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Boudreau M, Karakuzu A, Cohen-Adad J, Bozkurt E, Carr M, Castellaro M, Concha L, Doneva M, Dual SA, Ensworth A, Foias A, Fortier V, Gabr RE, Gilbert G, Glide-Hurst CK, Grech-Sollars M, Hu S, Jalnefjord O, Jovicich J, Keskin K, Koken P, Kolokotronis A, Kukran S, Lee NG, Levesque IR, Li B, Ma D, Mädler B, Maforo NG, Near J, Pasaye E, Ramirez-Manzanares A, Statton B, Stehning C, Tambalo S, Tian Y, Wang C, Weiss K, Zakariaei N, Zhang S, Zhao Z, Stikov N. Repeat it without me: Crowdsourcing the T 1 mapping common ground via the ISMRM reproducibility challenge. Magn Reson Med 2024; 92:1115-1127. [PMID: 38730562 DOI: 10.1002/mrm.30111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 03/21/2024] [Accepted: 03/23/2024] [Indexed: 05/13/2024]
Abstract
PURPOSE T1 mapping is a widely used quantitative MRI technique, but its tissue-specific values remain inconsistent across protocols, sites, and vendors. The ISMRM Reproducible Research and Quantitative MR study groups jointly launched a challenge to assess the reproducibility of a well-established inversion-recovery T1 mapping technique, using acquisition details from a seminal T1 mapping paper on a standardized phantom and in human brains. METHODS The challenge used the acquisition protocol from Barral et al. (2010). Researchers collected T1 mapping data on the ISMRM/NIST phantom and/or in human brains. Data submission, pipeline development, and analysis were conducted using open-source platforms. Intersubmission and intrasubmission comparisons were performed. RESULTS Eighteen submissions (39 phantom and 56 human datasets) on scanners by three MRI vendors were collected at 3 T (except one, at 0.35 T). The mean coefficient of variation was 6.1% for intersubmission phantom measurements, and 2.9% for intrasubmission measurements. For humans, the intersubmission/intrasubmission coefficient of variation was 5.9/3.2% in the genu and 16/6.9% in the cortex. An interactive dashboard for data visualization was also developed: https://rrsg2020.dashboards.neurolibre.org. CONCLUSION The T1 intersubmission variability was twice as high as the intrasubmission variability in both phantoms and human brains, indicating that the acquisition details in the original paper were insufficient to reproduce a quantitative MRI protocol. This study reports the inherent uncertainty in T1 measures across independent research groups, bringing us one step closer to a practical clinical baseline of T1 variations in vivo.
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Affiliation(s)
- Mathieu Boudreau
- NeuroPoly Lab, Polytechnique Montréal, Montréal, Quebec, Canada
- Montreal Heart Institute, Montréal, Quebec, Canada
| | - Agah Karakuzu
- NeuroPoly Lab, Polytechnique Montréal, Montréal, Quebec, Canada
| | - Julien Cohen-Adad
- NeuroPoly Lab, Polytechnique Montréal, Montréal, Quebec, Canada
- Montreal Heart Institute, Montréal, Quebec, Canada
- Unité de Neuroimagerie Fonctionnelle, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada
- Mila-Quebec AI Institute, Montréal, Québec, Canada
- Centre de Recherche du CHU Sainte-Justine, Université de Montréal, Montréal, Québec, Canada
| | - Ecem Bozkurt
- Magnetic Resonance Engineering Laboratory, University of Southern California, Los Angeles, California, USA
| | - Madeline Carr
- Medical Physics, Ingham Institute for Applied Medical Research, Liverpool, Australia
- Department of Medical Physics, Liverpool and Macarthur Cancer Therapy Centers, Liverpool, Australia
| | - Marco Castellaro
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Luis Concha
- Institute of Neurobiology, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro, Mexico
| | | | - Seraina A Dual
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Alex Ensworth
- Medical Physics Unit, McGill University, Montréal, Québec, Canada
- University of British Columbia, Vancouver, British Columbia, Canada
| | - Alexandru Foias
- NeuroPoly Lab, Polytechnique Montréal, Montréal, Quebec, Canada
| | - Véronique Fortier
- Department of Medical Imaging, McGill University Health Center, Montréal, Québec, Canada
- Department of Radiology, McGill University, Montréal, Québec, Canada
| | - Refaat E Gabr
- Department of Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston, McGovern Medical School, Houston, Texas, USA
| | | | - Carri K Glide-Hurst
- Department of Human Oncology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Matthew Grech-Sollars
- Center for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Siyuan Hu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Oscar Jalnefjord
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Jorge Jovicich
- Center for Mind/Brain Sciences, University of Trento, Trento, Italy
| | - Kübra Keskin
- Magnetic Resonance Engineering Laboratory, University of Southern California, Los Angeles, California, USA
| | | | - Anastasia Kolokotronis
- Medical Physics Unit, McGill University, Montréal, Québec, Canada
- Hopital Maisonneuve-Rosemont, Montréal, Québec, Canada
| | - Simran Kukran
- Bioengineering, Imperial College London, London, UK
- Radiotherapy and Imaging, Institute of Cancer Research, Imperial College London, London, UK
| | - Nam G Lee
- Magnetic Resonance Engineering Laboratory, University of Southern California, Los Angeles, California, USA
| | - Ives R Levesque
- Medical Physics Unit, McGill University, Montréal, Québec, Canada
- Research Institute of the McGill University Health Center, Montréal, Québec, Canada
| | - Bochao Li
- Magnetic Resonance Engineering Laboratory, University of Southern California, Los Angeles, California, USA
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | | | - Nyasha G Maforo
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, California, USA
- Physics and Biology in Medicine IDP, University of California Los Angeles, Los Angeles, California, USA
| | - Jamie Near
- Douglas Brain Imaging Center, Montréal, Québec, Canada
- Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Erick Pasaye
- Institute of Neurobiology, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro, Mexico
| | | | - Ben Statton
- Medical Research Council, London Institute of Medical Sciences, Imperial College London, London, UK
| | | | - Stefano Tambalo
- Center for Mind/Brain Sciences, University of Trento, Trento, Italy
| | - Ye Tian
- Magnetic Resonance Engineering Laboratory, University of Southern California, Los Angeles, California, USA
| | - Chenyang Wang
- Department of Radiation Oncology-CNS Service, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Kilian Weiss
- Clinical Science, Philips Healthcare, Hamburg, Germany
| | - Niloufar Zakariaei
- Department of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia, Canada
| | - Shuo Zhang
- Clinical Science, Philips Healthcare, Hamburg, Germany
| | - Ziwei Zhao
- Magnetic Resonance Engineering Laboratory, University of Southern California, Los Angeles, California, USA
| | - Nikola Stikov
- NeuroPoly Lab, Polytechnique Montréal, Montréal, Quebec, Canada
- Montreal Heart Institute, Montréal, Quebec, Canada
- Center for Advanced Interdisciplinary Research, Ss. Cyril and Methodius University, Skopje, North Macedonia
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Keenan KE, Tasdelen B, Javed A, Ramasawmy R, Rizzo R, Martin MN, Stupic KF, Seiberlich N, Campbell-Washburn AE, Nayak KS. T1 and T2 measurements across multiple 0.55T MRI systems using open-source vendor-neutral sequences. Magn Reson Med 2024. [PMID: 39219179 DOI: 10.1002/mrm.30281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Revised: 07/18/2024] [Accepted: 08/14/2024] [Indexed: 09/04/2024]
Abstract
PURPOSE To compare T1 and T2 measurements across commercial and prototype 0.55T MRI systems in both phantom and healthy participants using the same vendor-neutral pulse sequences, reconstruction, and analysis methods. METHODS Standard spin echo measurements and abbreviated protocol measurements of T1, B1, and T2 were made on two prototype 0.55 T systems and two commercial 0.55T systems using an ISMRM/NIST system phantom. Additionally, five healthy participants were imaged at each system using the abbreviated protocol for T1, B1, and T2 measurement. The phantom measurements were compared to NMR-based reference measurements to determine accuracy, and both phantom and in vivo measurements were compared to assess reproducibility and differences between the prototype and commercial systems. RESULTS Vendor-neutral sequences were implemented across all four systems, and the code for pulse sequences and reconstruction is freely available. For participants, there was no difference in the mean T1 and T2 relaxation times between the prototype and commercial systems. In the phantom, there were no significant differences between the prototype and commercial systems for T1 and T2 measurements using the abbreviated protocol. CONCLUSION Quantitative T1 and T2 measurements at 0.55T in phantom and healthy participants are not statistically different across the prototype and commercial systems.
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Affiliation(s)
- Kathryn E Keenan
- National Institute of Standards and Technology, Boulder, Colorado, USA
| | - Bilal Tasdelen
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA
| | - Ahsan Javed
- Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Rajiv Ramasawmy
- Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Rudy Rizzo
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Michele N Martin
- National Institute of Standards and Technology, Boulder, Colorado, USA
| | - Karl F Stupic
- National Institute of Standards and Technology, Boulder, Colorado, USA
| | - Nicole Seiberlich
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Adrienne E Campbell-Washburn
- Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Krishna S Nayak
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA
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Sakai NS, Bray TJP, Taylor SA. Quantitative Magnetic Resonance Imaging (qMRI) of the Small Bowel in Crohn's Disease: State-of-the-Art and Future Directions. J Magn Reson Imaging 2024. [PMID: 38970359 DOI: 10.1002/jmri.29511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 05/07/2024] [Accepted: 05/10/2024] [Indexed: 07/08/2024] Open
Abstract
Crohn's disease (CD) is a chronic inflammatory disease of the gastrointestinal tract in which repeated episodes of acute inflammation may lead to long-term bowel damage. Cross-sectional imaging is used in conjunction with endoscopy to diagnose and monitor disease and detect complications. Magnetic resonance imaging (MRI) has demonstrable utility in evaluating inflammatory activity. However, subjective interpretation of conventional MR sequences is limited in its ability to fully phenotype the underlying histopathological processes in chronic disease. In particular, conventional MRI can be confounded by the presence of mural fibrosis and muscle hypertrophy, which can mask or sometimes mimic inflammation. Quantitative MRI (qMRI) methods provide a means to better differentiate mural inflammation from fibrosis and improve quantification of these processes. qMRI may also provide more objective measures of disease activity and enable better tailoring of treatment. Here, we review quantitative MRI methods for imaging the small bowel in CD and consider the path to their clinical translation. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Naomi S Sakai
- Centre for Medical Imaging, University College London, London, UK
| | - Timothy J P Bray
- Centre for Medical Imaging, University College London, London, UK
| | - Stuart A Taylor
- Centre for Medical Imaging, University College London, London, UK
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Keenan KE, Jordanova KV, Ogier SE, Tamada D, Bruhwiler N, Starekova J, Riek J, McCracken PJ, Hernando D. Phantoms for Quantitative Body MRI: a review and discussion of the phantom value. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01181-8. [PMID: 38896407 DOI: 10.1007/s10334-024-01181-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 05/18/2024] [Accepted: 06/11/2024] [Indexed: 06/21/2024]
Abstract
In this paper, we review the value of phantoms for body MRI in the context of their uses for quantitative MRI methods research, clinical trials, and clinical imaging. Certain uses of phantoms are common throughout the body MRI community, including measuring bias, assessing reproducibility, and training. In addition to these uses, phantoms in body MRI methods research are used for novel methods development and the design of motion compensation and mitigation techniques. For clinical trials, phantoms are an essential part of quality management strategies, facilitating the conduct of ethically sound, reliable, and regulatorily compliant clinical research of both novel MRI methods and therapeutic agents. In the clinic, phantoms are used for development of protocols, mitigation of cost, quality control, and radiotherapy. We briefly review phantoms developed for quantitative body MRI, and finally, we review open questions regarding the most effective use of a phantom for body MRI.
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Affiliation(s)
- Kathryn E Keenan
- Physical Measurement Laboratory, National Institute of Standards and Technology, NIST, 325 Broadway, Boulder, CO, 80305, USA.
| | - Kalina V Jordanova
- Physical Measurement Laboratory, National Institute of Standards and Technology, NIST, 325 Broadway, Boulder, CO, 80305, USA
| | - Stephen E Ogier
- Physical Measurement Laboratory, National Institute of Standards and Technology, NIST, 325 Broadway, Boulder, CO, 80305, USA
- Department of Physics, University of Colorado Boulder, Boulder, CO, USA
| | | | - Natalie Bruhwiler
- Physical Measurement Laboratory, National Institute of Standards and Technology, NIST, 325 Broadway, Boulder, CO, 80305, USA
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Li X, Huang W, Holmes JH. Dynamic Contrast-Enhanced (DCE) MRI. Magn Reson Imaging Clin N Am 2024; 32:47-61. [PMID: 38007282 DOI: 10.1016/j.mric.2023.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2023]
Abstract
The non-invasive dynamic contrast-enhanced MRI (DCE-MRI) method provides valuable insights into tissue perfusion and vascularity. Primarily used in oncology, DCE-MRI is typically utilized to assess morphology and contrast agent (CA) kinetics in the tissue of interest. Interpretation of the temporal signatures of DCE-MRI data includes qualitative, semi-quantitative, and quantitative approaches. Recent advances in MRI technology allow simultaneous high spatial and temporal resolutions in DCE-MRI data acquisition on most vendor platforms, enabling the more desirable approach of quantitative data analysis using pharmacokinetic (PK) modeling. Many technical factors, including signal-to-noise ratio, temporal resolution, quantifications of arterial input function and native tissue T1, and PK model selection, need to be carefully considered when performing quantitative DCE-MRI. Standardization in data acquisition and analysis is especially important in multi-center studies.
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Affiliation(s)
- Xin Li
- Advanced Imaging Research Center, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA
| | - Wei Huang
- Advanced Imaging Research Center, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA
| | - James H Holmes
- Radiology, Biomedical Engineering, and Holden Cancer Center, University of Iowa, 169 Newton Road, Iowa City, IA 52242, USA.
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6
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Wang Y, Greer JS, Zhou L, Lin SQ, Hulsey KM, Udayakumar D, Madhuranthakam AJ. A 3D-printed phantom for quality-controlled reproducibility measurements of arterial spin labeled perfusion. Magn Reson Med 2024; 91:819-827. [PMID: 37815014 PMCID: PMC10841664 DOI: 10.1002/mrm.29886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 08/29/2023] [Accepted: 09/22/2023] [Indexed: 10/11/2023]
Abstract
PURPOSE To develop a portable MR perfusion phantom for quality-controlled assessment and reproducibility of arterial spin labeled (ASL) perfusion measurement. METHODS A 3D-printed perfusion phantom was developed that mimics the branching of arterial vessels, capillaries, and a chamber containing cellulose sponge representing tissue characteristics. A peristaltic pump circulated distilled water through the phantom, and was first evaluated at 300, 400, and 500 mL/min. Longitudinal reproducibility of perfusion was performed using 2D pseudo-continuous ASL at 20 post-label delays (PLDs, ranging between 0.2 and 7.8 s at 0.4-s intervals) over a period of 16 weeks, with three repetitions each week. Multi-PLD data were fitted into a general kinetic model for perfusion quantification (f) and arterial transit time (ATT). Intraclass correlation coefficient was used to assess intersession reproducibility. RESULTS MR perfusion signals acquired in the 3D-printed perfusion phantom agreed well with the experimental conditions, with progressively increasing signal intensities and decreasing ATT for pump flow rates from 300 to 500 mL/min. The perfusion signal at 400 mL/min and the general kinetic model-derived f and ATT maps were similar across all PLDs for both intrasession and intersession reproducibility. Across all 48 experimental time points, the average f was 75.55 ± 3.83 × 10-3 mL/mL/s, the corresponding ATT was 2.10 ± 0.20 s, and the T1 was 1.84 ± 0.102 s. Intraclass correlation coefficient was 0.92 (95% confidence interval 0.83-0.97) for f, 0.96 (0.91-0.99) for ATT, and 0.94 (0.88-0.98) for T1 , demonstrating excellent reproducibility. CONCLUSION A simple, portable 3D-printed perfusion phantom with excellent reproducibility of 2D pseudo-continuous ASL measurements was demonstrated that can serve for quality-controlled and reliable measurements of ASL perfusion.
