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Barbieri M, Hooijmans MT, Moulin K, Cork TE, Ennis DB, Gold GE, Kogan F, Mazzoli V. A deep learning approach for fast muscle water T2 mapping with subject specific fat T2 calibration from multi-spin-echo acquisitions. Sci Rep 2024; 14:8253. [PMID: 38589478 PMCID: PMC11002020 DOI: 10.1038/s41598-024-58812-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 04/03/2024] [Indexed: 04/10/2024] Open
Abstract
This work presents a deep learning approach for rapid and accurate muscle water T2 with subject-specific fat T2 calibration using multi-spin-echo acquisitions. This method addresses the computational limitations of conventional bi-component Extended Phase Graph fitting methods (nonlinear-least-squares and dictionary-based) by leveraging fully connected neural networks for fast processing with minimal computational resources. We validated the approach through in vivo experiments using two different MRI vendors. The results showed strong agreement of our deep learning approach with reference methods, summarized by Lin's concordance correlation coefficients ranging from 0.89 to 0.97. Further, the deep learning method achieved a significant computational time improvement, processing data 116 and 33 times faster than the nonlinear least squares and dictionary methods, respectively. In conclusion, the proposed approach demonstrated significant time and resource efficiency improvements over conventional methods while maintaining similar accuracy. This methodology makes the processing of water T2 data faster and easier for the user and will facilitate the utilization of the use of a quantitative water T2 map of muscle in clinical and research studies.
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Affiliation(s)
- Marco Barbieri
- Department of Radiology, Stanford University, Stanford, CA, USA.
| | - Melissa T Hooijmans
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Kevin Moulin
- Department of Cardiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Tyler E Cork
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Daniel B Ennis
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Feliks Kogan
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Valentina Mazzoli
- Department of Radiology, Stanford University, Stanford, CA, USA
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
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Kogan F, Yoon D, Teeter MG, Chaudhari AJ, Hales L, Barbieri M, Gold GE, Vainberg Y, Goyal A, Watkins L. Correction to: Multimodal positron emission tomography (PET) imaging in non-oncologic musculoskeletal radiology. Skeletal Radiol 2024:10.1007/s00256-024-04667-7. [PMID: 38557699 DOI: 10.1007/s00256-024-04667-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Affiliation(s)
- Feliks Kogan
- Department of Radiology, Stanford University, Stanford, CA, USA.
| | - Daehyun Yoon
- Department of Radiology, University of California-San Francisco, San Francisco, CA, USA
| | - Matthew G Teeter
- Department of Medical Biophysics, Western University, London, ON, Canada
| | | | - Laurel Hales
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Marco Barbieri
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Yael Vainberg
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Ananya Goyal
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Lauren Watkins
- Department of Radiology, Stanford University, Stanford, CA, USA
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Kogan F, Yoon D, Teeter MG, Chaudhari AJ, Hales L, Barbieri M, Gold GE, Vainberg Y, Goyal A, Watkins L. Multimodal positron emission tomography (PET) imaging in non-oncologic musculoskeletal radiology. Skeletal Radiol 2024:10.1007/s00256-024-04640-4. [PMID: 38492029 DOI: 10.1007/s00256-024-04640-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 02/27/2024] [Accepted: 02/28/2024] [Indexed: 03/18/2024]
Abstract
Musculoskeletal (MSK) disorders are associated with large impacts on patient's pain and quality of life. Conventional morphological imaging of tissue structure is limited in its ability to detect pain generators, early MSK disease, and rapidly assess treatment efficacy. Positron emission tomography (PET), which offers unique capabilities to evaluate molecular and metabolic processes, can provide novel information about early pathophysiologic changes that occur before structural or even microstructural changes can be detected. This sensitivity not only makes it a powerful tool for detection and characterization of disease, but also a tool able to rapidly assess the efficacy of therapies. These benefits have garnered more attention to PET imaging of MSK disorders in recent years. In this narrative review, we discuss several applications of multimodal PET imaging in non-oncologic MSK diseases including arthritis, osteoporosis, and sources of pain and inflammation. We also describe technical considerations and recent advancements in technology and radiotracers as well as areas of emerging interest for future applications of multimodal PET imaging of MSK conditions. Overall, we present evidence that the incorporation of PET through multimodal imaging offers an exciting addition to the field of MSK radiology and will likely prove valuable in the transition to an era of precision medicine.
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Affiliation(s)
- Feliks Kogan
- Department of Radiology, Stanford University, Stanford, CA, USA.
| | - Daehyun Yoon
- Department of Radiology, University of California-San Francisco, San Francisco, CA, USA
| | - Matthew G Teeter
- Department of Medical Biophysics, Western University, London, ON, Canada
| | | | - Laurel Hales
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Marco Barbieri
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Yael Vainberg
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Ananya Goyal
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Lauren Watkins
- Department of Radiology, Stanford University, Stanford, CA, USA
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Zbýň Š, Ludwig KD, Watkins LE, Lagore RL, Nowacki A, Tóth F, Tompkins MA, Zhang L, Adriany G, Gold GE, Shea KG, Nagel AM, Carlson CS, Metzger GJ, Ellermann JM. Changes in tissue sodium concentration and sodium relaxation times during the maturation of human knee cartilage: Ex vivo 23 Na MRI study at 10.5 T. Magn Reson Med 2024; 91:1099-1114. [PMID: 37997011 PMCID: PMC10751033 DOI: 10.1002/mrm.29930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 10/30/2023] [Accepted: 10/31/2023] [Indexed: 11/25/2023]
Abstract
PURPOSE To evaluate the influence of skeletal maturation on sodium (23 Na) MRI relaxation parameters and the accuracy of tissue sodium concentration (TSC) quantification in human knee cartilage. METHODS Twelve pediatric knee specimens were imaged with whole-body 10.5 T MRI using a density-adapted 3D radial projection sequence to evaluate 23 Na parameters: B1 + , T1 , biexponentialT 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and TSC. Water, collagen, and sulfated glycosaminoglycan (sGAG) content were calculated from osteochondral biopsies. The TSC was corrected for B1 + , relaxation, and water content. The literature-based TSC (TSCLB ) used previously published values for corrections, whereas the specimen-specific TSC (TSCSP ) used measurements from individual specimens. 23 Na parameters were evaluated in eight cartilage compartments segmented on proton images. Associations between 23 Na parameters, TSCLB - TSCSP difference, biochemical content, and age were determined. RESULTS From birth to 12 years, cartilage water content decreased by 18%; collagen increased by 59%; and sGAG decreased by 36% (all R2 ≥ 0.557). The shortT 2 * $$ {\mathrm{T}}_2^{\ast } $$ (T 2 * S $$ {{\mathrm{T}}_2^{\ast}}_{\mathrm{S}} $$ ) decreased by 72%, and the signal fraction relaxing withT 2 * S $$ {{\mathrm{T}}_2^{\ast}}_{\mathrm{S}} $$ (fT 2 * S $$ {{\mathrm{fT}}_2^{\ast}}_{\mathrm{S}} $$ ) increased by 55% during the first 5 years but remained relatively stable after that. TSCSP was significantly correlated with sGAG content from biopsies (R2 = 0.739). Depending on age, TSCLB showed higher or lower values than TSCSP . The TSCLB - TSCSP difference was significantly correlated withT 2 * S $$ {{\mathrm{T}}_2^{\ast}}_{\mathrm{S}} $$ (R2 = 0.850),fT 2 * S $$ {{\mathrm{fT}}_2^{\ast}}_{\mathrm{S}} $$ (R2 = 0.651), and water content (R2 = 0.738). CONCLUSION TSC and relaxation parameters measured with 23 Na MRI provide noninvasive information about changes in sGAG content and collagen matrix during cartilage maturation. Cartilage TSC quantification assuming fixed relaxation may be feasible in children older than 5 years.
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Affiliation(s)
- Štefan Zbýň
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN
- Department of Radiology, University of Minnesota, Minneapolis, MN
- Department of Biomedical Engineering, Cleveland Clinic, Cleveland, OH
| | - Kai D. Ludwig
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN
- Department of Radiology, University of Minnesota, Minneapolis, MN
| | - Lauren E. Watkins
- Department of Radiology, Department of Bioengineering, Stanford University, Palo Alto, CA
- Steadman Philippon Research Institute, Vail, CO
| | - Russell L. Lagore
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN
| | - Amanda Nowacki
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN
- University of Texas, Austin, TX
| | - Ferenc Tóth
- Department of Veterinary Clinical Sciences, University of Minnesota, St. Paul, MN
| | - Marc A. Tompkins
- Department of Orthopedic Surgery, University of Minnesota, Minneapolis, MN
| | - Lin Zhang
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN
| | - Gregor Adriany
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN
| | - Garry E. Gold
- Department of Radiology, Department of Bioengineering, Stanford University, Palo Alto, CA
| | - Kevin G. Shea
- Lucile Packard Children’s Hospital, Stanford University School of Medicine, Palo Alto, CA
| | - Armin M. Nagel
- Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Cathy S. Carlson
- Department of Veterinary Clinical Sciences, University of Minnesota, St. Paul, MN
| | - Gregory J. Metzger
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN
| | - Jutta M. Ellermann
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN
- Department of Radiology, University of Minnesota, Minneapolis, MN
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Yoon MA, Gold GE, Chaudhari AS. Accelerated Musculoskeletal Magnetic Resonance Imaging. J Magn Reson Imaging 2023. [PMID: 38156716 DOI: 10.1002/jmri.29205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 12/13/2023] [Accepted: 12/14/2023] [Indexed: 01/03/2024] Open
Abstract
With a substantial growth in the use of musculoskeletal MRI, there has been a growing need to improve MRI workflow, and faster imaging has been suggested as one of the solutions for a more efficient examination process. Consequently, there have been considerable advances in accelerated MRI scanning methods. This article aims to review the basic principles and applications of accelerated musculoskeletal MRI techniques including widely used conventional acceleration methods, more advanced deep learning-based techniques, and new approaches to reduce scan time. Specifically, conventional accelerated MRI techniques, including parallel imaging, compressed sensing, and simultaneous multislice imaging, and deep learning-based accelerated MRI techniques, including undersampled MR image reconstruction, super-resolution imaging, artifact correction, and generation of unacquired contrast images, are discussed. Finally, new approaches to reduce scan time, including synthetic MRI, novel sequences, and new coil setups and designs, are also reviewed. We believe that a deep understanding of these fast MRI techniques and proper use of combined acceleration methods will synergistically improve scan time and MRI workflow in daily practice. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Min A Yoon
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
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Nosrat C, Gao KT, Bhattacharjee R, Pedoia V, Koff MF, Gold GE, Potter HG, Majumdar S. Multiparametric MRI of Knees in Collegiate Basketball Players: Associations With Morphological Abnormalities and Functional Deficits. Orthop J Sports Med 2023; 11:23259671231216490. [PMID: 38107843 PMCID: PMC10722938 DOI: 10.1177/23259671231216490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 06/29/2023] [Indexed: 12/19/2023] Open
Abstract
Background Rates of cartilage degeneration in asymptomatic elite basketball players are significantly higher compared with the general population due to excessive loads on the knee. Compositional quantitative magnetic resonance imaging (qMRI) techniques can identify local biochemical changes of macromolecules observed in cartilage degeneration. Purpose/Hypothesis The purpose of this study was to utilize multiparametric qMRI to (1) quantify how T1ρ and T2 relaxation times differ based on the presence of anatomic abnormalities and (2) correlate T1ρ and T2 with self-reported functional deficits. It was hypothesized that prolonged relaxation times will be associated with knees with MRI-graded abnormalities and knees belonging to basketball players with greater self-reported functional deficits. Study Design Cross-sectional study; Level of evidence, 3. Methods A total of 75 knees from National Collegiate Athletic Association Division I basketball players (40 female, 35 male) were included in this multicenter study. All players completed the Knee injury and Osteoarthritis Outcome Score (KOOS) and had bilateral knee MRI scans taken. T1ρ and T2 were calculated on a voxel-by-voxel basis. The cartilage surfaces were segmented into 6 compartments: lateral femoral condyle, lateral tibia, medial femoral condyle, medial tibia (MT), patella (PAT), and trochlea (TRO). Lesions from the MRI scans were graded for imaging abnormalities, and statistical parametric mapping was performed to study cross-sectional differences based on MRI scan grading of anatomic knee abnormalities. Pearson partial correlations between relaxation times and KOOS subscore values were computed, obtaining r value statistical parametric mappings and P value clusters. Results Knees without patellar tendinosis displayed significantly higher T1ρ in the PAT compared with those with patellar tendinosis (average percentage difference, 10.4%; P = .02). Significant prolongation of T1ρ was observed in the MT, TRO, and PAT of knees without compared with those with quadriceps tendinosis (average percentage difference, 12.7%, 13.3%, and 13.4%, respectively; P ≤ .05). A weak correlation was found between the KOOS-Symptoms subscale values and T1ρ/T2. Conclusion Certain tissues that bear the brunt of impact developed tendinosis but spared cartilage degeneration. Whereas participants reported minimal functional deficits, their high-impact activities resulted in structural damage that may lead to osteoarthritis after their collegiate careers.
