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Molendowska M, Palombo M, Foley KG, Narahari K, Fasano F, Jones DK, Alexander DC, Panagiotaki E, Tax CMW. Diffusion MRI in prostate cancer with ultra-strong whole-body gradients. NMR IN BIOMEDICINE 2024:e5229. [PMID: 39191529 DOI: 10.1002/nbm.5229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 06/07/2024] [Accepted: 07/15/2024] [Indexed: 08/29/2024]
Abstract
Diffusion-weighted MRI (dMRI) is universally recommended for the detection and classification of prostate cancer (PCa), with PI-RADS recommendations to acquire b-values of ≥1.4 ms/μm2. However, clinical dMRI suffers from a low signal-to-noise ratio (SNR) as the consequence of prolonged echo times (TEs) attributable to the limited gradient power in the range of 40-80 mT/m. To overcome this, MRI systems with strong gradients have been designed but so far have mainly been applied in the brain. The aim of this work was to assess the feasibility, data quality, SNR and contrast-to-noise ratio (CNR) of measurements in PCa with a 300 mT/m whole-body system. A cohort of men without and with diagnosed PCa were imaged on a research-only 3T Connectom Siemens MRI system equipped with a gradient amplitude of 300 mT/m. dMRI at high b-values were acquired using high gradient amplitudes and compared with gradient capabilities mimicking clinical systems. Data artefacts typically amplified with stronger gradients were assessed and their correction evaluated. The SNR gains and lesion-to-healthy tissue CNR were statistically tested investigating the effect of protocol and b-value. The diagnostic quality of the images for different dMRI protocols was assessed by an experienced radiologist using a 5-point Likert scale and an adapted PI-QUAL scoring system. The strong gradients for prostate dMRI allowed a significant gain in SNR per unit time compared with clinical gradients. Furthermore, a 1.6-2.1-fold increase in CNR was observed. Despite the more pronounced artefacts typically associated with strong gradients, a satisfactory correction could be achieved. Smoother and less biased parameter maps were obtained with protocols at shorter TEs. The results of this study show that dMRI in PCa with a whole-body 300-mT/m scanner is feasible without a report of physiological effects, SNR and CNR can be improved compared with lower gradient strengths, and artefacts do not negate the benefits of strong gradients and can be ameliorated. This assessment provides the first essential step towards unveiling the full potential of cutting-edge scanners, now increasingly becoming available, to advance early detection and diagnostic precision.
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Affiliation(s)
- Malwina Molendowska
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
| | - Marco Palombo
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK
| | - Kieran G Foley
- Division of Cancer and Genetics, School of Medicine, Cardiff University, Cardiff, UK
| | - Krishna Narahari
- Cardiff and Vale University Health Board, Heath Park Campus, Cardiff, UK
| | - Fabrizio Fasano
- Siemens Healthcare Ltd, Camberley, UK
- Siemens Healthcare GmbH, Erlangen, Germany
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, University College London, London, UK
| | | | - Chantal M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
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Liu Q, Gagoski B, Shaik IA, Westin CF, Wilde EA, Schneider W, Bilgic B, Grissom WA, Nielsen JF, Zaitsev M, Rathi Y, Ning L. Time-division multiplexing (TDM) sequence removes bias in T 2 estimation and relaxation-diffusion measurements. Magn Reson Med 2024. [PMID: 39136245 DOI: 10.1002/mrm.30246] [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: 04/09/2024] [Revised: 07/24/2024] [Accepted: 07/25/2024] [Indexed: 08/21/2024]
Abstract
PURPOSE To compare the performance of multi-echo (ME) and time-division multiplexing (TDM) sequences for accelerated relaxation-diffusion MRI (rdMRI) acquisition and to examine their reliability in estimating accurate rdMRI microstructure measures. METHOD The ME, TDM, and the reference single-echo (SE) sequences with six TEs were implemented using Pulseq with single-band (SB) and multi-band 2 (MB2) acceleration factors. On a diffusion phantom, the image intensities of the three sequences were compared, and the differences were quantified using the normalized RMS error (NRMSE). Shinnar-Le Roux (SLR) pulses were implemented for the SB-ME and SB-SE sequences to investigate the impact of slice profiles on ME sequences. For the in-vivo brain scan, besides the image intensity comparison and T2-estimates, different methods were used to assess sequence-related effects on microstructure estimation, including the relaxation diffusion imaging moment (REDIM) and the maximum-entropy relaxation diffusion distribution (MaxEnt-RDD). RESULTS TDM performance was similar to the gold standard SE acquisition, whereas ME showed greater biases (3-4× larger NRMSEs for phantom, 2× for in-vivo). T2 values obtained from TDM closely matched SE, whereas ME sequences underestimated the T2 relaxation time. TDM provided similar diffusion and relaxation parameters as SE using REDIM, whereas SB-ME exhibited a 60% larger bias in the map and on average 3.5× larger bias in the covariance between relaxation-diffusion coefficients. CONCLUSION Our analysis demonstrates that TDM provides a more accurate estimation of relaxation-diffusion measurements while accelerating the acquisitions by a factor of 2 to 3.
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Affiliation(s)
- Qiang Liu
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Borjan Gagoski
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Imam Ahmed Shaik
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Carl-Fredrik Westin
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Elisabeth A Wilde
- Va Salt Lake City Health Care System, Informatics, Decision-Enhancement and Analytic Sciences Center, Salt Lake City, Utah, USA
- Department of Neurology, University of Utah, Salt Lake City, Utah, USA
| | | | - Berkin Bilgic
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard/MIT Health Sciences and Technology, Cambridge, Massachusetts, USA
| | - William A Grissom
- Department of Biomedical Engineering, Case School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
| | - Jon-Fredrik Nielsen
- Functional MRI Laboratory, Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Maxim Zaitsev
- Division of Medical Physics, Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Lipeng Ning
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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Chatterjee A, Dwivedi DK. MRI-based virtual pathology of the prostate. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01163-w. [PMID: 38856839 DOI: 10.1007/s10334-024-01163-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 04/26/2024] [Accepted: 04/29/2024] [Indexed: 06/11/2024]
Abstract
Prostate cancer poses significant diagnostic challenges, with conventional methods like prostate-specific antigen (PSA) screening and transrectal ultrasound (TRUS)-guided biopsies often leading to overdiagnosis or miss clinically significant cancers. Multiparametric MRI (mpMRI) has emerged as a more reliable tool. However, it is limited by high inter-observer variability and radiologists missing up to 30% of clinically significant cancers. This article summarizes a few of these recent advancements in quantitative MRI techniques that look at the "Virtual Pathology" of the prostate with an aim to enhance prostate cancer detection and characterization. These techniques include T2 relaxation-based techniques such as luminal water imaging, diffusion based such as vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) and restriction spectrum imaging or combined relaxation-diffusion techniques such as hybrid multi-dimensional MRI (HM-MRI), time-dependent diffusion imaging, and diffusion-relaxation correlation spectrum imaging. These methods provide detailed insights into underlying prostate microstructure and tissue composition and have shown improved diagnostic accuracy over conventional MRI. These innovative MRI methods hold potential for augmenting mpMRI, reducing variability in diagnosis, and paving the way for MRI as a 'virtual histology' tool in prostate cancer diagnosis. However, they require further validation in larger multi-center clinical settings and rigorous in-depth radiological-pathology correlation are needed for broader implementation.
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Affiliation(s)
- Aritrick Chatterjee
- Department of Radiology, University of Chicago, 5841 South Maryland Avenue, MC 2026, Chicago, IL, 60637, USA.
- Sanford J. Grossman Center of Excellence in Prostate Imaging and Image Guided Therapy, University of Chicago, Chicago, IL, USA.
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Cao Y, Davarani SN, You D, Feiweier T, Casper K, Balis U, Udager A, Balter J, Mierzwa M. In Vivo Microstructure Imaging in Oropharyngeal Squamous Cell Carcinoma Using the Random Walk With Barriers Model. J Magn Reson Imaging 2024; 59:929-938. [PMID: 37366349 DOI: 10.1002/jmri.28831] [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: 02/16/2023] [Revised: 05/16/2023] [Accepted: 05/18/2023] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND Apparent diffusion coefficient is not specifically sensitive to tumor microstructure and therapy-induced cellular changes. PURPOSE To investigate time-dependent diffusion imaging with the short-time-limit random walk with barriers model (STL-RWBM) for quantifying microstructure parameters and early cancer cellular response to therapy. STUDY TYPE Prospective. POPULATION Twenty-seven patients (median age of 58 years and 7.4% of females) with p16+/p16- oropharyngeal/oral cavity squamous cell carcinomas (OPSCC/OCSCC) underwent MRI scans before therapy, of which 16 patients had second scans at 2 weeks of the 7-weeks chemoradiation therapy (CRT). FIELD STRENGTH/SEQUENCE 3-T, diffusion sequence with oscillating gradient spine echo (OGSE) and pulse gradient spin echo (PGSE). ASSESSMENT Diffusion weighted images were acquired using OGSE and PGSE. Effective diffusion times were derived for the STL-RWBM to estimate free diffusion coefficient D0 , volume-to-surface area ratio of cellular membranes V/S, and cell membrane permeability κ. Mean values of these parameters were calculated in tumor volumes. STATISTICAL TESTS Tumor microstructure parameters were compared with clinical stages of p16+ I-II OPSCC, p16+ III OPSCC, and p16- IV OCSCC by Spearman's rank correlation and with digital pathological analysis of a resected tissue sample. Tumor microstructure parameter responses during CRT in the 16 patients were assessed by paired t-tests. A P-value of <0.05 was considered statistically significant. RESULTS The derived effective diffusion times affected estimated values of V/S and κ by 40%. The tumor V/S values were significantly correlated with clinical stages (r = 0.47) as an increase from low to high clinical stages. The in vivo estimated cell size agreed with one from pathological analysis of a tissue sample. Early tumor cellular responses showed a significant increase in D0 (14%, P = 0.03) and non-significant increases in κ (56%, P = 0.6) and V/S (10%, P = 0.1). DATA CONCLUSION Effective diffusion time estimation might impact microstructure parameter estimation. The tumor V/S was correlated with OPSCC/OCSCC clinical stages. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Yue Cao
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | | | - Daekeun You
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | | | - Keith Casper
- Department of Otolaryngology, University of Michigan, Ann Arbor, Michigan, USA
| | - Ulysses Balis
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
| | - Aaron Udager
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA
| | - James Balter
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | - Michelle Mierzwa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
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Zhang Z, Aygun E, Shih SF, Raman SS, Sung K, Wu HH. High-resolution prostate diffusion MRI using eddy current-nulled convex optimized diffusion encoding and random matrix theory-based denoising. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01147-w. [PMID: 38349453 PMCID: PMC11323217 DOI: 10.1007/s10334-024-01147-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 12/12/2023] [Accepted: 01/04/2024] [Indexed: 02/17/2024]
Abstract
OBJECTIVE To develop and evaluate a technique combining eddy current-nulled convex optimized diffusion encoding (ENCODE) with random matrix theory (RMT)-based denoising to accelerate and improve the apparent signal-to-noise ratio (aSNR) and apparent diffusion coefficient (ADC) mapping in high-resolution prostate diffusion-weighted MRI (DWI). MATERIALS AND METHODS: Eleven subjects with clinical suspicion of prostate cancer were scanned at 3T with high-resolution (HR) (in-plane: 1.0 × 1.0 mm2) ENCODE and standard-resolution (1.6 × 2.2 mm2) bipolar DWI sequences (both had 7 repetitions for averaging, acquisition time [TA] of 5 min 50 s). HR-ENCODE was retrospectively analyzed using three repetitions (accelerated effective TA of 2 min 30 s). The RMT-based denoising pipeline utilized complex DWI signals and Marchenko-Pastur distribution-based principal component analysis to remove additive Gaussian noise in images from multiple coils, b-values, diffusion encoding directions, and repetitions. HR-ENCODE with RMT-based denoising (HR-ENCODE-RMT) was compared with HR-ENCODE in terms of aSNR in prostate peripheral zone (PZ) and transition zone (TZ). Precision and accuracy of ADC were evaluated by the coefficient of variation (CoV) between repeated measurements and mean difference (MD) compared to the bipolar ADC reference, respectively. Differences were compared using two-sided Wilcoxon signed-rank tests (P < 0.05 considered significant). RESULTS HR-ENCODE-RMT yielded 62% and 56% higher median aSNR than HR-ENCODE (b = 800 s/mm2) in PZ and TZ, respectively (P < 0.001). HR-ENCODE-RMT achieved 63% and 70% lower ADC-CoV than HR-ENCODE in PZ and TZ, respectively (P < 0.001). HR-ENCODE-RMT ADC and bipolar ADC had low MD of 22.7 × 10-6 mm2/s in PZ and low MD of 90.5 × 10-6 mm2/s in TZ. CONCLUSIONS HR-ENCODE-RMT can shorten the acquisition time and improve the aSNR of high-resolution prostate DWI and achieve accurate and precise ADC measurements in the prostate.