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Affiliation(s)
- Yiming Wang
- Department of Radiology, UT Southwestern Medical Center, Dallas, Texas, USA
- Philips Healthcare, Shanghai, China
| | - Joshua S Greer
- Department of Radiology, UT Southwestern Medical Center, Dallas, Texas, USA
- Philips Healthcare, Cincinnati, Ohio, USA
| | - Limin Zhou
- Department of Radiology, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Sheng-Qing Lin
- Department of Radiology, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Keith M Hulsey
- Department of Radiology, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Durga Udayakumar
- Department of Radiology, UT Southwestern Medical Center, Dallas, Texas, USA
- Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Ananth J Madhuranthakam
- Department of Radiology, UT Southwestern Medical Center, Dallas, Texas, USA
- Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, Texas, USA
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7
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Pei H, Xia D, Xu X, Yang Y, Wang Y, Liu F, Feng L. Rapid 3D T 1 mapping using deep learning-assisted Look-Locker inversion recovery MRI. Magn Reson Med 2023; 90:569-582. [PMID: 37125662 PMCID: PMC10225330 DOI: 10.1002/mrm.29672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 03/01/2023] [Accepted: 03/28/2023] [Indexed: 05/02/2023]
Abstract
PURPOSE Conventional 3D Look-Locker inversion recovery (LLIR) T1 mapping requires multi-repetition data acquisition to reconstruct images at different inversion times for T1 fitting. To ensure B1 robustness, sufficient time of delay (TD) is needed between repetitions, which prolongs scan time. This work proposes a novel deep learning-assisted LLIR MRI approach for rapid 3D T1 mapping without TD. THEORY AND METHODS The proposed approach is based on the fact thatT 1 * $$ {\mathrm{T}}_1^{\ast } $$ , the effective T1 in LLIR imaging, is independent of TD and can be estimated from both LLIR imaging with and without TD, while accurate conversion ofT 1 * $$ {\mathrm{T}}_1^{\ast } $$ to T1 requires TD. Therefore, deep learning can be used to learn the conversion ofT 1 * $$ {\mathrm{T}}_1^{\ast } $$ to T1 , which eliminates the need for TD. This idea was implemented for inversion-recovery-prepared Golden-angel RAdial Sparse Parallel T1 mapping (GraspT1 ). 39 GraspT1 datasets with a TD of 6 s (GraspT1 -TD6) were used for training, which also incorporates additional anatomical images. The trained network was applied for T1 estimation in 14 GraspT1 datasets without TD (GraspT1 -TD0). The robustness of the trained network was also tested. RESULTS Deep learning-based T1 estimation from GraspT1 -TD0 is accurate compared to the reference. Incorporation of additional anatomical images improves the accuracy of T1 estimation. The technique is also robust against slight variation in spatial resolution, imaging orientation and scanner platform. CONCLUSION Our approach eliminates the need for TD in 3D LLIR imaging without affecting the T1 estimation accuracy. It represents a novel use of deep learning towards more efficient and robust 3D LLIR T1 mapping.
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Affiliation(s)
- Haoyang Pei
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University Grossman School of Medicine, New York, NY, USA
- Department of Electrical and Computer Engineering, NYU Tandon School of Engineering, New York, NY, USA
| | - Ding Xia
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Xiang Xu
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yang Yang
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
| | - Yao Wang
- Department of Electrical and Computer Engineering, NYU Tandon School of Engineering, New York, NY, USA
| | - Fang Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, MA, USA
| | - Li Feng
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University Grossman School of Medicine, New York, NY, USA
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Li T, Wang J, Yang Y, Glide-Hurst CK, Wen N, Cai J. Multi-parametric MRI for radiotherapy simulation. Med Phys 2023; 50:5273-5293. [PMID: 36710376 PMCID: PMC10382603 DOI: 10.1002/mp.16256] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 09/10/2022] [Accepted: 12/06/2022] [Indexed: 01/31/2023] Open
Abstract
Magnetic resonance imaging (MRI) has become an important imaging modality in the field of radiotherapy (RT) in the past decade, especially with the development of various novel MRI and image-guidance techniques. In this review article, we will describe recent developments and discuss the applications of multi-parametric MRI (mpMRI) in RT simulation. In this review, mpMRI refers to a general and loose definition which includes various multi-contrast MRI techniques. Specifically, we will focus on the implementation, challenges, and future directions of mpMRI techniques for RT simulation.
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Affiliation(s)
- Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jihong Wang
- Department of Radiation Physics, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Yingli Yang
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China
- SJTU-Ruijing-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Carri K Glide-Hurst
- Department of Radiation Oncology, University of Wisconsin, Madison, Wisconsin, USA
| | - Ning Wen
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China
- SJTU-Ruijing-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- The Global Institute of Future Technology, Shanghai Jiaotong University, Shanghai, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
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9
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Fukunaga K, Enzaki M, Komi M, Azuma M, Hirai T, Fujiwara Y. [Evaluation of the Accuracy of Relaxation Time Measurements Using 3D-QALAS at 3.0 T MRI and Comparison with 2D-MDME]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2023:2023-1343. [PMID: 37211403 DOI: 10.6009/jjrt.2023-1343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
PURPOSE Three-dimensional (3D) quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (QALAS) is a quantitative sequence used to measure relaxation times. The accuracy of the relaxation time measurement of 3D-QALAS at 3.0 T and the bias of 3D-QALAS have not yet been assessed. The purpose of this study was to clarify the accuracy of the relaxation time measurements using 3D-QALAS at 3.0 T MRI. METHODS The accuracy of the T1 and T2 values for 3D-QALAS was evaluated using a phantom. Subsequently, the T1 and T2 values and proton density of the brain parenchyma in healthy subjects were measured using 3D-QALAS and compared with those of 2D multi-dynamic multi-echo (MDME). RESULTS In the phantom study, the average T1 value of 3D-QALAS was 8.3% prolonged than that for conventional inversion recovery spin-echo; the average T2 value for 3D-QALAS was 18.4% shorter than that for multi-echo spin-echo. The in vivo assessment showed that the mean T1 and T2 values and PD for 3D-QALAS were prolonged by 5.3%, shortened by 9.6%, and increased by 7.0%, respectively, compared with those for 2D-MDME. CONCLUSION Although 3D-QALAS at 3.0 T has high accuracy T1 value, which is less than 1000 ms, the T1 value could be overestimated for tissues with it longer than that T1 value. The T2 value for 3D-QALAS could be underestimated for tissues with T2 values, and this tendency increases with longer T2 values.
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Affiliation(s)
- Kota Fukunaga
- Graduate School of Health Sciences, Kumamoto University
| | | | | | - Minako Azuma
- Department of Radiology, Faculty of Medicine, University of Miyazaki
| | - Toshinori Hirai
- Department of Diagnostic Radiology, Faculty of Medicine, Kumamoto University
| | - Yasuhiro Fujiwara
- Department of Medical Image Sciences, Faculty of Life Sciences, Kumamoto University
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10
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Yen YF, Stupic KF, Janicke MT, Greve DN, Mareyam A, Stockmann J, Polimeni JR, van der Kouwe A, Keenan KE. T1 relaxation time of ISMRM/NIST T1 phantom spheres at 7 T. NMR IN BIOMEDICINE 2023; 36:e4873. [PMID: 36347826 DOI: 10.1002/nbm.4873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 11/03/2022] [Accepted: 11/04/2022] [Indexed: 06/16/2023]
Abstract
T1 relaxation times of the 14 T1 phantom spheres that make up the standard International Society for Magnetic Resonance in Medicine (ISMRM)/National Institute of Standards and Technology (NIST) system phantom are reported at 7 T. T1 values of six of the 14 T1 spheres at 7 T (with T1 > 270 ms) have been reported previously, but, to the best of our knowledge, not all of the T1s of the 14 T1 spheres at 7 T have been reported before. Given the increasing number of 7-T MRI systems in clinical settings and the increasing need for T1 phantoms that cover a wide range of T1 relaxation times to evaluate rapid T1 mapping techniques at 7 T, it is of high interest to obtain accurate T1 values for all the ISMRM/NIST T1 spheres at 7 T. In this work, T1 relaxation time was measured on a 7-T MRI scanner using an inversion-recovery spin-echo pulse sequence and derived by curve fitting to a signal equation that exhibits insensitivity to B 1 + inhomogeneity. Day-to-day reproducibility was within 0.4% and differences between two different RF coils within 1.5%. T1s of a subset of the 14 spheres were also measured by NMR at 7 T for comparison, and the T1 results were consistent between the MRI and NMR measurements. T1 measurements performed at 3 T on the same 14 spheres using the same sequence and fitting method yielded good agreement (mean percentage difference of -0.4%) with the reference T1 values available from the NIST, reflecting the accuracy of the reported technique despite being without the standard phantom housing. We found that the T1 values of all 14 NiCl2 spheres are consistently lower at 7 T than at 3 T. Although our results were well reproduced, this study represents initial work to quantify the 7-T T1 values of all 14 NIST T1 spheres outside of the standard housing and does not warrant reproducibility of the ISMRM/NIST system phantom as a whole. A future study to assess the T1 values of a version of the ISMRM/NIST system phantom that fits inside typical commercial coils at 7 T will be very helpful. Nonetheless, the details on our acquisition and curve-fitting methods reported here allow the T1 measurements to be reproduced elsewhere. The T1 values of all 14 spheres reported here will be valuable for the development of quantitative MR fingerprinting and rapid T1 mapping for a large variety of research projects, not only in neuroimaging but also in body MRI, musculoskeletal MRI, and gadolinium contrast-enhanced MRI, each of which is concerned with much shortened T1.
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Affiliation(s)
- Yi-Fen Yen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Karl F Stupic
- Physical Measurement Laboratory, National Institute of Standards and Technology, Boulder, Colorado, USA
| | - Michael T Janicke
- Chemistry Division, Los Alamos National Laboratory, REFOCUS: Resonance Center for Chemical Signatures, Los Alamos, New Mexico, USA
| | - Douglas N Greve
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Azma Mareyam
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jason Stockmann
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Andre van der Kouwe
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Kathryn E Keenan
- Physical Measurement Laboratory, National Institute of Standards and Technology, Boulder, Colorado, USA
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11
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Edwards L, Waterton JC, Naish J, Short C, Semple T, Jm Parker G, Tibiletti M. Imaging human lung perfusion with contrast media: A meta-analysis. Eur J Radiol 2023; 164:110850. [PMID: 37178490 DOI: 10.1016/j.ejrad.2023.110850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 04/20/2023] [Accepted: 04/22/2023] [Indexed: 05/15/2023]
Abstract
PURPOSE To pool and summarise published data of pulmonary blood flow (PBF), pulmonary blood volume (PBV) and mean transit time (MTT) of the human lung, obtained with perfusion MRI or CT to provide reliable reference values of healthy lung tissue. In addition, the available data regarding diseased lung was investigated. METHODS PubMed was systematically searched to identify studies that quantified PBF/PBV/MTT in the human lung by injection of contrast agent, imaged by MRI or CT. Only data analysed by 'indicator dilution theory' were considered numerically. Weighted mean (wM), weighted standard deviation (wSD) and weighted coefficient of variance (wCoV) were obtained for healthy volunteers (HV), weighted according to the size of the datasets. Signal to concentration conversion method, breath holding method and presence of 'pre-bolus' were noted. RESULTS PBV was obtained from 313 measurements from 14 publications (wM: 13.97 ml/100 ml, wSD: 4.21 ml/100 ml, wCoV 0.30). MTT was obtained from 188 measurements from 10 publications (wM: 5.91 s, wSD: 1.84 s wCoV 0.31). PBF was obtained from 349 measurements from 14 publications (wM: 246.26 ml/100 ml ml/min, wSD: 93.13 ml/100 ml ml/min, wCoV 0.38). PBV and PBF were higher when the signal was normalised than when it was not. No significant differences were found for PBV and PBF between breathing states or between pre-bolus and no pre-bolus. Data for diseased lung were insufficient for meta-analysis. CONCLUSION Reference values for PBF, MTT and PBV were obtained in HV. The literature data are insufficient to draw strong conclusions regarding disease reference values.
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Affiliation(s)
- Lucy Edwards
- Bioxydyn Limited, St James Tower, 7 Charlotte Street, Manchester, M1 4DZ, UK
| | - John C Waterton
- Bioxydyn Limited, St James Tower, 7 Charlotte Street, Manchester, M1 4DZ, UK; Centre for Imaging Sciences, University of Manchester, Manchester, UK
| | - Josephine Naish
- Bioxydyn Limited, St James Tower, 7 Charlotte Street, Manchester, M1 4DZ, UK; MCMR, Manchester University NHS Foundation Trust, Wythenshawe, Manchester, UK
| | - Christopher Short
- ECFS CTN - LCI Core Facility, Imperial College London, London, UK; Departments of Imaging, Royal Brompton Hospital, Sydney Street, London SW3 6NP, London, UK
| | - Thomas Semple
- Department of Radiology, The Royal Brompton Hospital, London, UK; National Heart and Lung Institute, Imperial College London, London, UK; Centre for Paediatrics and Child Health, Imperial College London, London, UK
| | - Geoff Jm Parker
- Bioxydyn Limited, St James Tower, 7 Charlotte Street, Manchester, M1 4DZ, UK; Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
| | - Marta Tibiletti
- Bioxydyn Limited, St James Tower, 7 Charlotte Street, Manchester, M1 4DZ, UK
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12
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Laothamatas I, Al Mubarak H, Reddy A, Wax R, Badani K, Taouli B, Bane O, Lewis S. Multiparametric MRI of Solid Renal Masses: Principles and Applications of Advanced Quantitative and Functional Methods for Tumor Diagnosis and Characterization. J Magn Reson Imaging 2023. [PMID: 37052601 DOI: 10.1002/jmri.28718] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 03/23/2023] [Accepted: 03/23/2023] [Indexed: 04/14/2023] Open
Abstract
Solid renal masses (SRMs) are increasingly detected and encompass both benign and malignant masses, with renal cell carcinoma (RCC) being the most common malignant SRM. Most patients with SRMs will undergo management without a priori pathologic confirmation. There is an unmet need to noninvasively diagnose and characterize RCCs, as significant variability in clinical behavior is observed and a wide range of differing management options exist. Cross-sectional imaging modalities, including magnetic resonance imaging (MRI), are increasingly used for SRM characterization. Multiparametric (mp) MRI techniques can provide insight into tumor biology by probing different physiologic/pathophysiologic processes noninvasively. These include sequences that probe tissue microstructure, including intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and T1 relaxometry; oxygen metabolism (blood oxygen level dependent [BOLD-MRI]); as well as vascular flow and perfusion (dynamic contrast-enhanced MRI [DCE-MRI] and arterial spin labeling [ASL]). In this review, we will discuss each mpMRI method in terms of its principles, roles, and discuss the results of human studies for SRM assessment. Future validation of these methods may help to enable a personalized management approach for patients with SRM in the emerging era of precision medicine. EVIDENCE LEVEL: 5. TECHNICAL EFFICACY: 2.
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Affiliation(s)
- Indira Laothamatas
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Haitham Al Mubarak
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Arthi Reddy
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Rebecca Wax
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ketan Badani
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Bachir Taouli
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Octavia Bane
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Sara Lewis
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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13
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Korte JC, Chin Z, Carr M, Holloway L, Franich R. Magnetic resonance biomarker assessment software (MR-BIAS): an automated open-source tool for the ISMRM/NIST system phantom. Phys Med Biol 2023; 68. [PMID: 36796102 DOI: 10.1088/1361-6560/acbcbb] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 02/16/2023] [Indexed: 02/18/2023]
Abstract
Objective.To provide an open-source software for repeatable and efficient quantification ofT1andT2relaxation times with the ISMRM/NIST system phantom. Quantitative magnetic resonance imaging (qMRI) biomarkers have the potential to improve disease detection, staging and monitoring of treatment response. Reference objects, such as the system phantom, play a major role in translating qMRI methods into the clinic. The currently available open-source software for ISMRM/NIST system phantom analysis, Phantom Viewer (PV), includes manual steps that are subject to variability.Approach.We developed the Magnetic Resonance BIomarker Assessment Software (MR-BIAS) to automatically extract system phantom relaxation times. The inter-observer variability (IOV) and time efficiency of MR-BIAS and PV was observed in six volunteers analysing three phantom datasets. The IOV was measured with the coefficient of variation (CV) of percent bias (%bias) inT1andT2with respect to NMR reference values. The accuracy of MR-BIAS was compared to a custom script from a published study of twelve phantom datasets. This included comparison of overall bias and %bias for variable inversion recovery (T1VIR), variable flip angle (T1VFA) and multiple spin-echo (T2MSE) relaxation models.Main results.MR-BIAS had a lower mean CV withT1VIR(0.03%) andT2MSE(0.05%) in comparison to PV withT1VIR(1.28%) andT2MSE(4.55%). The mean analysis duration was 9.7 times faster for MR-BIAS (0.8 min) than PV (7.6 min). There was no statistically significant difference in the overall bias, or the %bias for the majority of ROIs, as calculated by MR-BIAS or the custom script for all models.Significance.MR-BIAS has demonstrated repeatable and efficient analysis of the ISMRM/NIST system phantom, with comparable accuracy to previous studies. The software is freely available to the MRI community, providing a framework to automate required analysis tasks, with the flexibility to explore open questions and accelerate biomarker research.