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Affiliation(s)
- Cameron Nosrat
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Kenneth T. Gao
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Rupsa Bhattacharjee
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Valentina Pedoia
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Matthew F. Koff
- Department of Radiology and Imaging, Hospital for Special Surgery, New York City, New York, USA
| | - Garry E. Gold
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Hollis G. Potter
- Department of Radiology and Imaging, Hospital for Special Surgery, New York City, New York, USA
| | - Sharmila Majumdar
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
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Rubin EB, Schmidt AM, Koff MF, Kogan F, Gao K, Majumdar S, Potter H, Gold GE. Advanced MRI Approaches for Evaluating Common Lower Extremity Injuries in Basketball Players: Current and Emerging Techniques. J Magn Reson Imaging 2023. [PMID: 37854004 DOI: 10.1002/jmri.29019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 08/31/2023] [Accepted: 09/01/2023] [Indexed: 10/20/2023] Open
Abstract
Magnetic resonance imaging (MRI) can provide accurate and non-invasive diagnoses of lower extremity injuries in athletes. Sport-related injuries commonly occur in and around the knee and can affect the articular cartilage, patellar tendon, hamstring muscles, and bone. Sports medicine physicians utilize MRI to evaluate and diagnose injury, track recovery, estimate return to sport timelines, and assess the risk of recurrent injury. This article reviews the current literature and describes novel developments of quantitative MRI tools that can further advance our understanding of sports injury diagnosis, prevention, and treatment while minimizing injury risk and rehabilitation time. Innovative approaches for enhancing the early diagnosis and treatment of musculoskeletal injuries in basketball players span a spectrum of techniques. These encompass the utilization of T2 , T1ρ , and T2 * quantitative MRI, along with dGEMRIC and Na-MRI to assess articular cartilage injuries, 3D-Ultrashort echo time MRI for patellar tendon injuries, diffusion tensor imaging for acute myotendinous injuries, and sagittal short tau inversion recovery and axial long-axis T1 -weighted, and 3D Cube sequences for bone stress imaging. Future studies should further refine and validate these MR-based quantitative techniques while exploring the lifelong cumulative impact of basketball on players' knees. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Elka B Rubin
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Andrew M Schmidt
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Matthew F Koff
- Department of Radiology and Imaging, Hospital for Special Surgery, New York City, New York, USA
| | - Feliks Kogan
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Kenneth Gao
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Sharmila Majumdar
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Hollis Potter
- Department of Radiology and Imaging, Hospital for Special Surgery, New York City, New York, USA
| | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
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Schmidt AM, Desai AD, Watkins LE, Crowder HA, Black MS, Mazzoli V, Rubin EB, Lu Q, MacKay JW, Boutin RD, Kogan F, Gold GE, Hargreaves BA, Chaudhari AS. Generalizability of Deep Learning Segmentation Algorithms for Automated Assessment of Cartilage Morphology and MRI Relaxometry. J Magn Reson Imaging 2023; 57:1029-1039. [PMID: 35852498 PMCID: PMC9849481 DOI: 10.1002/jmri.28365] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 07/06/2022] [Accepted: 07/07/2022] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Deep learning (DL)-based automatic segmentation models can expedite manual segmentation yet require resource-intensive fine-tuning before deployment on new datasets. The generalizability of DL methods to new datasets without fine-tuning is not well characterized. PURPOSE Evaluate the generalizability of DL-based models by deploying pretrained models on independent datasets varying by MR scanner, acquisition parameters, and subject population. STUDY TYPE Retrospective based on prospectively acquired data. POPULATION Overall test dataset: 59 subjects (26 females); Study 1: 5 healthy subjects (zero females), Study 2: 8 healthy subjects (eight females), Study 3: 10 subjects with osteoarthritis (eight females), Study 4: 36 subjects with various knee pathology (10 females). FIELD STRENGTH/SEQUENCE A 3-T, quantitative double-echo steady state (qDESS). ASSESSMENT Four annotators manually segmented knee cartilage. Each reader segmented one of four qDESS datasets in the test dataset. Two DL models, one trained on qDESS data and another on Osteoarthritis Initiative (OAI)-DESS data, were assessed. Manual and automatic segmentations were compared by quantifying variations in segmentation accuracy, volume, and T2 relaxation times for superficial and deep cartilage. STATISTICAL TESTS Dice similarity coefficient (DSC) for segmentation accuracy. Lin's concordance correlation coefficient (CCC), Wilcoxon rank-sum tests, root-mean-squared error-coefficient-of-variation to quantify manual vs. automatic T2 and volume variations. Bland-Altman plots for manual vs. automatic T2 agreement. A P value < 0.05 was considered statistically significant. RESULTS DSCs for the qDESS-trained model, 0.79-0.93, were higher than those for the OAI-DESS-trained model, 0.59-0.79. T2 and volume CCCs for the qDESS-trained model, 0.75-0.98 and 0.47-0.95, were higher than respective CCCs for the OAI-DESS-trained model, 0.35-0.90 and 0.13-0.84. Bland-Altman 95% limits of agreement for superficial and deep cartilage T2 were lower for the qDESS-trained model, ±2.4 msec and ±4.0 msec, than the OAI-DESS-trained model, ±4.4 msec and ±5.2 msec. DATA CONCLUSION The qDESS-trained model may generalize well to independent qDESS datasets regardless of MR scanner, acquisition parameters, and subject population. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Andrew M Schmidt
- Department of Radiology, Stanford University, Palo Alto, California, USA
| | - Arjun D Desai
- Department of Radiology, Stanford University, Palo Alto, California, USA
- Electrical Engineering, Stanford University, Palo Alto, California, USA
| | - Lauren E Watkins
- Department of Radiology, Stanford University, Palo Alto, California, USA
- Bioengineering, Stanford University, Palo Alto, California, USA
| | - Hollis A Crowder
- Mechanical Engineering, Stanford University, Palo Alto, California, USA
| | - Marianne S Black
- Department of Radiology, Stanford University, Palo Alto, California, USA
- Mechanical Engineering, Stanford University, Palo Alto, California, USA
| | - Valentina Mazzoli
- Department of Radiology, Stanford University, Palo Alto, California, USA
| | - Elka B Rubin
- Department of Radiology, Stanford University, Palo Alto, California, USA
| | - Quin Lu
- Philips Healthcare North America, Gainesville, Florida, USA
| | - James W MacKay
- Department of Radiology, University of Cambridge, Cambridge, UK
- Norwich Medical School, University of East Anglia, Norwich, UK
| | - Robert D Boutin
- Department of Radiology, Stanford University, Palo Alto, California, USA
| | - Feliks Kogan
- Department of Radiology, Stanford University, Palo Alto, California, USA
| | - Garry E Gold
- Department of Radiology, Stanford University, Palo Alto, California, USA
- Bioengineering, Stanford University, Palo Alto, California, USA
| | - Brian A Hargreaves
- Department of Radiology, Stanford University, Palo Alto, California, USA
- Electrical Engineering, Stanford University, Palo Alto, California, USA
- Bioengineering, Stanford University, Palo Alto, California, USA
| | - Akshay S Chaudhari
- Department of Radiology, Stanford University, Palo Alto, California, USA
- Biomedical Data Science, Stanford University, Palo Alto, California, USA
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Barbieri M, Chaudhari AS, Moran CJ, Gold GE, Hargreaves BA, Kogan F. A method for measuring B 0 field inhomogeneity using quantitative double-echo in steady-state. Magn Reson Med 2023; 89:577-593. [PMID: 36161727 PMCID: PMC9712261 DOI: 10.1002/mrm.29465] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 08/31/2022] [Accepted: 09/01/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE To develop and validate a method forB 0 $$ {B}_0 $$ mapping for knee imaging using the quantitative Double-Echo in Steady-State (qDESS) exploiting the phase difference (Δ θ $$ \Delta \theta $$ ) between the two echoes acquired. Contrary to a two-gradient-echo (2-GRE) method,Δ θ $$ \Delta \theta $$ depends only on the first echo time. METHODS Bloch simulations were applied to investigate robustness to noise of the proposed methodology and all imaging studies were validated with phantoms and in vivo simultaneous bilateral knee acquisitions. Two phantoms and five healthy subjects were scanned using qDESS, water saturation shift referencing (WASSR), and multi-GRE sequences.Δ B 0 $$ \Delta {B}_0 $$ maps were calculated with the qDESS and the 2-GRE methods and compared against those obtained with WASSR. The comparison was quantitatively assessed exploiting pixel-wise difference maps, Bland-Altman (BA) analysis, and Lin's concordance coefficient (ρ c $$ {\rho}_c $$ ). For in vivo subjects, the comparison was assessed in cartilage using average values in six subregions. RESULTS The proposed method for measuringΔ B 0 $$ \Delta {B}_0 $$ inhomogeneities from a qDESS acquisition providedΔ B 0 $$ \Delta {B}_0 $$ maps that were in good agreement with those obtained using WASSR.Δ B 0 $$ \Delta {B}_0 $$ ρ c $$ {\rho}_c $$ values were≥ $$ \ge $$ 0.98 and 0.90 in phantoms and in vivo, respectively. The agreement between qDESS and WASSR was comparable to that of a 2-GRE method. CONCLUSION The proposed method may allow B0 correction for qDESST 2 $$ {T}_2 $$ mapping using an inherently co-registeredΔ B 0 $$ \Delta {B}_0 $$ map without requiring an additional B0 measurement sequence. More generally, the method may help shorten knee imaging protocols that require an auxiliaryΔ B 0 $$ \Delta {B}_0 $$ map by exploiting a qDESS acquisition that also providesT 2 $$ {T}_2 $$ measurements and high-quality morphological imaging.
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Affiliation(s)
- Marco Barbieri
- Department of Radiology, Stanford University, Stanford, CA, U.S.A
| | - Akshay S. Chaudhari
- Department of Radiology, Stanford University, Stanford, CA, U.S.A
- Department of Biomedical Data Science, Stanford University, Stanford, CA, U.S.A
| | | | - Garry E. Gold
- Department of Radiology, Stanford University, Stanford, CA, U.S.A
- Department of Bioengineering, Stanford University, Stanford, CA, U.S.A
| | - Brian A. Hargreaves
- Department of Radiology, Stanford University, Stanford, CA, U.S.A
- Department of Bioengineering, Stanford University, Stanford, CA, U.S.A
- Department of Electrical Engineering, Stanford University, Stanford, CA, U.S.A
| | - Feliks Kogan
- Department of Radiology, Stanford University, Stanford, CA, U.S.A
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10
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Uhlrich SD, Kolesar JA, Kidziński Ł, Boswell MA, Silder A, Gold GE, Delp SL, Beaupre GS. Personalization improves the biomechanical efficacy of foot progression angle modifications in individuals with medial knee osteoarthritis. J Biomech 2022; 144:111312. [PMID: 36191434 PMCID: PMC9889103 DOI: 10.1016/j.jbiomech.2022.111312] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 08/12/2022] [Accepted: 09/13/2022] [Indexed: 02/02/2023]
Abstract
Modifying the foot progression angle during walking can reduce the knee adduction moment, a surrogate measure of medial knee loading. However, not all individuals reduce their knee adduction moment with the same modification. This study evaluates whether a personalized approach to prescribing foot progression angle modifications increases the proportion of individuals with medial knee osteoarthritis who reduce their knee adduction moment, compared to a non-personalized approach. Individuals with medial knee osteoarthritis (N=107) walked with biofeedback instructing them to toe-in and toe-out by 5° and 10° relative to their self-selected angle. We selected individuals' personalized foot progression angle as the modification that maximally reduced their larger knee adduction moment peak. Additionally, we used lasso regression to identify which secondary kinematic changes made a 10° toe-in gait modification more effective at reducing the first knee adduction moment peak. Seventy percent of individuals reduced their larger knee adduction moment peak by at least 5% with a personalized foot progression angle modification, which was more than (p≤0.002) the 23-57% of individuals who reduced it with a uniformly assigned 5° or 10° toe-in or toe-out modification. When toeing-in, greater reductions in the first knee adduction moment peak were related to an increased frontal-plane tibia angle (knee more medial than ankle), a more valgus knee abduction angle, reduced contralateral pelvic drop, and a more medialized center of pressure in the foot reference frame. In summary, personalization increases the proportion of individuals with medial knee osteoarthritis who may benefit from a foot progression angle modification.
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Affiliation(s)
- Scott D Uhlrich
- Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, United States; Department of Bioengineering, Stanford University, Stanford, CA 94305, United States; Musculoskeletal Research Laboratory, VA Palo Alto Healthcare System, Palo Alto, CA 94304, United States.
| | - Julie A Kolesar
- Department of Bioengineering, Stanford University, Stanford, CA 94305, United States; Musculoskeletal Research Laboratory, VA Palo Alto Healthcare System, Palo Alto, CA 94304, United States
| | - Łukasz Kidziński
- Department of Bioengineering, Stanford University, Stanford, CA 94305, United States
| | - Melissa A Boswell
- Department of Bioengineering, Stanford University, Stanford, CA 94305, United States
| | - Amy Silder
- Department of Bioengineering, Stanford University, Stanford, CA 94305, United States; Musculoskeletal Research Laboratory, VA Palo Alto Healthcare System, Palo Alto, CA 94304, United States
| | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, CA 94305, United States
| | - Scott L Delp
- Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, United States; Department of Bioengineering, Stanford University, Stanford, CA 94305, United States; Department of Orthopaedic Surgery, Stanford University, Stanford, CA 94305, United States
| | - Gary S Beaupre
- Department of Bioengineering, Stanford University, Stanford, CA 94305, United States; Musculoskeletal Research Laboratory, VA Palo Alto Healthcare System, Palo Alto, CA 94304, United States
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11
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Watkins LE, Haddock B, MacKay JW, Baker J, Uhlrich SD, Mazzoli V, Gold GE, Kogan F. [ 18F]Sodium fluoride PET-MRI detects increased metabolic bone response to whole-joint loading stress in osteoarthritic knees. Osteoarthritis Cartilage 2022; 30:1515-1525. [PMID: 36031138 PMCID: PMC9922526 DOI: 10.1016/j.joca.2022.08.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 07/27/2022] [Accepted: 08/11/2022] [Indexed: 02/02/2023]
Abstract
OBJECTIVE Altered joint function is a hallmark of osteoarthritis (OA). Imaging techniques for joint function are limited, but [18F]sodium fluoride (NaF) PET-MRI may assess the acute joint response to loading stresses. [18F]NaF PET-MRI was used to study the acute joint response to exercise in OA knees, and compare relationships between regions of increased uptake after loading and structural OA progression two years later. METHODS In this prospective study, 10 participants with knee OA (59 ± 8 years; 8 female) were scanned twice consecutively using a PET-MR system and performed a one-legged squat exercise between scans. Changes in tracer uptake measures in 9 bone regions were compared between knees that did and did not exercise with a mixed-effects model. Areas of focally large changes in uptake between scans (ROIfocal, ΔSUVmax > 3) were identified and the presence of structural MRI features was noted. Five participants returned two years later to assess structural change on MRI. RESULTS There was a significant increase in [18F]NaF uptake in OA exercised knees (SUV P < 0.001, KiP = 0.002, K1P < 0.001) that differed by bone region. CONCLUSION There were regional differences in the acute bone metabolic response to exercise and areas of focally large changes in the metabolic bone response that might be representative of whole-joint dysfunction.
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Affiliation(s)
- L E Watkins
- Department of Radiology, Stanford University, Stanford CA, USA
| | | | - J W MacKay
- Norwich Medical School, University of East Anglia, Norwich, United Kingdom
| | - J Baker
- Department of Radiology, Stanford University, Stanford CA, USA
| | - S D Uhlrich
- Department of Mechanical Engineering, Stanford University, Stanford CA, USA
| | - V Mazzoli
- Department of Radiology, Stanford University, Stanford CA, USA
| | - G E Gold
- Department of Radiology, Stanford University, Stanford CA, USA
| | - F Kogan
- Department of Radiology, Stanford University, Stanford CA, USA.
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12
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Hall ME, Wang AS, Gold GE, Levenston ME. Contrast solution properties and scan parameters influence the apparent diffusivity of computed tomography contrast agents in articular cartilage. J R Soc Interface 2022; 19:20220403. [PMID: 35919981 PMCID: PMC9346352 DOI: 10.1098/rsif.2022.0403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
The inability to detect early degenerative changes to the articular cartilage surface that commonly precede bulk osteoarthritic degradation is an obstacle to early disease detection for research or clinical diagnosis. Leveraging a known artefact that blurs tissue boundaries in clinical arthrograms, contrast agent (CA) diffusivity can be derived from computed tomography arthrography (CTa) scans. We combined experimental and computational approaches to study protocol variations that may alter the CTa-derived apparent diffusivity. In experimental studies on bovine cartilage explants, we examined how CA dilution and transport direction (absorption versus desorption) influence the apparent diffusivity of untreated and enzymatically digested cartilage. Using multiphysics simulations, we examined mechanisms underlying experimental observations and the effects of image resolution, scan interval and early scan termination. The apparent diffusivity during absorption decreased with increasing CA concentration by an amount similar to the increase induced by tissue digestion. Models indicated that osmotically-induced fluid efflux strongly contributed to the concentration effect. Simulated changes to spatial resolution, scan spacing and total scan time all influenced the apparent diffusivity, indicating the importance of consistent protocols. With careful control of imaging protocols and interpretations guided by transport models, CTa-derived diffusivity offers promise as a biomarker for early degenerative changes.
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Affiliation(s)
- Mary E Hall
- Department of Mechanical Engineering, Stanford University, Stanford, CA, USA
| | - Adam S Wang
- Department of Radiology, Stanford University, Stanford, CA, USA.,Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, CA, USA.,Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Marc E Levenston
- Department of Mechanical Engineering, Stanford University, Stanford, CA, USA.,Department of Radiology, Stanford University, Stanford, CA, USA
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13
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Abstract
Knee effusion is a common comorbidity in osteoarthritis. To quantify the amount of effusion, semi quantitative assessment scales have been developed that classify fluid levels on an integer scale from 0 to 3. In this work, we investigated the use of a neural network (NN) that used MRI Osteoarthritis Knee Scores effusion-synovitis (MOAKS-ES) values to distinguish physiologic fluid levels from higher fluid levels in MR images of the knee. We evaluate its effectiveness on low-resolution images to examine its potential in low-field, low-cost MRI. We created a dense NN (dNN) for detecting effusion, defined as a nonzero MOAKS-ES score, from MRI scans. Both the training and performance evaluation of the network were conducted using public radiological data from the Osteoarthritis Initiative (OAI). The model was trained using sagittal turbo-spin-echo (TSE) MR images from 1628 knees. The accuracy was compared to VGG16, a commonly used convolutional classification network. Robustness of the dNN was assessed by adding zero-mean Gaussian noise to the test images with a standard deviation of 5-30% of the maximum test data intensity. Also, inference was performed on a test data set of 163 knees, which includes a smaller test set of 36 knees that was also assessed by a musculoskeletal radiologist and the performance of the dNN and the radiologist compared. For the larger test data set, the dNN performed with an average accuracy of 62%. In addition, the network proved robust to noise, classifying the noisy images with minimal degradation to accuracy. When given MRI scans with 5% Gaussian noise, the network performed similarly, with an average accuracy of 61%. For the smaller 36-knee test data set, assessed both by the dNN and by a radiologist, the network performed better than the radiologist on average. Classifying knee effusion from low-resolution images with a similar accuracy as a human radiologist using neural networks is feasible, suggesting automatic assessment of images from low-cost, low-field scanners as a potentially useful assessment tool.