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Affiliation(s)
- Zhaohuan Zhang
- Department of Radiological Sciences, University of California Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Elif Aygun
- Department of Radiological Sciences, University of California Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Shu-Fu Shih
- Department of Radiological Sciences, University of California Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Steven S Raman
- Department of Radiological Sciences, University of California Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA
| | - Kyunghyun Sung
- Department of Radiological Sciences, University of California Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Holden H Wu
- Department of Radiological Sciences, University of California Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA.
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA.
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Hanzlikova P, Vilimek D, Vilimkova Kahankova R, Ladrova M, Skopelidou V, Ruzickova Z, Martinek R, Cvek J. Longitudinal analysis of T2 relaxation time variations following radiotherapy for prostate cancer. Heliyon 2024; 10:e24557. [PMID: 38298676 PMCID: PMC10828070 DOI: 10.1016/j.heliyon.2024.e24557] [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: 07/07/2023] [Revised: 12/02/2023] [Accepted: 01/10/2024] [Indexed: 02/02/2024] Open
Abstract
Aim of this paper is to evaluate short and long-term changes in T 2 relaxation times after radiotherapy in patients with low and intermediate risk localized prostate cancer. A total of 24 patients were selected for this retrospective study. Each participant underwent 1.5T magnetic resonance imaging on seven separate occasions: initially after the implantation of gold fiducials, the required step for Cyberknife therapy guidance, followed by MRI scans two weeks post-therapy and monthly thereafter. As part of each MRI scan, the prostate region was manually delineated, and the T 2 relaxation times were calculated for quantitative analysis. The T 2 relaxation times between individual follow-ups were analyzed using Repeated Measures Analysis of Variance that revealed a significant difference across all measurements (F (6, 120) = 0.611, p << 0.001). A Bonferroni post hoc test revealed significant differences in median T 2 values between the baseline and subsequent measurements, particularly between pre-therapy (M 0 ) and two weeks post-therapy (M 1 ), as well as during the monthly interval checks (M 2 - M 6 ). Some cases showed a delayed decrease in relaxation times, indicating the prolonged effects of therapy. The changes in T 2 values during the course of radiotherapy can help in monitoring radiotherapy response in unconfirmed patients, quantifying the scarring process, and recognizing the therapy failure.
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Affiliation(s)
- Pavla Hanzlikova
- Department of Radiology, University Hospital Ostrava, Czech Republic
- Department of Imaging Methods, Faculty of Medicine, University of Ostrava, Ostrava, Czech Republic
| | - Dominik Vilimek
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, 17. listopadu 15, Ostrava – Poruba, 708 00, Czech Republic
| | - Radana Vilimkova Kahankova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, 17. listopadu 15, Ostrava – Poruba, 708 00, Czech Republic
| | - Martina Ladrova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, 17. listopadu 15, Ostrava – Poruba, 708 00, Czech Republic
| | - Valeria Skopelidou
- Institute of Molecular and Clinical Pathology and Medical Genetics, University Hospital Ostrava, 70852, Ostrava, Czech Republic
- Institute of Molecular and Clinical Pathology and Medical Genetics, Faculty of Medicine, University of Ostrava, 70300, Ostrava, Czech Republic
| | - Zuzana Ruzickova
- Faculty of Medicine, University of Ostrava, 70300 Ostrava, Czech Republic
- Department of Oncology, University Hospital Ostrava, 70852 Ostrava, Czech Republic
| | - Radek Martinek
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB - Technical University of Ostrava, 17. listopadu 15, Ostrava – Poruba, 708 00, Czech Republic
| | - Jakub Cvek
- Faculty of Medicine, University of Ostrava, 70300 Ostrava, Czech Republic
- Department of Oncology, University Hospital Ostrava, 70852 Ostrava, Czech Republic
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Fokkinga E, Hernandez-Tamames JA, Ianus A, Nilsson M, Tax CMW, Perez-Lopez R, Grussu F. Advanced Diffusion-Weighted MRI for Cancer Microstructure Assessment in Body Imaging, and Its Relationship With Histology. J Magn Reson Imaging 2023. [PMID: 38032021 DOI: 10.1002/jmri.29144] [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: 08/11/2023] [Revised: 10/30/2023] [Accepted: 10/31/2023] [Indexed: 12/01/2023] Open
Abstract
Diffusion-weighted magnetic resonance imaging (DW-MRI) aims to disentangle multiple biological signal sources in each imaging voxel, enabling the computation of innovative maps of tissue microstructure. DW-MRI model development has been dominated by brain applications. More recently, advanced methods with high fidelity to histology are gaining momentum in other contexts, for example, in oncological applications of body imaging, where new biomarkers are urgently needed. The objective of this article is to review the state-of-the-art of DW-MRI in body imaging (ie, not including the nervous system) in oncology, and to analyze its value as compared to reference colocalized histology measurements, given that demonstrating the histological validity of any new DW-MRI method is essential. In this article, we review the current landscape of DW-MRI techniques that extend standard apparent diffusion coefficient (ADC), describing their acquisition protocols, signal models, fitting settings, microstructural parameters, and relationship with histology. Preclinical, clinical, and in/ex vivo studies were included. The most used techniques were intravoxel incoherent motion (IVIM; 36.3% of used techniques), diffusion kurtosis imaging (DKI; 16.7%), vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT; 13.3%), and imaging microstructural parameters using limited spectrally edited diffusion (IMPULSED; 11.7%). Another notable category of techniques relates to innovative b-tensor diffusion encoding or joint diffusion-relaxometry. The reviewed approaches provide histologically meaningful indices of cancer microstructure (eg, vascularization/cellularity) which, while not necessarily accurate numerically, may still provide useful sensitivity to microscopic pathological processes. Future work of the community should focus on improving the inter-/intra-scanner robustness, and on assessing histological validity in broader contexts. LEVEL OF EVIDENCE: NA TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Ella Fokkinga
- Biomedical Engineering, Track Medical Physics, Delft University of Technology, Delft, The Netherlands
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Juan A Hernandez-Tamames
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
| | - Andrada Ianus
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | - Markus Nilsson
- Department of Diagnostic Radiology, Clinical Sciences Lund, Lund, Sweden
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Center (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Raquel Perez-Lopez
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Francesco Grussu
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
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Lemberskiy G, Chandarana H, Bruno M, Ginocchio LA, Huang C, Tong A, Keerthivasan MB, Fieremans E, Novikov DS. Feasibility of Accelerated Prostate Diffusion-Weighted Imaging on 0.55 T MRI Enabled With Random Matrix Theory Denoising. Invest Radiol 2023; 58:720-729. [PMID: 37222526 PMCID: PMC10527232 DOI: 10.1097/rli.0000000000000979] [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] [Indexed: 05/25/2023]
Abstract
INTRODUCTION Prostate cancer diffusion weighted imaging (DWI) MRI is typically performed at high-field strength (3.0 T) in order to overcome low signal-to-noise ratio (SNR). In this study, we demonstrate the feasibility of prostate DWI at low field enabled by random matrix theory (RMT)-based denoising, relying on the MP-PCA algorithm applied during image reconstruction from multiple coils. METHODS Twenty-one volunteers and 2 prostate cancer patients were imaged with a 6-channel pelvic surface array coil and an 18-channel spine array on a prototype 0.55 T system created by ramping down a commercial magnetic resonance imaging system (1.5 T MAGNETOM Aera Siemens Healthcare) with 45 mT/m gradients and 200 T/m/s slew rate. Diffusion-weighted images were acquired with 4 non-collinear directions, for which b = 50 s/mm 2 was used with 8 averages and b = 1000 s/mm 2 with 40 averages; 2 extra b = 50 s/mm 2 were used as part of the dynamic field correction. Standard and RMT-based reconstructions were applied on DWI over different ranges of averages. Accuracy/precision was evaluated using the apparent diffusion coefficient (ADC), and image quality was evaluated over 5 separate reconstructions by 3 radiologists with a 5-point Likert scale. For the 2 patients, we compare image quality and lesion visibility of the RMT reconstruction versus the standard one on 0.55 T and on clinical 3.0 T. RESULTS The RMT-based reconstruction in this study reduces the noise floor by a factor of 5.8, thereby alleviating the bias on prostate ADC. Moreover, the precision of the ADC in prostate tissue after RMT increases over a range of 30%-130%, with the increase in both signal-to-noise ratio and precision being more prominent for a low number of averages. Raters found that the images were consistently of moderate to good overall quality (3-4 on the Likert scale). Moreover, they determined that b = 1000 s/mm 2 images from a 1:55-minute scan with the RMT-based reconstruction were on par with the corresponding images from a 14:20-minute scan with standard reconstruction. Prostate cancer was visible on ADC and calculated b = 1500 images even with the abbreviated 1:55-minute scan reconstructed with RMT. CONCLUSIONS Prostate imaging using DWI is feasible at low field and can be performed more rapidly with noninferior image quality compared with standard reconstruction.