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Affiliation(s)
- James C Korte
- Peter MacCallum Cancer Centre, Department of Physical Sciences, Melbourne, Australia.,The University of Melbourne, Department of Biomedical Engineering, Melbourne, Australia
| | - Zachary Chin
- RMIT University, School of Science, Melbourne, Australia
| | - Madeline Carr
- University of Wollongong, Centre for Medical Radiation Physics, Wollongong, Australia.,Liverpool and Macarthur Cancer Therapy Centres and Ingham Institute, Liverpool, Sydney, Australia.,GenesisCare, Sydney, New South Wales, Australia
| | - Lois Holloway
- University of Wollongong, Centre for Medical Radiation Physics, Wollongong, Australia.,Liverpool and Macarthur Cancer Therapy Centres and Ingham Institute, Liverpool, Sydney, Australia
| | - Rick Franich
- Peter MacCallum Cancer Centre, Department of Physical Sciences, Melbourne, Australia.,RMIT University, School of Science, Melbourne, Australia
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14
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DiCarlo JC, Jarrett AM, Kazerouni AS, Virostko J, Sorace A, Slavkova KP, Woodard S, Avery S, Patt D, Goodgame B, Yankeelov TE. Analysis of simplicial complexes to determine when to sample for quantitative DCE MRI of the breast. Magn Reson Med 2023; 89:1134-1150. [PMID: 36321574 PMCID: PMC9792438 DOI: 10.1002/mrm.29511] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 10/09/2022] [Accepted: 10/13/2022] [Indexed: 12/27/2022]
Abstract
PURPOSE A method is presented to select the optimal time points at which to measure DCE-MRI signal intensities, leaving time in the MR exam for high-spatial resolution image acquisition. THEORY Simplicial complexes are generated from the Kety-Tofts model pharmacokinetic parameters Ktrans and ve . A geometric search selects optimal time points for accurate estimation of perfusion parameters. METHODS The DCE-MRI data acquired in women with invasive breast cancer (N = 27) were used to retrospectively compare parameter maps fit to full and subsampled time courses. Simplicial complexes were generated for a fixed range of Kety-Tofts model parameters and for the parameter ranges weighted by estimates from the fully sampled data. The largest-area manifolds determined the optimal three time points for each case. Simulations were performed along with retrospectively subsampled data fits. The agreement was computed between the model parameters fit to three points and those fit to all points. RESULTS The optimal three-point sample times were from the data-informed simplicial complex analysis and determined to be 65, 204, and 393 s after arrival of the contrast agent to breast tissue. In the patient data, tumor-median parameter values fit using all points and the three selected time points agreed with concordance correlation coefficients of 0.97 for Ktrans and 0.67 for ve . CONCLUSION It is possible to accurately estimate pharmacokinetic parameters from three properly selected time points inserted into a clinical DCE-MRI breast exam. This technique can provide guidance on when to capture images for quantitative data between high-spatial-resolution DCE-MRI images.
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Affiliation(s)
- Julie C. DiCarlo
- The Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Texas, USA
| | - Angela M. Jarrett
- The Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, USA
| | | | - John Virostko
- The Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Texas, USA
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, USA
| | - Anna Sorace
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, USA
- O’Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Kalina P. Slavkova
- Department of Physics, The University of Texas at Austin, Austin, TX, USA
| | - Stefanie Woodard
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Sarah Avery
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, USA
- Austin Radiological Association, Austin, TX, USA
| | | | - Boone Goodgame
- Department of Oncology, University of Texas at Austin, Austin, Texas, USA
- Department of Internal Medicine, University of Texas at Austin, Austin, Texas, USA
- Ascension Seton Medical Center, Austin, TX, USA
| | - Thomas E. Yankeelov
- The Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Texas, USA
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, USA
- Department of Oncology, University of Texas at Austin, Austin, Texas, USA
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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15
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Nguyen TB, Zakhari N, Velasco Sandoval S, Guarnizo-Capera A, Alexios Gulak M, Woulfe J, Jansen G, Thornhill R, Majtenyi N, Cron GO. Diagnostic Accuracy of Arterial Spin-Labeling, Dynamic Contrast-Enhanced, and DSC Perfusion Imaging in the Diagnosis of Recurrent High-Grade Gliomas: A Prospective Study. AJNR Am J Neuroradiol 2023; 44:134-142. [PMID: 36702501 PMCID: PMC9891339 DOI: 10.3174/ajnr.a7771] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 12/30/2022] [Indexed: 01/27/2023]
Abstract
BACKGROUND AND PURPOSE For patients with high-grade gliomas, the appearance of a new, enhancing lesion after surgery and chemoradiation represents a diagnostic dilemma. We hypothesized that MR perfusion without and with contrast can differentiate tumor recurrence from radiation necrosis. MATERIALS AND METHODS In this prospective study, we performed 3 MR perfusion methods: arterial spin-labeling, DSC, and dynamic contrast enhancement. For each lesion, we measured CBF from arterial spin-labeling, uncorrected relative CBV, and leakage-corrected relative CBV from DSC imaging. The volume transfer constant and plasma volume were obtained from dynamic contrast-enhanced imaging without and with T1 mapping using modified Look-Locker inversion recovery (MOLLI). The diagnosis of tumor recurrence or radiation necrosis was determined by either histopathology for patients who underwent re-resection or radiologic follow-up for patients who did not have re-resection. RESULTS There were 26 patients with 32 lesions, 19 lesions with tumor recurrence and 13 lesions with radiation necrosis. Compared with radiation necrosis, lesions with tumor recurrence had higher CBF (P = .033), leakage-corrected relative CBV (P = .048), and plasma volume using MOLLI T1 mapping (P = .012). For differentiating tumor recurrence from radiation necrosis, the areas under the curve were 0.81 for CBF, 0.80 for plasma volume using MOLLI T1 mapping, and 0.71 for leakage-corrected relative CBV. A correlation was found between CBF and leakage-corrected relative CBV (r s = 0.54), volume transfer constant, and plasma volume (0.50 < r s< 0.77) but not with uncorrected relative CBV (r s = 0.20, P = .29). CONCLUSIONS In the differentiation of tumor recurrence from radiation necrosis in a newly enhancing lesion, the diagnostic value of arterial spin-labeling-derived CBF is similar to that of DSC and dynamic contrast-enhancement-derived blood volume.
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Affiliation(s)
- T B Nguyen
- From the Department of Radiology (T.B.N., N.Z., R.T.), Radiation Oncology and Medical Physics
- University of Ottawa (T.B.N., N.Z., J.W., G.J., R.T.), Ottawa, Ontario, Canada
- The Ottawa Hospital Research Institute (T.B.N., J.W., G.J., R.T.), Ottawa, Ontario, Canada
| | - N Zakhari
- From the Department of Radiology (T.B.N., N.Z., R.T.), Radiation Oncology and Medical Physics
- University of Ottawa (T.B.N., N.Z., J.W., G.J., R.T.), Ottawa, Ontario, Canada
| | - S Velasco Sandoval
- Division of Neuroradiology (S.V.S., A.G.-C.), Department of Radiology, Hospital Universitario Fundación Santa Fe de Bogotá, Bogotá, D.C., Colombia
| | - A Guarnizo-Capera
- Division of Neuroradiology (S.V.S., A.G.-C.), Department of Radiology, Hospital Universitario Fundación Santa Fe de Bogotá, Bogotá, D.C., Colombia
| | - M Alexios Gulak
- Department of Anesthesiology and Pain Medicine (M.A.G.), University of Toronto, Toronto, Ontario, Canada
| | - J Woulfe
- Department of Pathology and Laboratory Medicine (J.W., G.J.), The Ottawa Hospital, Ottawa, Ontario, Canada
- University of Ottawa (T.B.N., N.Z., J.W., G.J., R.T.), Ottawa, Ontario, Canada
- The Ottawa Hospital Research Institute (T.B.N., J.W., G.J., R.T.), Ottawa, Ontario, Canada
| | - G Jansen
- Department of Pathology and Laboratory Medicine (J.W., G.J.), The Ottawa Hospital, Ottawa, Ontario, Canada
- University of Ottawa (T.B.N., N.Z., J.W., G.J., R.T.), Ottawa, Ontario, Canada
- The Ottawa Hospital Research Institute (T.B.N., J.W., G.J., R.T.), Ottawa, Ontario, Canada
| | - R Thornhill
- From the Department of Radiology (T.B.N., N.Z., R.T.), Radiation Oncology and Medical Physics
- University of Ottawa (T.B.N., N.Z., J.W., G.J., R.T.), Ottawa, Ontario, Canada
- The Ottawa Hospital Research Institute (T.B.N., J.W., G.J., R.T.), Ottawa, Ontario, Canada
| | - N Majtenyi
- Department of Medical Physics (N.M.), Grand River Regional Cancer Centre, Kitchener, Ontario, Canada
| | - G O Cron
- Stanford University (G.O.C.), Stanford, California
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16
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Altinmakas E, Bane O, Hectors SJ, Issa R, Carbonell G, Abboud G, Schiano TD, Thung S, Fischman A, Kelly MD, Friedman SL, Kennedy P, Taouli B. Performance of native and gadoxetate-enhanced liver and spleen T 1 mapping for noninvasive diagnosis of clinically significant portal hypertension: preliminary results. ABDOMINAL RADIOLOGY (NEW YORK) 2022; 47:3758-3769. [PMID: 36085378 DOI: 10.1007/s00261-022-03645-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 08/03/2022] [Accepted: 08/03/2022] [Indexed: 01/18/2023]
Abstract
PURPOSE In this preliminary study, our aim was to assess the utility of quantitative native-T1 (T1-pre), iron-corrected T1 (cT1) of the liver/spleen and T1 mapping of the liver obtained during hepatobiliary phase (T1-HBP) post-gadoxetate disodium, compared to spleen size/volume and APRI (aspartate aminotransferase-to-platelet ratio index) for noninvasive diagnosis of clinically significant portal hypertension [CSPH, defined as hepatic venous pressure gradient (HVPG) ≥ 10 mm Hg]. METHODS Forty-nine patients (M/F: 27/22, mean age 53y) with chronic liver disease, HVPG measurement and MRI were included. Breath-held T1 and cT1 measurements were obtained using an inversion recovery Look-Locker sequence and a T2* corrected modified Look-Locker sequence, respectively. Liver T1-pre (n = 49), spleen T1 (obtained pre-contrast, n = 47), liver and spleen cT1 (both obtained pre-contrast, n = 30), liver T1-HBP (obtained 20 min post gadoxetate disodium injection, n = 36) and liver T1 uptake (ΔT1, n = 36) were measured. Spleen size/volume and APRI were also obtained. Spearman correlation coefficients were used to assess the correlation between each of liver/spleen T1/cT1 parameters, spleen size/volume and APRI with HVPG. ROC analysis was performed to determine the performance of measured parameters for diagnosis of CSPH. RESULTS There were 12/49 (24%) patients with CSPH. Liver T1-pre (r = 0.287, p = 0.045), liver T1-HBP (r = 0.543, p = 0.001), liver ΔT1 (r = - 0.437, p = 0.008), spleen T1 (r = 0.311, p = 0.033) and APRI (r = 0.394, p = 0.005) were all significantly correlated with HVPG, while liver cT1, spleen cT1 and spleen size/volume were not. The highest AUCs for the diagnosis of CSPH were achieved with liver T1-HBP, liver ΔT1 and spleen T1: 0.881 (95%CI 0.76-1.0, p = 0.001), 0.852 (0.72-0.98, p = 0.002) and 0.781 (0.60-0.95, p = 0.004), respectively. CONCLUSION Our preliminary results demonstrate the potential of liver T1 mapping obtained during HBP post gadoxetate disodium for the diagnosis of CSPH. These results require further validation.
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Affiliation(s)
- Emre Altinmakas
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA.,BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Radiology, Koc University School of Medicine, Istanbul, Turkey
| | - Octavia Bane
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA.,BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stefanie J Hectors
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA.,BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rayane Issa
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA
| | - Guillermo Carbonell
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Radiology, Virgen de La Arrixaca University Clinical Hospital, University of Murcia, Murcia, Spain
| | - Ghadi Abboud
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA.,BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Thomas D Schiano
- Icahn School of Medicine at Mount Sinai, Recanati/Miller Transplantation Institute, New York, NY, USA
| | - Swan Thung
- Department of Pathology, Molecular and Cell Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Aaron Fischman
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA
| | | | - Scott L Friedman
- Division of Liver Diseases, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Paul Kennedy
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA.,BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bachir Taouli
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA. .,BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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Rahmanzadeh R, Weigel M, Lu PJ, Melie-Garcia L, Nguyen TD, Cagol A, La Rosa F, Barakovic M, Lutti A, Wang Y, Bach Cuadra M, Radue EW, Gaetano L, Kappos L, Kuhle J, Magon S, Granziera C. A comparative assessment of myelin-sensitive measures in multiple sclerosis patients and healthy subjects. Neuroimage Clin 2022; 36:103177. [PMID: 36067611 PMCID: PMC9468574 DOI: 10.1016/j.nicl.2022.103177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 08/22/2022] [Accepted: 08/27/2022] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Multiple Sclerosis (MS) is a common neurological disease primarily characterized by myelin damage in lesions and in normal - appearing white and gray matter (NAWM, NAGM). Several quantitative MRI (qMRI) methods are sensitive to myelin characteristics by measuring specific tissue biophysical properties. However, there are currently few studies assessing the relative reproducibility and sensitivity of qMRI measures to MS pathology in vivo in patients. METHODS We performed two studies. The first study assessed of the sensitivity of qMRI measures to MS pathology: in this work, we recruited 150 MS and 100 healthy subjects, who underwent brain MRI at 3 T including quantitative T1 mapping (qT1), quantitative susceptibility mapping (QSM), magnetization transfer saturation imaging (MTsat) and myelin water imaging for myelin water fraction (MWF). The sensitivity of qMRIs to MS focal pathology (MS lesions vs peri-plaque white/gray matter (PPWM/PPGM)) was studied lesion-wise; the sensitivity to diffuse normal appearing (NA) pathology was measured using voxel-wise threshold-free cluster enhancement (TFCE) in NAWM and vertex-wise inflated cortex analysis in NAGM. Furthermore, the sensitivity of qMRI to the identification of lesion tissue was investigated using a voxel-wise logistic regression analysis to distinguish MS lesion and PP voxels. The second study assessed the reproducibility of myelin-sensitive qMRI measures in a single scanner. To evaluate the intra-session and inter-session reproducibility of qMRI measures, we have investigated 10 healthy subjects, who underwent two brain 3 T MRIs within the same day (without repositioning), and one after 1-week interval. Five region of interest (ROIs) in white and deep grey matter areas were segmented, and inter- and intra- session reproducibility was studied using the intra-class correlation coefficient (ICC). Further, we also investigated the voxel-wise reproducibility of qMRI measures in NAWM and NAGM. RESULTS qT1 and QSM showed the highest sensitivity to distinguish MS focal WM and cortical pathology from peri-plaque WM (P < 0.0001), although QSM also showed the highest variance when applied to lesions. MWF and MTsat exhibited the highest sensitivity to NAWM pathology (P < 0.01). On the other hand, qT1 appeared to be the most sensitive measure to NAGM pathology (P < 0.01). All myelin-sensitive qMRI measures exhibited high inter/intra sessional ICCs in various WM and deep GM ROIs, in NAWM and in NAGM (ICC 0.82 ± 0.12). CONCLUSION This work shows that the applied qT1, MWF, MTsat and QSM are highly reproducible and exhibit differential sensitivity to focal and diffuse WM and GM pathology in MS patients.
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Affiliation(s)
- Reza Rahmanzadeh
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Matthias Weigel
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland,Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Po-Jui Lu
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Lester Melie-Garcia
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Thanh D. Nguyen
- Department of Radiology, Weill Cornell Medical College, New York, NY, USA
| | - Alessandro Cagol
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Francesco La Rosa
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland,CIBM Center for Biomedical Imaging, Lausanne, Switzerland,Radiology Department, Lausanne University and University Hospital, Lausanne, Switzerland
| | - Muhamed Barakovic
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Antoine Lutti
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Yi Wang
- Department of Radiology, Weill Cornell Medical College, New York, NY, USA
| | - Meritxell Bach Cuadra
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland,CIBM Center for Biomedical Imaging, Lausanne, Switzerland,Radiology Department, Lausanne University and University Hospital, Lausanne, Switzerland
| | - Ernst-Wilhelm Radue
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | | | - Ludwig Kappos
- Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Jens Kuhle
- Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Stefano Magon
- Pharmaceutical Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Cristina Granziera
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland,Neurologic Clinic and Policlinic, MS Center and Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland,Corresponding author.
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18
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Dynamic Contrast-Enhanced MRI in the Abdomen of Mice with High Temporal and Spatial Resolution Using Stack-of-Stars Sampling and KWIC Reconstruction. Tomography 2022; 8:2113-2128. [PMID: 36136874 PMCID: PMC9498490 DOI: 10.3390/tomography8050178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 08/17/2022] [Accepted: 08/19/2022] [Indexed: 11/17/2022] Open
Abstract
Application of quantitative dynamic contrast-enhanced (DCE) MRI in mouse models of abdominal cancer is challenging due to the effects of RF inhomogeneity, image corruption from rapid respiratory motion and the need for high spatial and temporal resolutions. Here we demonstrate a DCE protocol optimized for such applications. The method consists of three acquisitions: (1) actual flip-angle B1 mapping, (2) variable flip-angle T1 mapping and (3) acquisition of the DCE series using a motion-robust radial strategy with k-space weighted image contrast (KWIC) reconstruction. All three acquisitions employ spoiled radial imaging with stack-of-stars sampling (SoS) and golden-angle increments between the views. This scheme is shown to minimize artifacts due to respiratory motion while simultaneously facilitating view-sharing image reconstruction for the dynamic series. The method is demonstrated in a genetically engineered mouse model of pancreatic ductal adenocarcinoma and yielded mean perfusion parameters of Ktrans = 0.23 ± 0.14 min−1 and ve = 0.31 ± 0.17 (n = 22) over a wide range of tumor sizes. The SoS-sampled DCE method is shown to produce artifact-free images with good SNR leading to robust estimation of DCE parameters.