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Affiliation(s)
- Sandhya Raman
- grid.32224.350000 0004 0386 9924A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA USA
| | - Garry E. Gold
- grid.168010.e0000000419368956Department of Radiology, Stanford University, Stanford, CA USA
| | - Matthew S. Rosen
- grid.32224.350000 0004 0386 9924A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA USA ,grid.38142.3c000000041936754XHarvard Medical School, Boston, MA USA ,grid.38142.3c000000041936754XDepartment of Physics, Harvard University, Cambridge, MA USA
| | - Bragi Sveinsson
- grid.32224.350000 0004 0386 9924A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA USA ,grid.38142.3c000000041936754XHarvard Medical School, Boston, MA USA
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14
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Sandford HJC, MacKay JW, Watkins LE, Gold GE, Kogan F, Mazzoli V. Gadolinium-free assessment of synovitis using diffusion tensor imaging. NMR Biomed 2022; 35:e4614. [PMID: 34549476 PMCID: PMC8688337 DOI: 10.1002/nbm.4614] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Revised: 08/14/2021] [Accepted: 08/16/2021] [Indexed: 05/08/2023]
Abstract
The dynamic contrast-enhanced (DCE)-MRI parameter Ktrans can quantify the intensity of synovial inflammation (synovitis) in knees with osteoarthritis (OA), but requires the use of gadolinium-based contrast agent (GBCA). Diffusion tensor imaging (DTI) measures the diffusion of water molecules with parameters mean diffusivity (MD) and fractional anisotropy (FA), and has been proposed as a method to detect synovial inflammation without the use of GBCA. The purpose of this study is to (1) determine the ability of DTI to quantify the intensity of synovitis in OA by comparing MD and FA with our imaging gold standard Ktrans within the synovium and (2) compare DTI and DCE-MRI measures with the semi-quantitative grading of OA severity with the Kellgren-Lawrence (KL) and MRI Osteoarthritis Knee Score (MOAKS) systems, in order to assess the relationship between synovitis intensity and OA severity. Within the synovium, MD showed a significant positive correlation with Ktrans (r = 0.79, p < 0.001), while FA showed a significant negative correlation with Ktrans (r = -0.72, p = 0.0026). These results show that DTI is able to quantify the intensity of synovitis within the whole synovium without the use of exogenous contrast agent. Additionally, MD, FA, and Ktrans values did not vary significantly when knees were separated by KL grade (p = 0.15, p = 0.32, p = 0.41, respectively), while MD (r = 0.60, p = 0.018) and Ktrans (r = 0.62, p = 0.013) had a significant positive correlation and FA (r = -0.53, p = 0.043) had a negative correlation with MOAKS. These comparisons indicate that quantitative measures of the intensity of synovitis may provide information in addition to morphological assessment to evaluate OA severity. Using DTI to quantify the intensity of synovitis without GBCA may be helpful to facilitate a broader clinical assessment of the severity of OA.
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Affiliation(s)
| | - James W. MacKay
- Norwich Medical School, University of East Anglia, Norwich, UK
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Lauren E. Watkins
- Department of Radiology, Stanford University, Stanford, California
- Department of Bioengineering, Stanford University, Stanford, California
| | - Garry E. Gold
- Department of Radiology, Stanford University, Stanford, California
- Department of Bioengineering, Stanford University, Stanford, California
- Department of Orthopaedic Surgery, Stanford University, Stanford, California
| | - Feliks Kogan
- Department of Radiology, Stanford University, Stanford, California
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15
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Hall ME, Black MS, Gold GE, Levenston ME. Validation of watershed-based segmentation of the cartilage surface from sequential CT arthrography scans. Quant Imaging Med Surg 2022; 12:1-14. [PMID: 34993056 DOI: 10.21037/qims-20-1062] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 07/12/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND This study investigated the utility of a 2-dimensional watershed algorithm for identifying the cartilage surface in computed tomography (CT) arthrograms of the knee up to 33 minutes after an intra-articular iohexol injection as boundary blurring increased. METHODS A 2D watershed algorithm was applied to CT arthrograms of 3 bovine stifle joints taken 3, 8, 18, and 33 minutes after iohexol injection and used to segment tibial cartilage. Thickness measurements were compared to a reference standard thickness measurement and the 3-minute time point scan. RESULTS 77.2% of cartilage thickness measurements were within 0.2 mm (1 voxel) of the thickness calculated in the reference scan at the 3-minute time point. 42% fewer voxels could be segmented from the 33-minute scan than the 3-minute scan due to diffusion of the contrast agent out of the joint space and into the cartilage, leading to blurring of the cartilage boundary. The traced watershed lines were closer to the location of the cartilage surface in areas where tissues were in direct contact with each other (cartilage-cartilage or cartilage-meniscus contact). CONCLUSIONS The use of watershed dam lines to guide cartilage segmentation shows promise for identifying cartilage boundaries from CT arthrograms in areas where soft tissues are in direct contact with each other.
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Affiliation(s)
- Mary E Hall
- Department of Mechanical Engineering, Stanford University, Stanford, CA, USA
| | - Marianne S Black
- Department of Mechanical Engineering, Stanford University, Stanford, CA, USA.,Department of Radiology, Stanford University, Stanford, CA, USA
| | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, CA, USA.,Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Marc E Levenston
- Department of Mechanical Engineering, Stanford University, Stanford, CA, USA.,Department of Radiology, Stanford University, Stanford, CA, USA.,Department of Bioengineering, Stanford University, Stanford, CA, USA
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16
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Thomas KA, Krzemiński D, Kidziński Ł, Paul R, Rubin EB, Halilaj E, Black MS, Chaudhari A, Gold GE, Delp SL. Open Source Software for Automatic Subregional Assessment of Knee Cartilage Degradation Using Quantitative T2 Relaxometry and Deep Learning. Cartilage 2021; 13:747S-756S. [PMID: 34496667 PMCID: PMC8808775 DOI: 10.1177/19476035211042406] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
OBJECTIVE We evaluated a fully automated femoral cartilage segmentation model for measuring T2 relaxation values and longitudinal changes using multi-echo spin-echo (MESE) magnetic resonance imaging (MRI). We open sourced this model and developed a web app available at https://kl.stanford.edu into which users can drag and drop images to segment them automatically. DESIGN We trained a neural network to segment femoral cartilage from MESE MRIs. Cartilage was divided into 12 subregions along medial-lateral, superficial-deep, and anterior-central-posterior boundaries. Subregional T2 values and four-year changes were calculated using a radiologist's segmentations (Reader 1) and the model's segmentations. These were compared using 28 held-out images. A subset of 14 images were also evaluated by a second expert (Reader 2) for comparison. RESULTS Model segmentations agreed with Reader 1 segmentations with a Dice score of 0.85 ± 0.03. The model's estimated T2 values for individual subregions agreed with those of Reader 1 with an average Spearman correlation of 0.89 and average mean absolute error (MAE) of 1.34 ms. The model's estimated four-year change in T2 for individual subregions agreed with Reader 1 with an average correlation of 0.80 and average MAE of 1.72 ms. The model agreed with Reader 1 at least as closely as Reader 2 agreed with Reader 1 in terms of Dice score (0.85 vs. 0.75) and subregional T2 values. CONCLUSIONS Assessments of cartilage health using our fully automated segmentation model agreed with those of an expert as closely as experts agreed with one another. This has the potential to accelerate osteoarthritis research.
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Affiliation(s)
- Kevin A. Thomas
- Department of Biomedical Data Science,
Stanford University, Stanford, CA, USA,Kevin A. Thomas, Department of Biomedical
Data Science, Stanford University, Clark Center, Room S331, 318 Campus Drive,
Stanford, CA 94305, USA.
| | - Dominik Krzemiński
- Cardiff University Brain Research
Imaging Centre, Cardiff University, Cardiff, Wales, UK
| | - Łukasz Kidziński
- Department of Bioengineering, Stanford
University, Stanford, CA, USA
| | - Rohan Paul
- Department of Biomedical Data Science,
Stanford University, Stanford, CA, USA
| | - Elka B. Rubin
- Department of Radiology, Stanford
University, Stanford, CA, USA
| | - Eni Halilaj
- Department of Mechanical Engineering,
Carnegie Mellon University, Pittsburgh, PA, USA
| | | | - Akshay Chaudhari
- Department of Biomedical Data Science,
Stanford University, Stanford, CA, USA,Department of Radiology, Stanford
University, Stanford, CA, USA
| | - Garry E. Gold
- Department of Bioengineering, Stanford
University, Stanford, CA, USA,Department of Radiology, Stanford
University, Stanford, CA, USA,Department of Orthopaedic Surgery,
Stanford University, Stanford, CA, USA
| | - Scott L. Delp
- Department of Bioengineering, Stanford
University, Stanford, CA, USA,Department of Orthopaedic Surgery,
Stanford University, Stanford, CA, USA,Department of Mechanical Engineering,
Stanford University, Stanford, CA, USA
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17
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Crowder HA, Mazzoli V, Black MS, Watkins LE, Kogan F, Hargreaves BA, Levenston ME, Gold GE. Characterizing the transient response of knee cartilage to running: Decreases in cartilage T 2 of female recreational runners. J Orthop Res 2021; 39:2340-2352. [PMID: 33483997 PMCID: PMC8295402 DOI: 10.1002/jor.24994] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 11/20/2020] [Accepted: 01/19/2021] [Indexed: 02/04/2023]
Abstract
Cartilage transmits and redistributes biomechanical loads in the knee joint during exercise. Exercise-induced loading alters cartilage hydration and is detectable using quantitative magnetic resonance imaging (MRI), where T2 relaxation time (T2 ) is influenced by cartilage collagen composition, fiber orientation, and changes in the extracellular matrix. This study characterized short-term transient responses of healthy knee cartilage to running-induced loading using bilateral scans and image registration. Eleven healthy female recreational runners (33.73 ± 4.22 years) and four healthy female controls (27.25 ± 1.38 years) were scanned on a 3T GE MRI scanner with quantitative 3D double-echo in steady-state before running over-ground (runner group) or resting (control group) for 40 min. Subjects were scanned immediately post-activity at 5-min intervals for 60 min. T2 times were calculated for femoral, tibial, and patellar cartilage at each time point and analyzed using a mixed-effects model and Bonferroni post hoc. There were immediate decreases in T2 (mean ± SEM) post-run in superficial femoral cartilage of at least 3.3% ± 0.3% (p = .002) between baseline and Time 0 that remained for 25 min, a decrease in superficial tibial cartilage T2 of 2.9% ± 0.4% (p = .041) between baseline and Time 0, and a decrease in superficial patellar cartilage T2 of 3.6% ± 0.3% (p = .020) 15 min post-run. There were decreases in the medial posterior region of superficial femoral cartilage T2 of at least 5.3 ± 0.2% (p = .022) within 5 min post-run that remained at 60 min post-run. These results increase understanding of transient responses of healthy cartilage to repetitive, exercise-induced loading and establish preliminary recommendations for future definitive studies of cartilage response to running.
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Affiliation(s)
- Hollis A. Crowder
- Department of Mechanical Engineering, Stanford University, Stanford, California, USA,Department of Radiology, Stanford University, Stanford, California, USA
| | - Valentina Mazzoli
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Marianne S. Black
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Lauren E. Watkins
- Department of Radiology, Stanford University, Stanford, California, USA,Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Feliks Kogan
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Brian A. Hargreaves
- Department of Radiology, Stanford University, Stanford, California, USA,Department of Bioengineering, Stanford University, Stanford, California, USA,Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Marc E. Levenston
- Department of Mechanical Engineering, Stanford University, Stanford, California, USA,Department of Radiology, Stanford University, Stanford, California, USA
| | - Garry E. Gold
- Department of Radiology, Stanford University, Stanford, California, USA
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18
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Sveinsson B, Chaudhari AS, Zhu B, Koonjoo N, Torriani M, Gold GE, Rosen MS. Synthesizing Quantitative T2 Maps in Right Lateral Knee Femoral Condyles from Multicontrast Anatomic Data with a Conditional Generative Adversarial Network. Radiol Artif Intell 2021; 3:e200122. [PMID: 34617020 PMCID: PMC8489449 DOI: 10.1148/ryai.2021200122] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 04/11/2021] [Accepted: 05/03/2021] [Indexed: 04/09/2023]
Abstract
PURPOSE To develop a proof-of-concept convolutional neural network (CNN) to synthesize T2 maps in right lateral femoral condyle articular cartilage from anatomic MR images by using a conditional generative adversarial network (cGAN). MATERIALS AND METHODS In this retrospective study, anatomic images (from turbo spin-echo and double-echo in steady-state scans) of the right knee of 4621 patients included in the 2004-2006 Osteoarthritis Initiative were used as input to a cGAN-based CNN, and a predicted CNN T2 was generated as output. These patients included men and women of all ethnicities, aged 45-79 years, with or at high risk for knee osteoarthritis incidence or progression who were recruited at four separate centers in the United States. These data were split into 3703 (80%) for training, 462 (10%) for validation, and 456 (10%) for testing. Linear regression analysis was performed between the multiecho spin-echo (MESE) and CNN T2 in the test dataset. A more detailed analysis was performed in 30 randomly selected patients by means of evaluation by two musculoskeletal radiologists and quantification of cartilage subregions. Radiologist assessments were compared by using two-sided t tests. RESULTS The readers were moderately accurate in distinguishing CNN T2 from MESE T2, with one reader having random-chance categorization. CNN T2 values were correlated to the MESE values in the subregions of 30 patients and in the bulk analysis of all patients, with best-fit line slopes between 0.55 and 0.83. CONCLUSION With use of a neural network-based cGAN approach, it is feasible to synthesize T2 maps in femoral cartilage from anatomic MRI sequences, giving good agreement with MESE scans.See also commentary by Yi and Fritz in this issue.Keywords: Cartilage Imaging, Knee, Experimental Investigations, Quantification, Vision, Application Domain, Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms© RSNA, 2021.
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19
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Chaudhari AS, Sandino CM, Cole EK, Larson DB, Gold GE, Vasanawala SS, Lungren MP, Hargreaves BA, Langlotz CP. Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices. J Magn Reson Imaging 2021; 54:357-371. [PMID: 32830874 PMCID: PMC8639049 DOI: 10.1002/jmri.27331] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 07/27/2020] [Accepted: 07/31/2020] [Indexed: 12/16/2022] Open
Abstract
Artificial intelligence algorithms based on principles of deep learning (DL) have made a large impact on the acquisition, reconstruction, and interpretation of MRI data. Despite the large number of retrospective studies using DL, there are fewer applications of DL in the clinic on a routine basis. To address this large translational gap, we review the recent publications to determine three major use cases that DL can have in MRI, namely, that of model-free image synthesis, model-based image reconstruction, and image or pixel-level classification. For each of these three areas, we provide a framework for important considerations that consist of appropriate model training paradigms, evaluation of model robustness, downstream clinical utility, opportunities for future advances, as well recommendations for best current practices. We draw inspiration for this framework from advances in computer vision in natural imaging as well as additional healthcare fields. We further emphasize the need for reproducibility of research studies through the sharing of datasets and software. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
| | - Christopher M Sandino
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Elizabeth K Cole
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - David B Larson
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | | | - Matthew P Lungren
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Brian A Hargreaves
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
- Department of Biomedical Informatics, Stanford University, Stanford, California, USA
| | - Curtis P Langlotz
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Biomedical Informatics, Stanford University, Stanford, California, USA
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20
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Gambhir SS, Ge TJ, Vermesh O, Spitler R, Gold GE. Continuous health monitoring: An opportunity for precision health. Sci Transl Med 2021; 13:13/597/eabe5383. [PMID: 34108250 DOI: 10.1126/scitranslmed.abe5383] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 05/19/2021] [Indexed: 01/15/2023]
Abstract
Continuous health monitoring and integrated diagnostic devices, worn on the body and used in the home, will help to identify and prevent early manifestations of disease. However, challenges lie ahead in validating new health monitoring technologies and in optimizing data analytics to extract actionable conclusions from continuously obtained health data.