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Affiliation(s)
- Gregory Lemberskiy
- Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine
| | - Hersh Chandarana
- Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine
| | - Mary Bruno
- Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine
| | - Luke A. Ginocchio
- Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine
| | - Chenchan Huang
- Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine
| | - Angela Tong
- Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine
| | | | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine
| | - Dmitry S. Novikov
- Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine
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9
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Enríquez-Mier-Y-Terán FE, Chatterjee A, Antic T, Oto A, Karczmar G, Bourne R. Multi-model sequential analysis of MRI data for microstructure prediction in heterogeneous tissue. Sci Rep 2023; 13:16486. [PMID: 37779137 PMCID: PMC10543593 DOI: 10.1038/s41598-023-43329-x] [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: 12/09/2022] [Accepted: 09/22/2023] [Indexed: 10/03/2023] Open
Abstract
We propose a general method for combining multiple models to predict tissue microstructure, with an exemplar using in vivo diffusion-relaxation MRI data. The proposed method obviates the need to select a single 'optimum' structure model for data analysis in heterogeneous tissues where the best model varies according to local environment. We break signal interpretation into a three-stage sequence: (1) application of multiple semi-phenomenological models to predict the physical properties of tissue water pools contributing to the observed signal; (2) from each Stage-1 semi-phenomenological model, application of a tissue microstructure model to predict the relative volumes of tissue structure components that make up each water pool; and (3) aggregation of the predictions of tissue structure, with weightings based on model likelihood and fractional volumes of the water pools from Stage-1. The multiple model approach is expected to reduce prediction variance in tissue regions where a complex model is overparameterised, and bias where a model is underparameterised. The separation of signal characterisation (Stage-1) from biological assignment (Stage-2) enables alternative biological interpretations of the observed physical properties of the system, by application of different tissue structure models. The proposed method is exemplified with human prostate diffusion-relaxation MRI data, but has potential application to a wide range of analyses where a single model may not be optimal throughout the sampled domain.
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Affiliation(s)
- Francisco E Enríquez-Mier-Y-Terán
- School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, 2008, Australia
- The Brain and Mind Centre, The University of Sydney, Sydney, 2050, Australia
| | - Aritrick Chatterjee
- Department of Radiology, University of Chicago, 5841 South Maryland Avenue, MC 2026, Chicago, 60637, IL, USA
- Sanford J. Grossman Center of Excellence in Prostate Imaging and Image Guided Therapy, University of Chicago, Chicago, 60637, IL, USA
| | - Tatjana Antic
- Department of Pathology, University of Chicago, Chicago, 60637, IL, USA
| | - Aytekin Oto
- Department of Radiology, University of Chicago, 5841 South Maryland Avenue, MC 2026, Chicago, 60637, IL, USA
| | - Gregory Karczmar
- Department of Radiology, University of Chicago, 5841 South Maryland Avenue, MC 2026, Chicago, 60637, IL, USA
| | - Roger Bourne
- Discipline of Medical Imaging Science, Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, 2006, Australia.
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10
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Wu D, Turnbill V, Lee HH, Wang X, Ba R, Walczak P, Martin LJ, Fieremans E, Novikov DS, Northington FJ, Zhang J. In vivo Mapping of Cellular Resolution Neuropathology in Brain Ischemia by Diffusion MRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.08.552374. [PMID: 37609182 PMCID: PMC10441332 DOI: 10.1101/2023.08.08.552374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Non-invasive mapping of cellular pathology can provide critical diagnostic and prognostic information. Recent developments in diffusion MRI have produced new tools for examining tissue microstructure at a level well below the imaging resolution. Here, we report the use of diffusion time ( t )-dependent diffusion kurtosis imaging ( t DKI) to simultaneously assess the morphology and transmembrane permeability of cells and their processes in the context of pathological changes in hypoxic-ischemic brain (HI) injury. Through Monte Carlo simulations and cell culture organoid imaging, we demonstrate feasibility in measuring effective size and permeability changes based on the peak and tail of t DKI curves. In a mouse model of HI, in vivo imaging at 11.7T detects a marked shift of the t DKI peak to longer t in brain edema, suggesting swelling and beading associated with the astrocytic processes and neuronal neurites. Furthermore, we observed a faster decrease of the t DKI tail in injured brain regions, reflecting increased membrane permeability that was associated with upregulated water exchange upon astrocyte activation at acute stage as well as necrosis with disrupted membrane integrity at subacute stage. Such information, unavailable with conventional diffusion MRI at a single t, can predict salvageable tissues. For a proof-of-concept, t DKI at 3T on an ischemic stroke patient suggested increased membrane permeability in the stroke region. This work therefore demonstrates the potential of t DKI for in vivo detection of the pathological changes in microstructural morphology and transmembrane permeability after ischemic injury using a clinically translatable protocol.
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Xu J, Xie J, Semmineh NB, Devan SP, Jiang X, Gore JC. Diffusion time dependency of extracellular diffusion. Magn Reson Med 2023; 89:2432-2440. [PMID: 36740894 PMCID: PMC10392121 DOI: 10.1002/mrm.29594] [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: 04/24/2022] [Revised: 12/10/2022] [Accepted: 01/09/2023] [Indexed: 02/07/2023]
Abstract
PURPOSE To quantify the variations of the power-law dependences on diffusion time t or gradient frequencyf $$ f $$ of extracellular water diffusion measured by diffusion MRI (dMRI). METHODS Model cellular systems containing only extracellular water were used to investigate thet / f $$ t/f $$ dependence ofD ex $$ {D}_{ex} $$ , the extracellular diffusion coefficient. Computer simulations used a randomly packed tissue model with realistic intracellular volume fractions and cell sizes. DMRI measurements were performed on samples consisting of liposomes containing heavy water(D2 O, deuterium oxide) dispersed in regular water (H2 O).D ex $$ {D}_{ex} $$ was obtained over a broadt $$ t $$ range (∼1-1000 ms) and then fit power-law equationsD ex ( t ) = D const + const · t - ϑ t $$ {D}_{ex}(t)={D}_{\mathrm{const}}+\mathrm{const}\cdotp {t}^{-{\vartheta}_t} $$ andD ex ( f ) = D const + const · f ϑ f $$ {D}_{ex}(f)={D}_{\mathrm{const}}+\mathrm{const}\cdotp {f}^{\vartheta_f} $$ . RESULTS Both simulated and experimental results suggest that no single power-law adequately describes the behavior ofD ex $$ {D}_{ex} $$ over the range of diffusion times of most interest in practical dMRI. Previous theoretical predictions are accurate over only limitedt $$ t $$ ranges; for example,θ t = θ f = - 1 2 $$ {\theta}_t={\theta}_f=-\frac{1}{2} $$ is valid only for short times, whereasθ t = 1 $$ {\theta}_t=1 $$ orθ f = 3 2 $$ {\theta}_f=\frac{3}{2} $$ is valid only for long times but cannot describe other ranges simultaneously. For the specifict $$ t $$ range of 5-70 ms used in typical human dMRI measurements,θ t = θ f = 1 $$ {\theta}_t={\theta}_f=1 $$ matches the data well empirically. CONCLUSION The optimal power-law fit of extracellular diffusion varies with diffusion time. The dependency obtained at short or longt $$ t $$ limits cannot be applied to typical dMRI measurements in human cancer or liver. It is essential to determine the appropriate diffusion time range when modeling extracellular diffusion in dMRI-based quantitative microstructural imaging.
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Affiliation(s)
- Junzhong Xu
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
- Department of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee
| | - Jingping Xie
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee
| | | | - Sean P. Devan
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Xiaoyu Jiang
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
| | - John C. Gore
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
- Department of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee
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12
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Sun K, Dan G, Zhong Z, Zhou XJ. Multi-readout DWI with a reduced FOV for studying the coupling between diffusion and T 2 * relaxation in the prostate. Magn Reson Med 2023; 90:250-258. [PMID: 36932652 DOI: 10.1002/mrm.29636] [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: 10/13/2022] [Revised: 02/16/2023] [Accepted: 02/23/2023] [Indexed: 03/19/2023]
Abstract
PURPOSE To develop a DWI sequence with multiple readout echo-trains in a single shot (multi-readout DWI) over a reduced FOV, and to demonstrate its ability to achieve high data acquisition efficiency in the study of coupling between diffusion and relaxation in the human prostate. METHODS The proposed multi-readout DWI sequence plays out multiple EPI readout echo-trains after a Stejskal-Tanner diffusion preparation module. Each EPI readout echo-train corresponded to a distinct effective TE. To maintain a high spatial resolution with a relatively short echo-train for each readout, a 2D RF pulse was used to limit the FOV. Experiments were performed on the prostate of six healthy subjects to acquire a set of images with three b values (0, 500, and 1000 s/mm2 ) and three TEs (63.0, 78.8, and 94.6 ms), producing three ADC maps at different TEs and three T 2 * $$ {T}_2^{\ast } $$ maps at different b values. RESULTS Multi-readout DWI enabled a threefold acceleration without compromising the spatial resolution when compared with a conventional single-readout sequence. Images with three b values and three TEs were obtained in 3 min 40 s with an adequate SNR (≥ 26.9). The ADC values (1.45 ± 0.13, 1.52 ± 0.14, and 1.58 ± 0.15 μm 2 / ms $$ {\upmu \mathrm{m}}^2/\mathrm{ms} $$ ; P < 0.01) exhibited an increasing trend as TEs increased (63.0 ms, 78.8 ms, and 94.6 ms), whereas T 2 * $$ {T}_2^{\ast } $$ values (74.78 ± 13.21, 63.21 ± 7.84, and 56.61 ± 5.05 ms; P < 0.01) decreases as the b values increased (0, 500, and 1000 s/mm2 ). CONCLUSION The multi-readout DWI sequence over a reduced FOV provides a time-efficient technique to study the coupling between diffusion and relaxation times.