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19
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Karakuzu A, Biswas L, Cohen-Adad J, Stikov N. Vendor-neutral sequences and fully transparent workflows improve inter-vendor reproducibility of quantitative MRI. Magn Reson Med 2022; 88:1212-1228. [PMID: 35657066 DOI: 10.1002/mrm.29292] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 04/18/2022] [Accepted: 04/19/2022] [Indexed: 12/20/2022]
Abstract
PURPOSE We developed an end-to-end workflow that starts with a vendor-neutral acquisition and tested the hypothesis that vendor-neutral sequences decrease inter-vendor variability of T1, magnetization transfer ratio (MTR), and magnetization transfer saturation-index (MTsat) measurements. METHODS We developed and deployed a vendor-neutral 3D spoiled gradient-echo (SPGR) sequence on three clinical scanners by two MRI vendors. We then acquired T1 maps on the ISMRM-NIST system phantom, as well as T1, MTR, and MTsat maps in three healthy participants. We performed hierarchical shift function analysis in vivo to characterize the differences between scanners when the vendor-neutral sequence is used instead of commercial vendor implementations. Inter-vendor deviations were compared for statistical significance to test the hypothesis. RESULTS In the phantom, the vendor-neutral sequence reduced inter-vendor differences from 8% to 19.4% to 0.2% to 5% with an overall accuracy improvement, reducing ground truth T1 deviations from 7% to 11% to 0.2% to 4%. In vivo, we found that the variability between vendors is significantly reduced (p = 0.015) for all maps (T1, MTR, and MTsat) using the vendor-neutral sequence. CONCLUSION We conclude that vendor-neutral workflows are feasible and compatible with clinical MRI scanners. The significant reduction of inter-vendor variability using vendor-neutral sequences has important implications for qMRI research and for the reliability of multicenter clinical trials.
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Affiliation(s)
- Agah Karakuzu
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, Quebec, Canada.,Montréal Heart Institute, Montréal, Quebec, Canada
| | - Labonny Biswas
- Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, Quebec, Canada.,Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montréal, Quebec, Canada.,Mila - Quebec AI Institute, Montreal, Quebec, Canada
| | - Nikola Stikov
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, Quebec, Canada.,Montréal Heart Institute, Montréal, Quebec, Canada.,Center for Advanced Interdisciplinary Research, Ss. Cyril and Methodius University, Skopje, North Macedonia
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20
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Henriksen OM, del Mar Álvarez-Torres M, Figueiredo P, Hangel G, Keil VC, Nechifor RE, Riemer F, Schmainda KM, Warnert EAH, Wiegers EC, Booth TC. High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 1: Perfusion and Diffusion Techniques. Front Oncol 2022; 12:810263. [PMID: 35359414 PMCID: PMC8961422 DOI: 10.3389/fonc.2022.810263] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 01/05/2022] [Indexed: 01/16/2023] Open
Abstract
Objective Summarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and highlight the latest bench-to-bedside developments. Methods Experts in advanced MRI techniques applied to high-grade glioma treatment response assessment convened through a European framework. Current evidence regarding the potential for monitoring biomarkers in adult high-grade glioma is reviewed, and individual modalities of perfusion, permeability, and microstructure imaging are discussed (in Part 1 of two). In Part 2, we discuss modalities related to metabolism and/or chemical composition, appraise the clinic readiness of the individual modalities, and consider post-processing methodologies involving the combination of MRI approaches (multiparametric imaging) or machine learning (radiomics). Results High-grade glioma vasculature exhibits increased perfusion, blood volume, and permeability compared with normal brain tissue. Measures of cerebral blood volume derived from dynamic susceptibility contrast-enhanced MRI have consistently provided information about brain tumor growth and response to treatment; it is the most clinically validated advanced technique. Clinical studies have proven the potential of dynamic contrast-enhanced MRI for distinguishing post-treatment related effects from recurrence, but the optimal acquisition protocol, mode of analysis, parameter of highest diagnostic value, and optimal cut-off points remain to be established. Arterial spin labeling techniques do not require the injection of a contrast agent, and repeated measurements of cerebral blood flow can be performed. The absence of potential gadolinium deposition effects allows widespread use in pediatric patients and those with impaired renal function. More data are necessary to establish clinical validity as monitoring biomarkers. Diffusion-weighted imaging, apparent diffusion coefficient analysis, diffusion tensor or kurtosis imaging, intravoxel incoherent motion, and other microstructural modeling approaches also allow treatment response assessment; more robust data are required to validate these alone or when applied to post-processing methodologies. Conclusion Considerable progress has been made in the development of these monitoring biomarkers. Many techniques are in their infancy, whereas others have generated a larger body of evidence for clinical application.
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Affiliation(s)
- Otto M. Henriksen
- Department of Clinical Physiology, Nuclear Medicine and PET, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | | | - Patricia Figueiredo
- Department of Bioengineering and Institute for Systems and Robotics-Lisboa, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Gilbert Hangel
- Department of Neurosurgery, Medical University, Vienna, Austria
- High-Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University, Vienna, Austria
| | - Vera C. Keil
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, Netherlands
| | - Ruben E. Nechifor
- International Institute for the Advanced Studies of Psychotherapy and Applied Mental Health, Department of Clinical Psychology and Psychotherapy, Babes-Bolyai University, Cluj-Napoca, Romania
| | - Frank Riemer
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Kathleen M. Schmainda
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, United States
| | | | - Evita C. Wiegers
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Thomas C. Booth
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School of Biomedical Engineering and Imaging Sciences, St. Thomas’ Hospital, King’s College London, London, United Kingdom
- Department of Neuroradiology, King’s College Hospital NHS Foundation Trust, London, United Kingdom
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21
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Tadimalla S, Wilson DJ, Shelley D, Bainbridge G, Saysell M, Mendichovszky IA, Graves MJ, Guthrie JA, Waterton JC, Parker GJM, Sourbron SP. Bias, Repeatability and Reproducibility of Liver T 1 Mapping With Variable Flip Angles. J Magn Reson Imaging 2022; 56:1042-1052. [PMID: 35224803 PMCID: PMC9545852 DOI: 10.1002/jmri.28127] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 02/09/2022] [Accepted: 02/10/2022] [Indexed: 12/16/2022] Open
Abstract
Background Three‐dimensional variable flip angle (VFA) methods are commonly used for T1 mapping of the liver, but there is no data on the accuracy, repeatability, and reproducibility of this technique in this organ in a multivendor setting. Purpose To measure bias, repeatability, and reproducibility of VFA T1 mapping in the liver. Study Type Prospective observational. Population Eight healthy volunteers, four women, with no known liver disease. Field Strength/Sequence 1.5‐T and 3.0‐T; three‐dimensional steady‐state spoiled gradient echo with VFAs; Look‐Locker. Assessment Traveling volunteers were scanned twice each (30 minutes to 3 months apart) on six MRI scanners from three vendors (GE Healthcare, Philips Medical Systems, and Siemens Healthineers) at two field strengths. The maximum period between the first and last scans among all volunteers was 9 months. Volunteers were instructed to abstain from alcohol intake for at least 72 hours prior to each scan and avoid high cholesterol foods on the day of the scan. Statistical Tests Repeated measures ANOVA, Student t‐test, Levene's test of variances, and 95% significance level. The percent error relative to literature liver T1 in healthy volunteers was used to assess bias. The relative error (RE) due to intrascanner and interscanner variation in T1 measurements was used to assess repeatability and reproducibility. Results The 95% confidence interval (CI) on the mean bias and mean repeatability RE of VFA T1 in the healthy liver was 34 ± 6% and 10 ± 3%, respectively. The 95% CI on the mean reproducibility RE at 1.5 T and 3.0 T was 29 ± 7% and 25 ± 4%, respectively. Data Conclusion Bias, repeatability, and reproducibility of VFA T1 mapping in the liver in a multivendor setting are similar to those reported for breast, prostate, and brain. Level of Evidence 1 Technical Efficacy Stage 1
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Affiliation(s)
- Sirisha Tadimalla
- Institute of Medical Physics, University of Sydney, Sydney, Australia.,Department of Biomedical Imaging Sciences, University of Leeds, Leeds, UK
| | | | | | | | | | | | - Martin J Graves
- Department of Radiology, University of Cambridge, Cambridge, UK
| | | | - John C Waterton
- Bioxydyn Ltd, Manchester, UK.,Centre for Imaging Sciences, Division of Informatics Imaging and Data Sciences, School of Health Sciences, Faculty of Biology Medicine and Health, University of Manchester, Manchester Academic Health Sciences Centre, Manchester, UK
| | - Geoffrey J M Parker
- Bioxydyn Ltd, Manchester, UK.,Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Steven P Sourbron
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
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22
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Luo S, Sha Y, Wu J, Lin N, Pan Y, Zhang F, Huang W. Differentiation of malignant from benign orbital tumours using dual-energy CT. Clin Radiol 2022; 77:307-313. [PMID: 35094818 DOI: 10.1016/j.crad.2021.12.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 12/24/2021] [Indexed: 11/17/2022]
Abstract
AIM To investigate the diagnostic accuracy of dual-energy computed tomography (DECT)-derived iodine concentration (IC), effective atomic number (Zeff), and spectral attenuation information for differentiating malignant and benign orbital tumours. MATERIALS AND METHODS Data from 41 patients with orbital tumours from November 2019 to March 2021 were analysed retrospectively. Each patient underwent contrast-enhanced DECT using a 128-section dual-source computed tomography (DSCT) system. Dual-energy information, including IC, normalised iodine concentration (NIC), Zeff, virtual monoenergetic images (VMIs) reconstructed from 40 to 120 keV and slope (k) value were determined. Quantitative measurement of DECT parameters was undertaken by two independent radiologists blinded to clinical data. Differences in parameters were assessed using independent sample t-test. Diagnosis performance was calculated by the receiver operating characteristic (ROC) curve analysis. Radiation doses of conventional CT and DECT were compared by paired t-tests. RESULTS Forty-one patients with histopathologically confirmed tumours were enrolled, including 10 malignant cases and 21 benign cases. Malignant orbital tumours exhibited significantly greater IC, NIC, Zeff, CT attenuation of VMIs at 40-105 keV, and k values compared to benign orbital tumours (p<0.05). In ROC analyses, 40 keV VMI demonstrated the highest diagnostic performance of single parameters (area under the ROC curve [AUC], 0.940), and combined parameters achieved the best performance (AUC, 0.971; sensitivity, 90%; specificity, 93.55%). Radiation doses were significantly reduced in DECT than conventional CT (p<0.001). CONCLUSIONS Quantitative DECT analysis can be a useful technique, which yields excellent diagnostic accuracy, in the differentiation of malignant and benign orbital tumours with low radiation dose.
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Affiliation(s)
- S Luo
- Shanghai Institution of Medical Imaging, Fudan University, No. 180 Fenglin Road, Shanghai, 200032, China; Department of Radiology, Eye & ENT Hospital, Fudan University, No. 83 Fenyang Road, Shanghai, 200031, China
| | - Y Sha
- Department of Radiology, Eye & ENT Hospital, Fudan University, No. 83 Fenyang Road, Shanghai, 200031, China.
| | - J Wu
- Siemens Healthineers, No. 399, West Haiyang Road, Shanghai, 200126, China
| | - N Lin
- Department of Radiology, Eye & ENT Hospital, Fudan University, No. 83 Fenyang Road, Shanghai, 200031, China
| | - Y Pan
- Department of Radiology, Eye & ENT Hospital, Fudan University, No. 83 Fenyang Road, Shanghai, 200031, China
| | - F Zhang
- Department of Radiology, Eye & ENT Hospital, Fudan University, No. 83 Fenyang Road, Shanghai, 200031, China
| | - W Huang
- Department of Radiology, Eye & ENT Hospital, Fudan University, No. 83 Fenyang Road, Shanghai, 200031, China
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23
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Woodall RT, Sahoo P, Cui Y, Chen BT, Shiroishi MS, Lavini C, Frankel P, Gutova M, Brown CE, Munson JM, Rockne RC. Repeatability of tumor perfusion kinetics from dynamic contrast-enhanced MRI in glioblastoma. Neurooncol Adv 2022; 3:vdab174. [PMID: 34988454 PMCID: PMC8715899 DOI: 10.1093/noajnl/vdab174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Background Dynamic contrast-enhanced MRI (DCE-MRI) parameters have been shown to be biomarkers for treatment response in glioblastoma (GBM). However, variations in analysis and measurement methodology complicate determination of biological changes measured via DCE. The aim of this study is to quantify DCE-MRI variations attributable to analysis methodology and image quality in GBM patients. Methods The Extended Tofts model (eTM) and Leaky Tracer Kinetic Model (LTKM), with manually and automatically segmented vascular input functions (VIFs), were used to calculate perfusion kinetic parameters from 29 GBM patients with double-baseline DCE-MRI data. DCE-MRI images were acquired 2-5 days apart with no change in treatment. Repeatability of kinetic parameters was quantified with Bland-Altman and percent repeatability coefficient (%RC) analysis. Results The perfusion parameter with the least RC was the plasma volume fraction (v p ), with a %RC of 53%. The extra-cellular extra-vascular volume fraction (v e ) %RC was 82% and 81%, for extended Tofts-Kety Model (eTM) and LTKM respectively. The %RC of the volume transfer rate constant (K trans ) was 72% for the eTM, and 82% for the LTKM, respectively. Using an automatic VIF resulted in smaller %RCs for all model parameters, as compared to manual VIF. Conclusions As much as 72% change in K trans (eTM, autoVIF) can be attributable to non-biological changes in the 2-5 days between double-baseline imaging. Poor K trans repeatability may result from inferior temporal resolution and short image acquisition time. This variation suggests DCE-MRI repeatability studies should be performed institutionally, using an automatic VIF method and following quantitative imaging biomarkers alliance guidelines.
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Affiliation(s)
- Ryan T Woodall
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope, Duarte, California, USA
| | - Prativa Sahoo
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope, Duarte, California, USA
| | - Yujie Cui
- Division of Biostatistics, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope, Duarte, California, USA
| | - Bihong T Chen
- Department of Diagnostic Radiology, City of Hope, Duarte, California, USA
| | - Mark S Shiroishi
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Cristina Lavini
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, the Netherlands
| | - Paul Frankel
- Division of Biostatistics, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope, Duarte, California, USA
| | - Margarita Gutova
- Department of Stem Cell Biology and Regenerative Medicine, Beckman Research Institute, City of Hope, Duarte, California, USA
| | - Christine E Brown
- Department of Hematology & Hematopoietic Cell Transplantation, Beckman Research Institute, City of Hope, Duarte, California, USA.,Department of Immuno-Oncology, Beckman Research Institute, City of Hope, Duarte, California, USA
| | - Jennifer M Munson
- Department of Biomedical Engineering & Mechanics, Fralin Biomedical Research Institute, Virginia Tech, Roanoke, Virginia, USA
| | - Russell C Rockne
- Division of Mathematical Oncology, Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope, Duarte, California, USA
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24
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Cicolari D, Lizio D, Pedrotti P, Moioli MT, Lascialfari A, Mariani M, Torresin A. A method for T 1 and T 2 relaxation times validation and harmonization as a support to MRI mapping. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2022; 334:107110. [PMID: 34844075 DOI: 10.1016/j.jmr.2021.107110] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 11/09/2021] [Accepted: 11/17/2021] [Indexed: 06/13/2023]
Abstract
We present a proof-of-concept study focusing on a method for the intra- and inter-center validation and harmonization of data obtained from MRI T1 and T2 maps. The method is based on a set of MnCl2 samples that provide in-scan ground-truth reference values regardless of the details of the MRI protocol. The relaxation times of MnCl2 aqueous solutions were first measured by means of an NMR laboratory relaxometer, as a function of concentration and temperature. The obtained T1 and T2 values, once renormalized at the scanner temperature, were used as reference values for the MRI mapping measurements of the MnCl2 relaxation times. By using different clinical MRI scanners and sequences, we found a good agreement for standard and turbo sequences (limits of agreement: 5% for IR, SE, IR-TSE; 10% for TSE), while an under-estimation and an over-estimation were found respectively for MOLLI and T2-prep TrueFISP, as already reported in the literature. The linearity of the relaxation rates with the concentration predicted by the Solomon-Bloembergen-Morgan theory was observed for every dataset at all temperatures, except for T2-prep TrueFISP maps results. Some preliminary results of an in vivo experiment are also presented.