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Affiliation(s)
- Sanjiv S Gambhir
- Department of Radiology, Molecular Imaging Program at Stanford, Stanford University School of Medicine, Stanford, CA 94305, USA.,Canary Center at Stanford for Cancer Early Detection, Stanford University School of Medicine, Palo Alto, CA 94304, USA.,Department of Bioengineering and Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, USA.,Precision Health and Integrated Diagnostics Center, Stanford University, Stanford, CA 94305, USA
| | - T Jessie Ge
- Department of Urology, Stanford University School of Medicine, Stanford, CA 94305, USA.,Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
| | - Ophir Vermesh
- Department of Radiology, Molecular Imaging Program at Stanford, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Ryan Spitler
- Department of Radiology, Molecular Imaging Program at Stanford, Stanford University School of Medicine, Stanford, CA 94305, USA. .,Canary Center at Stanford for Cancer Early Detection, Stanford University School of Medicine, Palo Alto, CA 94304, USA.,Precision Health and Integrated Diagnostics Center, Stanford University, Stanford, CA 94305, USA
| | - Garry E Gold
- Department of Radiology, Molecular Imaging Program at Stanford, Stanford University School of Medicine, Stanford, CA 94305, USA.,Precision Health and Integrated Diagnostics Center, Stanford University, Stanford, CA 94305, USA
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21
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Gao KT, Pedoia V, Young KA, Kogan F, Koff MF, Gold GE, Potter HG, Majumdar S. Multiparametric MRI characterization of knee articular cartilage and subchondral bone shape in collegiate basketball players. J Orthop Res 2021; 39:1512-1522. [PMID: 32910520 PMCID: PMC8359246 DOI: 10.1002/jor.24851] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 07/31/2020] [Accepted: 09/02/2020] [Indexed: 02/04/2023]
Abstract
Magnetic resonance imaging (MRI) is commonly used to evaluate the morphology of the knee in athletes with high-knee impact; however, complex repeated loading of the joint can lead to biochemical and structural degeneration that occurs before visible morphological changes. In this study, we utilized multiparametric quantitative MRI to compare morphology and composition of articular cartilage and subchondral bone shape between young athletes with high-knee impact (basketball players; n = 40) and non-knee impact (swimmers; n = 25). We implemented voxel-based relaxometry to register all cases to a single reference space and performed a localized compositional analysis of T 1ρ - and T 2 -relaxation times on a voxel-by-voxel basis. Additionally, statistical shape modeling was employed to extract differences in subchondral bone shape between the two groups. Evaluation of cartilage composition demonstrated a significant prolongation of relaxation times in the medial femoral and tibial compartments and in the posterolateral femur of basketball players in comparison to relaxation times in the same cartilage compartments of swimmers. The compositional analysis also showed depth-dependent differences with prolongation of the superficial layer in basketball players. For subchondral bone shape, three total modes were found to be significantly different between groups and related to the relative sizes of the tibial plateaus, intercondylar eminences, and the curvature and concavity of the patellar lateral facet. In summary, this study identified several characteristics associated with a high-knee impact which may expand our understanding of local degenerative patterns in this population.
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Affiliation(s)
- Kenneth T. Gao
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Valentina Pedoia
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | | | - Feliks Kogan
- Department of RadiologyStanford UniversityStanfordCaliforniaUSA
| | - Matthew F. Koff
- Department of Radiology and ImagingHospital for Special SurgeryNew York CityNew YorkUSA
| | - Garry E. Gold
- Department of RadiologyStanford UniversityStanfordCaliforniaUSA
| | - Hollis G. Potter
- Department of Radiology and ImagingHospital for Special SurgeryNew York CityNew YorkUSA
| | - Sharmila Majumdar
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
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22
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Chaudhari AS, Grissom MJ, Fang Z, Sveinsson B, Lee JH, Gold GE, Hargreaves BA, Stevens KJ. Diagnostic Accuracy of Quantitative Multicontrast 5-Minute Knee MRI Using Prospective Artificial Intelligence Image Quality Enhancement. AJR Am J Roentgenol 2021; 216:1614-1625. [PMID: 32755384 PMCID: PMC8862596 DOI: 10.2214/ajr.20.24172] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
BACKGROUND. Potential approaches for abbreviated knee MRI, including prospective acceleration with deep learning, have achieved limited clinical implementation. OBJECTIVE. The objective of this study was to evaluate the interreader agreement between conventional knee MRI and a 5-minute 3D quantitative double-echo steady-state (qDESS) sequence with automatic T2 mapping and deep learning super-resolutionaugmentation and to compare the diagnostic performance of the two methods regarding findings from arthroscopic surgery. METHODS. Fifty-one patients with knee pain underwent knee MRI that included an additional 3D qDESS sequence with automatic T2 mapping. Fourier interpolation was followed by prospective deep learning super resolution to enhance qDESS slice resolution twofold. A musculoskeletal radiologist and a radiology resident performed independent retrospective evaluations of articular cartilage, menisci, ligaments, bones, extensor mechanism, and synovium using conventional MRI. Following a 2-month washout period, readers reviewed qDESS images alone followed by qDESS with the automatic T2 maps. Interreader agreement between conventional MRI and qDESS was computed using percentage agreement and Cohen kappa. The sensitivity and specificity of conventional MRI, qDESS alone, and qDESS plus T2 mapping were compared with arthroscopic findings using exact McNemar tests. RESULTS. Conventional MRI and qDESS showed 92% agreement in evaluating all tissues. Kappa was 0.79 (95% CI, 0.76-0.81) across all imaging findings. In 43 patients who underwent arthroscopy, sensitivity and specificity were not significantly different (p = .23 to > .99) between conventional MRI (sensitivity, 58-93%; specificity, 27-87%) and qDESS alone (sensitivity, 54-90%; specificity, 23-91%) for cartilage, menisci, ligaments, and synovium. For grade 1 cartilage lesions, sensitivity and specificity were 33% and 56%, respectively, for conventional MRI; 23% and 53% for qDESS (p = .81); and 46% and 39% for qDESS with T2 mapping (p = .80). For grade 2A lesions, values were 27% and 53% for conventional MRI, 26% and 52% for qDESS (p = .02), and 58% and 40% for qDESS with T2 mapping (p < .001). CONCLUSION. The qDESS method prospectively augmented with deep learning showed strong interreader agreement with conventional knee MRI and near-equivalent diagnostic performance regarding arthroscopy. The ability of qDESS to automatically generate T2 maps increases sensitivity for cartilage abnormalities. CLINICAL IMPACT. Using prospective artificial intelligence to enhance qDESS image quality may facilitate an abbreviated knee MRI protocol while generating quantitative T2 maps.
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Affiliation(s)
- Akshay S Chaudhari
- Department of Radiology, Lucas Center for Imaging, Stanford University, 1201 Welch Rd, PS 055B, Stanford, CA 94305
| | | | | | - Bragi Sveinsson
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA
- Department of Radiology, Harvard Medical School, Boston, MA
| | - Jin Hyung Lee
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA
- Department of Bioengineering, Stanford University, Stanford, CA
- Department of Neurosurgery, Stanford University, Stanford, CA
- Department of Electrical Engineering, Stanford University, Stanford, CA
| | - Garry E Gold
- Department of Radiology, Lucas Center for Imaging, Stanford University, 1201 Welch Rd, PS 055B, Stanford, CA 94305
- Department of Bioengineering, Stanford University, Stanford, CA
- Department of Orthopaedic Surgery, Stanford University, Redwood City, CA
| | - Brian A Hargreaves
- Department of Radiology, Lucas Center for Imaging, Stanford University, 1201 Welch Rd, PS 055B, Stanford, CA 94305
- Department of Bioengineering, Stanford University, Stanford, CA
- Department of Electrical Engineering, Stanford University, Stanford, CA
| | - Kathryn J Stevens
- Department of Radiology, Lucas Center for Imaging, Stanford University, 1201 Welch Rd, PS 055B, Stanford, CA 94305
- Department of Orthopaedic Surgery, Stanford University, Redwood City, CA
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23
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Verschueren J, Van Langeveld SJ, Dragoo JL, Bierma-Zeinstra SMA, Reijman M, Gold GE, Oei EHG. T2 relaxation times of knee cartilage in 109 patients with knee pain and its association with disease characteristics. Acta Orthop 2021; 92:335-340. [PMID: 33538221 PMCID: PMC8231385 DOI: 10.1080/17453674.2021.1882131] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Background and purpose - Quantitative T2 mapping MRI of cartilage has proven value for the assessment of early osteoarthritis changes in research. We evaluated knee cartilage T2 relaxation times in a clinical population with knee complaints and its association with patients and disease characteristics and clinical symptoms.Patients and methods - In this cross-sectional study, T2 mapping knee scans of 109 patients with knee pain who were referred for an MRI by an orthopedic surgeon were collected. T2 relaxation times were calculated in 6 femoral and tibial regions of interest of full-thickness tibiofemoral cartilage. Its associations with age, sex, BMI, duration of complaints, disease onset (acute/chronic), and clinical symptoms were assessed with multivariate regression analysis. Subgroups were created of patients with abnormalities expected to cause predominantly medial or lateral tibiofemoral cartilage changes.Results - T2 relaxation times increased statistically significantly with higher age and BMI. In patients with expected medial cartilage damage, the medial femoral T2 values were significantly higher than the lateral; in patients with expected lateral cartilage damage the lateral tibial T2 values were significantly higher. A traumatic onset of knee complaints was associated with an acute elevation. No significant association was found with clinical symptoms.Interpretation - Our study demonstrates age, BMI, and type of injury-dependent T2 relaxation times and emphasizes the importance of acknowledging these variations when performing T2 mapping in a clinical population.
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Affiliation(s)
- Joost Verschueren
- Department of Orthopedic Surgery, Erasmus MC University Medical Center Rotterdam, The Netherlands; ,Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Center Rotterdam, The Netherlands;;
| | - Stephan J Van Langeveld
- Department of Orthopedic Surgery, Erasmus MC University Medical Center Rotterdam, The Netherlands;
| | - Jason L Dragoo
- Department of Orthopedic Surgery, University of Colorado, Denver, CO, USA;
| | - Sita M A Bierma-Zeinstra
- Department of Orthopedic Surgery, Erasmus MC University Medical Center Rotterdam, The Netherlands; ,Department of General Practice, Erasmus MC University Medical Center Rotterdam, The Netherlands;
| | - Max Reijman
- Department of Orthopedic Surgery, Erasmus MC University Medical Center Rotterdam, The Netherlands;
| | - Garry E Gold
- Department of Radiology, Stanford University Medical Center, CA, USA; ,Department of Bioengineering, Stanford University Medical Center, CA, USA; ,Department of Orthopedic Surgery, Stanford University Medical Center, CA, USA
| | - Edwin H G Oei
- Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Center Rotterdam, The Netherlands;; ,Correspondence:
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24
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Desai AD, Caliva F, Iriondo C, Mortazi A, Jambawalikar S, Bagci U, Perslev M, Igel C, Dam EB, Gaj S, Yang M, Li X, Deniz CM, Juras V, Regatte R, Gold GE, Hargreaves BA, Pedoia V, Chaudhari AS. The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset. Radiol Artif Intell 2021; 3:e200078. [PMID: 34235438 PMCID: PMC8231759 DOI: 10.1148/ryai.2021200078] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 01/08/2021] [Accepted: 01/25/2021] [Indexed: 02/05/2023]
Abstract
PURPOSE To organize a multi-institute knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression. MATERIALS AND METHODS A dataset partition consisting of three-dimensional knee MRI from 88 retrospective patients at two time points (baseline and 1-year follow-up) with ground truth articular (femoral, tibial, and patellar) cartilage and meniscus segmentations was standardized. Challenge submissions and a majority-vote ensemble were evaluated against ground truth segmentations using Dice score, average symmetric surface distance, volumetric overlap error, and coefficient of variation on a holdout test set. Similarities in automated segmentations were measured using pairwise Dice coefficient correlations. Articular cartilage thickness was computed longitudinally and with scans. Correlation between thickness error and segmentation metrics was measured using the Pearson correlation coefficient. Two empirical upper bounds for ensemble performance were computed using combinations of model outputs that consolidated true positives and true negatives. RESULTS Six teams (T 1-T 6) submitted entries for the challenge. No differences were observed across any segmentation metrics for any tissues (P = .99) among the four top-performing networks (T 2, T 3, T 4, T 6). Dice coefficient correlations between network pairs were high (> 0.85). Per-scan thickness errors were negligible among networks T 1-T 4 (P = .99), and longitudinal changes showed minimal bias (< 0.03 mm). Low correlations (ρ < 0.41) were observed between segmentation metrics and thickness error. The majority-vote ensemble was comparable to top-performing networks (P = .99). Empirical upper-bound performances were similar for both combinations (P = .99). CONCLUSION Diverse networks learned to segment the knee similarly, where high segmentation accuracy did not correlate with cartilage thickness accuracy and voting ensembles did not exceed individual network performance.See also the commentary by Elhalawani and Mak in this issue.Keywords: Cartilage, Knee, MR-Imaging, Segmentation © RSNA, 2020Supplemental material is available for this article.
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Affiliation(s)
- Arjun D. Desai
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Francesco Caliva
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Claudia Iriondo
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Aliasghar Mortazi
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Sachin Jambawalikar
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Ulas Bagci
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Mathias Perslev
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Christian Igel
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Erik B. Dam
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Sibaji Gaj
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Mingrui Yang
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Xiaojuan Li
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Cem M. Deniz
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Vladimir Juras
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Ravinder Regatte
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Garry E. Gold
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Brian A. Hargreaves
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Valentina Pedoia
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - Akshay S. Chaudhari
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
| | - on behalf of the IWOAI Segmentation Challenge Writing Group
- From the Departments of Radiology (A.D.D., G.E.G., B.A.H., A.S.C.)
and Electrical Engineering (A.D.D., B.A.H.), Stanford University, Lucas Center
for Imaging, 1201 Welch Rd, PS 055B, Stanford, CA 94305; Department of
Radiology, University of California, San Francisco, San Francisco, Calif (F.C.,
C. Iriondo, V.P.); Berkeley Joint Graduate Group in Bioengineering, University
of California, Berkeley, Berkeley, Calif (C. Iriondo); Department of Computer
Science, University of Central Florida, Orlando, Fla (A.M., U.B.); Department of
Radiology, Northwestern University, Chicago, Ill (U.B.); Department of
Radiology, Columbia University, New York, NY (S.J.); Department of Computer
Science, University of Copenhagen, Copenhagen, Denmark (M.P., C. Igel, E.B.D.);
Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio (S.G.,
M.Y., X.L.); Department of Radiology, New York University Langone Health, New
York, NY (C.M.D., R.R.); and Department of Biomedical Imaging and Image-guided
Therapy, High-Field MR Centre, Medical University of Vienna, Vienna, Austria
(V.J.)
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25
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Rubin EB, Mazzoli V, Black MS, Young K, Desai AD, Koff MF, Sreedhar A, Kogan F, Safran MR, Vincentini DJ, Knox KA, Yamada T, McCabe A, Majumdar S, Potter HG, Gold GE. Effects of the Competitive Season and Off-Season on Knee Articular Cartilage in Collegiate Basketball Players Using Quantitative MRI: A Multicenter Study. J Magn Reson Imaging 2021; 54:840-851. [PMID: 33763929 DOI: 10.1002/jmri.27610] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 03/05/2021] [Accepted: 03/09/2021] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Injuries to the articular cartilage in the knee are common in jumping athletes, particularly high-level basketball players. Unfortunately, these are often diagnosed at a late stage of the disease process, after tissue loss has already occurred. PURPOSE/HYPOTHESIS To evaluate longitudinal changes in knee articular cartilage and knee function in National Collegiate Athletic Association (NCAA) basketball players and their evolution over the competitive season and off-season. STUDY TYPE Longitudinal, multisite cohort study. POPULATION Thirty-two NCAA Division 1 athletes: 22 basketball players and 10 swimmers. FIELD STRENGTH/SEQUENCE Bilateral magnetic resonance imaging (MRI) using a combined T1ρ and T2 magnetization-prepared angle-modulated portioned k-space spoiled gradient-echo snapshots (MAPSS) sequence at 3T. ASSESSMENT We calculated T2 and T1ρ relaxation times to compare compositional cartilage changes between three timepoints: preseason 1, postseason 1, and preseason 2. Knee Osteoarthritis Outcome Scores (KOOS) were used to assess knee health. STATISTICAL TESTS One-way variance model hypothesis test, general linear model, and chi-squared test. RESULTS In the femoral articular cartilage of all athletes, we saw a global decrease in T2 and T1ρ relaxation times during the competitive season (all P < 0.05) and an increase in T2 and T1ρ relaxation times during the off-season (all P < 0.05). In the basketball players' femoral cartilage, the anterior and central compartments respectively had the highest T2 and T1ρ relaxation times following the competitive season and off-season. The basketball players had significantly lower KOOS measures in every domain compared with the swimmers: Pain (P < 0.05), Symptoms (P < 0.05), Function in Daily Living (P < 0.05), Function in Sport/Recreation (P < 0.05), and Quality of Life (P < 0.05). CONCLUSION Our results indicate that T2 and T1ρ MRI can detect significant seasonal changes in the articular cartilage of basketball players and that there are regional differences in the articular cartilage that are indicative of basketball-specific stress on the femoral cartilage. This study demonstrates the potential of quantitative MRI to monitor global and regional cartilage health in athletes at risk of developing cartilage problems. LEVEL OF EVIDENCE 2 Technical Efficacy Stage: 2.