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Affiliation(s)
- Kaibao Sun
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Guangyu Dan
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, Illinois, USA.,Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Zheng Zhong
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, Illinois, USA.,Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Xiaohong Joe Zhou
- Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, Illinois, USA.,Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, Illinois, USA.,Departments of Radiology and Neurosurgery, University of Illinois College of Medicine at Chicago, Chicago, Illinois, USA
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13
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Palombo M, Valindria V, Singh S, Chiou E, Giganti F, Pye H, Whitaker HC, Atkinson D, Punwani S, Alexander DC, Panagiotaki E. Joint estimation of relaxation and diffusion tissue parameters for prostate cancer with relaxation-VERDICT MRI. Sci Rep 2023; 13:2999. [PMID: 36810476 PMCID: PMC9943845 DOI: 10.1038/s41598-023-30182-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 02/17/2023] [Indexed: 02/23/2023] Open
Abstract
This work presents a biophysical model of diffusion and relaxation MRI for prostate called relaxation vascular, extracellular and restricted diffusion for cytometry in tumours (rVERDICT). The model includes compartment-specific relaxation effects providing T1/T2 estimates and microstructural parameters unbiased by relaxation properties of the tissue. 44 men with suspected prostate cancer (PCa) underwent multiparametric MRI (mp-MRI) and VERDICT-MRI followed by targeted biopsy. We estimate joint diffusion and relaxation prostate tissue parameters with rVERDICT using deep neural networks for fast fitting. We tested the feasibility of rVERDICT estimates for Gleason grade discrimination and compared with classic VERDICT and the apparent diffusion coefficient (ADC) from mp-MRI. The rVERDICT intracellular volume fraction fic discriminated between Gleason 3 + 3 and 3 + 4 (p = 0.003) and Gleason 3 + 4 and ≥ 4 + 3 (p = 0.040), outperforming classic VERDICT and the ADC from mp-MRI. To evaluate the relaxation estimates we compare against independent multi-TE acquisitions, showing that the rVERDICT T2 values are not significantly different from those estimated with the independent multi-TE acquisition (p > 0.05). Also, rVERDICT parameters exhibited high repeatability when rescanning five patients (R2 = 0.79-0.98; CV = 1-7%; ICC = 92-98%). The rVERDICT model allows for accurate, fast and repeatable estimation of diffusion and relaxation properties of PCa sensitive enough to discriminate Gleason grades 3 + 3, 3 + 4 and ≥ 4 + 3.
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Affiliation(s)
- Marco Palombo
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK.
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK.
| | - Vanya Valindria
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Saurabh Singh
- Centre for Medical Imaging, University College London, London, UK
| | - Eleni Chiou
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Francesco Giganti
- Division of Surgery and Interventional Science, University College London, London, UK
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK
| | - Hayley Pye
- Molecular Diagnostics and Therapeutics Group, Division of Surgery & Interventional Science, University College London, London, UK
| | - Hayley C Whitaker
- Molecular Diagnostics and Therapeutics Group, Division of Surgery & Interventional Science, University College London, London, UK
| | - David Atkinson
- Centre for Medical Imaging, University College London, London, UK
| | - Shonit Punwani
- Centre for Medical Imaging, University College London, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Eleftheria Panagiotaki
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
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14
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Fan Q, Eichner C, Afzali M, Mueller L, Tax CMW, Davids M, Mahmutovic M, Keil B, Bilgic B, Setsompop K, Lee HH, Tian Q, Maffei C, Ramos-Llordén G, Nummenmaa A, Witzel T, Yendiki A, Song YQ, Huang CC, Lin CP, Weiskopf N, Anwander A, Jones DK, Rosen BR, Wald LL, Huang SY. Mapping the human connectome using diffusion MRI at 300 mT/m gradient strength: Methodological advances and scientific impact. Neuroimage 2022; 254:118958. [PMID: 35217204 PMCID: PMC9121330 DOI: 10.1016/j.neuroimage.2022.118958] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 01/27/2022] [Accepted: 01/31/2022] [Indexed: 12/20/2022] Open
Abstract
Tremendous efforts have been made in the last decade to advance cutting-edge MRI technology in pursuit of mapping structural connectivity in the living human brain with unprecedented sensitivity and speed. The first Connectom 3T MRI scanner equipped with a 300 mT/m whole-body gradient system was installed at the Massachusetts General Hospital in 2011 and was specifically constructed as part of the Human Connectome Project. Since that time, numerous technological advances have been made to enable the broader use of the Connectom high gradient system for diffusion tractography and tissue microstructure studies and leverage its unique advantages and sensitivity to resolving macroscopic and microscopic structural information in neural tissue for clinical and neuroscientific studies. The goal of this review article is to summarize the technical developments that have emerged in the last decade to support and promote large-scale and scientific studies of the human brain using the Connectom scanner. We provide a brief historical perspective on the development of Connectom gradient technology and the efforts that led to the installation of three other Connectom 3T MRI scanners worldwide - one in the United Kingdom in Cardiff, Wales, another in continental Europe in Leipzig, Germany, and the latest in Asia in Shanghai, China. We summarize the key developments in gradient hardware and image acquisition technology that have formed the backbone of Connectom-related research efforts, including the rich array of high-sensitivity receiver coils, pulse sequences, image artifact correction strategies and data preprocessing methods needed to optimize the quality of high-gradient strength diffusion MRI data for subsequent analyses. Finally, we review the scientific impact of the Connectom MRI scanner, including advances in diffusion tractography, tissue microstructural imaging, ex vivo validation, and clinical investigations that have been enabled by Connectom technology. We conclude with brief insights into the unique value of strong gradients for diffusion MRI and where the field is headed in the coming years.
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Affiliation(s)
- Qiuyun Fan
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Cornelius Eichner
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neuropsychology, Leipzig, Germany
| | - Maryam Afzali
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, Wales, UK; Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds LS2 9JT, UK
| | - Lars Mueller
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds LS2 9JT, UK
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, Wales, UK; Image Sciences Institute, University Medical Center (UMC) Utrecht, Utrecht, the Netherlands
| | - Mathias Davids
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA; Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Mirsad Mahmutovic
- Institute of Medical Physics and Radiation Protection (IMPS), TH-Mittelhessen University of Applied Sciences (THM), Giessen, Germany
| | - Boris Keil
- Institute of Medical Physics and Radiation Protection (IMPS), TH-Mittelhessen University of Applied Sciences (THM), Giessen, Germany
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kawin Setsompop
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Hong-Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Chiara Maffei
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Gabriel Ramos-Llordén
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | | | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Yi-Qiao Song
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA USA
| | - Chu-Chung Huang
- Key Laboratory of Brain Functional Genomics (MOE & STCSM), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China; Shanghai Changning Mental Health Center, Shanghai, China
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany
| | - Alfred Anwander
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neuropsychology, Leipzig, Germany
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, Wales, UK
| | - Bruce R Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA.
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15
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Wu D, Jiang K, Li H, Zhang Z, Ba R, Zhang Y, Hsu YC, Sun Y, Zhang YD. Time-Dependent Diffusion MRI for Quantitative Microstructural Mapping of Prostate Cancer. Radiology 2022; 303:578-587. [PMID: 35258368 DOI: 10.1148/radiol.211180] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Recently developed time-dependent diffusion MRI has potential in characterizing cellular tissue microstructures; however, its value in imaging prostate cancer (PCa) remains unknown. Purpose To investigate the feasibility of time-dependent diffusion MRI-based microstructural mapping for noninvasively characterizing cellular properties of PCa and for discriminating between clinically significant PCa and clinically insignificant disease. Materials and Methods Men with a clinical suspicion of PCa were enrolled prospectively between October 2019 and August 2020. Time-dependent diffusion MRI data were acquired with pulsed and oscillating gradient diffusion MRI sequences at an equivalent diffusion time of 7.5-30 msec on a 3.0-T scanner. Time-dependent diffusion MRI-based microstructural parameters, including cell diameter, intracellular volume fraction, cellularity, and diffusivities, were estimated with a two-compartment model. These were compared for different International Society of Urological Pathology grade groups (GGs), and their performance in discriminating clinically significant PCa (GG >1) from clinically insignificant disease (benign and GG 1) was determined with a linear discriminant analysis. The fitted microstructural parameters were validated by means of correlation with histopathologic measurements. Results In the 48 enrolled men, the time-dependent diffusion MRI measurements showed that higher GG was correlated with higher intracellular volume fraction and higher cellularity (intracellular volume fraction = 0.22, 0.36, 0.34, 0.37, and 0.40 in GGs 1-5, respectively; P < .001 at one-way analysis of variance), while lower cell diameter was found at higher GGs (diameter = 23.4, 18.3, 19.2, 17.9, and 18.5 μm in GGs 1-5, respectively; P = .002). Among all measurements derived from time-dependent diffusion MRI, cellularity achieved the highest diagnostic performance, with an accuracy of 92% (44 of 48 participants) and area under the receiver operating characteristic curve of 0.96 (95% CI: 0.87, 0.99) in discriminating clinically significant PCa from clinically insignificant disease. Microstructural mapping was supported by positive correlations between time-dependent diffusion MRI-based and pathologic examination-based intracellular volume fraction (r = 0.83; P < .001). Conclusion Time-dependent diffusion MRI-based microstructural mapping correlates with pathologic findings and demonstrates promise for characterizing prostate cancer. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Chatterjee and Oto in this issue.
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Affiliation(s)
- Dan Wu
- From the Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China (D.W., Z.Z., R.B., Y.Z.); Departments of Radiology (K.J., Y.D.Z.) and Pathology (H.L.), the First Affiliated Hospital with Nanjing Medical University & AI Lab, Medical Imaging College, Nanjing Medical University, Nanjing 210009, China; and MR Collaboration, Siemens Healthcare, Shanghai, China (Y.C.H., Y.S.)
| | - Kewen Jiang
- From the Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China (D.W., Z.Z., R.B., Y.Z.); Departments of Radiology (K.J., Y.D.Z.) and Pathology (H.L.), the First Affiliated Hospital with Nanjing Medical University & AI Lab, Medical Imaging College, Nanjing Medical University, Nanjing 210009, China; and MR Collaboration, Siemens Healthcare, Shanghai, China (Y.C.H., Y.S.)
| | - Hai Li
- From the Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China (D.W., Z.Z., R.B., Y.Z.); Departments of Radiology (K.J., Y.D.Z.) and Pathology (H.L.), the First Affiliated Hospital with Nanjing Medical University & AI Lab, Medical Imaging College, Nanjing Medical University, Nanjing 210009, China; and MR Collaboration, Siemens Healthcare, Shanghai, China (Y.C.H., Y.S.)
| | - Zelin Zhang
- From the Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China (D.W., Z.Z., R.B., Y.Z.); Departments of Radiology (K.J., Y.D.Z.) and Pathology (H.L.), the First Affiliated Hospital with Nanjing Medical University & AI Lab, Medical Imaging College, Nanjing Medical University, Nanjing 210009, China; and MR Collaboration, Siemens Healthcare, Shanghai, China (Y.C.H., Y.S.)
| | - Ruicheng Ba
- From the Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China (D.W., Z.Z., R.B., Y.Z.); Departments of Radiology (K.J., Y.D.Z.) and Pathology (H.L.), the First Affiliated Hospital with Nanjing Medical University & AI Lab, Medical Imaging College, Nanjing Medical University, Nanjing 210009, China; and MR Collaboration, Siemens Healthcare, Shanghai, China (Y.C.H., Y.S.)
| | - Yi Zhang
- From the Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China (D.W., Z.Z., R.B., Y.Z.); Departments of Radiology (K.J., Y.D.Z.) and Pathology (H.L.), the First Affiliated Hospital with Nanjing Medical University & AI Lab, Medical Imaging College, Nanjing Medical University, Nanjing 210009, China; and MR Collaboration, Siemens Healthcare, Shanghai, China (Y.C.H., Y.S.)
| | - Yi-Cheng Hsu
- From the Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China (D.W., Z.Z., R.B., Y.Z.); Departments of Radiology (K.J., Y.D.Z.) and Pathology (H.L.), the First Affiliated Hospital with Nanjing Medical University & AI Lab, Medical Imaging College, Nanjing Medical University, Nanjing 210009, China; and MR Collaboration, Siemens Healthcare, Shanghai, China (Y.C.H., Y.S.)
| | - Yi Sun
- From the Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China (D.W., Z.Z., R.B., Y.Z.); Departments of Radiology (K.J., Y.D.Z.) and Pathology (H.L.), the First Affiliated Hospital with Nanjing Medical University & AI Lab, Medical Imaging College, Nanjing Medical University, Nanjing 210009, China; and MR Collaboration, Siemens Healthcare, Shanghai, China (Y.C.H., Y.S.)
| | - Yu-Dong Zhang
- From the Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China (D.W., Z.Z., R.B., Y.Z.); Departments of Radiology (K.J., Y.D.Z.) and Pathology (H.L.), the First Affiliated Hospital with Nanjing Medical University & AI Lab, Medical Imaging College, Nanjing Medical University, Nanjing 210009, China; and MR Collaboration, Siemens Healthcare, Shanghai, China (Y.C.H., Y.S.)