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Affiliation(s)
- Davide Cicolari
- University of Pavia, Department of Physics, and INFN-Pavia Unit, Via Bassi 6, 27100 Pavia, Italy.
| | - Domenico Lizio
- ASST Grande Ospedale Metropolitano Niguarda, Department of Medical Physics, P.zza Ospedale Maggiore 3, 20162 Milan, Italy.
| | - Patrizia Pedrotti
- ASST Grande Ospedale Metropolitano Niguarda, Department of Cardiology, P.zza Ospedale Maggiore 3, 20162 Milan, Italy.
| | - Monica Teresa Moioli
- ASST Grande Ospedale Metropolitano Niguarda, Department of Medical Physics, P.zza Ospedale Maggiore 3, 20162 Milan, Italy.
| | - Alessandro Lascialfari
- University of Pavia, Department of Physics, and INFN-Pavia Unit, Via Bassi 6, 27100 Pavia, Italy.
| | - Manuel Mariani
- University of Pavia, Department of Physics, and INFN-Pavia Unit, Via Bassi 6, 27100 Pavia, Italy.
| | - Alberto Torresin
- ASST Grande Ospedale Metropolitano Niguarda, Department of Medical Physics, P.zza Ospedale Maggiore 3, 20162 Milan, Italy; University of Milan, Department of Physics, Via Celoria 16, 20133 Milan, Italy.
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25
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Statton BK, Smith J, Finnegan ME, Koerzdoerfer G, Quest RA, Grech-Sollars M. Temperature dependence, accuracy, and repeatability of T 1 and T 2 relaxation times for the ISMRM/NIST system phantom measured using MR fingerprinting. Magn Reson Med 2021; 87:1446-1460. [PMID: 34752644 DOI: 10.1002/mrm.29065] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 09/23/2021] [Accepted: 10/14/2021] [Indexed: 01/09/2023]
Abstract
PURPOSE Before MR fingerprinting (MRF) can be adopted clinically, the derived quantitative values must be proven accurate and repeatable over a range of T1 and T2 values and temperatures. Correct assessment of accuracy and precision as well as comparison between measurements can only be performed when temperature is either controlled or corrected for. The purpose of this study was to investigate the temperature dependence of T1 and T2 MRF values and evaluate the accuracy and repeatability of temperature-corrected relaxation values derived from a B1 -corrected MRF-fast imaging with steady-state precession implementation using 2 different dictionary sizes. METHODS The International Society of MR in Medicine/National Institute of Standards and Technology phantom was scanned using an MRF sequence of 2 different lengths, a variable flip angle T1 , and a multi-echo spin echo T2 at 14 temperatures ranging from 15°C to 28°C and investigated with a linear regression model. Temperature-corrected accuracy was evaluated by correlating T1 and T2 times from each MRF dictionary with reference values. Repeatability was assessed using the coefficient of variation, with measurements taken over 30 separate sessions. RESULTS There was a statistically significant fit of the model for MRF-derived T1 and T2 and temperature (p < 0.05) for all the spheres with a T1 > 500 ms. Both MRF methods showed a strong linear correlation with reference values for T1 (R2 = 0.996) and T2 (R2 = 0.982). MRF repeatability for T1 values was ≤1.4% and for T2 values was ≤3.4%. CONCLUSION MRF demonstrated relaxation times with a temperature dependence similar to that of conventional mapping methods. Temperature-corrected T1 and T2 values from both dictionaries showed adequate accuracy and excellent repeatability in this phantom study.
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Affiliation(s)
- Ben K Statton
- Medical Research Council, London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| | - Joely Smith
- Department of Imaging, Imperial College Healthcare NHS Trust, London, United Kingdom.,Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Mary E Finnegan
- Department of Imaging, Imperial College Healthcare NHS Trust, London, United Kingdom.,Department of Bioengineering, Imperial College London, London, United Kingdom
| | | | - Rebecca A Quest
- Department of Imaging, Imperial College Healthcare NHS Trust, London, United Kingdom.,Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Matthew Grech-Sollars
- Department of Medical Physics, Royal Surrey NHS Foundation Trust, Guildford, United Kingdom.,Department of Surgery and Cancer, Imperial College London, London, United Kingdom
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26
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Jarrett AM, Kazerouni AS, Wu C, Virostko J, Sorace AG, DiCarlo JC, Hormuth DA, Ekrut DA, Patt D, Goodgame B, Avery S, Yankeelov TE. Quantitative magnetic resonance imaging and tumor forecasting of breast cancer patients in the community setting. Nat Protoc 2021; 16:5309-5338. [PMID: 34552262 PMCID: PMC9753909 DOI: 10.1038/s41596-021-00617-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 08/12/2021] [Indexed: 02/07/2023]
Abstract
This protocol describes a complete data acquisition, analysis and computational forecasting pipeline for employing quantitative MRI data to predict the response of locally advanced breast cancer to neoadjuvant therapy in a community-based care setting. The methodology has previously been successfully applied to a heterogeneous patient population. The protocol details how to acquire the necessary images followed by registration, segmentation, quantitative perfusion and diffusion analysis, model calibration, and prediction. The data collection portion of the protocol requires ~25 min of scanning, postprocessing requires 2-3 h, and the model calibration and prediction components require ~10 h per patient depending on tumor size. The response of individual breast cancer patients to neoadjuvant therapy is forecast by application of a biophysical, reaction-diffusion mathematical model to these data. Successful application of the protocol results in coregistered MRI data from at least two scan visits that quantifies an individual tumor's size, cellularity and vascular properties. This enables a spatially resolved prediction of how a particular patient's tumor will respond to therapy. Expertise in image acquisition and analysis, as well as the numerical solution of partial differential equations, is required to carry out this protocol.
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Affiliation(s)
- Angela M Jarrett
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA
- Livestrong Cancer Institutes, Austin, TX, USA
| | - Anum S Kazerouni
- Departments of Biomedical Engineering, Austin, TX, USA
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA
| | - John Virostko
- Livestrong Cancer Institutes, Austin, TX, USA
- Departments of Diagnostic Medicine, Austin, TX, USA
- Departments of Oncology, Austin, TX, USA
| | - Anna G Sorace
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, USA
- O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Julie C DiCarlo
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA
- Livestrong Cancer Institutes, Austin, TX, USA
| | - David A Hormuth
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA
- Livestrong Cancer Institutes, Austin, TX, USA
| | - David A Ekrut
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA
| | | | - Boone Goodgame
- Departments of Oncology, Austin, TX, USA
- Departments of Internal Medicine, The University of Texas at Austin, Austin, Texas, USA
- Seton Hospital, Austin, TX, USA
| | - Sarah Avery
- Austin Radiological Association, Austin, TX, USA
| | - Thomas E Yankeelov
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA.
- Livestrong Cancer Institutes, Austin, TX, USA.
- Departments of Biomedical Engineering, Austin, TX, USA.
- Departments of Diagnostic Medicine, Austin, TX, USA.
- Departments of Oncology, Austin, TX, USA.
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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27
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Carr ME, Keenan KE, Rai R, Metcalfe P, Walker A, Holloway L. Determining the longitudinal accuracy and reproducibility of T 1 and T 2 in a 3T MRI scanner. J Appl Clin Med Phys 2021; 22:143-150. [PMID: 34562341 PMCID: PMC8598150 DOI: 10.1002/acm2.13432] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Revised: 08/17/2021] [Accepted: 09/07/2021] [Indexed: 11/09/2022] Open
Abstract
Purpose To determine baseline accuracy and reproducibility of T1 and T2 relaxation times over 12 months on a dedicated radiotherapy MRI scanner. Methods An International Society of Magnetic Resonance in Medicine/National Institute of Standards and Technology (ISMRM/NIST) System Phantom was scanned monthly on a 3T MRI scanner for 1 year. T1 was measured using inversion recovery (T1‐IR) and variable flip angle (T1‐VFA) sequences and T2 was measured using a multi‐echo spin echo (T2‐SE) sequence. For each vial in the phantom, accuracy errors (%bias) were determined by the relative differences in measured T1 and T2 times compared to reference values. Reproducibility was measured by the coefficient of variation (CV) of T1 and T2 measurements across monthly scans. Accuracy and reproducibility were mainly assessed on vials with relaxation times expected to be in physiological ranges at 3T. Results A strong linear correlation between measured and reference relaxation times was found for all sequences tested (R2 > 0.997). Baseline bias (and CV[%]) for T1‐IR, T1‐VFA and T2‐SE sequences were +2.0% (2.1), +6.5% (4.2), and +8.5% (1.9), respectively. Conclusions The accuracy and reproducibility of T1 and T2 on the scanner were considered sufficient for the sequences tested. No longitudinal trends of variation were deduced, suggesting less frequent measurements are required following the establishment of baselines.
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Affiliation(s)
- Madeline E Carr
- Centre for Medical and Radiation Physics, University of Wollongong, Wollongong, Australia.,Ingham Institute for Applied Medical Research, Liverpool, Australia.,Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
| | - Kathryn E Keenan
- National Institute of Standards and Technology, Boulder, Colorado, USA
| | - Robba Rai
- Ingham Institute for Applied Medical Research, Liverpool, Australia.,Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia.,South Western Sydney Clinical School, University of New South Wales, Liverpool, Australia
| | - Peter Metcalfe
- Centre for Medical and Radiation Physics, University of Wollongong, Wollongong, Australia.,Ingham Institute for Applied Medical Research, Liverpool, Australia
| | - Amy Walker
- Centre for Medical and Radiation Physics, University of Wollongong, Wollongong, Australia.,Ingham Institute for Applied Medical Research, Liverpool, Australia.,Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia.,South Western Sydney Clinical School, University of New South Wales, Liverpool, Australia
| | - Lois Holloway
- Centre for Medical and Radiation Physics, University of Wollongong, Wollongong, Australia.,Ingham Institute for Applied Medical Research, Liverpool, Australia.,Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia.,South Western Sydney Clinical School, University of New South Wales, Liverpool, Australia.,Institute of Medical Physics, University of Sydney, Camperdown, Australia
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28
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Accuracy and repeatability of QRAPMASTER and MRF-vFA. Magn Reson Imaging 2021; 83:196-207. [PMID: 34506911 DOI: 10.1016/j.mri.2021.09.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 09/03/2021] [Accepted: 09/05/2021] [Indexed: 11/23/2022]
Abstract
Our purpose is to evaluate bias and repeatability of the quantitative MRI sequences QRAPMASTER, based on steady-state imaging, and variable Flip Angle MRF (MRF-VFA), based on the transient response. Both techniques are assessed with a standardized phantom and five volunteers on 1.5 T and 3 T clinical scanners. All scans were repeated eight times in consecutive weeks. In the phantom, the mean bias±95% confidence interval for T1 values with QRAPMASTER was 10 ± 10% on 1.5 T and 4 ± 13% on 3.0 T. The mean bias for T1 values with MRF-vFA was 21 ± 17% on 1.5 T and 9 ± 9% on 3.0 T. For T2 values the mean bias with QRAPMASTER was 12 ± 3% on 1.5 T and 23 ± 1% on 3.0 T. For T2 values the mean bias with MRF-vFA was 17 ± 1% on 1.5 T and 19 ± 2% on 3.0 T. QRAPMASTER estimated lower T1 and T2 values than MRF-vFA. Repeatability was good with low coefficients of variation (CoV). Mean CoV ± 95% confidence interval for T1 were 3.2 ± 0.4% on 1.5 T and 4.5 ± 0.8% on 3.0 T with QRAPMASTER and 2.7% ± 0.2% on 1.5 T and 2.5 ± 0.2% on 3.0 T with MRF-vFA. For T2 were 3.3 ± 1.9% on 1.5 T and 3.2 ± 0.6% on 3.0 T with QRAPMASTER and 2.0 ± 0.4% on 1.5 T and 5.7 ± 1.0% on 3.0 T with MRF-vFA. The in-vivo T1 and T2 are in the range of values previously reported by other authors. The in-vivo mean CoV ± 95% confidence interval in gray matter were for T1 1.7 ± 0.2% using QRAPMASTER and 0.7 ± 0.5% using MRF-vFA and for T2 were 0.9 ± 0.4% using QRAPMASTER and 2.4 ± 0.5% using MRF-vFA. In white matter were for T1 0.9 ± 0.3% using QRAPMASTER and 1.3 ± 1.1% using MRF-vFA and for T2 were 0.7 ± 0.4% using QRAPMASTER and 2.4 ± 0.4% using MRF-vFA. A GLM analysis showed that the variations in T1 and T2 mainly depend on the field strength and the subject, but not on the follow-up repetition in different days. This confirms the high repeatability of QRAPMASTER and MRF-vFA. In summary, QRAPMASTER and MRF-vFA on both systems were highly repeatable with moderate accuracy, providing results comparable to standard references. While repeatability was similar for both methods, QRAPMASTER was more accurate. QRAPMASTER is a tested commercial product but MRF-vFA is 4.77 times faster, which would ease the inclusion of quantitative relaxometry.
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Wang C, Padgett KR, Su MY, Mellon EA, Maziero D, Chang Z. Multi-parametric MRI (mpMRI) for treatment response assessment of radiation therapy. Med Phys 2021; 49:2794-2819. [PMID: 34374098 DOI: 10.1002/mp.15130] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 06/23/2021] [Accepted: 06/28/2021] [Indexed: 11/11/2022] Open
Abstract
Magnetic resonance imaging (MRI) plays an important role in the modern radiation therapy (RT) workflow. In comparison with computed tomography (CT) imaging, which is the dominant imaging modality in RT, MRI possesses excellent soft-tissue contrast for radiographic evaluation. Based on quantitative models, MRI can be used to assess tissue functional and physiological information. With the developments of scanner design, acquisition strategy, advanced data analysis, and modeling, multiparametric MRI (mpMRI), a combination of morphologic and functional imaging modalities, has been increasingly adopted for disease detection, localization, and characterization. Integration of mpMRI techniques into RT enriches the opportunities to individualize RT. In particular, RT response assessment using mpMRI allows for accurate characterization of both tissue anatomical and biochemical changes to support decision-making in monotherapy of radiation treatment and/or systematic cancer management. In recent years, accumulating evidence have, indeed, demonstrated the potentials of mpMRI in RT response assessment regarding patient stratification, trial benchmarking, early treatment intervention, and outcome modeling. Clinical application of mpMRI for treatment response assessment in routine radiation oncology workflow, however, is more complex than implementing an additional imaging protocol; mpMRI requires additional focus on optimal study design, practice standardization, and unified statistical reporting strategy to realize its full potential in the context of RT. In this article, the mpMRI theories, including image mechanism, protocol design, and data analysis, will be reviewed with a focus on the radiation oncology field. Representative works will be discussed to demonstrate how mpMRI can be used for RT response assessment. Additionally, issues and limits of current works, as well as challenges and potential future research directions, will also be discussed.
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Affiliation(s)
- Chunhao Wang
- Department of Radiation Oncology, Duke University, Durham, North Carolina, USA
| | - Kyle R Padgett
- Department of Radiation Oncology, University of Miami, Miami, Florida, USA.,Department of Radiology, University of Miami, Miami, Florida, USA
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, California, USA.,Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Eric A Mellon
- Department of Radiation Oncology, University of Miami, Miami, Florida, USA
| | - Danilo Maziero
- Department of Radiation Oncology, University of Miami, Miami, Florida, USA
| | - Zheng Chang
- Department of Radiation Oncology, Duke University, Durham, North Carolina, USA
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30
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Shoghi KI, Badea CT, Blocker SJ, Chenevert TL, Laforest R, Lewis MT, Luker GD, Manning HC, Marcus DS, Mowery YM, Pickup S, Richmond A, Ross BD, Vilgelm AE, Yankeelov TE, Zhou R. Co-Clinical Imaging Resource Program (CIRP): Bridging the Translational Divide to Advance Precision Medicine. ACTA ACUST UNITED AC 2021; 6:273-287. [PMID: 32879897 PMCID: PMC7442091 DOI: 10.18383/j.tom.2020.00023] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The National Institutes of Health’s (National Cancer Institute) precision medicine initiative emphasizes the biological and molecular bases for cancer prevention and treatment. Importantly, it addresses the need for consistency in preclinical and clinical research. To overcome the translational gap in cancer treatment and prevention, the cancer research community has been transitioning toward using animal models that more fatefully recapitulate human tumor biology. There is a growing need to develop best practices in translational research, including imaging research, to better inform therapeutic choices and decision-making. Therefore, the National Cancer Institute has recently launched the Co-Clinical Imaging Research Resource Program (CIRP). Its overarching mission is to advance the practice of precision medicine by establishing consensus-based best practices for co-clinical imaging research by developing optimized state-of-the-art translational quantitative imaging methodologies to enable disease detection, risk stratification, and assessment/prediction of response to therapy. In this communication, we discuss our involvement in the CIRP, detailing key considerations including animal model selection, co-clinical study design, need for standardization of co-clinical instruments, and harmonization of preclinical and clinical quantitative imaging pipelines. An underlying emphasis in the program is to develop best practices toward reproducible, repeatable, and precise quantitative imaging biomarkers for use in translational cancer imaging and therapy. We will conclude with our thoughts on informatics needs to enable collaborative and open science research to advance precision medicine.