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Affiliation(s)
- Elka B Rubin
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Valentina Mazzoli
- Department of Radiology, Stanford University, Stanford, California, USA.,Musculoskeletal Research Laboratory, VA Palo Alto Healthcare System, Palo Alto, California, USA
| | - Marianne S Black
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Katherine Young
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Arjun D Desai
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Matthew F Koff
- Department of Radiology and Imaging, Hospital for Special Surgery, New York, New York, USA
| | - Ashwin Sreedhar
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Feliks Kogan
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Marc R Safran
- Department of Orthopaedic Surgery, Stanford University, Redwood City, California, USA
| | - Dominic J Vincentini
- Stanford Department of Athletics, Stanford University, Stanford, California, USA
| | - Katelin A Knox
- Stanford Department of Athletics, Stanford University, Stanford, California, USA
| | - Tomoo Yamada
- Stanford Department of Athletics, Stanford University, Stanford, California, USA
| | - Andrew McCabe
- Santa Clara Department of Athletics, Santa Clara University, Santa Clara, California, USA
| | - Sharmila Majumdar
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
| | - Hollis G Potter
- Department of Radiology and Imaging, Hospital for Special Surgery, New York, New York, USA
| | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, California, USA
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26
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Boswell MA, Uhlrich SD, Kidziński Ł, Thomas K, Kolesar JA, Gold GE, Beaupre GS, Delp SL. A neural network to predict the knee adduction moment in patients with osteoarthritis using anatomical landmarks obtainable from 2D video analysis. Osteoarthritis Cartilage 2021; 29:346-356. [PMID: 33422707 PMCID: PMC7925428 DOI: 10.1016/j.joca.2020.12.017] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 10/30/2020] [Accepted: 12/28/2020] [Indexed: 02/02/2023]
Abstract
OBJECTIVE The knee adduction moment (KAM) can inform treatment of medial knee osteoarthritis; however, measuring the KAM requires an expensive gait analysis laboratory. We evaluated the feasibility of predicting the peak KAM during natural and modified walking patterns using the positions of anatomical landmarks that could be identified from video analysis. METHOD Using inverse dynamics, we calculated the KAM for 86 individuals (64 with knee osteoarthritis, 22 without) walking naturally and with foot progression angle modifications. We trained a neural network to predict the peak KAM using the 3-dimensional positions of 13 anatomical landmarks measured with motion capture (3D neural network). We also trained models to predict the peak KAM using 2-dimensional subsets of the dataset to simulate 2-dimensional video analysis (frontal and sagittal plane neural networks). Model performance was evaluated on a held-out, 8-person test set that included steps from all trials. RESULTS The 3D neural network predicted the peak KAM for all test steps with r2( Murray et al., 2012) 2 = 0.78. This model predicted individuals' average peak KAM during natural walking with r2( Murray et al., 2012) 2 = 0.86 and classified which 15° foot progression angle modifications reduced the peak KAM with accuracy = 0.85. The frontal plane neural network predicted peak KAM with similar accuracy (r2( Murray et al., 2012) 2 = 0.85) to the 3D neural network, but the sagittal plane neural network did not (r2( Murray et al., 2012) 2 = 0.14). CONCLUSION Using the positions of anatomical landmarks from motion capture, a neural network accurately predicted the peak KAM during natural and modified walking. This study demonstrates the feasibility of measuring the peak KAM using positions obtainable from 2D video analysis.
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Affiliation(s)
| | - Scott D. Uhlrich
- Department of Mechanical Engineering, Stanford University, Stanford, CA, USA,Musculoskeletal Research Lab, VA Palo Alto Healthcare System, Palo Alto, CA, USA
| | - Łukasz Kidziński
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Kevin Thomas
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Julie A. Kolesar
- Department of Bioengineering, Stanford University, Stanford, CA, USA,Musculoskeletal Research Lab, VA Palo Alto Healthcare System, Palo Alto, CA, USA
| | - Garry E. Gold
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Gary S. Beaupre
- Department of Bioengineering, Stanford University, Stanford, CA, USA,Musculoskeletal Research Lab, VA Palo Alto Healthcare System, Palo Alto, CA, USA
| | - Scott L. Delp
- Department of Bioengineering, Stanford University, Stanford, CA, USA,Department of Mechanical Engineering, Stanford University, Stanford, CA, USA,Department of Orthopaedic Surgery, Stanford University, Stanford, CA, USA
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27
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Sonn GA, Fan RE, Kunder CA, Gold GE, James KM, Parker ID, Carlson JM, Cannizzaro SM, James TW. MR method for measuring microscopic histologic soft tissue textures. Magn Reson Med 2021; 86:308-319. [PMID: 33608954 DOI: 10.1002/mrm.28731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 01/22/2021] [Accepted: 01/25/2021] [Indexed: 11/09/2022]
Abstract
PURPOSE Provide a direct, non-invasive diagnostic measure of microscopic tissue texture in the size scale between tens of microns and the much larger scale measurable by clinical imaging. This paper presents a method and data demonstrating the ability to measure these microscopic pathologic tissue textures (histology) in the presence of subject motion in an MR scanner. This size range is vital to diagnosing a wide range of diseases. THEORY/METHODS MR micro-Texture (MRµT) resolves these textures by a combination of measuring a targeted set of k-values to characterize texture-as in diffraction analysis of materials, performing a selective internal excitation to isolate a volume of interest (VOI), applying a high k-value phase encode to the excited spins in the VOI, and acquiring each individual k-value data point in a single excitation-providing motion immunity and extended acquisition time for maximizing signal-to-noise ratio. Additional k-value measurements from the same tissue can be made to characterize the tissue texture in the VOI-there is no need for these additional measurements to be spatially coherent as there is no image to be reconstructed. This method was applied to phantoms and tissue specimens including human prostate tissue. RESULTS Data demonstrating resolution <50 µm, motion immunity, and clearly differentiating between normal and cancerous tissue textures are presented. CONCLUSION The data reveal textural differences not resolvable by standard MR imaging. As MRµT is a pulse sequence, it is directly translatable to MRI scanners currently in clinical practice to meet the need for further improvement in cancer imaging.
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Affiliation(s)
- Geoffrey A Sonn
- Department of Urology, Stanford School of Medicine, Stanford, California, USA.,Department of Radiology, Stanford School of Medicine, Stanford, California, USA
| | - Richard E Fan
- Department of Urology, Stanford School of Medicine, Stanford, California, USA
| | - Christian A Kunder
- Department of Pathology, Stanford School of Medicine, Stanford, California, USA
| | - Garry E Gold
- Department of Radiology, Stanford School of Medicine, Stanford, California, USA
| | | | - Ian D Parker
- Formerly at BioProtonics, now at Samsung Research America, Mountain View, California, USA
| | - Jean M Carlson
- Department of Physics, University of California, Santa Barbara, California, USA
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28
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Mazzoli V, Moulin K, Kogan F, Hargreaves BA, Gold GE. Diffusion Tensor Imaging of Skeletal Muscle Contraction Using Oscillating Gradient Spin Echo. Front Neurol 2021; 12:608549. [PMID: 33658976 PMCID: PMC7917051 DOI: 10.3389/fneur.2021.608549] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 01/08/2021] [Indexed: 01/01/2023] Open
Abstract
Diffusion tensor imaging (DTI) measures water diffusion in skeletal muscle tissue and allows for muscle assessment in a broad range of neuromuscular diseases. However, current DTI measurements, typically performed using pulsed gradient spin echo (PGSE) diffusion encoding, are limited to the assessment of non-contracted musculature, therefore providing limited insight into muscle contraction mechanisms and contraction abnormalities. In this study, we propose the use of an oscillating gradient spin echo (OGSE) diffusion encoding strategy for DTI measurements to mitigate the effect of signal voids in contracted muscle and to obtain reliable diffusivity values. Two OGSE sequences with encoding frequencies of 25 and 50 Hz were tested in the lower leg of five healthy volunteers with relaxed musculature and during active dorsiflexion and plantarflexion, and compared with a conventional PGSE approach. A significant reduction of areas of signal voids using OGSE compared with PGSE was observed in the tibialis anterior for the scans obtained in active dorsiflexion and in the soleus during active plantarflexion. The use of PGSE sequences led to unrealistically elevated axial diffusivity values in the tibialis anterior during dorsiflexion and in the soleus during plantarflexion, while the corresponding values obtained using the OGSE sequences were significantly reduced. Similar findings were seen for radial diffusivity, with significantly higher diffusivity measured in plantarflexion in the soleus muscle using the PGSE sequence. Our preliminary results indicate that DTI with OGSE diffusion encoding is feasible in human musculature and allows to quantitatively assess diffusion properties in actively contracting skeletal muscle. OGSE holds great potential to assess microstructural changes occurring in the skeletal muscle during contraction, and for non-invasive assessment of contraction abnormalities in patients with muscle diseases.
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Affiliation(s)
- Valentina Mazzoli
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Kevin Moulin
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Feliks Kogan
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Brian A Hargreaves
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, CA, United States
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29
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de Vries BA, Breda SJ, Sveinsson B, McWalter EJ, Meuffels DE, Krestin GP, Hargreaves BA, Gold GE, Oei EHG. Detection of knee synovitis using non-contrast-enhanced qDESS compared with contrast-enhanced MRI. Arthritis Res Ther 2021; 23:55. [PMID: 33581741 PMCID: PMC7881494 DOI: 10.1186/s13075-021-02436-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 02/02/2021] [Indexed: 01/15/2023] Open
Abstract
Background To assess diagnostic accuracy of quantitative double-echo in steady-state (qDESS) MRI for detecting synovitis in knee osteoarthritis (OA). Methods Patients with different degrees of radiographic knee OA were included prospectively. All underwent MRI with both qDESS and contrast-enhanced T1-weighted magnetic resonance imaging (CE-MRI). A linear combination of the two qDESS images can be used to create an image that displays contrast between synovium and the synovial fluid. Synovitis on both qDESS and CE-MRI was assessed semi-quantitatively, using a whole-knee synovitis sum score, indicating no/equivocal, mild, moderate, and severe synovitis. The correlation between sum scores of qDESS and CE-MRI (reference standard) was determined using Spearman’s rank correlation coefficient and intraclass correlation coefficient for absolute agreement. Receiver operating characteristic analysis was performed to assess the diagnostic performance of qDESS for detecting different degrees of synovitis, with CE-MRI as reference standard. Results In the 31 patients included, very strong correlation was found between synovitis sum scores on qDESS and CE-MRI (ρ = 0.96, p < 0.001), with high absolute agreement (0.84 (95%CI 0.14–0.95)). Mean sum score (SD) values on qDESS 5.16 (3.75) were lower than on CE-MRI 7.13 (4.66), indicating systematically underestimated synovitis severity on qDESS. For detecting mild synovitis or higher, high sensitivity and specificity were found for qDESS (1.00 (95%CI 0.80–1.00) and 0.909 (0.571–1.00), respectively). For detecting moderate synovitis or higher, sensitivity and specificity were good (0.727 (95%CI 0.393–0.927) and 1.00 (0.800–1.00), respectively). Conclusion qDESS MRI is able to, however with an underestimation, detect synovitis in patients with knee OA.
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Affiliation(s)
- Bas A de Vries
- Department of Radiology & Nuclear Medicine, Erasmus MC, Doctor Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Stephan J Breda
- Department of Radiology & Nuclear Medicine, Erasmus MC, Doctor Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands.,Department of Orthopedic Surgery, Erasmus MC, Rotterdam, The Netherlands
| | - Bragi Sveinsson
- Department of Radiology, Massachusetts General Hospital, Boston, USA.,Harvard Medical School, Boston, USA
| | - Emily J McWalter
- Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, Canada
| | - Duncan E Meuffels
- Department of Orthopedic Surgery, Erasmus MC, Rotterdam, The Netherlands
| | - Gabriel P Krestin
- Department of Radiology & Nuclear Medicine, Erasmus MC, Doctor Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | | | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, USA
| | - Edwin H G Oei
- Department of Radiology & Nuclear Medicine, Erasmus MC, Doctor Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands.
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30
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Sveinsson B, Gold GE, Hargreaves BA, Yoon D. Utilizing shared information between gradient-spoiled and RF-spoiled steady-state MRI signals. Phys Med Biol 2021; 66:01NT03. [PMID: 33246317 DOI: 10.1088/1361-6560/abce8a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
This work presents an analytical relationship between gradient-spoiled and RF-spoiled steady-state signals. The two echoes acquired in double-echo in steady-state scans are shown to lie on a line in the signal plane, where the two axes represent the amplitudes of each echo. The location along the line depends on the amount of spoiling and the diffusivity. The line terminates in a point corresponding to an RF-spoiled signal. In addition to the main contribution of demonstrating this signal relationship, we also include the secondary contribution of preliminary results from an example application of the relationship, in the form of a heuristic denoising method when both types of scans are performed. This is investigated in simulations, phantom scans, and in vivo scans. For the signal model, the main topic of this study, simulations confirmed its accuracy and explored its dependency on signal parameters and image noise. For the secondary topic of its preliminary application to reduce noise, simulations demonstrated the denoising method giving a reduction in noise-induced standard deviation of about 30%. The relative effect of the method on the signals is shown to depend on the slope of the described line, which is demonstrated to be zero at the Ernst angle. The phantom scans show a similar effect as the simulations. In vivo scans showed a slightly lower average improvement of about 28%.
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Affiliation(s)
- Bragi Sveinsson
- Athinoula A. Martinos Center, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States of America. Harvard Medical School, Boston, MA, United States of America
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31
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Chaudhari AS, Kogan F, Pedoia V, Majumdar S, Gold GE, Hargreaves BA. Rapid Knee MRI Acquisition and Analysis Techniques for Imaging Osteoarthritis. J Magn Reson Imaging 2020; 52:1321-1339. [PMID: 31755191 PMCID: PMC7925938 DOI: 10.1002/jmri.26991] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 10/22/2019] [Accepted: 10/22/2019] [Indexed: 12/16/2022] Open
Abstract
Osteoarthritis (OA) of the knee is a major source of disability that has no known treatment or cure. Morphological and compositional MRI is commonly used for assessing the bone and soft tissues in the knee to enhance the understanding of OA pathophysiology. However, it is challenging to extend these imaging methods and their subsequent analysis techniques to study large population cohorts due to slow and inefficient imaging acquisition and postprocessing tools. This can create a bottleneck in assessing early OA changes and evaluating the responses of novel therapeutics. The purpose of this review article is to highlight recent developments in tools for enhancing the efficiency of knee MRI methods useful to study OA. Advances in efficient MRI data acquisition and reconstruction tools for morphological and compositional imaging, efficient automated image analysis tools, and hardware improvements to further drive efficient imaging are discussed in this review. For each topic, we discuss the current challenges as well as potential future opportunities to alleviate these challenges. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 3.
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Affiliation(s)
| | - Feliks Kogan
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Valentina Pedoia
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
- Center of Digital Health Innovation (CDHI), University of California San Francisco, San Francisco, California, USA
| | - Sharmila Majumdar
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
- Center of Digital Health Innovation (CDHI), University of California San Francisco, San Francisco, California, USA
| | - Garry E. Gold
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Brian A. Hargreaves
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
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Abstract
AbstractIdentifying the source of a person's pain is a significant clinical challenge because the physical sensation of pain is believed to be subjective and difficult to quantify. The experience of pain is not only modulated by the individual's threshold to painful stimuli but also a product of the person's affective contributions, such as fear, anxiety, and previous experiences. Perhaps then to quantify pain is to examine the degree of nociception and pro-nociceptive inflammation, that is, the extent of cellular, chemical, and molecular changes that occur in pain-generating processes. Measuring changes in the local density of receptors, ion channels, mediators, and inflammatory/immune cells that are involved in the painful phenotype using targeted, highly sensitive, and specific positron emission tomography (PET) radiotracers is therefore a promising approach toward objectively identifying peripheral pain generators. Although several preclinical radiotracer candidates are being developed, a growing number of ongoing clinical PET imaging approaches can measure the degree of target concentration and thus serve as a readout for sites of pain generation. Further, when PET is combined with the spatial and contrast resolution afforded by magnetic resonance imaging, nuclear medicine physicians and radiologists can potentially identify pain drivers with greater accuracy and confidence. Clinical PET imaging approaches with fluorine-18 fluorodeoxyglucose, fluorine-18 sodium fluoride, and sigma-1 receptor PET radioligand and translocator protein radioligands to isolate the source of pain are described here.