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16
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Grussu F, Bernatowicz K, Casanova-Salas I, Castro N, Nuciforo P, Mateo J, Barba I, Perez-Lopez R. Diffusion MRI signal cumulants and hepatocyte microstructure at fixed diffusion time: Insights from simulations, 9.4T imaging, and histology. Magn Reson Med 2022; 88:365-379. [PMID: 35181943 PMCID: PMC9303340 DOI: 10.1002/mrm.29174] [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: 07/29/2021] [Revised: 12/21/2021] [Accepted: 01/07/2022] [Indexed: 11/09/2022]
Abstract
Purpose Relationships between diffusion‐weighted MRI signals and hepatocyte microstructure were investigated to inform liver diffusion MRI modeling, focusing on the following question: Can cell size and diffusivity be estimated at fixed diffusion time, realistic SNR, and negligible contribution from extracellular/extravascular water and exchange? Methods Monte Carlo simulations were performed within synthetic hepatocytes for varying cell size/diffusivity L/D0, and clinical protocols (single diffusion encoding; maximum b‐value: {1000, 1500, 2000} s/mm2; 5 unique gradient duration/separation pairs; SNR = {∞, 100, 80, 40, 20}), accounting for heterogeneity in (D0,L) and perfusion contamination. Diffusion (D) and kurtosis (K) coefficients were calculated, and relationships between (D0,L) and (D,K) were visualized. Functions mapping (D,K) to (D0,L) were computed to predict unseen (D0,L) values, tested for their ability to classify discrete cell‐size contrasts, and deployed on 9.4T ex vivo MRI‐histology data of fixed mouse livers Results Relationships between (D,K) and (D0,L) are complex and depend on the diffusion encoding. Functions mapping D,K to (D0,L) captures salient characteristics of D0(D,K) and L(D,K) dependencies. Mappings are not always accurate, but they enable just under 70% accuracy in a three‐class cell‐size classification task (for SNR = 20, bmax = 1500 s/mm2, δ = 20 ms, and Δ = 75 ms). MRI detects cell‐size contrasts in the mouse livers that are confirmed by histology, but overestimates the largest cell sizes. Conclusion Salient information about liver cell size and diffusivity may be retrieved from minimal diffusion encodings at fixed diffusion time, in experimental conditions and pathological scenarios for which extracellular, extravascular water and exchange are negligible.
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Affiliation(s)
- Francesco Grussu
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Kinga Bernatowicz
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Irene Casanova-Salas
- Prostate Cancer Translational Research Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Natalia Castro
- Prostate Cancer Translational Research Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Paolo Nuciforo
- Molecular Oncology Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Joaquin Mateo
- Prostate Cancer Translational Research Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Ignasi Barba
- NMR Lab, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Raquel Perez-Lopez
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain.,Department of Radiology, Hospital Universitari Vall d'Hebron, Barcelona, Spain
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17
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Wang W, Metzler R, Cherstvy AG. Anomalous diffusion, aging, and nonergodicity of scaled Brownian motion with fractional Gaussian noise: overview of related experimental observations and models. Phys Chem Chem Phys 2022; 24:18482-18504. [DOI: 10.1039/d2cp01741e] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
How does a systematic time-dependence of the diffusion coefficient $D (t)$ affect the ergodic and statistical characteristics of fractional Brownian motion (FBM)? Here, we examine how the behavior of the...
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18
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Nonparametric D-R 1-R 2 distribution MRI of the living human brain. Neuroimage 2021; 245:118753. [PMID: 34852278 DOI: 10.1016/j.neuroimage.2021.118753] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 11/17/2021] [Accepted: 11/22/2021] [Indexed: 11/23/2022] Open
Abstract
Diffusion-relaxation correlation NMR can simultaneously characterize both the microstructure and the local chemical composition of complex samples that contain multiple populations of water. Recent developments on tensor-valued diffusion encoding and Monte Carlo inversion algorithms have made it possible to transfer diffusion-relaxation correlation NMR from small-bore scanners to clinical MRI systems. Initial studies on clinical MRI systems employed 5D D-R1 and D-R2 correlation to characterize healthy brain in vivo. However, these methods are subject to an inherent bias that originates from not including R2 or R1 in the analysis, respectively. This drawback can be remedied by extending the concept to 6D D-R1-R2 correlation. In this work, we present a sparse acquisition protocol that records all data necessary for in vivo 6D D-R1-R2 correlation MRI across 633 individual measurements within 25 min-a time frame comparable to previous lower-dimensional acquisition protocols. The data were processed with a Monte Carlo inversion algorithm to obtain nonparametric 6D D-R1-R2 distributions. We validated the reproducibility of the method in repeated measurements of healthy volunteers. For a post-therapy glioblastoma case featuring cysts, edema, and partially necrotic remains of tumor, we present representative single-voxel 6D distributions, parameter maps, and artificial contrasts over a wide range of diffusion-, R1-, and R2-weightings based on the rich information contained in the D-R1-R2 distributions.
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19
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Slator PJ, Palombo M, Miller KL, Westin C, Laun F, Kim D, Haldar JP, Benjamini D, Lemberskiy G, de Almeida Martins JP, Hutter J. Combined diffusion-relaxometry microstructure imaging: Current status and future prospects. Magn Reson Med 2021; 86:2987-3011. [PMID: 34411331 PMCID: PMC8568657 DOI: 10.1002/mrm.28963] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 06/25/2021] [Accepted: 07/20/2021] [Indexed: 12/15/2022]
Abstract
Microstructure imaging seeks to noninvasively measure and map microscopic tissue features by pairing mathematical modeling with tailored MRI protocols. This article reviews an emerging paradigm that has the potential to provide a more detailed assessment of tissue microstructure-combined diffusion-relaxometry imaging. Combined diffusion-relaxometry acquisitions vary multiple MR contrast encodings-such as b-value, gradient direction, inversion time, and echo time-in a multidimensional acquisition space. When paired with suitable analysis techniques, this enables quantification of correlations and coupling between multiple MR parameters-such as diffusivity, T 1 , T 2 , and T 2 ∗ . This opens the possibility of disentangling multiple tissue compartments (within voxels) that are indistinguishable with single-contrast scans, enabling a new generation of microstructural maps with improved biological sensitivity and specificity.
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Affiliation(s)
- Paddy J. Slator
- Centre for Medical Image ComputingDepartment of Computer ScienceUniversity College LondonLondonUK
| | - Marco Palombo
- Centre for Medical Image ComputingDepartment of Computer ScienceUniversity College LondonLondonUK
| | - Karla L. Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
| | - Carl‐Fredrik Westin
- Department of RadiologyBrigham and Women’s HospitalHarvard Medical SchoolBostonMAUSA
| | - Frederik Laun
- Institute of RadiologyUniversity Hospital ErlangenFriedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Daeun Kim
- Ming Hsieh Department of Electrical and Computer EngineeringUniversity of Southern CaliforniaLos AngelesCAUSA
- Signal and Image Processing InstituteUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Justin P. Haldar
- Ming Hsieh Department of Electrical and Computer EngineeringUniversity of Southern CaliforniaLos AngelesCAUSA
- Signal and Image Processing InstituteUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Dan Benjamini
- The Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentBethesdaMDUSA
- The Center for Neuroscience and Regenerative MedicineUniformed Service University of the Health SciencesBethesdaMDUSA
| | | | - Joao P. de Almeida Martins
- Division of Physical Chemistry, Department of ChemistryLund UniversityLundSweden
- Department of Radiology and Nuclear MedicineSt. Olav’s University HospitalTrondheimNorway
| | - Jana Hutter
- Centre for Biomedical EngineeringSchool of Biomedical Engineering and ImagingKing’s College LondonLondonUK
- Centre for the Developing BrainSchool of Biomedical Engineering and ImagingKing’s College LondonLondonUK
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20
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Huang CC, Hsu CCH, Zhou FL, Kusmia S, Drakesmith M, Parker GJM, Lin CP, Jones DK. Validating pore size estimates in a complex microfiber environment on a human MRI system. Magn Reson Med 2021; 86:1514-1530. [PMID: 33960501 PMCID: PMC7613441 DOI: 10.1002/mrm.28810] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 03/18/2021] [Accepted: 03/26/2021] [Indexed: 12/19/2022]
Abstract
PURPOSE Recent advances in diffusion-weighted MRI provide "restricted diffusion signal fraction" and restricting pore size estimates. Materials based on co-electrospun oriented hollow cylinders have been introduced to provide validation for such methods. This study extends this work, exploring accuracy and repeatability using an extended acquisition on a 300 mT/m gradient human MRI scanner, in substrates closely mimicking tissue, that is, non-circular cross-sections, intra-voxel fiber crossing, intra-voxel distributions of pore-sizes, and smaller pore-sizes overall. METHODS In a single-blind experiment, diffusion-weighted data were collected from a biomimetic phantom on a 3T Connectom system using multiple gradient directions/diffusion times. Repeated scans established short-term and long-term repeatability. The total scan time (54 min) matched similar protocols used in human studies. The number of distinct fiber populations was estimated using spherical deconvolution, and median pore size estimated through the combination of CHARMED and AxCaliber3D framework. Diffusion-based estimates were compared with measurements derived from scanning electron microscopy. RESULTS The phantom contained substrates with different orientations, fiber configurations, and pore size distributions. Irrespective of one or two populations within the voxel, the pore-size estimates (~5 μm) and orientation-estimates showed excellent agreement with the median values of pore-size derived from scanning electron microscope and phantom configuration. Measurement repeatability depended on substrate complexity, with lower values seen in samples containing crossing-fibers. Sample-level repeatability was found to be good. CONCLUSION While no phantom mimics tissue completely, this study takes a step closer to validating diffusion microstructure measurements for use in vivo by demonstrating the ability to quantify microgeometry in relatively complex configurations.