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Affiliation(s)
- Kooresh I Shoghi
- Department of Radiology, Washington University School of Medicine, St. Louis, MO
| | - Cristian T Badea
- Department of Radiology, Center for In Vivo Microscopy, Duke University Medical Center, Durham, NC
| | - Stephanie J Blocker
- Department of Radiology, Center for In Vivo Microscopy, Duke University Medical Center, Durham, NC
| | | | - Richard Laforest
- Department of Radiology, Washington University School of Medicine, St. Louis, MO
| | - Michael T Lewis
- Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX
| | - Gary D Luker
- Department of Radiology, University of Michigan, Ann Arbor, MI
| | - H Charles Manning
- Vanderbilt Center for Molecular Probes-Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN
| | - Daniel S Marcus
- Department of Radiology, Washington University School of Medicine, St. Louis, MO
| | - Yvonne M Mowery
- Department of Radiation Oncology, Duke University Medical Center, Durham, Durham, NC
| | - Stephen Pickup
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania.,Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA
| | - Ann Richmond
- Department of Pharmacology, Vanderbilt School of Medicine, Nashville, TN
| | - Brian D Ross
- Department of Radiology, University of Michigan, Ann Arbor, MI
| | - Anna E Vilgelm
- Department of Pathology, The Ohio State University, Columbus, OH
| | - Thomas E Yankeelov
- Departments of Biomedical Engineering, Diagnostic Medicine, and Oncology, Oden Institute for Computational Engineering and Sciences, Austin, TX; and.,Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX
| | - Rong Zhou
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania.,Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA
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31
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Lena B, Bos C, Ferrer CJ, Moonen CTW, Viergever MA, Bartels LW. Rapid 2D variable flip angle method for accurate and precise T 1 measurements over a wide range of T 1 values. NMR IN BIOMEDICINE 2021; 34:e4542. [PMID: 34031938 PMCID: PMC8365751 DOI: 10.1002/nbm.4542] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 04/23/2021] [Accepted: 04/26/2021] [Indexed: 05/07/2023]
Abstract
PURPOSE To perform dynamic T1 mapping using a 2D variable flip angle (VFA) method, a correction for the slice profile effect is needed. In this work we investigated the impact of flip angle selection and excitation RF pulse profile on the performance of slice profile correction when applied to T1 mapping over a range of T1 values. METHODS A correction of the slice profile effect is proposed, based on Bloch simulation of steady-state signals. With this correction, Monte Carlo simulations were performed to assess the accuracy and precision of 2D VFA T1 mapping in the presence of noise, for RF pulses with time-bandwidth products of 2, 3 and 10 and with flip angle pairs in the range [1°-90°]. To evaluate its performance over a wide range of T1 , maximum errors were calculated for six T1 values between 50 ms and 1250 ms. The method was demonstrated using in vitro and in vivo experiments. RESULTS Without corrections, 2D VFA severely underestimates T1 . Slice profile errors were effectively reduced with the correction based on simulations, both in vitro and in vivo. The precision and accuracy of the method depend on the nominal T1 values, the FA pair, and the RF pulse shape. FA pairs leading to <5% errors in T1 can be identified for the common RF shapes, for T1 values between 50 ms and 1250 ms. CONCLUSIONS 2D VFA T1 mapping with Bloch-simulation-based correction can deliver T1 estimates that are accurate and precise to within 5% over a wide T1 range.
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Affiliation(s)
- Beatrice Lena
- Image Sciences Institute, Imaging and Oncology DivisionUniversity Medical Center UtrechtUtrechtThe Netherlands
- Image Guided Molecular Interventions Group, Imaging and Oncology DivisionUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Clemens Bos
- Image Guided Molecular Interventions Group, Imaging and Oncology DivisionUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Cyril J. Ferrer
- Image Guided Molecular Interventions Group, Imaging and Oncology DivisionUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Chrit T. W. Moonen
- Image Guided Molecular Interventions Group, Imaging and Oncology DivisionUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Max A. Viergever
- Image Sciences Institute, Imaging and Oncology DivisionUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Lambertus W. Bartels
- Image Sciences Institute, Imaging and Oncology DivisionUniversity Medical Center UtrechtUtrechtThe Netherlands
- Image Guided Molecular Interventions Group, Imaging and Oncology DivisionUniversity Medical Center UtrechtUtrechtThe Netherlands
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32
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Keenan KE, Gimbutas Z, Dienstfrey A, Stupic KF, Boss MA, Russek SE, Chenevert TL, Prasad PV, Guo J, Reddick WE, Cecil KM, Shukla-Dave A, Aramburu Nunez D, Shridhar Konar A, Liu MZ, Jambawalikar SR, Schwartz LH, Zheng J, Hu P, Jackson EF. Multi-site, multi-platform comparison of MRI T1 measurement using the system phantom. PLoS One 2021; 16:e0252966. [PMID: 34191819 PMCID: PMC8244851 DOI: 10.1371/journal.pone.0252966] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 05/26/2021] [Indexed: 11/19/2022] Open
Abstract
Recent innovations in quantitative magnetic resonance imaging (MRI) measurement methods have led to improvements in accuracy, repeatability, and acquisition speed, and have prompted renewed interest to reevaluate the medical value of quantitative T1. The purpose of this study was to determine the bias and reproducibility of T1 measurements in a variety of MRI systems with an eye toward assessing the feasibility of applying diagnostic threshold T1 measurement across multiple clinical sites. We used the International Society of Magnetic Resonance in Medicine/National Institute of Standards and Technology (ISMRM/NIST) system phantom to assess variations of T1 measurements, using a slow, reference standard inversion recovery sequence and a rapid, commonly-available variable flip angle sequence, across MRI systems at 1.5 tesla (T) (two vendors, with number of MRI systems n = 9) and 3 T (three vendors, n = 18). We compared the T1 measurements from inversion recovery and variable flip angle scans to ISMRM/NIST phantom reference values using Analysis of Variance (ANOVA) to test for statistical differences between T1 measurements grouped according to MRI scanner manufacturers and/or static field strengths. The inversion recovery method had minor over- and under-estimations compared to the NMR-measured T1 values at both 1.5 T and 3 T. Variable flip angle measurements had substantially greater deviations from the NMR-measured T1 values than the inversion recovery measurements. At 3 T, the measured variable flip angle T1 for one vendor is significantly different than the other two vendors for most of the samples throughout the clinically relevant range of T1. There was no consistent pattern of discrepancy between vendors. We suggest establishing rigorous quality control procedures for validating quantitative MRI methods to promote confidence and stability in associated measurement techniques and to enable translation of diagnostic threshold from the research center to the entire clinical community.
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Affiliation(s)
- Kathryn E. Keenan
- National Institute of Standards and Technology, Boulder, Colorado, United State of America
- * E-mail:
| | - Zydrunas Gimbutas
- National Institute of Standards and Technology, Boulder, Colorado, United State of America
| | - Andrew Dienstfrey
- National Institute of Standards and Technology, Boulder, Colorado, United State of America
| | - Karl F. Stupic
- National Institute of Standards and Technology, Boulder, Colorado, United State of America
| | - Michael A. Boss
- American College of Radiology, Center for Research and Innovation, Philadelphia, Pennsylvania, United State of America
| | - Stephen E. Russek
- National Institute of Standards and Technology, Boulder, Colorado, United State of America
| | | | - P. V. Prasad
- NorthShore University Health System, Evanston, Illinois, United State of America
| | - Junyu Guo
- St. Jude Children’s Research Hospital, Memphis, Tennessee, United State of America
| | - Wilburn E. Reddick
- St. Jude Children’s Research Hospital, Memphis, Tennessee, United State of America
| | - Kim M. Cecil
- Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine Cincinnati, Ohio, United State of America
| | - Amita Shukla-Dave
- Memorial Sloan Kettering Cancer Center, New York, New York, United State of America
| | - David Aramburu Nunez
- Memorial Sloan Kettering Cancer Center, New York, New York, United State of America
| | | | - Michael Z. Liu
- Columbia University Medical Center, New York, New York, United State of America
| | | | | | - Jie Zheng
- Washington University in St. Louis, St. Louis, Missouri, United State of America
| | - Peng Hu
- University of California, Los Angeles, California, United State of America
| | - Edward F. Jackson
- University of Wisconsin, Madison, Wisconsin, United State of America
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33
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Granziera C, Wuerfel J, Barkhof F, Calabrese M, De Stefano N, Enzinger C, Evangelou N, Filippi M, Geurts JJG, Reich DS, Rocca MA, Ropele S, Rovira À, Sati P, Toosy AT, Vrenken H, Gandini Wheeler-Kingshott CAM, Kappos L. Quantitative magnetic resonance imaging towards clinical application in multiple sclerosis. Brain 2021; 144:1296-1311. [PMID: 33970206 PMCID: PMC8219362 DOI: 10.1093/brain/awab029] [Citation(s) in RCA: 73] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 10/25/2020] [Accepted: 11/16/2020] [Indexed: 12/11/2022] Open
Abstract
Quantitative MRI provides biophysical measures of the microstructural integrity of the CNS, which can be compared across CNS regions, patients, and centres. In patients with multiple sclerosis, quantitative MRI techniques such as relaxometry, myelin imaging, magnetization transfer, diffusion MRI, quantitative susceptibility mapping, and perfusion MRI, complement conventional MRI techniques by providing insight into disease mechanisms. These include: (i) presence and extent of diffuse damage in CNS tissue outside lesions (normal-appearing tissue); (ii) heterogeneity of damage and repair in focal lesions; and (iii) specific damage to CNS tissue components. This review summarizes recent technical advances in quantitative MRI, existing pathological validation of quantitative MRI techniques, and emerging applications of quantitative MRI to patients with multiple sclerosis in both research and clinical settings. The current level of clinical maturity of each quantitative MRI technique, especially regarding its integration into clinical routine, is discussed. We aim to provide a better understanding of how quantitative MRI may help clinical practice by improving stratification of patients with multiple sclerosis, and assessment of disease progression, and evaluation of treatment response.
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Affiliation(s)
- Cristina Granziera
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Jens Wuerfel
- Medical Image Analysis Center, Basel, Switzerland
- Quantitative Biomedical Imaging Group (qbig), Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, multiple sclerosis Center Amsterdam, Amsterdam University Medical Center, Amsterdam, The Netherlands
- UCL Institutes of Healthcare Engineering and Neurology, London, UK
| | - Massimiliano Calabrese
- Neurology B, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Nicola De Stefano
- Neurology, Department of Medicine, Surgery and Neuroscience, University of Siena, Italy
| | - Christian Enzinger
- Department of Neurology and Division of Neuroradiology, Medical University of Graz, Graz, Austria
| | - Nikos Evangelou
- Division of Clinical Neuroscience, University of Nottingham, Nottingham, UK
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, multiple sclerosis Center Amsterdam, Neuroscience Amsterdam, Amsterdam University Medical Centers, location VUmc, Amsterdam, The Netherlands
| | - Daniel S Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health (NIH), Bethesda, MD, USA
| | - Maria A Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Stefan Ropele
- Neuroimaging Research Unit, Department of Neurology, Medical University of Graz, Graz, Austria
| | - Àlex Rovira
- Section of Neuroradiology (Department of Radiology), Vall d'Hebron University Hospital and Research Institute, Barcelona, Spain
| | - Pascal Sati
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health (NIH), Bethesda, MD, USA
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Ahmed T Toosy
- Queen Square multiple sclerosis Centre, Department of Neuroinflammation, Queen Square Institute of Neurology, University College London, London, UK
| | - Hugo Vrenken
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, multiple sclerosis Center Amsterdam, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Claudia A M Gandini Wheeler-Kingshott
- Queen Square multiple sclerosis Centre, Department of Neuroinflammation, Queen Square Institute of Neurology, University College London, London, UK
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
- Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy
| | - Ludwig Kappos
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
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Wang YF, Tadimalla S, Hayden AJ, Holloway L, Haworth A. Artificial intelligence and imaging biomarkers for prostate radiation therapy during and after treatment. J Med Imaging Radiat Oncol 2021; 65:612-626. [PMID: 34060219 DOI: 10.1111/1754-9485.13242] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 04/18/2021] [Accepted: 05/02/2021] [Indexed: 12/15/2022]
Abstract
Magnetic resonance imaging (MRI) is increasingly used in the management of prostate cancer (PCa). Quantitative MRI (qMRI) parameters, derived from multi-parametric MRI, provide indirect measures of tumour characteristics such as cellularity, angiogenesis and hypoxia. Using Artificial Intelligence (AI), relevant information and patterns can be efficiently identified in these complex data to develop quantitative imaging biomarkers (QIBs) of tumour function and biology. Such QIBs have already demonstrated potential in the diagnosis and staging of PCa. In this review, we explore the role of these QIBs in monitoring treatment response during and after PCa radiotherapy (RT). Recurrence of PCa after RT is not uncommon, and early detection prior to development of metastases provides an opportunity for salvage treatments with curative intent. However, the current method of monitoring treatment response using prostate-specific antigen levels lacks specificity. QIBs, derived from qMRI and developed using AI techniques, can be used to monitor biological changes post-RT providing the potential for accurate and early diagnosis of recurrent disease.
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Affiliation(s)
- Yu-Feng Wang
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | - Sirisha Tadimalla
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
| | - Amy J Hayden
- Sydney West Radiation Oncology, Westmead Hospital, Wentworthville, New South Wales, Australia
- Faculty of Medicine, Western Sydney University, Sydney, New South Wales, Australia
- Faculty of Medicine, Health & Human Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Lois Holloway
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
- Liverpool and Macarthur Cancer Therapy Centre, Liverpool Hospital, Liverpool, New South Wales, Australia
| | - Annette Haworth
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
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Ke Q, Liao CC, Tan XH, Guo BP, Cen H, Li LQ. Diagnostic accuracy of pelvic magnetic resonance imaging for the assessment of bone marrow involvement in diffuse large B-cell lymphoma. PLoS One 2021; 16:e0252226. [PMID: 34043683 PMCID: PMC8158887 DOI: 10.1371/journal.pone.0252226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 05/12/2021] [Indexed: 12/03/2022] Open
Abstract
Purpose We investigated the efficacy of pelvic magnetic resonance imaging (MRI) in the diagnosis of bone marrow involvement (BMinv) in diffuse large B-cell lymphoma (DLBCL) patients. Patients and methods This was a retrospective study of data from a previous study (NCT02733887). We included 171 patients who underwent bone marrow biopsy (BMB) and bone marrow smear (BMS), pelvic MRI, and whole-body positron emission tomography-computed tomography (PET/CT) from January 2016 to December 2019 at a single center. BMB/BMS and whole-body PET/CT results were used as reference standards against which we calculated the diagnostic value of pelvic MRI for BMinv in DLBCL patients. A chi-square test was used to compare detection rates, and a receiver operating characteristic curve was used to evaluate diagnostic value of pelvic MRI. Propensity-score matching was performed according to clinical information, and Kaplan-Meier curves were constructed to compare progression-free survival (PFS) and overall survival (OS) of patients. Results The BMinv detection rate of pelvic MRI (42/171) was higher (P = 0.029) than that of BMB/BMS (25/171), and similar to that of PET/CT (44/171; P = 0.901). The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of pelvic MRI were 83.33%, 98.37%, 94.15%, 95.24%, and 93.80%, respectively. Median PFS values were as follows: BMB/BMS-positive, 17.8 months vs. BMB/BMS-negative, 26.9 months (P = 0.092); PET/CT-positive, 24.8 months vs. PET/CT-negative, 33.0 months (P = 0.086); pelvic MRI-positive, 24.9 months vs. pelvic MRI-negative, 33.1 months (P<0.001). Median OS values were as follows: BMB/BMS-positive, 22.3 months vs. BMB/BMS-negative, 29.8 months (P = 0.240); PET/CT-positive, 27.9 months vs. PET/CT-negative, 33.9 months (P = 0.365); pelvic MRI-positive, 27.3 months vs. pelvic MRI-negative, 35.8 months (P = 0.062). Conclusion Pelvic MRI is effective for detecting BMinv in DLBCL patients, providing a more accurate indication of PFS than BMB/BMS and PET/CT do. It may ultimately be used to improve the accuracy of clinical staging, guide patient treatment, and evaluate prognosis.