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Affiliation(s)
- Daehyun Yoon
- Division of Musculoskeletal Radiology, Department of Radiology, Stanford University School of Medicine, Stanford, California
| | - Feliks Kogan
- Division of Musculoskeletal Radiology, Department of Radiology, Stanford University School of Medicine, Stanford, California
| | - Garry E. Gold
- Division of Musculoskeletal Radiology, Department of Radiology, Stanford University School of Medicine, Stanford, California
| | - Sandip Biswal
- Division of Musculoskeletal Radiology, Department of Radiology, Stanford University School of Medicine, Stanford, California
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Watkins LE, Rubin EB, Mazzoli V, Uhlrich SD, Desai AD, Black M, Ho GK, Delp SL, Levenston ME, Beaupré GS, Gold GE, Kogan F. Rapid volumetric gagCEST imaging of knee articular cartilage at 3 T: evaluation of improved dynamic range and an osteoarthritic population. NMR Biomed 2020; 33:e4310. [PMID: 32445515 PMCID: PMC7347437 DOI: 10.1002/nbm.4310] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 03/03/2020] [Accepted: 03/20/2020] [Indexed: 05/22/2023]
Abstract
Chemical exchange saturation transfer of glycosaminoglycans, gagCEST, is a quantitative MR technique that has potential for assessing cartilage proteoglycan content at field strengths of 7 T and higher. However, its utility at 3 T remains unclear. The objective of this work was to implement a rapid volumetric gagCEST sequence with higher gagCEST asymmetry at 3 T to evaluate its sensitivity to osteoarthritic changes in knee articular cartilage and in comparison with T2 and T1ρ measures. We hypothesize that gagCEST asymmetry at 3 T decreases with increasing severity of osteoarthritis (OA). Forty-two human volunteers, including 10 healthy subjects and 32 subjects with medial OA, were included in the study. Knee Injury and Osteoarthritis Outcome Scores (KOOS) were assessed for all subjects, and Kellgren-Lawrence grading was performed for OA volunteers. Healthy subjects were scanned consecutively at 3 T to assess the repeatability of the volumetric gagCEST sequence at 3 T. For healthy and OA subjects, gagCEST asymmetry and T2 and T1ρ relaxation times were calculated for the femoral articular cartilage to assess sensitivity to OA severity. Volumetric gagCEST imaging had higher gagCEST asymmetry than single-slice acquisitions (p = 0.015). The average scan-rescan coefficient of variation was 6.8%. There were no significant differences in average gagCEST asymmetry between younger and older healthy controls (p = 0.655) or between healthy controls and OA subjects (p = 0.310). T2 and T1ρ relaxation times were elevated in OA subjects (p < 0.001 for both) compared with healthy controls and both were moderately correlated with total KOOS scores (rho = -0.181 and rho = -0.332 respectively). The gagCEST technique developed here, with volumetric scan times under 10 min and high gagCEST asymmetry at 3 T, did not vary significantly between healthy subjects and those with mild-moderate OA. This further supports a limited utility for gagCEST imaging at 3 T for assessment of early changes in cartilage composition in OA.
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Affiliation(s)
| | - Elka B Rubin
- Radiology, Stanford University, Stanford, California, USA
| | | | - Scott D Uhlrich
- Mechanical Engineering, Stanford University, Stanford, California, USA
| | - Arjun D Desai
- Electrical Engineering, Stanford University, Stanford, California, USA
| | - Marianne Black
- Radiology, Stanford University, Stanford, California, USA
- Mechanical Engineering, Stanford University, Stanford, California, USA
| | - Gabe K Ho
- Bioengineering, Stanford University, Stanford, California, USA
| | - Scott L Delp
- Bioengineering, Stanford University, Stanford, California, USA
- Mechanical Engineering, Stanford University, Stanford, California, USA
- Orthopaedic Surgery, Stanford University, Stanford, California, USA
| | - Marc E Levenston
- Bioengineering, Stanford University, Stanford, California, USA
- Mechanical Engineering, Stanford University, Stanford, California, USA
| | - Gary S Beaupré
- Bioengineering, Stanford University, Stanford, California, USA
- Veteran Affairs Palo Alto Health Care System, Palo Alto, California, USA
| | - Garry E Gold
- Bioengineering, Stanford University, Stanford, California, USA
- Radiology, Stanford University, Stanford, California, USA
- Orthopaedic Surgery, Stanford University, Stanford, California, USA
| | - Feliks Kogan
- Radiology, Stanford University, Stanford, California, USA
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Langner JL, Black MS, MacKay JW, Hall KE, Safran MR, Kogan F, Gold GE. The prevalence of femoroacetabular impingement anatomy in Division 1 aquatic athletes who tread water. J Hip Preserv Surg 2020; 7:233-241. [PMID: 33163207 PMCID: PMC7605769 DOI: 10.1093/jhps/hnaa009] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 01/22/2020] [Indexed: 12/15/2022] Open
Abstract
Femoroacetabular impingement (FAI) is a disorder that causes hip pain and disability in young patients, particularly athletes. Increased stress on the hip during development has been associated with increased risk of cam morphology. The specific forces involved are unclear, but may be due to continued rotational motion, like the eggbeater kick. The goal of this prospective cohort study was to use magnetic resonance imaging (MRI) to identify the prevalence of FAI anatomy in athletes who tread water and compare it to the literature on other sports. With university IRB approval, 20 Division 1 water polo players and synchronized swimmers (15 female, 5 male), ages 18-23 years (mean age 20.7 ± 1.4), completed the 33-item International Hip Outcome Tool and underwent non-contrast MRI scans of both hips using a 3 Tesla scanner. Recruitment was based on sport, with both symptomatic and asymptomatic individuals included. Cam and pincer morphology were identified. The Wilcoxon Signed-Rank/Rank Sum tests were used to assess outcomes. Seventy per cent (14/20) of subjects reported pain in their hips yet only 15% (3/20) sought clinical evaluation. Cam morphology was present in 67.5% (27/40) of hips, while 22.5% (9/40) demonstrated pincer morphology. The prevalence of cam morphology in water polo players and synchronized swimmers is greater than that reported for the general population and at a similar level as some other sports. From a clinical perspective, acknowledgment of the high prevalence of cam morphology in water polo players and synchronized swimmers should be considered when these athletes present with hip pain.
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Thomas KA, Kidziński Ł, Halilaj E, Fleming SL, Venkataraman GR, Oei EHG, Gold GE, Delp SL. Automated Classification of Radiographic Knee Osteoarthritis Severity Using Deep Neural Networks. Radiol Artif Intell 2020; 2:e190065. [PMID: 32280948 DOI: 10.1148/ryai.2020190065] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Revised: 10/30/2019] [Accepted: 11/06/2019] [Indexed: 11/11/2022]
Abstract
Purpose To develop an automated model for staging knee osteoarthritis severity from radiographs and to compare its performance to that of musculoskeletal radiologists. Materials and Methods Radiographs from the Osteoarthritis Initiative staged by a radiologist committee using the Kellgren-Lawrence (KL) system were used. Before using the images as input to a convolutional neural network model, they were standardized and augmented automatically. The model was trained with 32 116 images, tuned with 4074 images, evaluated with a 4090-image test set, and compared to two individual radiologists using a 50-image test subset. Saliency maps were generated to reveal features used by the model to determine KL grades. Results With committee scores used as ground truth, the model had an average F1 score of 0.70 and an accuracy of 0.71 for the full test set. For the 50-image subset, the best individual radiologist had an average F1 score of 0.60 and an accuracy of 0.60; the model had an average F1 score of 0.64 and an accuracy of 0.66. Cohen weighted κ between the committee and model was 0.86, comparable to intraexpert repeatability. Saliency maps identified sites of osteophyte formation as influential to predictions. Conclusion An end-to-end interpretable model that takes full radiographs as input and predicts KL scores with state-of-the-art accuracy, performs as well as musculoskeletal radiologists, and does not require manual image preprocessing was developed. Saliency maps suggest the model's predictions were based on clinically relevant information. Supplemental material is available for this article. © RSNA, 2020.
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Affiliation(s)
- Kevin A Thomas
- Departments of Biomedical Data Science (K.A.T., S.L.F., G.R.V.), Bioengineering (Ł.K., S.L.D.), and Radiology (G.E.G.), Stanford University, Clark Center, 318 Campus Dr, Room S321, Stanford, CA 94305; Department of Radiology, Erasmus University Rotterdam, Rotterdam, the Netherlands (E.H.G.O.); and Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pa (E.H.)
| | - Łukasz Kidziński
- Departments of Biomedical Data Science (K.A.T., S.L.F., G.R.V.), Bioengineering (Ł.K., S.L.D.), and Radiology (G.E.G.), Stanford University, Clark Center, 318 Campus Dr, Room S321, Stanford, CA 94305; Department of Radiology, Erasmus University Rotterdam, Rotterdam, the Netherlands (E.H.G.O.); and Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pa (E.H.)
| | - Eni Halilaj
- Departments of Biomedical Data Science (K.A.T., S.L.F., G.R.V.), Bioengineering (Ł.K., S.L.D.), and Radiology (G.E.G.), Stanford University, Clark Center, 318 Campus Dr, Room S321, Stanford, CA 94305; Department of Radiology, Erasmus University Rotterdam, Rotterdam, the Netherlands (E.H.G.O.); and Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pa (E.H.)
| | - Scott L Fleming
- Departments of Biomedical Data Science (K.A.T., S.L.F., G.R.V.), Bioengineering (Ł.K., S.L.D.), and Radiology (G.E.G.), Stanford University, Clark Center, 318 Campus Dr, Room S321, Stanford, CA 94305; Department of Radiology, Erasmus University Rotterdam, Rotterdam, the Netherlands (E.H.G.O.); and Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pa (E.H.)
| | - Guhan R Venkataraman
- Departments of Biomedical Data Science (K.A.T., S.L.F., G.R.V.), Bioengineering (Ł.K., S.L.D.), and Radiology (G.E.G.), Stanford University, Clark Center, 318 Campus Dr, Room S321, Stanford, CA 94305; Department of Radiology, Erasmus University Rotterdam, Rotterdam, the Netherlands (E.H.G.O.); and Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pa (E.H.)
| | - Edwin H G Oei
- Departments of Biomedical Data Science (K.A.T., S.L.F., G.R.V.), Bioengineering (Ł.K., S.L.D.), and Radiology (G.E.G.), Stanford University, Clark Center, 318 Campus Dr, Room S321, Stanford, CA 94305; Department of Radiology, Erasmus University Rotterdam, Rotterdam, the Netherlands (E.H.G.O.); and Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pa (E.H.)
| | - Garry E Gold
- Departments of Biomedical Data Science (K.A.T., S.L.F., G.R.V.), Bioengineering (Ł.K., S.L.D.), and Radiology (G.E.G.), Stanford University, Clark Center, 318 Campus Dr, Room S321, Stanford, CA 94305; Department of Radiology, Erasmus University Rotterdam, Rotterdam, the Netherlands (E.H.G.O.); and Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pa (E.H.)
| | - Scott L Delp
- Departments of Biomedical Data Science (K.A.T., S.L.F., G.R.V.), Bioengineering (Ł.K., S.L.D.), and Radiology (G.E.G.), Stanford University, Clark Center, 318 Campus Dr, Room S321, Stanford, CA 94305; Department of Radiology, Erasmus University Rotterdam, Rotterdam, the Netherlands (E.H.G.O.); and Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pa (E.H.)
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Chaudhari AS, Stevens KJ, Wood JP, Chakraborty AK, Gibbons EK, Fang Z, Desai AD, Lee JH, Gold GE, Hargreaves BA. Utility of deep learning super-resolution in the context of osteoarthritis MRI biomarkers. J Magn Reson Imaging 2020; 51:768-779. [PMID: 31313397 PMCID: PMC6962563 DOI: 10.1002/jmri.26872] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 07/02/2019] [Accepted: 07/03/2019] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Super-resolution is an emerging method for enhancing MRI resolution; however, its impact on image quality is still unknown. PURPOSE To evaluate MRI super-resolution using quantitative and qualitative metrics of cartilage morphometry, osteophyte detection, and global image blurring. STUDY TYPE Retrospective. POPULATION In all, 176 MRI studies of subjects at varying stages of osteoarthritis. FIELD STRENGTH/SEQUENCE Original-resolution 3D double-echo steady-state (DESS) and DESS with 3× thicker slices retrospectively enhanced using super-resolution and tricubic interpolation (TCI) at 3T. ASSESSMENT A quantitative comparison of femoral cartilage morphometry was performed for the original-resolution DESS, the super-resolution, and the TCI scans in 17 subjects. A reader study by three musculoskeletal radiologists assessed cartilage image quality, overall image sharpness, and osteophytes incidence in all three sets of scans. A referenceless blurring metric evaluated blurring in all three image dimensions for the three sets of scans. STATISTICAL TESTS Mann-Whitney U-tests compared Dice coefficients (DC) of segmentation accuracy for the DESS, super-resolution, and TCI images, along with the image quality readings and blurring metrics. Sensitivity, specificity, and diagnostic odds ratio (DOR) with 95% confidence intervals compared osteophyte detection for the super-resolution and TCI images, with the original-resolution as a reference. RESULTS DC for the original-resolution (90.2 ± 1.7%) and super-resolution (89.6 ± 2.0%) were significantly higher (P < 0.001) than TCI (86.3 ± 5.6%). Segmentation overlap of super-resolution with the original-resolution (DC = 97.6 ± 0.7%) was significantly higher (P < 0.0001) than TCI overlap (DC = 95.0 ± 1.1%). Cartilage image quality for sharpness and contrast levels, and the through-plane quantitative blur factor for super-resolution images, was significantly (P < 0.001) better than TCI. Super-resolution osteophyte detection sensitivity of 80% (76-82%), specificity of 93% (92-94%), and DOR of 32 (22-46) was significantly higher (P < 0.001) than TCI sensitivity of 73% (69-76%), specificity of 90% (89-91%), and DOR of 17 (13-22). DATA CONCLUSION Super-resolution appears to consistently outperform naïve interpolation and may improve image quality without biasing quantitative biomarkers. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:768-779.
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Affiliation(s)
| | - Kathryn J Stevens
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
| | - Jeff P Wood
- Austin Radiological Association, Austin, Texas, USA
| | | | - Eric K Gibbons
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, USA
| | | | - Arjun D Desai
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Jin Hyung Lee
- Department of Neurology & Neurological Sciences, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
- Department of Neurosurgery, Stanford University, Stanford, California, USA
| | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Brian A Hargreaves
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
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Bonaretti S, Gold GE, Beaupre GS. pyKNEEr: An image analysis workflow for open and reproducible research on femoral knee cartilage. PLoS One 2020; 15:e0226501. [PMID: 31978052 PMCID: PMC6980400 DOI: 10.1371/journal.pone.0226501] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Accepted: 11/27/2019] [Indexed: 02/04/2023] Open
Abstract
Transparent research in musculoskeletal imaging is fundamental to reliably investigate diseases such as knee osteoarthritis (OA), a chronic disease impairing femoral knee cartilage. To study cartilage degeneration, researchers have developed algorithms to segment femoral knee cartilage from magnetic resonance (MR) images and to measure cartilage morphology and relaxometry. The majority of these algorithms are not publicly available or require advanced programming skills to be compiled and run. However, to accelerate discoveries and findings, it is crucial to have open and reproducible workflows. We present pyKNEEr, a framework for open and reproducible research on femoral knee cartilage from MR images. pyKNEEr is written in python, uses Jupyter notebook as a user interface, and is available on GitHub with a GNU GPLv3 license. It is composed of three modules: 1) image preprocessing to standardize spatial and intensity characteristics; 2) femoral knee cartilage segmentation for intersubject, multimodal, and longitudinal acquisitions; and 3) analysis of cartilage morphology and relaxometry. Each module contains one or more Jupyter notebooks with narrative, code, visualizations, and dependencies to reproduce computational environments. pyKNEEr facilitates transparent image-based research of femoral knee cartilage because of its ease of installation and use, and its versatility for publication and sharing among researchers. Finally, due to its modular structure, pyKNEEr favors code extension and algorithm comparison. We tested our reproducible workflows with experiments that also constitute an example of transparent research with pyKNEEr, and we compared pyKNEEr performances to existing algorithms in literature review visualizations. We provide links to executed notebooks and executable environments for immediate reproducibility of our findings.