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Affiliation(s)
- Chu-Chung Huang
- Key Laboratory of Brain Functional Genomics (MOE & STCSM), Affiliated Mental Health Center (ECNU), Institute of Cognitive Neuroscience, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
- Shanghai Changning Mental Health Center, Shanghai, China
| | - Chih-Chin Heather Hsu
- Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Feng-Lei Zhou
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
- School of Pharmacy, University College London, London, United Kingdom
| | - Slawomir Kusmia
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
- Epilepsy Society MRI Unit, Chalfont St Peter, United Kingdom
| | - Mark Drakesmith
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Geoff J. M. Parker
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
- Department of Neuroinflammation, Queen Square Institute of Neurology, University College London, London, United Kingdom
- Bioxydyn Limited, Manchester, United Kingdom
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
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21
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Bhuiyan EH, Dewdney A, Weinreb J, Galiana G. Feasibility of diffusion weighting with a local inside-out nonlinear gradient coil for prostate MRI. Med Phys 2021; 48:5804-5818. [PMID: 34287937 DOI: 10.1002/mp.15100] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 04/04/2021] [Accepted: 06/23/2021] [Indexed: 11/09/2022] Open
Abstract
PURPOSE Prostate cancer remains the 2nd leading cancer killer of men, yet it is also a disease with a high rate of overtreatment. Diffusion weighted imaging (DWI) has shown promise as a reliable, grade-sensitive imaging method, but it is limited by low image quality. Currently, DWI quality image is directly related to low gradient amplitudes, since weak gradients must be compensated with long echo times. METHODS We propose a new type of MRI accessory, an "inside-out" and nonlinear gradient, whose sole purpose is to deliver diffusion encoding to a region of interest. Performance was simulated in OPERA and the resulting fields were used to simulate DWI with two compartment and kurtosis models. Experiments with a nonlinear head gradient prove the accuracy of DWI and ADC maps diffusion encoded with nonlinear gradients. RESULTS Simulations validated thermal and mechanical safety while showing a 5 to 10-fold increase in gradient strength over prostate. With these strengths, lesion CNR in ADC maps approximately doubled for a range of anatomical positions. Proof-of-principle experiments show that spatially varying b-values can be corrected for accurate DWI and ADC. CONCLUSIONS Dedicated nonlinear diffusion encoding hardware could improve prostate DWI.
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Affiliation(s)
| | | | - Jeffrey Weinreb
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA
| | - Gigi Galiana
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA
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22
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Li C, Li N, Li Z, Shen L. Diagnostic accuracy of high b-value diffusion weighted imaging for patients with prostate cancer: a diagnostic comprehensive analysis. Aging (Albany NY) 2021; 13:16404-16424. [PMID: 34156972 PMCID: PMC8266335 DOI: 10.18632/aging.203164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Accepted: 05/31/2021] [Indexed: 12/04/2022]
Abstract
We performed a meta-analysis to assess the diagnostic accuracy of high b-value diffusion-weighted imaging for patients with prostate cancer. A comprehensive literature search of the PubMed, Excerpta Medica Database, Cochrane Library, China National Knowledge Infrastructure, China Biology Medicine disc, and Wanfang databases from January 1, 1995, to April 30, 2021, was conducted. The quality of the retrieved papers was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2. The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and their 95% confidence intervals (CIs) were evaluated using bivariate mixed effects models. A total of twenty-four articles matched the selection criteria and were finally included after screening the titles, abstracts, and full texts of 641 initial articles. The pooled sensitivity and specificity (95% CI) were 0.84 (0.80-0.87) and 0.87 (0.81-0.91), respectively. The pooled positive and negative likelihood ratios (95% CI) were 6.4 (4.4-9.3) and 0.19 (0.16-0.23), respectively. The diagnostic odds ratio was 34 (95% CI: 22-51). The area under the summary receiver operator characteristic curve was 0.91 (95% CI: 0.88-0.93). Subgroup analysis presents similar results. The diagnostic accuracy of high b-value diffusion-weighted imaging was similarly high in the qualitative and quantitative evaluation of prostate cancer.
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Affiliation(s)
- Chao Li
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan Province 410008, China
| | - Na Li
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan Province 410008, China
| | - Zhanzhan Li
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan Province 410008, China
| | - Liangfang Shen
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan Province 410008, China
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23
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Nilsson M, Eklund G, Szczepankiewicz F, Skorpil M, Bryskhe K, Westin CF, Lindh C, Blomqvist L, Jäderling F. Mapping prostatic microscopic anisotropy using linear and spherical b-tensor encoding: A preliminary study. Magn Reson Med 2021; 86:2025-2033. [PMID: 34056750 PMCID: PMC9272946 DOI: 10.1002/mrm.28856] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 04/12/2021] [Accepted: 05/05/2021] [Indexed: 12/24/2022]
Abstract
Purpose: Tensor-valued diffusion encoding provides more specific information than conventional diffusion-weighted imaging (DWI), but has mainly been applied in neuroimaging studies. This study aimed to assess its potential for the imaging of prostate cancer (PCa). Methods: Seventeen patients with histologically proven PCa were enrolled. DWI of the prostate was performed with linear and spherical tensor encoding using a maximal b-value of 1.5 ms/μm2 and a voxel size of 3 × 3 × 4 mm3. The gamma-distribution model was used to estimate the mean diffusivity (MD), the isotropic kurtosis (MKI), and the anisotropic kurtosis (MKA). Regions of interest were placed in MR-defined cancerous tissues, as well as in apparently healthy tissues in the peripheral and transitional zones (PZs and TZs). Results: DWI with linear and spherical encoding yielded different image contrasts at high b-values, which enabled the estimation of MKA and MKI. Compared with healthy tissue (PZs and TZs combined) the cancers displayed a significantly lower MD (P < .05), higher MKI (P < 10−5), and lower MKA (P < .05). Compared with the TZ, tissue in the PZ showed lower MD (P < 10−3) and higher MKA (P < 10−3). No significant differences were found between cancers of different Gleason scores, possibly because of the limited sample size. Conclusion: Tensor-valued diffusion encoding enabled mapping of MKA and MKI in the prostate. The elevated MKI in PCa compared with normal tissues suggests an elevated heterogeneity in the cancers. Increased in-plane resolution could improve tumor delineation in future studies.
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Affiliation(s)
- Markus Nilsson
- Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
| | | | | | - Mikael Skorpil
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.,Department of Neuroradiology, Karolinska University Hospital, Solna, Stockholm, Sweden
| | | | - Carl-Fredrik Westin
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Claes Lindh
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Lennart Blomqvist
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.,Department of Diagnostic Radiology, Karolinska University Hospital, Solna, Sweden
| | - Fredrik Jäderling
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.,Department of Diagnostic Radiology, Karolinska University Hospital, Solna, Sweden.,Department of Radiology, Capio S:t Görans Hospital, Stockholm, Sweden
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24
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Reymbaut A, Critchley J, Durighel G, Sprenger T, Sughrue M, Bryskhe K, Topgaard D. Toward nonparametric diffusion- T1 characterization of crossing fibers in the human brain. Magn Reson Med 2021; 85:2815-2827. [PMID: 33301195 PMCID: PMC7898694 DOI: 10.1002/mrm.28604] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 10/26/2020] [Accepted: 10/27/2020] [Indexed: 12/24/2022]
Abstract
PURPOSE To estimate T 1 for each distinct fiber population within voxels containing multiple brain tissue types. METHODS A diffusion- T 1 correlation experiment was carried out in an in vivo human brain using tensor-valued diffusion encoding and multiple repetition times. The acquired data were inverted using a Monte Carlo algorithm that retrieves nonparametric distributions P ( D , R 1 ) of diffusion tensors and longitudinal relaxation rates R 1 = 1 / T 1 . Orientation distribution functions (ODFs) of the highly anisotropic components of P ( D , R 1 ) were defined to visualize orientation-specific diffusion-relaxation properties. Finally, Monte Carlo density-peak clustering (MC-DPC) was performed to quantify fiber-specific features and investigate microstructural differences between white matter fiber bundles. RESULTS Parameter maps corresponding to P ( D , R 1 ) 's statistical descriptors were obtained, exhibiting the expected R 1 contrast between brain tissue types. Our ODFs recovered local orientations consistent with the known anatomy and indicated differences in R 1 between major crossing fiber bundles. These differences, confirmed by MC-DPC, were in qualitative agreement with previous model-based works but seem biased by the limitations of our current experimental setup. CONCLUSIONS Our Monte Carlo framework enables the nonparametric estimation of fiber-specific diffusion- T 1 features, thereby showing potential for characterizing developmental or pathological changes in T 1 within a given fiber bundle, and for investigating interbundle T 1 differences.
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Affiliation(s)
- Alexis Reymbaut
- Department of Physical ChemistryLund UniversityLundSweden
- Random Walk Imaging ABLundSweden
| | | | | | - Tim Sprenger
- Karolinska InstituteStockholmSweden
- GE HealthcareStockholmSweden
| | | | | | - Daniel Topgaard
- Department of Physical ChemistryLund UniversityLundSweden
- Random Walk Imaging ABLundSweden
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25
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Li Y, Kim M, Lawrence TS, Parmar H, Cao Y. Microstructure Modeling of High b-Value Diffusion-Weighted Images in Glioblastoma. ACTA ACUST UNITED AC 2021; 6:34-43. [PMID: 32280748 PMCID: PMC7138521 DOI: 10.18383/j.tom.2020.00018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Apparent diffusion coefficient has limits to differentiate solid tumor from normal tissue or edema in glioblastoma (GBM). This study investigated a microstructure model (MSM) in GBM using a clinically available diffusion imaging technique. The MSM was modified to integrate with bi-polar diffusion gradient waveforms, and applied to 30 patients with newly diagnosed GBM. Diffusion-weighted (DW) images acquired on a 3 T scanner with b-values from 0 to 2500 s/mm2 were fitted in volumes of interest (VOIs) of solid tumor to obtain the apparent restriction size of intracellular water (ARS), the fractional volume of intracellular water (Vin), and extracellular (Dex) water diffusivity. The parameters in solid tumor were compared with those of other tissue types by Students’ t test. For comparison, DW images were fitted by conventional mono-exponential and bi-exponential models. ARS, Dex, and Vin from the MSM in tumor VOIs were significantly greater than those in WM, GM, and edema (P values of .01–.001). ARS values in solid tumors (from 21.6 to 34.5 um) had absolutely no overlap with those in all other tissue types (from 0.9 to 3.5 um). Vin values showed a descending order from solid tumor (from 0.32 to 0.52) to WM, GM, and edema (from 0.05 to 0.25), consisting with the descending cellularity in these tissue types. The parameters from mono-exponential and bi-exponential models could not significantly differentiate solid tumor from all other tissue types, particularly from edema. Further development and histopathological validation of the MSM will warrant its role in clinical management of GBM.