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Affiliation(s)
- Qing Ke
- Department of Hematology/Oncology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Cheng-Cheng Liao
- Department of Hematology/Oncology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Xiao-Hong Tan
- Department of Hematology/Oncology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Bao-Ping Guo
- Department of Hematology/Oncology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Hong Cen
- Department of Hematology/Oncology, Guangxi Medical University Cancer Hospital, Nanning, China
- * E-mail: (HC); (LQL)
| | - Le-Qun Li
- Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, China
- * E-mail: (HC); (LQL)
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Lazari A, Lipp I. Can MRI measure myelin? Systematic review, qualitative assessment, and meta-analysis of studies validating microstructural imaging with myelin histology. Neuroimage 2021; 230:117744. [PMID: 33524576 PMCID: PMC8063174 DOI: 10.1016/j.neuroimage.2021.117744] [Citation(s) in RCA: 84] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 01/05/2021] [Accepted: 01/09/2021] [Indexed: 12/16/2022] Open
Abstract
Recent years have seen an increased understanding of the importance of myelination in healthy brain function and neuropsychiatric diseases. Non-invasive microstructural magnetic resonance imaging (MRI) holds the potential to expand and translate these insights to basic and clinical human research, but the sensitivity and specificity of different MR markers to myelination is a subject of debate. To consolidate current knowledge on the topic, we perform a systematic review and meta-analysis of studies that validate microstructural imaging by combining it with myelin histology. We find meta-analytic evidence for correlations between various myelin histology metrics and markers from different MRI modalities, including fractional anisotropy, radial diffusivity, macromolecular pool, magnetization transfer ratio, susceptibility and longitudinal relaxation rate, but not mean diffusivity. Meta-analytic correlation effect sizes range widely, between R2 = 0.26 and R2 = 0.82. However, formal comparisons between MRI-based myelin markers are limited by methodological variability, inconsistent reporting and potential for publication bias, thus preventing the establishment of a single most sensitive strategy to measure myelin with MRI. To facilitate further progress, we provide a detailed characterisation of the evaluated studies as an online resource. We also share a set of 12 recommendations for future studies validating putative MR-based myelin markers and deploying them in vivo in humans.
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Affiliation(s)
- Alberto Lazari
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Ilona Lipp
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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Stupic KF, Ainslie M, Boss MA, Charles C, Dienstfrey AM, Evelhoch JL, Finn P, Gimbutas Z, Gunter JL, Hill DLG, Jack CR, Jackson EF, Karaulanov T, Keenan KE, Liu G, Martin MN, Prasad PV, Rentz NS, Yuan C, Russek SE. A standard system phantom for magnetic resonance imaging. Magn Reson Med 2021; 86:1194-1211. [PMID: 33847012 PMCID: PMC8252537 DOI: 10.1002/mrm.28779] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Revised: 02/01/2021] [Accepted: 03/02/2021] [Indexed: 01/20/2023]
Abstract
Purpose A standard MRI system phantom has been designed and fabricated to assess scanner performance, stability, comparability and assess the accuracy of quantitative relaxation time imaging. The phantom is unique in having traceability to the International System of Units, a high level of precision, and monitoring by a national metrology institute. Here, we describe the phantom design, construction, imaging protocols, and measurement of geometric distortion, resolution, slice profile, signal‐to‐noise ratio (SNR), proton‐spin relaxation times, image uniformity and proton density. Methods The system phantom, designed by the International Society of Magnetic Resonance in Medicine ad hoc committee on Standards for Quantitative MR, is a 200 mm spherical structure that contains a 57‐element fiducial array; two relaxation time arrays; a proton density/SNR array; resolution and slice‐profile insets. Standard imaging protocols are presented, which provide rapid assessment of geometric distortion, image uniformity, T1 and T2 mapping, image resolution, slice profile, and SNR. Results Fiducial array analysis gives assessment of intrinsic geometric distortions, which can vary considerably between scanners and correction techniques. This analysis also measures scanner/coil image uniformity, spatial calibration accuracy, and local volume distortion. An advanced resolution analysis gives both scanner and protocol contributions. SNR analysis gives both temporal and spatial contributions. Conclusions A standard system phantom is useful for characterization of scanner performance, monitoring a scanner over time, and to compare different scanners. This type of calibration structure is useful for quality assurance, benchmarking quantitative MRI protocols, and to transition MRI from a qualitative imaging technique to a precise metrology with documented accuracy and uncertainty.
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Affiliation(s)
- Karl F Stupic
- Physical Measurement Laboratory, National Institute of Standards and Technology, Boulder, Colorado, USA
| | - Maureen Ainslie
- Department of Radiology, Duke University, Durham, North Carolina, USA
| | - Michael A Boss
- American College of Radiology, Philadelphia, Pennsylvania, USA
| | - Cecil Charles
- Department of Radiology, Duke University, Durham, North Carolina, USA
| | - Andrew M Dienstfrey
- Physical Measurement Laboratory, National Institute of Standards and Technology, Boulder, Colorado, USA
| | | | - Paul Finn
- University of California, Los Angeles, California, USA
| | - Zydrunas Gimbutas
- Physical Measurement Laboratory, National Institute of Standards and Technology, Boulder, Colorado, USA
| | | | - Derek L G Hill
- Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Edward F Jackson
- Medical Physics, University of Wisconsin, Madison, Wisconsin, USA
| | | | - Kathryn E Keenan
- Physical Measurement Laboratory, National Institute of Standards and Technology, Boulder, Colorado, USA
| | - Guoying Liu
- National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, Maryland, USA
| | - Michele N Martin
- Physical Measurement Laboratory, National Institute of Standards and Technology, Boulder, Colorado, USA
| | | | - Nikki S Rentz
- Physical Measurement Laboratory, National Institute of Standards and Technology, Boulder, Colorado, USA
| | - Chun Yuan
- Radiology, University of Washington, Seattle, Washington, USA
| | - Stephen E Russek
- Physical Measurement Laboratory, National Institute of Standards and Technology, Boulder, Colorado, USA
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Kim MM, Aryal MP, Sun Y, Parmar HA, Li P, Schipper M, Wahl DR, Lawrence TS, Cao Y. Response assessment during chemoradiation using a hypercellular/hyperperfused imaging phenotype predicts survival in patients with newly diagnosed glioblastoma. Neuro Oncol 2021; 23:1537-1546. [PMID: 33599755 DOI: 10.1093/neuonc/noab038] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Adversely prognostic hypercellular and hyperperfused regions of glioblastoma (GBM) predict progression-free survival, and are a novel target for dose-intensified chemoradiation (chemoRT) recently implemented in a phase II clinical trial. As a secondary aim, we hypothesized that dose-intensified chemoRT would induce greater mid-treatment response of hypercellular/hyperperfused tumor regions vs standard chemoradiation, and that early response would improve overall survival (OS). METHODS Forty-nine patients with newly diagnosed GBM underwent prospective, multiparametric high b value diffusion-weighted MRI (DW-MRI) and perfusion dynamic contrast-enhanced MRI (DCE-MRI) pre-RT and 3-4 weeks into RT. The hypercellular tumor volume (TVHCV, mean contralateral normal brain + 2SD) and hyperperfused tumor volume (TVCBV, contralateral normal frontal gray matter + 1SD) were generated using automated thresholding. Twenty-six patients were enrolled on a dose-escalation trial targeting TVHCV/TVCBV with 75 Gy in 30 fractions, and 23 non-trial patients comprised the control group. OS was estimated using the Kaplan-Meier method and compared using the log-rank test. The effect of TVHCV/TVCBV and Gd-enhanced tumor volume on OS was assessed using multivariable Cox proportional-hazard regression. RESULTS Most patients had gross total (47%) or subtotal resection (37%), 25% were MGMT-methylated. Patients treated on the dose-escalation trial had significantly greater reduction in TVHCV/TVCBV (41% reduction, IQR 17%-75%) vs non-trial patients (6% reduction, IQR 6%-22%, P = .002). An increase in TVHCV/TVCBV during chemoRT was associated with worse OS (adjusted hazard ratio [aHR] 1.2, 95%CI 1.0-1.4, P = .02), while pre-treatment tumor volumes (P > .5) and changes in Gd-enhanced volume (P = .9) were not. CONCLUSIONS Multiparametric MRI permits identification of therapeutic resistance during chemoRT and supports adaptive strategies in future trials.
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Affiliation(s)
- Michelle M Kim
- Department of Radiation Oncology, The University of Michigan, Ann Arbor, Michigan, USA
| | - Madhava P Aryal
- Department of Radiation Oncology, The University of Michigan, Ann Arbor, Michigan, USA
| | - Yilun Sun
- Department of Radiation Oncology, The University of Michigan, Ann Arbor, Michigan, USA.,Department of Biostatistics, The University of Michigan, Ann Arbor, Michigan, USA
| | - Hemant A Parmar
- Department of Radiology, The University of Michigan, Ann Arbor, Michigan, USA
| | - Pin Li
- Department of Biostatistics, The University of Michigan, Ann Arbor, Michigan, USA
| | - Matthew Schipper
- Department of Biostatistics, The University of Michigan, Ann Arbor, Michigan, USA
| | - Daniel R Wahl
- Department of Radiation Oncology, The University of Michigan, Ann Arbor, Michigan, USA
| | - Theodore S Lawrence
- Department of Radiation Oncology, The University of Michigan, Ann Arbor, Michigan, USA
| | - Yue Cao
- Department of Radiation Oncology, The University of Michigan, Ann Arbor, Michigan, USA.,Department of Radiology, The University of Michigan, Ann Arbor, Michigan, USA.,Department of Biomedical Engineering, The University of Michigan, Ann Arbor, Michigan, USA
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Jona G, Furman‐Haran E, Schmidt R. Realistic head-shaped phantom with brain-mimicking metabolites for 7 T spectroscopy and spectroscopic imaging. NMR IN BIOMEDICINE 2021; 34:e4421. [PMID: 33015864 PMCID: PMC7757235 DOI: 10.1002/nbm.4421] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 08/30/2020] [Accepted: 09/14/2020] [Indexed: 06/11/2023]
Abstract
PURPOSE Moving to ultra-high fields (≥7 T), the inhomogeneity of both RF (B1 ) and static (B0 ) magnetic fields increases, which further motivates us to design a realistic head-shaped phantom, especially for spectroscopic imaging. Such phantoms provide images similar to the human brain and serve as a reliable tool for developing and examining methods in MRI. This study aims to develop and characterize a realistic head-shaped phantom filled with brain-mimicking metabolites for MRS and magnetic resonance spectroscopic imaging in a 7 T MRI scanner. METHODS A 3D head-shaped container with three sections-mimicking brain, muscle and precranial lipid-was constructed. The phantom was designed to provide robustness to heating, mechanical damage and leakage, with easy refilling. The head's shape and the agarose mixture were optimized to provide B0 and B1 distributions and T1 /T2 relaxation values similar to those of human brain. Eight brain-tissue-mimicking metabolites were included for spectroscopy. The phantom was evaluated for localized spectroscopy, fast spectroscopic imaging and fat suppression. RESULTS The B0 and B1 maps showed distribution similar to that of human brain, with increased B0 inhomogeneity near the nasal and ear areas and reduced B1 in the temporal lobe and brain stem regions, as expected in vivo. The metabolites' concentrations were verified by single-voxel spectroscopy, showing an average deviation of 11%. Fast spectroscopic imaging and imaging with fat suppression were demonstrated. CONCLUSION A 3D head-shaped phantom for human brain imaging and spectroscopic imaging in 7 T MRI was demonstrated, making it a realistic phantom for methodology development at 7 T.
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Affiliation(s)
- Ghil Jona
- Life Sciences Core FacilitiesWeizmann Institute of ScienceRehovotIsrael
| | - Edna Furman‐Haran
- Life Sciences Core FacilitiesWeizmann Institute of ScienceRehovotIsrael
- The Azrieli National Institute for Human Brain Imaging and ResearchWeizmann Institute of ScienceRehovotIsrael
| | - Rita Schmidt
- The Azrieli National Institute for Human Brain Imaging and ResearchWeizmann Institute of ScienceRehovotIsrael
- Neurobiology DepartmentWeizmann Institute of ScienceRehovotIsrael
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Quantitative MRI: Defining repeatability, reproducibility and accuracy for prostate cancer imaging biomarker development. Magn Reson Imaging 2021; 77:169-179. [PMID: 33388362 DOI: 10.1016/j.mri.2020.12.018] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 11/25/2020] [Accepted: 12/29/2020] [Indexed: 01/09/2023]
Abstract
INTRODUCTION Quantitative MRI (qMRI) parameters have been increasingly used to develop predictive models to accurately monitor treatment response in prostate cancer after radiotherapy. To reliably detect changes in signal due to treatment response, predictive models require qMRI parameters with high repeatability and reproducibility. The purpose of this study was to measure qMRI parameter uncertainties in both commercial and in-house developed phantoms to guide the development of robust predictive models for monitoring treatment response. MATERIALS AND METHODS ADC, T1, and R2* values were acquired across three 3 T scanners with a prostate-specific qMRI protocol using the NIST/ISMRM system phantom, RSNA/NIST diffusion phantom, and an in-house phantom. A B1 field map was acquired to correct for flip angle inhomogeneity in T1 maps. All sequences were repeated in each scan to assess within-session repeatability. Weekly scans were acquired on one scanner for three months with the in-house phantom. Between-session repeatability was measured with test-retest scans 6-months apart on all scanners with all phantoms. Accuracy, defined as percentage deviation from reference value for ADC and T1, was evaluated using the system and diffusion phantoms. Repeatability and reproducibility coefficients of variation (%CV) were calculated for all qMRI parameters on all phantoms. RESULTS Overall, repeatability CV of ADC was <2.40%, reproducibility CV was <3.98%, and accuracy ranged between -8.0% to 2.7% across all scanners. Applying B1 correction on T1 measurements significantly improved the repeatability and reproducibility (p<0.05) but increased error in accuracy (p<0.001). Repeatability and reproducibility of R2* was <4.5% and <7.3% respectively in the system phantom across all scanners. CONCLUSION Repeatability, reproducibility, and accuracy in qMRI parameters from a prostate-specific protocol was estimated using both commercial and in-house phantoms. Results from this work will be used to identify robust qMRI parameters for use in the development of predictive models to longitudinally monitor treatment response for prostate cancer in current and future clinical trials.
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Prospective Imaging Trial Assessing Gadoteridol Retention in the Deep Brain Nuclei of Women Undergoing Breast MRI. Acad Radiol 2020; 27:1734-1741. [PMID: 32107123 DOI: 10.1016/j.acra.2020.01.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 12/27/2019] [Accepted: 01/07/2020] [Indexed: 11/23/2022]
Abstract
RATIONALE AND OBJECTIVES To assess for indirect evidence of gadoteridol retention in the deep brain nuclei of women undergoing serial screening breast MRI. METHODS This HIPAA-compliant prospective observational noninferiority imaging trial was approved by the IRB. From December 2016 to March 2018, 12 consented subjects previously exposed to 0-1 doses of gadoteridol (group 1) and 7 consented subjects previously exposed to ≥4 doses of gadoteridol (group 2) prospectively underwent research-specific unenhanced brain MRI including T1w spin echo imaging and T1 mapping. Inclusion criteria were: (1) planned breast MRI with gadoteridol, (2) no gadolinium exposure other than gadoteridol, (3) able to undergo MRI, (4) no neurological illness, (5) no metastatic disease, (6) no chemotherapy. Regions of interest were manually drawn in the globus pallidus, thalamus, dentate nucleus, and pons. Globus pallidus/thalamus and dentate nucleus/pons signal intensities and T1-time ratios were calculated using established methods and correlated with cumulative gadoteridol dose (mL). RESULTS All subjects were female (mean age: 50 ± 12 years) and previously had received an average of 0.5 ± 0.5 (group 1) and 5.9 ± 2.1 (group 2) doses of gadoteridol (cumulative dose: 8 ± 8 and 82 ± 31 mL, respectively), with the last dose an average of 492 ± 299 days prior to scanning. There was no significant correlation between cumulative gadoteridol dose (mL) and deep brain nuclei signal intensity at T1w spin echo imaging (p = 0.365-0.512) or T1 mapping (p = 0.197-0.965). CONCLUSION We observed no indirect evidence of gadolinium retention in the deep brain nuclei of women undergoing screening breast MRI with gadoteridol.
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Kennedy P, Bane O, Hectors SJ, Fischman A, Schiano T, Lewis S, Taouli B. Noninvasive imaging assessment of portal hypertension. Abdom Radiol (NY) 2020; 45:3473-3495. [PMID: 32926209 PMCID: PMC10124623 DOI: 10.1007/s00261-020-02729-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 08/16/2020] [Accepted: 08/30/2020] [Indexed: 02/07/2023]
Abstract
Portal hypertension (PH) is a spectrum of complications of chronic liver disease (CLD) and cirrhosis, with manifestations including ascites, gastroesophageal varices, splenomegaly, hypersplenism, hepatic hydrothorax, hepatorenal syndrome, hepatopulmonary syndrome and portopulmonary hypertension. PH can vary in severity and is diagnosed via invasive hepatic venous pressure gradient measurement (HVPG), which is considered the reference standard. Accurate diagnosis of PH and assessment of severity are highly relevant as patients with clinically significant portal hypertension (CSPH) are at higher risk for developing acute variceal bleeding and mortality. In this review, we discuss current and upcoming noninvasive imaging methods for diagnosis and assessment of severity of PH.