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Affiliation(s)
- Serena Bonaretti
- Department of Radiology, Stanford University, Stanford, CA, United States of America
- Musculoskeletal Research Laboratory, VA Palo Alto Health Care System, Palo Alto, CA, United States of America
| | - Garry E. Gold
- Department of Radiology, Stanford University, Stanford, CA, United States of America
| | - Gary S. Beaupre
- Musculoskeletal Research Laboratory, VA Palo Alto Health Care System, Palo Alto, CA, United States of America
- Department of Bioengineering, Stanford University, Stanford, CA, United States of America
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Eijgenraam SM, Chaudhari AS, Reijman M, Bierma-Zeinstra SMA, Hargreaves BA, Runhaar J, Heijboer FWJ, Gold GE, Oei EHG. Time-saving opportunities in knee osteoarthritis: T 2 mapping and structural imaging of the knee using a single 5-min MRI scan. Eur Radiol 2019; 30:2231-2240. [PMID: 31844957 PMCID: PMC7062657 DOI: 10.1007/s00330-019-06542-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Revised: 10/09/2019] [Accepted: 10/23/2019] [Indexed: 12/22/2022]
Abstract
Objectives To assess the discriminative power of a 5-min quantitative double-echo steady-state (qDESS) sequence for simultaneous T2 measurements of cartilage and meniscus, and structural knee osteoarthritis (OA) assessment, in a clinical OA population, using radiographic knee OA as reference standard. Methods Fifty-three subjects were included and divided over three groups based on radiographic and clinical knee OA: 20 subjects with no OA (Kellgren-Lawrence grade (KLG) 0), 18 with mild OA (KLG2), and 15 with moderate OA (KLG3). All patients underwent a 5-min qDESS scan. We measured T2 relaxation times in four cartilage and four meniscus regions of interest (ROIs) and performed structural OA evaluation with the MRI Osteoarthritis Knee Score (MOAKS) using qDESS with multiplanar reformatting. Between-group differences in T2 values and MOAKS were calculated using ANOVA. Correlations of the reference standard (i.e., radiographic knee OA) with T2 and MOAKS were assessed with correlation analyses for ordinal variables. Results In cartilage, mean T2 values were 36.1 ± SD 4.3, 40.6 ± 5.9, and 47.1 ± 4.3 ms for no, mild, and moderate OA, respectively (p < 0.001). In menisci, mean T2 values were 15 ± 3.6, 17.5 ± 3.8, and 20.6 ± 4.7 ms for no, mild, and moderate OA, respectively (p < 0.001). Statistically significant correlations were found between radiographic OA and T2 and between radiographic OA and MOAKS in all ROIs (p < 0.05). Conclusion Quantitative T2 and structural assessment of cartilage and meniscus, using a single 5-min qDESS scan, can distinguish between different grades of radiographic OA, demonstrating the potential of qDESS as an efficient tool for OA imaging. Key Points • Quantitative T2values of cartilage and meniscus as well as structural assessment of the knee with a single 5-min quantitative double-echo steady-state (qDESS) scan can distinguish between different grades of knee osteoarthritis (OA). • Quantitative and structural qDESS-based measurements correlate significantly with the reference standard, radiographic degree of OA, for all cartilage and meniscus regions. • By providing quantitative measurements and diagnostic image quality in one rapid MRI scan, qDESS has great potential for application in large-scale clinical trials in knee OA. Electronic supplementary material The online version of this article (10.1007/s00330-019-06542-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Susanne M Eijgenraam
- Deptartment of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Dr. Molewaterplein 40, Room Nd-547, 3015, GD, Rotterdam, The Netherlands.,Deptartment of Orthopedic Surgery, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | | | - Max Reijman
- Deptartment of Orthopedic Surgery, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Sita M A Bierma-Zeinstra
- Deptartment of Orthopedic Surgery, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.,Deptartment of General Practice, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Brian A Hargreaves
- Deptartment of Radiology, Stanford University, Stanford, CA, USA.,Deptartment of Electrical Engineering, Stanford University, Stanford, CA, USA.,Deptartment of Bioengineering, Stanford University, Stanford, CA, USA
| | - Jos Runhaar
- Deptartment of General Practice, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Frank W J Heijboer
- Deptartment of Orthopedic Surgery, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Garry E Gold
- Deptartment of Radiology, Stanford University, Stanford, CA, USA.,Deptartment of Bioengineering, Stanford University, Stanford, CA, USA.,Deptartment of Orthopedic Surgery, Stanford University, Stanford, CA, USA
| | - Edwin H G Oei
- Deptartment of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Dr. Molewaterplein 40, Room Nd-547, 3015, GD, Rotterdam, The Netherlands.
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Nathani A, Gold GE, Monu U, Hargreaves B, Finlay AK, Rubin EB, Safran MR. Does Injection of Hyaluronic Acid Protect Against Early Cartilage Injury Seen After Marathon Running? A Randomized Controlled Trial Utilizing High-Field Magnetic Resonance Imaging. Am J Sports Med 2019; 47:3414-3422. [PMID: 31634003 DOI: 10.1177/0363546519879138] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
BACKGROUND Previous studies have shown that runners demonstrate elevated T2 and T1ρ values on magnetic resonance imaging (MRI) after running a marathon, with the greatest changes in the patellofemoral and medial compartment, which can persist after 3 months of reduced activity. Additionally, marathon running has been shown to increase serum inflammatory markers. Hyaluronic acid (HA) purportedly improves viscoelasticity of synovial fluid, serving as a lubricant while also having chondroprotective and anti-inflammatory effects. PURPOSE/HYPOTHESIS The purpose was to investigate whether intra-articular HA injection can protect articular cartilage from injury attributed to marathon running. The hypothesis was that the addition of intra-articular HA 1 week before running a marathon would reduce the magnitude of early cartilage breakdown measured by MRI. STUDY DESIGN Randomized controlled trial; Level of evidence, 2. METHODS After institutional review board approval, 20 runners were randomized into receiving an intra-articular injection of HA or normal saline (NS) 1 week before running a marathon. Exclusionary criteria included any prior knee injury or surgery and having run >3 prior marathons. Baseline 3-T knee MRI was obtained within 48 hours before the marathon (approximately 5 days after injection). Follow-up 3-T MRI scans of the same knee were obtained 48 to 72 hours and 3 months after the marathon. The T2 and T1ρ relaxation times of articular cartilage were measured in 8 locations-the medial and lateral compartments (including 2 areas of each femoral condyle) and the patellofemoral joint. The statistical analysis compared changes in T2 and T1ρ relaxation times (ms) from baseline to immediate and 3-month postmarathon scans between the HA and NS groups with repeated measures analysis of variance. RESULTS Fifteen runners completed the study: 6 women and 2 men in the HA group (mean age, 31 years; range, 23-50 years) and 6 women and 1 man in the NS group (mean age, 27 years; range, 20-49 years). There were no gross morphologic MRI changes after running the marathon. Postmarathon studies revealed no statistically significant changes between the HA and NS groups in all articular cartilage areas of the knee on both T2 and T1ρ relaxation times. CONCLUSION Increased T2 and T1ρ relaxation times have been observed in marathon runners, suggesting early cartilage injury. The addition of intra-articular HA did not significantly affect relaxation times in all areas of the knee when compared with an NS control.
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Affiliation(s)
- Amit Nathani
- Department of Orthopaedic Surgery, Sports Medicine and Shoulder Surgery, Stanford University, Redwood City, California, USA
| | - Garry E Gold
- Department of Orthopaedic Surgery, Sports Medicine and Shoulder Surgery, Stanford University, Redwood City, California, USA.,Department of Radiology, Stanford University, Stanford, California, USA.,Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Uchechukwuka Monu
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Brian Hargreaves
- Department of Radiology, Stanford University, Stanford, California, USA.,Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Andrea K Finlay
- Department of Orthopaedic Surgery, Sports Medicine and Shoulder Surgery, Stanford University, Redwood City, California, USA
| | - Elka B Rubin
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Marc R Safran
- Department of Orthopaedic Surgery, Sports Medicine and Shoulder Surgery, Stanford University, Redwood City, California, USA
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40
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Kogan F, Broski SM, Yoon D, Gold GE. Applications of PET-MRI in musculoskeletal disease. J Magn Reson Imaging 2019; 48:27-47. [PMID: 29969193 DOI: 10.1002/jmri.26183] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Accepted: 04/19/2018] [Indexed: 12/26/2022] Open
Abstract
New integrated PET-MRI systems potentially provide a complete imaging modality for diagnosis and evaluation of musculoskeletal disease. MRI is able to provide excellent high-resolution morphologic information with multiple contrast mechanisms that has made it the imaging modality of choice in evaluation of many musculoskeletal disorders. PET offers incomparable abilities to provide quantitative information about molecular and physiologic changes that often precede structural and biochemical changes. In combination, hybrid PET-MRI can enhance imaging of musculoskeletal disorders through early detection of disease as well as improved diagnostic sensitivity and specificity. The purpose of this article is to review emerging applications of PET-MRI in musculoskeletal disease. Both clinical applications of malignant musculoskeletal disease as well as new opportunities to incorporate the molecular capabilities of nuclear imaging into studies of nononcologic musculoskeletal disease are discussed. Lastly, we discuss some of the technical considerations and challenges of PET-MRI as they specifically relate to musculoskeletal disease. LEVEL OF EVIDENCE 5 TECHNICAL EFFICACY: Stage 3 J. Magn. Reson. Imaging 2018;48:27-47.
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Affiliation(s)
- Feliks Kogan
- Department of Radiology, Stanford University, Stanford, California, USA
| | | | - Daehyun Yoon
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, California, USA.,Department of Bioengineering, Stanford University, Stanford, California, USA.,Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
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Chaudhari AS, Stevens KJ, Sveinsson B, Wood JP, Beaulieu CF, Oei EH, Rosenberg JK, Kogan F, Alley MT, Gold GE, Hargreaves BA. Combined 5-minute double-echo in steady-state with separated echoes and 2-minute proton-density-weighted 2D FSE sequence for comprehensive whole-joint knee MRI assessment. J Magn Reson Imaging 2019; 49:e183-e194. [PMID: 30582251 PMCID: PMC7850298 DOI: 10.1002/jmri.26582] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Accepted: 11/01/2018] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Clinical knee MRI protocols require upwards of 15 minutes of scan time. PURPOSE/HYPOTHESIS To compare the imaging appearance of knee abnormalities depicted with a 5-minute 3D double-echo in steady-state (DESS) sequence with separate echo images, with that of a routine clinical knee MRI protocol. A secondary goal was to compare the imaging appearance of knee abnormalities depicted with 5-minute DESS paired with a 2-minute coronal proton-density fat-saturated (PDFS) sequence. STUDY TYPE Prospective. SUBJECTS Thirty-six consecutive patients (19 male) referred for a routine knee MRI. FIELD STRENGTH/SEQUENCES DESS and PDFS at 3T. ASSESSMENT Five musculoskeletal radiologists evaluated all images for the presence of internal knee derangement using DESS, DESS+PDFS, and the conventional imaging protocol, and their associated diagnostic confidence of the reading. STATISTICAL TESTS Differences in positive and negative percent agreement (PPA and NPA, respectively) and 95% confidence intervals (CIs) for DESS and DESS+PDFS compared with the conventional protocol were calculated and tested using exact McNemar tests. The percentage of observations where DESS or DESS+PDFS had equivalent confidence ratings to DESS+Conv were tested with exact symmetry tests. Interreader agreement was calculated using Krippendorff's alpha. RESULTS DESS had a PPA of 90% (88-92% CI) and NPA of 99% (99-99% CI). DESS+PDFS had increased PPA of 99% (95-99% CI) and NPA of 100% (99-100% CI) compared with DESS (both P < 0.001). DESS had equivalent diagnostic confidence to DESS+Conv in 94% of findings, whereas DESS+PDFS had equivalent diagnostic confidence in 99% of findings (both P < 0.001). All readers had moderate concordance for all three protocols (Krippendorff's alpha 47-48%). DATA CONCLUSION Both 1) 5-minute 3D-DESS with separated echoes and 2) 5-minute 3D-DESS paired with a 2-minute coronal PDFS sequence depicted knee abnormalities similarly to a routine clinical knee MRI protocol, which may be a promising technique for abbreviated knee MRI. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.
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Affiliation(s)
- Akshay S. Chaudhari
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Kathryn J. Stevens
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
| | - Bragi Sveinsson
- Department of Radiology, Stanford University, Stanford, California, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Jeff P. Wood
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Christopher F. Beaulieu
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
| | - Edwin H.G. Oei
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | | | - Feliks Kogan
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Marcus T. Alley
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Garry E. Gold
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
| | - Brian A. Hargreaves
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
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Abstract
Although computed tomography (CT) and MR imaging alone have been used extensively to evaluate various musculoskeletal disorders, hybrid imaging modalities of PET-CT and PET-MR imaging were recently developed, combining the advantages of each method: molecular information from PET and anatomical information from CT or MR imaging. Furthermore, different radiotracers can be used in PET to uncover different disease mechanisms. In this article, potential applications of PET-CT and PET-MR imaging for benign musculoskeletal disorders are organized by benign cell proliferation/dysplasia, diabetic foot complications, joint prostheses, degeneration, inflammation, and trauma, metabolic bone disorders, and pain (acute and chronic) and peripheral nerve imaging.
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Affiliation(s)
- James S Yoder
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Feliks Kogan
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Bioengineering, Stanford University, Stanford, CA, USA; Department of Orthopaedic Surgery, Stanford University, Stanford, CA, USA.
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Pal S, Besier TF, Gold GE, Fredericson M, Delp SL, Beaupre GS. Patellofemoral cartilage stresses are most sensitive to variations in vastus medialis muscle forces. Comput Methods Biomech Biomed Engin 2018; 22:206-216. [PMID: 30596523 DOI: 10.1080/10255842.2018.1544629] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
The purpose of this study was to evaluate the effects of variations in quadriceps muscle forces on patellofemoral stress. We created subject-specific finite element models for 21 individuals with chronic patellofemoral pain and 16 pain-free control subjects. We extracted three-dimensional geometries from high resolution magnetic resonance images and registered the geometries to magnetic resonance images from an upright weight bearing squat with the knees flexed at 60°. We estimated quadriceps muscle forces corresponding to 60° knee flexion during a stair climb task from motion analysis and electromyography-driven musculoskeletal modelling. We applied the quadriceps muscle forces to our finite element models and evaluated patellofemoral cartilage stress. We quantified cartilage stress using an energy-based effective stress, a scalar quantity representing the local stress intensity in the tissue. We used probabilistic methods to evaluate the effects of variations in quadriceps muscle forces from five trials of the stair climb task for each subject. Patellofemoral effective stress was most sensitive to variations in forces in the two branches of the vastus medialis muscle. Femur cartilage effective stress was most sensitive to variations in vastus medialis forces in 29/37 (78%) subjects, and patella cartilage effective stress was most sensitive to variations in vastus medialis forces in 21/37 (57%) subjects. Femur cartilage effective stress was more sensitive to variations in vastus medialis longus forces in subjects classified as maltrackers compared to normal tracking subjects (p = 0.006). This study provides new evidence of the importance of the vastus medialis muscle in the treatment of patellofemoral pain.