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Affiliation(s)
- Yuan Li
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI
| | - Michelle Kim
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI
| | - Theodore S Lawrence
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI
| | - Hemant Parmar
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI
| | - Yue Cao
- Departments of Radiation Oncology, Radiology, and Biomedical Engineering, University of Michigan, Ann Arbor, MI
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Barakovic M, Tax CMW, Rudrapatna U, Chamberland M, Rafael-Patino J, Granziera C, Thiran JP, Daducci A, Canales-Rodríguez EJ, Jones DK. Resolving bundle-specific intra-axonal T 2 values within a voxel using diffusion-relaxation tract-based estimation. Neuroimage 2021; 227:117617. [PMID: 33301934 PMCID: PMC7615251 DOI: 10.1016/j.neuroimage.2020.117617] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 11/23/2020] [Accepted: 11/29/2020] [Indexed: 02/06/2023] Open
Abstract
At the typical spatial resolution of MRI in the human brain, approximately 60-90% of voxels contain multiple fiber populations. Quantifying microstructural properties of distinct fiber populations within a voxel is therefore challenging but necessary. While progress has been made for diffusion and T1-relaxation properties, how to resolve intra-voxel T2 heterogeneity remains an open question. Here a novel framework, named COMMIT-T2, is proposed that uses tractography-based spatial regularization with diffusion-relaxometry data to estimate multiple intra-axonal T2 values within a voxel. Unlike previously-proposed voxel-based T2 estimation methods, which (when applied in white matter) implicitly assume just one fiber bundle in the voxel or the same T2 for all bundles in the voxel, COMMIT-T2 can recover specific T2 values for each unique fiber population passing through the voxel. In this approach, the number of recovered unique T2 values is not determined by a number of model parameters set a priori, but rather by the number of tractography-reconstructed streamlines passing through the voxel. Proof-of-concept is provided in silico and in vivo, including a demonstration that distinct tract-specific T2 profiles can be recovered even in the three-way crossing of the corpus callosum, arcuate fasciculus, and corticospinal tract. We demonstrate the favourable performance of COMMIT-T2 compared to that of voxelwise approaches for mapping intra-axonal T2 exploiting diffusion, including a direction-averaged method and AMICO-T2, a new extension to the previously-proposed Accelerated Microstructure Imaging via Convex Optimization (AMICO) framework.
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Affiliation(s)
- Muhamed Barakovic
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, Wales, UK; Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, Wales, UK
| | - Umesh Rudrapatna
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, Wales, UK
| | - Maxime Chamberland
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, Wales, UK
| | - Jonathan Rafael-Patino
- Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Jean-Philippe Thiran
- Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, 1005 Lausanne, Switzerland
| | | | - Erick J Canales-Rodríguez
- Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; FIDMAG Germanes Hospitalàries Research Foundation, CIBERSAM, Barcelona, Spain.
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, Wales, UK; Mary MacKillop Institute for Health Research, Faculty of Health Sciences, Australian Catholic University, Melbourne, Australia
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27
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Reymbaut A, Caron AV, Gilbert G, Szczepankiewicz F, Nilsson M, Warfield SK, Descoteaux M, Scherrer B. Magic DIAMOND: Multi-fascicle diffusion compartment imaging with tensor distribution modeling and tensor-valued diffusion encoding. Med Image Anal 2021; 70:101988. [PMID: 33611054 DOI: 10.1016/j.media.2021.101988] [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: 02/14/2020] [Revised: 01/25/2021] [Accepted: 01/29/2021] [Indexed: 01/05/2023]
Abstract
Diffusion tensor imaging provides increased sensitivity to microstructural tissue changes compared to conventional anatomical imaging but also presents limited specificity. To tackle this problem, the DIAMOND model subdivides the voxel content into diffusion compartments and draws from diffusion-weighted data to estimate compartmental non-central matrix-variate Gamma distributions of diffusion tensors. It models each sub-voxel fascicle separately, resolving crossing white-matter pathways and allowing for a fascicle-element (fixel) based analysis of microstructural features. Alternatively, specific features of the intra-voxel diffusion tensor distribution can be selectively measured using tensor-valued diffusion-weighted acquisition schemes. However, the impact of such schemes on estimating brain microstructural features has only been studied in a handful of parametric single-fascicle models. In this work, we derive a general Laplace transform for the non-central matrix-variate Gamma distribution, which enables the extension of DIAMOND to tensor-valued encoded data. We then evaluate this "Magic DIAMOND" model in silico and in vivo on various combinations of tensor-valued encoded data. Assessing uncertainty on parameter estimation via stratified bootstrap, we investigate both voxel-based and fixel-based metrics by carrying out multi-peak tractography. We demonstrate using in silico evaluations that tensor-valued diffusion encoding significantly improves Magic DIAMOND's accuracy. Most importantly, we show in vivo that our estimated metrics can be robustly mapped along tracks across regions of fiber crossing, which opens new perspectives for tractometry and microstructure mapping along specific white-matter tracts.
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Affiliation(s)
| | | | - Guillaume Gilbert
- MR Clinical Science, Philips Healthcare Canada, Markham, ON L6C 2S3, Canada
| | - Filip Szczepankiewicz
- Department of Clinical Sciences, Lund University, 22184, Lund, Sweden; Random Walk Imaging AB, 22224, Lund, Sweden
| | - Markus Nilsson
- Department of Clinical Sciences, Lund University, 22184, Lund, Sweden
| | - Simon K Warfield
- Department of Radiology, Boston Children's Hospital, Boston, MA 02115, United States
| | | | - Benoit Scherrer
- Department of Radiology, Boston Children's Hospital, Boston, MA 02115, United States
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Zibetti MVW, Helou ES, Sharafi A, Regatte RR. Fast multicomponent 3D-T 1ρ relaxometry. NMR IN BIOMEDICINE 2020; 33:e4318. [PMID: 32359000 PMCID: PMC7606711 DOI: 10.1002/nbm.4318] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 03/10/2020] [Accepted: 04/05/2020] [Indexed: 05/06/2023]
Abstract
NMR relaxometry can provide information about the relaxation of the magnetization in different tissues, increasing our understanding of molecular dynamics and biochemical composition in biological systems. In general, tissues have complex and heterogeneous structures composed of multiple pools. As a result, bulk magnetization returns to its original state with different relaxation times, in a multicomponent relaxation. Recovering the distribution of relaxation times in each voxel is a difficult inverse problem; it is usually unstable and requires long acquisition time, especially on clinical scanners. MRI can also be viewed as an inverse problem, especially when compressed sensing (CS) is used. The solution of these two inverse problems, CS and relaxometry, can be obtained very efficiently in a synergistically combined manner, leading to a more stable multicomponent relaxometry obtained with short scan times. In this paper, we will discuss the details of this technique from the viewpoint of inverse problems.
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Affiliation(s)
- Marcelo V W Zibetti
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, US
| | - Elias S Helou
- Institute of Mathematical Sciences and Computation, University of São Paulo, São Carlos, SP, Brazil
| | - Azadeh Sharafi
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, US
| | - Ravinder R Regatte
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, US
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Reymbaut A, Mezzani P, de Almeida Martins JP, Topgaard D. Accuracy and precision of statistical descriptors obtained from multidimensional diffusion signal inversion algorithms. NMR IN BIOMEDICINE 2020; 33:e4267. [PMID: 32067322 DOI: 10.1002/nbm.4267] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 01/15/2020] [Accepted: 01/16/2020] [Indexed: 05/22/2023]
Abstract
In biological tissues, typical MRI voxels comprise multiple microscopic environments, the local organization of which can be captured by microscopic diffusion tensors. The measured diffusion MRI signal can, therefore, be written as the multidimensional Laplace transform of an intravoxel diffusion tensor distribution (DTD). Tensor-valued diffusion encoding schemes have been designed to probe specific features of the DTD, and several algorithms have been introduced to invert such data and estimate statistical descriptors of the DTD, such as the mean diffusivity, the variance of isotropic diffusivities, and the mean squared diffusion anisotropy. However, the accuracy and precision of these estimations have not been assessed systematically and compared across methods. In this article, we perform and compare such estimations in silico for a one-dimensional Gamma fit, a generalized two-term cumulant approach, and two-dimensional and four-dimensional Monte-Carlo-based inversion techniques, using a clinically feasible tensor-valued acquisition scheme. In particular, we compare their performance at different signal-to-noise ratios (SNRs) for voxel contents varying in terms of the aforementioned statistical descriptors, orientational order, and fractions of isotropic and anisotropic components. We find that all inversion techniques share similar precision (except for a lower precision of the two-dimensional Monte Carlo inversion) but differ in terms of accuracy. While the Gamma fit exhibits infinite-SNR biases when the signal deviates strongly from monoexponentiality and is unaffected by orientational order, the generalized cumulant approach shows infinite-SNR biases when this deviation originates from the variance in isotropic diffusivities or from the low orientational order of anisotropic diffusion components. The two-dimensional Monte Carlo inversion shows remarkable accuracy in all systems studied, given that the acquisition scheme possesses enough directions to yield a rotationally invariant powder average. The four-dimensional Monte Carlo inversion presents no infinite-SNR bias, but suffers significantly from noise in the data, while preserving good contrast in most systems investigated.
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Affiliation(s)
- Alexis Reymbaut
- Physical Chemistry Department, Lund University, Lund, Sweden
- Random Walk Imaging AB, Lund, Sweden
| | - Paolo Mezzani
- Physical Chemistry Department, Lund University, Lund, Sweden
- Physics Department, Università degli Studi di Milano, Milan, Italy
| | | | - Daniel Topgaard
- Physical Chemistry Department, Lund University, Lund, Sweden
- Random Walk Imaging AB, Lund, Sweden
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30
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Grussu F, Battiston M, Veraart J, Schneider T, Cohen-Adad J, Shepherd TM, Alexander DC, Fieremans E, Novikov DS, Gandini Wheeler-Kingshott CAM. Multi-parametric quantitative in vivo spinal cord MRI with unified signal readout and image denoising. Neuroimage 2020; 217:116884. [PMID: 32360689 PMCID: PMC7378937 DOI: 10.1016/j.neuroimage.2020.116884] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 03/18/2020] [Accepted: 04/23/2020] [Indexed: 12/11/2022] Open
Abstract
Multi-parametric quantitative MRI (qMRI) of the spinal cord is a promising non-invasive tool to probe early microstructural damage in neurological disorders. It is usually performed in vivo by combining acquisitions with multiple signal readouts, which exhibit different thermal noise levels, geometrical distortions and susceptibility to physiological noise. This ultimately hinders joint multi-contrast modelling and makes the geometric correspondence of parametric maps challenging. We propose an approach to overcome these limitations, by implementing state-of-the-art microstructural MRI of the spinal cord with a unified signal readout in vivo (i.e. with matched spatial encoding parameters across a range of imaging contrasts). We base our acquisition on single-shot echo planar imaging with reduced field-of-view, and obtain data from two different vendors (vendor 1: Philips Achieva; vendor 2: Siemens Prisma). Importantly, the unified acquisition allows us to compare signal and noise across contrasts, thus enabling overall quality enhancement via multi-contrast image denoising methods. As a proof-of-concept, here we provide a demonstration with one such method, known as Marchenko-Pastur (MP) Principal Component Analysis (PCA) denoising. MP-PCA is a singular value (SV) decomposition truncation approach that relies on redundant acquisitions, i.e. such that the number of measurements is large compared to the number of components that are maintained in the truncated SV decomposition. Here we used in vivo and synthetic data to test whether a unified readout enables more efficient MP-PCA denoising of less redundant acquisitions, since these can be denoised jointly with more redundant ones. We demonstrate that a unified readout provides robust multi-parametric maps, including diffusion and kurtosis tensors from diffusion MRI, myelin metrics from two-pool magnetisation transfer, and T1 and T2 from relaxometry. Moreover, we show that MP-PCA improves the quality of our multi-contrast acquisitions, since it reduces the coefficient of variation (i.e. variability) by up to 17% for mean kurtosis, 8% for bound pool fraction (myelin-sensitive), and 13% for T1, while enabling more efficient denoising of modalities limited in redundancy (e.g. relaxometry). In conclusion, multi-parametric spinal cord qMRI with unified readout is feasible and provides robust microstructural metrics with matched resolution and distortions, whose quality benefits from multi-contrast denoising methods such as MP-PCA.