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Sorace AG, Elkassem AA, Galgano SJ, Lapi SE, Larimer BM, Partridge SC, Quarles CC, Reeves K, Napier TS, Song PN, Yankeelov TE, Woodard S, Smith AD. Imaging for Response Assessment in Cancer Clinical Trials. Semin Nucl Med 2020; 50:488-504. [PMID: 33059819 PMCID: PMC7573201 DOI: 10.1053/j.semnuclmed.2020.05.001] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The use of biomarkers is integral to the routine management of cancer patients, including diagnosis of disease, clinical staging and response to therapeutic intervention. Advanced imaging metrics with computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) are used to assess response during new drug development and in cancer research for predictive metrics of response. Key components and challenges to identifying an appropriate imaging biomarker are selection of integral vs integrated biomarkers, choosing an appropriate endpoint and modality, and standardization of the imaging biomarkers for cooperative and multicenter trials. Imaging biomarkers lean on the original proposed quantified metrics derived from imaging such as tumor size or longest dimension, with the most commonly implemented metrics in clinical trials coming from the Response Evaluation Criteria in Solid Tumors (RECIST) criteria, and then adapted versions such as immune-RECIST (iRECIST) and Positron Emission Tomography Response Criteria in Solid Tumors (PERCIST) for immunotherapy response and PET imaging, respectively. There have been many widely adopted biomarkers in clinical trials derived from MRI including metrics that describe cellularity and vascularity from diffusion-weighted (DW)-MRI apparent diffusion coefficient (ADC) and Dynamic Susceptibility Contrast (DSC) or dynamic contrast enhanced (DCE)-MRI (Ktrans, relative cerebral blood volume (rCBV)), respectively. Furthermore, Fluorodexoyglucose (FDG), fluorothymidine (FLT), and fluoromisonidazole (FMISO)-PET imaging, which describe molecular markers of glucose metabolism, proliferation and hypoxia have been implemented into various cancer types to assess therapeutic response to a wide variety of targeted- and chemotherapies. Recently, there have been many functional and molecular novel imaging biomarkers that are being developed that are rapidly being integrated into clinical trials (with anticipation of being implemented into clinical workflow in the future), such as artificial intelligence (AI) and machine learning computational strategies, antibody and peptide specific molecular imaging, and advanced diffusion MRI. These include prostate-specific membrane antigen (PSMA) and trastuzumab-PET, vascular tumor burden extracted from contrast-enhanced CT, diffusion kurtosis imaging, and CD8 or Granzyme B PET imaging. Further excitement surrounds theranostic procedures such as the combination of 68Ga/111In- and 177Lu-DOTATATE to use integral biomarkers to direct care and personalize therapy. However, there are many challenges in the implementation of imaging biomarkers that remains, including understand the accuracy, repeatability and reproducibility of both acquisition and analysis of these imaging biomarkers. Despite the challenges associated with the biological and technical validation of novel imaging biomarkers, a distinct roadmap has been created that is being implemented into many clinical trials to advance the development and implementation to create specific and sensitive novel imaging biomarkers of therapeutic response to continue to transform medical oncology.
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Affiliation(s)
- Anna G Sorace
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL; Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL; O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL.
| | - Asser A Elkassem
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL
| | - Samuel J Galgano
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL; O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL
| | - Suzanne E Lapi
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL; O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL; Department of Chemistry, University of Alabama at Birmingham, Birmingham, AL
| | - Benjamin M Larimer
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL; O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL
| | | | - C Chad Quarles
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ
| | - Kirsten Reeves
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL; Cancer Biology, University of Alabama at Birmingham, Birmingham, AL
| | - Tiara S Napier
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL; Cancer Biology, University of Alabama at Birmingham, Birmingham, AL
| | - Patrick N Song
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL
| | - Thomas E Yankeelov
- Department of Biomedical Engineering, University of Texas at Austin, Austin, TX; Department of Diagnostic Medicine, University of Texas at Austin, Austin, TX; Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX
| | - Stefanie Woodard
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL
| | - Andrew D Smith
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL; O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL
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van Houdt PJ, Kallehauge JF, Tanderup K, Nout R, Zaletelj M, Tadic T, van Kesteren ZJ, van den Berg CAT, Georg D, Côté JC, Levesque IR, Swamidas J, Malinen E, Telliskivi S, Brynolfsson P, Mahmood F, van der Heide UA. Phantom-based quality assurance for multicenter quantitative MRI in locally advanced cervical cancer. Radiother Oncol 2020; 153:114-121. [PMID: 32931890 DOI: 10.1016/j.radonc.2020.09.013] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 09/07/2020] [Accepted: 09/08/2020] [Indexed: 02/08/2023]
Abstract
BACKGROUND AND PURPOSE A wide variation of MRI systems is a challenge in multicenter imaging biomarker studies as it adds variation in quantitative MRI values. The aim of this study was to design and test a quality assurance (QA) framework based on phantom measurements, for the quantitative MRI protocols of a multicenter imaging biomarker trial of locally advanced cervical cancer. MATERIALS AND METHODS Fifteen institutes participated (five 1.5 T and ten 3 T scanners). Each institute optimized protocols for T2, diffusion-weighted imaging, T1, and dynamic contrast-enhanced (DCE-)MRI according to system possibilities, institutional preferences and study-specific constraints. Calibration phantoms with known values were used for validation. Benchmark protocols, similar on all systems, were used to investigate whether differences resulted from variations in institutional protocols or from system variations. Bias, repeatability (%RC), and reproducibility (%RDC) were determined. Ratios were used for T2 and T1 values. RESULTS The institutional protocols showed a range in bias of 0.88-0.98 for T2 (median %RC = 1%; %RDC = 12%), -0.007 to 0.029 × 10-3 mm2/s for the apparent diffusion coefficient (median %RC = 3%; %RDC = 18%), and 0.39-1.29 for T1 (median %RC = 1%; %RDC = 33%). For DCE a nonlinear vendor-specific relation was observed between measured and true concentrations with magnitude data, whereas the relation was linear when phase data was used. CONCLUSION We designed a QA framework for quantitative MRI protocols and demonstrated for a multicenter trial for cervical cancer that measurement of consistent T2 and apparent diffusion coefficient values is feasible despite protocol differences. For DCE-MRI and T1 mapping with the variable flip angle method, this was more challenging.
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Affiliation(s)
- Petra J van Houdt
- Department of Radiation Oncology, the Netherlands Cancer Institute, Amsterdam, the Netherlands.
| | | | - Kari Tanderup
- Department of Clinical Medicine, Aarhus University Hospital, Denmark
| | - Remi Nout
- Department of Radiation Oncology, Leiden University Medical Center, the Netherlands
| | - Marko Zaletelj
- Department of Radiotherapy, Institute of Oncology Ljubljana, Slovenia
| | - Tony Tadic
- Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, Canada
| | - Zdenko J van Kesteren
- Department of Radiation Oncology, Amsterdam University Medical Center, the Netherlands
| | | | - Dietmar Georg
- Division of Medical Radiation Physics, Department of Radiation Oncology, Medical University Of Vienna, Austria
| | - Jean-Charles Côté
- Department of Radiation Oncology, Centre Hospitalier de l'Universite de Montreal, Canada
| | - Ives R Levesque
- Medical Physics Unit and Gerald Bronfman Department of Oncology, McGill University, Montreal, Canada
| | - Jamema Swamidas
- Department of Radiation Oncology, Tata Memorial Centre, Mumbai, India
| | - Eirik Malinen
- Department of Medical Physics, Oslo University Hospital, Norway
| | - Sven Telliskivi
- Department of Radiation Oncology, North-Estonia Medical Centre, Tallinn, Estonia
| | - Patrik Brynolfsson
- Department of Translational Sciences, Skåne University Hospital, Lund, Sweden
| | - Faisal Mahmood
- Department of Oncology, Odense University Hospital, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Uulke A van der Heide
- Department of Radiation Oncology, the Netherlands Cancer Institute, Amsterdam, the Netherlands
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Wu C, Hormuth DA, Oliver TA, Pineda F, Lorenzo G, Karczmar GS, Moser RD, Yankeelov TE. Patient-Specific Characterization of Breast Cancer Hemodynamics Using Image-Guided Computational Fluid Dynamics. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:2760-2771. [PMID: 32086203 PMCID: PMC7438313 DOI: 10.1109/tmi.2020.2975375] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
The overall goal of this study is to employ quantitative magnetic resonance imaging (MRI) data to constrain a patient-specific, computational fluid dynamics (CFD) model of blood flow and interstitial transport in breast cancer. We develop image processing methodologies to generate tumor-related vasculature-interstitium geometry and realistic material properties, using dynamic contrast enhanced MRI (DCE-MRI) and diffusion weighted MRI (DW-MRI) data. These data are used to constrain CFD simulations for determining the tumor-associated blood supply and interstitial transport characteristics unique to each patient. We then perform a proof-of-principle statistical comparison between these hemodynamic characteristics in 11 malignant and 5 benign lesions from 12 patients. Significant differences between groups (i.e., malignant versus benign) were observed for the median of tumor-associated interstitial flow velocity ( P = 0.028 ), and the ranges of tumor-associated blood pressure (P = 0.016) and vascular extraction rate (P = 0.040). The implication is that malignant lesions tend to have larger magnitude of interstitial flow velocity, and higher heterogeneity in blood pressure and vascular extraction rate. Multivariable logistic models based on combinations of these hemodynamic data achieved excellent differentiation between malignant and benign lesions with an area under the receiver operator characteristic curve of 1.0, sensitivity of 1.0, and specificity of 1.0. This image-based model system is a fundamentally new way to map flow and pressure fields related to breast tumors using only non-invasive, clinically available imaging data and established laws of fluid mechanics. Furthermore, the results provide preliminary evidence for this methodology's utility for the quantitative characterization of breast cancer.
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Demerath T, Blackham K, Anastasopoulos C, Block K, Stieltjes B, Schubert T. Golden-Angle Radial Sparse Parallel (GRASP) MRI differentiates head & neck paragangliomas from schwannomas. Magn Reson Imaging 2020; 70:73-80. [DOI: 10.1016/j.mri.2020.04.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2020] [Revised: 03/30/2020] [Accepted: 04/10/2020] [Indexed: 11/24/2022]
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Zhang J, Winters K, Kiser K, Baboli M, Kim SG. Assessment of tumor treatment response using active contrast encoding (ACE)-MRI: Comparison with conventional DCE-MRI. PLoS One 2020; 15:e0234520. [PMID: 32520950 PMCID: PMC7286489 DOI: 10.1371/journal.pone.0234520] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Accepted: 05/26/2020] [Indexed: 12/01/2022] Open
Abstract
Purpose To investigate the validity of contrast kinetic parameter estimates from Active Contrast Encoding (ACE)-MRI against those from conventional Dynamic Contrast-Enhanced (DCE)-MRI for evaluation of tumor treatment response in mouse tumor models. Methods The ACE-MRI method that incorporates measurement of T1 and B1 into the enhancement curve washout region, was implemented on a 7T MRI scanner to measure tracer kinetic model parameters of 4T1 and GL261 tumors with treatment using bevacizumab and 5FU. A portion of the same ACE-MRI data was used for conventional DCE-MRI data analysis with a separately measured pre-contrast T1 map. Tracer kinetic model parameters, such as Ktrans (permeability area surface product) and ve (extracellular space volume fraction), estimated from ACE-MRI were compared with those from DCE-MRI, in terms of correlation and Bland-Altman analyses. Results A three-fold increase of the median Ktrans by treatment was observed in the flank 4T1 tumors by both ACE-MRI and DCE-MRI. In contrast, the brain tumors did not show a significant change by the treatment in either ACE-MRI or DCE-MRI. Ktrans and ve values of the tumors from ACE-MRI were strongly correlated with those from DCE-MRI methods with correlation coefficients of 0.92 and 0.78, respectively, for the median values of 17 tumors. The Bland-Altman plot analysis showed a mean difference of -0.01 min-1 for Ktrans with the 95% limits of agreement of -0.12 min-1 to 0.09 min-1, and -0.05 with -0.37 to 0.26 for ve. Conclusion The tracer kinetic model parameters estimated from ACE-MRI and their changes by treatment closely matched those of DCE-MRI, which suggests that ACE-MRI can be used in place of conventional DCE-MRI for tumor progression monitoring and treatment response evaluation with a reduced scan time.
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Affiliation(s)
- Jin Zhang
- Department of Radiology, Center for Biomedical Imaging, New York University School of Medicine, New York, New York, United States of America
| | - Kerryanne Winters
- Department of Radiology, Center for Biomedical Imaging, New York University School of Medicine, New York, New York, United States of America
| | - Karl Kiser
- Department of Radiology, Center for Biomedical Imaging, New York University School of Medicine, New York, New York, United States of America
| | - Mehran Baboli
- Department of Radiology, Center for Biomedical Imaging, New York University School of Medicine, New York, New York, United States of America
| | - Sungheon Gene Kim
- Department of Radiology, Center for Biomedical Imaging, New York University School of Medicine, New York, New York, United States of America
- * E-mail:
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Petralia G, Summers PE, Agostini A, Ambrosini R, Cianci R, Cristel G, Calistri L, Colagrande S. Dynamic contrast-enhanced MRI in oncology: how we do it. Radiol Med 2020; 125:1288-1300. [PMID: 32415476 DOI: 10.1007/s11547-020-01220-z] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 04/27/2020] [Indexed: 12/14/2022]
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Optimal Control Theory for Personalized Therapeutic Regimens in Oncology: Background, History, Challenges, and Opportunities. J Clin Med 2020; 9:jcm9051314. [PMID: 32370195 PMCID: PMC7290915 DOI: 10.3390/jcm9051314] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 04/25/2020] [Accepted: 04/28/2020] [Indexed: 12/13/2022] Open
Abstract
Optimal control theory is branch of mathematics that aims to optimize a solution to a dynamical system. While the concept of using optimal control theory to improve treatment regimens in oncology is not novel, many of the early applications of this mathematical technique were not designed to work with routinely available data or produce results that can eventually be translated to the clinical setting. The purpose of this review is to discuss clinically relevant considerations for formulating and solving optimal control problems for treating cancer patients. Our review focuses on two of the most widely used cancer treatments, radiation therapy and systemic therapy, as they naturally lend themselves to optimal control theory as a means to personalize therapeutic plans in a rigorous fashion. To provide context for optimal control theory to address either of these two modalities, we first discuss the major limitations and difficulties oncologists face when considering alternate regimens for their patients. We then provide a brief introduction to optimal control theory before formulating the optimal control problem in the context of radiation and systemic therapy. We also summarize examples from the literature that illustrate these concepts. Finally, we present both challenges and opportunities for dramatically improving patient outcomes via the integration of clinically relevant, patient-specific, mathematical models and optimal control theory.
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Bianchini L, Botta F, Origgi D, Rizzo S, Mariani M, Summers P, García-Polo P, Cremonesi M, Lascialfari A. PETER PHAN: An MRI phantom for the optimisation of radiomic studies of the female pelvis. Phys Med 2020; 71:71-81. [PMID: 32092688 DOI: 10.1016/j.ejmp.2020.02.003] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 01/29/2020] [Accepted: 02/04/2020] [Indexed: 01/26/2023] Open
Abstract
PURPOSE To develop a phantom for methodological radiomic investigation on Magnetic Resonance (MR) images of female patients affected by pelvic cancer. METHODS A pelvis-shaped container was filled with a MnCl2 solution reproducing the relaxation times (T1, T2) of muscle surrounding pelvic malignancies. Inserts simulating multi-textured lesions were embedded in the phantom. The relaxation times of muscle and tumour were measured on an MR scanner on healthy volunteers and patients; T1 and T2 of MnCl2 solutions were evaluated with a relaxometer to find the concentrations providing a match to in vivo relaxation times. Radiomic features were extracted from the phantom inserts and the patients' lesions. Their repeatability was assessed by multiple measurements. RESULTS Muscle T1 and T2 were 1128 (806-1378) and 51 (40-65) ms, respectively. The phantom reproduced in vivo values within 13% (T1) and 12% (T2). T1 and T2 of tumour tissue were 1637 (1396-2121) and 94 (79-101) ms, respectively. The phantom insert best mimicking the tumour agreed within 7% (T1) and 24% (T2) with in vivo values. Out of 1034 features, 75% (95%) had interclass correlation coefficient greater than 0.9 on T1 (T2)-weighted images, reducing to 33% (25%) if the phantom was repositioned. The most repeatable features on phantom showed values in agreement with the features extracted from patients' lesions. CONCLUSIONS We developed an MR phantom with inserts mimicking both relaxation times and texture of pelvic tumours. As exemplified with repeatability assessment, such phantom is useful to investigate features robustness and optimise the radiomic workflow on pelvic MR images.
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Affiliation(s)
- Linda Bianchini
- Department of Physics and INSTM RU, Università degli Studi di Milano, Italy.
| | - Francesca Botta
- Medical Physics Unit, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Daniela Origgi
- Medical Physics Unit, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Stefania Rizzo
- Clinica di Radiologia EOC, Istituto di Imaging della Svizzera Italiana, Sede Ospedale Regionale di Lugano, Switzerland
| | - Manuel Mariani
- Department of Physics and INSTM RU, Università degli Studi di Pavia, Italy
| | - Paul Summers
- Division of Radiology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Pablo García-Polo
- Southern Europe Global Research Organization, GE Healthcare, Madrid, Spain
| | - Marta Cremonesi
- Radiation Research Unit, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Alessandro Lascialfari
- Department of Physics and INSTM RU, Università degli Studi di Milano, Italy; Department of Physics and INSTM RU, Università degli Studi di Pavia, Italy
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