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Affiliation(s)
- Saikat Pal
- a Department of Biomedical Engineering , New Jersey Institute of Technology , Newark , NJ , USA
| | - Thor F Besier
- b Auckland Bioengineering Institute & Department of Engineering Science , University of Auckland , Auckland , New Zealand
| | - Garry E Gold
- c Department of Bioengineering , Stanford University , Stanford , CA , USA.,d Department of Radiology , Stanford University , Stanford , CA , USA.,e Department of Orthopaedic Surgery , Stanford University , Stanford , CA , USA
| | - Michael Fredericson
- e Department of Orthopaedic Surgery , Stanford University , Stanford , CA , USA
| | - Scott L Delp
- c Department of Bioengineering , Stanford University , Stanford , CA , USA.,e Department of Orthopaedic Surgery , Stanford University , Stanford , CA , USA.,f Mechanical Engineering Department , Stanford University , Stanford , CA , USA
| | - Gary S Beaupre
- c Department of Bioengineering , Stanford University , Stanford , CA , USA.,g Musculoskeletal Research Laboratory , VA Palo Alto Health Care System , Palo Alto , CA , USA
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Abstract
PURPOSE OF REVIEW This review article attempts to summarize the current state and applications of the hybrid imaging modality of PET-MRI to metabolic bone diseases. The advances of PET and MRI are also discussed for metabolic bone diseases as potentially applied via PET-MRI. RECENT FINDINGS Etiologies and mechanisms of metabolic bone disease can be complex where molecular changes precede structural changes. Although PET-MRI has yet to be applied directly to metabolic bone disease, possible applications exist since PET, specifically 18F-NaF PET, can quantitatively track changes in bone metabolism and is useful for assessing treatment, while MRI can give detailed information on bone water concentration, porosity, and architecture through novel techniques such as UTE and ZTE MRI. Earlier detection and further understanding of metabolic bone disease via PET and MRI could lead to better treatment and prevention. More research using this modality is needed to further understand how it can be implemented in this realm.
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Affiliation(s)
- James S Yoder
- Department of Radiology, Stanford University, 1201 Welch Rd, Stanford, CA, 94305, USA
| | - Feliks Kogan
- Department of Radiology, Stanford University, 1201 Welch Rd, Stanford, CA, 94305, USA
| | - Garry E Gold
- Department of Radiology, Stanford University, 1201 Welch Rd, Stanford, CA, 94305, USA.
- Bioengineering, Stanford University, Stanford, CA, USA.
- Orthopaedic Surgery, Stanford University, Stanford, CA, USA.
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Chaudhari AS, Fang Z, Kogan F, Wood J, Stevens KJ, Gibbons EK, Lee JH, Gold GE, Hargreaves BA. Super-resolution musculoskeletal MRI using deep learning. Magn Reson Med 2018; 80:2139-2154. [PMID: 29582464 PMCID: PMC6107420 DOI: 10.1002/mrm.27178] [Citation(s) in RCA: 171] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2017] [Revised: 02/14/2018] [Accepted: 02/22/2018] [Indexed: 12/22/2022]
Abstract
PURPOSE To develop a super-resolution technique using convolutional neural networks for generating thin-slice knee MR images from thicker input slices, and compare this method with alternative through-plane interpolation methods. METHODS We implemented a 3D convolutional neural network entitled DeepResolve to learn residual-based transformations between high-resolution thin-slice images and lower-resolution thick-slice images at the same center locations. DeepResolve was trained using 124 double echo in steady-state (DESS) data sets with 0.7-mm slice thickness and tested on 17 patients. Ground-truth images were compared with DeepResolve, clinically used tricubic interpolation, and Fourier interpolation methods, along with state-of-the-art single-image sparse-coding super-resolution. Comparisons were performed using structural similarity, peak SNR, and RMS error image quality metrics for a multitude of thin-slice downsampling factors. Two musculoskeletal radiologists ranked the 3 data sets and reviewed the diagnostic quality of the DeepResolve, tricubic interpolation, and ground-truth images for sharpness, contrast, artifacts, SNR, and overall diagnostic quality. Mann-Whitney U tests evaluated differences among the quantitative image metrics, reader scores, and rankings. Cohen's Kappa (κ) evaluated interreader reliability. RESULTS DeepResolve had significantly better structural similarity, peak SNR, and RMS error than tricubic interpolation, Fourier interpolation, and sparse-coding super-resolution for all downsampling factors (p < .05, except 4 × and 8 × sparse-coding super-resolution downsampling factors). In the reader study, DeepResolve significantly outperformed (p < .01) tricubic interpolation in all image quality categories and overall image ranking. Both readers had substantial scoring agreement (κ = 0.73). CONCLUSION DeepResolve was capable of resolving high-resolution thin-slice knee MRI from lower-resolution thicker slices, achieving superior quantitative and qualitative diagnostic performance to both conventionally used and state-of-the-art methods.
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Affiliation(s)
- Akshay S. Chaudhari
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | | | - Feliks Kogan
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Jeff Wood
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Kathryn J Stevens
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
| | - Eric K. Gibbons
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, USA
| | - Jin Hyung Lee
- Department of Bioengineering, Stanford University, Stanford, California, USA
- LVIS Corporation, Palo Alto, California, USA
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Garry E. Gold
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
| | - Brian A. Hargreaves
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
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Sveinsson B, Gold GE, Hargreaves BA, Yoon D. SNR-weighted regularization of ADC estimates from double-echo in steady-state (DESS). Magn Reson Med 2018; 81:711-718. [PMID: 30125389 DOI: 10.1002/mrm.27436] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Revised: 05/17/2018] [Accepted: 06/07/2018] [Indexed: 11/07/2022]
Abstract
PURPOSE To improve the homogeneity and consistency of apparent diffusion coefficient (ADC) estimates in cartilage from the double-echo in steady-state (DESS) sequence by applying SNR-weighted regularization during post-processing. METHODS An estimation method that linearizes ADC estimates from DESS is used in conjunction with a smoothness constraint to suppress noise-induced variation in ADC estimates. Simulations, phantom scans, and in vivo scans are used to demonstrate how the method reduces ADC variability. Conventional diffusion-weighted echo-planar imaging (DW EPI) maps are acquired for comparison of mean and standard deviation (SD) of the ADC estimate. RESULTS Simulations and phantom scans demonstrated that the SNR-weighted regularization can produce homogenous ADC maps at varying levels of SNR, whereas non-regularized maps only estimate ADC accurately at high SNR levels. The in vivo maps showed that the SNR-weighted regularization produced ADC maps with similar heterogeneity to maps produced with standard DW EPI, but without the distortion of such reference scans. CONCLUSION A linear approximation of a simplified model of the relationship between DESS signals allows for fast SNR-weighted regularization of ADC maps that reduces estimation error in relatively short T2 tissue such as cartilage.
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Affiliation(s)
- Bragi Sveinsson
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School, Boston, Massachusetts.,Department of Physics, Harvard University, Cambridge, Massachusetts
| | - Garry E Gold
- Department of Radiology, Stanford University, Stanford, California
| | | | - Daehyun Yoon
- Department of Radiology, Stanford University, Stanford, California
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Halilaj E, Hastie TJ, Gold GE, Delp SL. Physical activity is associated with changes in knee cartilage microstructure. Osteoarthritis Cartilage 2018; 26:770-774. [PMID: 29605382 PMCID: PMC6086595 DOI: 10.1016/j.joca.2018.03.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2017] [Revised: 02/27/2018] [Accepted: 03/22/2018] [Indexed: 02/02/2023]
Abstract
OBJECTIVE The purpose of this study was to determine if there is an association between objectively measured physical activity and longitudinal changes in knee cartilage microstructure. METHODS We used accelerometry and T2-weighted magnetic resonance imaging (MRI) data from the Osteoarthritis Initiative, restricting the analysis to men aged 45-60 years, with a body mass index (BMI) of 25-27 kg/m2 and no radiographic evidence of knee osteoarthritis. After computing 4-year changes in mean T2 relaxation time for six femoral cartilage regions and mean daily times spent in the sedentary, light, moderate, and vigorous activity ranges, we performed canonical correlation analysis (CCA) to find a linear combination of times spent in different activity intensity ranges (Activity Index) that was maximally correlated with a linear combination of regional changes in cartilage microstructure (Cartilage Microstructure Index). We used leave-one-out pre-validation to test the robustness of the model on new data. RESULTS Nineteen subjects satisfied the inclusion criteria. CCA identified an Activity Index and a Cartilage Microstructure Index that were significantly correlated (r = .82, P < .0001 on test data). Higher levels of sedentary time and vigorous activity were associated with greater medial-lateral differences in longitudinal T2 changes, whereas light activity was associated with smaller differences. CONCLUSIONS Physical activity is better associated with an index that contrasts microstructural changes in different cartilage regions than it is with univariate or cumulative changes, likely because this index separates the effect of activity, which is greater in the medial loadbearing region, from that of patient-specific natural aging.
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Affiliation(s)
- Eni Halilaj
- Postdoctoral Fellow, Department of Bioengineering, Stanford University
| | - Trevor J. Hastie
- John A. Overdeck Professor, Department of Statistics, Stanford University
| | - Garry E. Gold
- Professor, Department of Radiology, Stanford University
| | - Scott L. Delp
- James H. Clark Professor, Departments of Bioengineering, Mechanical Engineering, and Orthopaedic Surgery, Stanford University
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Kogan F, Fan AP, Monu U, Iagaru A, Hargreaves BA, Gold GE. Quantitative imaging of bone-cartilage interactions in ACL-injured patients with PET-MRI. Osteoarthritis Cartilage 2018; 26:790-796. [PMID: 29656143 PMCID: PMC6037170 DOI: 10.1016/j.joca.2018.04.001] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2017] [Revised: 02/10/2018] [Accepted: 04/04/2018] [Indexed: 02/07/2023]
Abstract
OBJECTIVE To investigate changes in bone metabolism by positron emission tomography (PET), as well as spatial relationships between bone metabolism and magnetic resonance imaging (MRI) quantitative markers of early cartilage degradation, in anterior cruciate ligament (ACL)-reconstructed knees. DESIGN Both knees of 15 participants with unilateral reconstructed ACL tears and unaffected contralateral knees were scanned using a simultaneous 3.0T PET-MRI system following injection of 18F-sodium fluoride (18F-NaF). The maximum pixel standardized uptake value (SUVmax) in the subchondral bone and the average T2 relaxation time in cartilage were measured in each knee in eight knee compartments. We tested differences in SUVmax and cartilage T2 relaxation times between the ACL-injured knee and the contralateral control knee as well as spatial relationships between these bone and cartilage changes. RESULTS Significantly increased subchondral bone 18F-NaF SUVmax and cartilage T2 times were observed in the ACL-reconstructed knees (median [inter-quartile-range (IQR)]: 5.0 [5.8], 36.8 [3.6] ms) compared to the contralateral knees (median [IQR]: 1.9 [1.4], 34.4 [3.8] ms). A spatial relationship between the two markers was also seen. Using the contralateral knee as a control, we observed a significant correlation of r = 0.59 between the difference in subchondral bone SUVmax (between injured and contralateral knees) and the adjacent cartilage T2 (between the two knees) [P < 0.001], with a slope of 0.49 ms/a.u. This correlation and slope were higher in deep layers (r = 0.73, slope = 0.60 ms/a.u.) of cartilage compared to superficial layers (r = 0.40, slope = 0.43 ms/a.u.). CONCLUSIONS 18F-NaF PET-MR imaging enables detection of increased subchondral bone metabolism in ACL-reconstructed knees and may serve as an important marker of early osteoarthritis (OA) progression. Spatial relationships observed between early OA changes across bone and cartilage support the need to study whole-joint disease mechanisms in OA.
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Affiliation(s)
- F Kogan
- Department of Radiology, Stanford University, Stanford, CA, USA.
| | - A P Fan
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - U Monu
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - A Iagaru
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - B A Hargreaves
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Bioengineering, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - G E Gold
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Bioengineering, Stanford University, Stanford, CA, USA; Department of Orthopaedic Surgery, Stanford University, Stanford, CA, USA
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Abstract
Purpose to create a custom-shaped graft through 3D tissue shape reconstruction and rapid-prototype molding methods using MRI data, and to test the accuracy of the custom-shaped graft against the original anatomical defect. Methods An iatrogenic defect on the distal femur was identified with a 1.5 Tesla MRI and its shape was reconstructed into a three-dimensional (3D) computer model by processing the 3D MRI data. First, the accuracy of the MRI-derived 3D model was tested against a laser-scan based 3D model of the defect. A custom-shaped polyurethane graft was fabricated from the laser-scan based 3D model by creating custom molds through computer aided design and rapid-prototyping methods. The polyurethane tissue was laser-scanned again to calculate the accuracy of this process compared to the original defect. Results The volumes of the defect models from MRI and laser-scan were 537 mm3 and 405 mm3, respectively, implying that the MRI model was 33% larger than the laser-scan model. The average (±SD) distance deviation of the exterior surface of the MRI model from the laser-scan model was 0.4±0.4 mm. The custom-shaped tissue created from the molds was qualitatively very similar to the original shape of the defect. The volume of the custom-shaped cartilage tissue was 463 mm3 which was 15% larger than the laser-scan model. The average (±SD) distance deviation between the two models was 0.04±0.19 mm. Conclusions This investigation proves the concept that custom-shaped engineered grafts can be fabricated from standard sequence 3-D MRI data with the use of CAD and rapid-prototyping technology. The accuracy of this technology may help solve the interfacial problem between native cartilage and graft, if the grafts are custom made for the specific defect. The major source of error in fabricating a 3D custom-shaped cartilage graft appears to be the accuracy of a MRI data itself; however, the precision of the model is expected to increase by the utilization of advanced MR sequences with higher magnet strengths.
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Affiliation(s)
- Seungbum Koo
- School of Mechanical Engineering, Chung-Ang University, Seoul - South Korea
| | | | - Garry E. Gold
- Department of Radiology, Stanford University, Stanford, California - USA
| | - Jason L. Dragoo
- Department of Orthopedic Surgery, Stanford University, Stanford, California - USA
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Titchenal MR, Williams AA, Chehab EF, Asay JL, Dragoo JL, Gold GE, McAdams TR, Andriacchi TP, Chu CR. Cartilage Subsurface Changes to Magnetic Resonance Imaging UTE-T2* 2 Years After Anterior Cruciate Ligament Reconstruction Correlate With Walking Mechanics Associated With Knee Osteoarthritis. Am J Sports Med 2018; 46:565-572. [PMID: 29293364 PMCID: PMC6548433 DOI: 10.1177/0363546517743969] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
BACKGROUND Anterior cruciate ligament (ACL) injury increases risk for posttraumatic knee osteoarthritis (OA). Quantitative ultrashort echo time enhanced T2* (UTE-T2*) mapping shows promise for early detection of potentially reversible subsurface cartilage abnormalities after ACL reconstruction (ACLR) but needs further validation against established clinical metrics of OA risk such as knee adduction moment (KAM) and mechanical alignment. HYPOTHESIS Elevated UTE-T2* values in medial knee cartilage 2 years after ACLR correlate with varus alignment and higher KAM during walking. STUDY DESIGN Cohort study (diagnosis); Level of evidence, 2. METHODS Twenty patients (mean age, 33.1 ± 10.5 years; 11 female) 2 years after ACLR underwent 3.0-T knee magnetic resonance imaging (MRI), radiography, and gait analysis, after which mechanical alignment was measured, KAM during walking was calculated, and UTE-T2* maps were generated. The mechanical axis and the first and second peaks of KAM (KAM1 and KAM2, respectively) were tested using linear regressions for correlations with deep UTE-T2* values in the central and posterior medial femoral condyle (cMFC and pMFC, respectively) and central medial tibial plateau (cMTP). UTE-T2* values from ACL-reconstructed patients were additionally compared with those of 14 uninjured participants (mean age, 30.9 ± 8.9 years; 6 female) using Mann-Whitney U and standard t tests. RESULTS Central weightbearing medial compartment cartilage of ACL-reconstructed knees was intact on morphological MRI. Mean UTE-T2* values were elevated in both the cMFC and pMFC of ACL-reconstructed knees compared with those of uninjured knees ( P = .003 and P = .012, respectively). In ACL-reconstructed knees, UTE-T2* values of cMFC cartilage positively correlated with increasing varus alignment ( R = 0.568). Higher UTE-T2* values in cMFC and cMTP cartilage of ACL-reconstructed knees also correlated with greater KAM1 ( R = 0.452 and R = 0.463, respectively) and KAM2 ( R = 0.465 and R = 0.764, respectively) and with KAM2 in pMFC cartilage ( R = 0.602). CONCLUSION Elevated deep UTE-T2* values of medial knee cartilage 2 years after ACLR correlate with 2 clinical markers of increased risk of medial knee OA. These results support the clinical utility of MRI UTE-T2* for early diagnosis of subsurface cartilage abnormalities. Longitudinal follow-up of larger cohorts is needed to determine the predictive and staging potential of UTE-T2* for posttraumatic OA.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Constance R. Chu
- Address correspondence to Constance R. Chu, MD, Stanford University, 450 Broadway Street, MC 6342, Redwood City, CA 94061, USA ()
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