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Affiliation(s)
- Francesco Grussu
- Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK; Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
| | - Marco Battiston
- Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Jelle Veraart
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, USA
| | | | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, Canada; Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, Canada
| | - Timothy M Shepherd
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, USA
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, USA
| | - Dmitry S Novikov
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, USA
| | - Claudia A M Gandini Wheeler-Kingshott
- Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK; Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy; Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
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31
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Barrick TR, Spilling CA, Ingo C, Madigan J, Isaacs JD, Rich P, Jones TL, Magin RL, Hall MG, Howe FA. Quasi-diffusion magnetic resonance imaging (QDI): A fast, high b-value diffusion imaging technique. Neuroimage 2020; 211:116606. [DOI: 10.1016/j.neuroimage.2020.116606] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 12/22/2019] [Accepted: 02/02/2020] [Indexed: 12/11/2022] Open
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Gurney‐Champion OJ, Rauh SS, Harrington K, Oelfke U, Laun FB, Wetscherek A. Optimal acquisition scheme for flow-compensated intravoxel incoherent motion diffusion-weighted imaging in the abdomen: An accurate and precise clinically feasible protocol. Magn Reson Med 2020; 83:1003-1015. [PMID: 31566262 PMCID: PMC6899942 DOI: 10.1002/mrm.27990] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 08/14/2019] [Accepted: 08/17/2019] [Indexed: 12/16/2022]
Abstract
PURPOSE Flow-compensated (FC) diffusion-weighted MRI (DWI) for intravoxel-incoherent motion (IVIM) modeling allows for a more detailed description of tissue microvasculature than conventional IVIM. The long acquisition time of current FC-IVIM protocols, however, has prohibited clinical application. Therefore, we developed an optimized abdominal FC-IVIM acquisition with a clinically feasible scan time. METHODS Precision and accuracy of the FC-IVIM parameters were assessed by fitting the FC-IVIM model to signal decay curves, simulated for different acquisition schemes. Diffusion-weighted acquisitions were added subsequently to the protocol, where we chose the combination of b-value, diffusion time and gradient profile (FC or bipolar) that resulted in the largest improvement to its accuracy and precision. The resulting two optimized FC-IVIM protocols with 25 and 50 acquisitions (FC-IVIMopt25 and FC-IVIMopt50 ), together with a complementary acquisition consisting of 50 diffusion-weighting (FC-IVIMcomp ), were acquired in repeated abdominal free-breathing FC-IVIM imaging of seven healthy volunteers. Intersession and intrasession within-subject coefficient of variation of the FC-IVIM parameters were compared for the liver, spleen, and kidneys. RESULTS Simulations showed that the performance of FC-IVIM improved in tissue with larger perfusion fraction and signal-to-noise ratio. The scan time of the FC-IVIMopt25 and FC-IVIMopt50 protocols were 8 and 16 min. The best in vivo performance was seen in FC-IVIMopt50 . The intersession within-subject coefficients of variation of FC-IVIMopt50 were 11.6%, 16.3%, 65.5%, and 36.0% for FC-IVIM model parameters diffusivity, perfusion fraction, characteristic time and blood flow velocity, respectively. CONCLUSIONS We have optimized the FC-IVIM protocol, allowing for clinically feasible scan times (8-16 min).
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Affiliation(s)
- Oliver J. Gurney‐Champion
- Joint Department of PhysicsThe Institute of Cancer Research and The Royal Marsden NHS Foundation TrustLondonUnited Kingdom
| | - Susanne S. Rauh
- Joint Department of PhysicsThe Institute of Cancer Research and The Royal Marsden NHS Foundation TrustLondonUnited Kingdom
- Institute of RadiologyUniversity Hospital Erlangen, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Kevin Harrington
- Targeted Therapy teamThe Institute of Cancer Research and The Royal Marsden NHS Foundation TrustLondonUnited Kingdom
| | - Uwe Oelfke
- Joint Department of PhysicsThe Institute of Cancer Research and The Royal Marsden NHS Foundation TrustLondonUnited Kingdom
| | - Frederik B. Laun
- Institute of RadiologyUniversity Hospital Erlangen, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Andreas Wetscherek
- Joint Department of PhysicsThe Institute of Cancer Research and The Royal Marsden NHS Foundation TrustLondonUnited Kingdom
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Zhang Z, Moulin K, Aliotta E, Shakeri S, Afshari Mirak S, Hosseiny M, Raman S, Ennis DB, Wu HH. Prostate diffusion MRI with minimal echo time using eddy current nulled convex optimized diffusion encoding. J Magn Reson Imaging 2019; 51:1526-1539. [PMID: 31625663 DOI: 10.1002/jmri.26960] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Accepted: 09/20/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Prostate diffusion-weighted imaging (DWI) using monopolar encoding is sensitive to eddy-current-induced distortion artifacts. Twice-refocused bipolar encoding suppresses eddy current artifacts, but increases echo time (TE), leading to lower signal-to-noise ratio (SNR). Optimization of the diffusion encoding might improve prostate DWI. PURPOSE To evaluate eddy current nulled convex optimized diffusion encoding (ENCODE) for prostate DWI with minimal TE. STUDY TYPE Prospective cohort study. POPULATION A diffusion phantom, an ex vivo prostate specimen, 10 healthy male subjects (27 ± 3 years old), and five prostate cancer patients (62 ± 7 years old). FIELD STRENGTH/SEQUENCE 3T; single-shot spin-echo echoplanar DWI. ASSESSMENT Eddy-current artifacts, TE, SNR, apparent diffusion coefficient (ADC), and image quality scores from three independent readers were compared between monopolar, bipolar, and ENCODE prostate DWI for standard-resolution (1.6 × 1.6 mm2 , partial Fourier factor [pF] = 6/8) and higher-resolution protocols (1.6 × 1.6 mm2 , pF = off; 1.0 × 1.0 mm2 , pF = 6/8). STATISTICAL TESTING SNR and ADC differences between techniques were tested with Kruskal-Wallis and Wilcoxon signed-rank tests (P < 0.05 considered significant). RESULTS Eddy current suppression with ENCODE was comparable to bipolar encoding (mean coefficient of variation across three diffusion directions of 9.4% and 9%). For a standard-resolution protocol, ENCODE achieved similar TE as monopolar and reduced TE by 14 msec compared to bipolar, resulting in 27% and 29% higher mean SNR in prostate transition zone (TZ) and peripheral zone (PZ) (P < 0.05) compared to bipolar, respectively. For higher-resolution protocols, ENCODE achieved the shortest TE (67 msec), with 17-21% and 58-70% higher mean SNR compared to monopolar (TE = 77 msec) and bipolar (TE = 102 msec) in PZ and TZ (P < 0.05). No significant differences were found in mean TZ (P = 0.91) and PZ ADC (P = 0.94) between the three techniques. ENCODE achieved similar or higher image quality scores than bipolar DWI in patients, with mean intraclass correlation coefficient of 0.77 for overall quality between three independent readers. DATA CONCLUSION ENCODE minimizes TE (improves SNR) and reduces eddy-current distortion for prostate DWI compared to monopolar and bipolar encoding. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:1526-1539.
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Affiliation(s)
- Zhaohuan Zhang
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA.,Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Kevin Moulin
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Eric Aliotta
- Department of Radiation Oncology, University of Virginia, Charlottesville, Virginia, USA
| | - Sepideh Shakeri
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA
| | - Sohrab Afshari Mirak
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA
| | - Melina Hosseiny
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA
| | - Steven Raman
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA
| | - Daniel B Ennis
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Holden H Wu
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California, USA.,Department of Bioengineering, University of California, Los Angeles, California, USA
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Wagner M, Doblas S, Poté N, Lambert SA, Ronot M, Garteiser P, Paradis V, Vilgrain V, Van Beers BE. Comparison of pulsed and oscillating gradient diffusion-weighted MRI for characterizing hepatocellular nodules in liver cirrhosis: ex vivo study in a rat model. J Magn Reson Imaging 2019; 51:1065-1074. [PMID: 31507025 DOI: 10.1002/jmri.26919] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Accepted: 08/07/2019] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND In contrast to classical pulsed gradient diffusion-weighted MRI, oscillating gradient diffusion-weighted MR imaging (DWI) is sensitive to short distance diffusion changes at the intracellular level. PURPOSE To compare the diagnostic performance of pulsed and oscillating DWI for characterizing hepatocellular nodules in a rat model of hepatic cirrhosis. STUDY TYPE Prospective, experimental study. ANIMAL MODEL Cirrhosis was induced by weekly intraperitoneal injection of diethylnitrosamine in Wistar rats. FIELD STRENGTH/SEQUENCE Ex vivo liver MRI was performed at 7T with T1 -weighted, T2 -weighted, pulsed, and oscillating gradient diffusion-weighted sequences. ASSESSMENT Apparent diffusion coefficient from pulsed (ADCpulsed ) and oscillating gradient (ADCoscillating ) sequences was calculated in 82 nodules identified on the T1 /T2 -weighted images and on pathological examination. Two pathologists classified the nodules in three categories: benign (regenerative and low-grade dysplastic nodules), with intermediate malignancy (high-grade dysplastic nodules and early hepatocellular carcinomas) and overtly malignant (progressed hepatocellular carcinomas). STATISTICAL TESTS Differences between groups were assessed with Kruskal-Wallis and Mann-Whitney tests. RESULTS ADC, mainly ADCoscillating , increased in the group of nodules with intermediate malignancy (ADCpulsed : 0.75 ± 0.25 × 10-3 mm2 /s vs. 0.64 ± 0.07 × 10-3 mm2 /s in benign nodules, P = 0.025; ADCoscillating : 0.81 ± 0.20 × 10-3 mm2 /s vs. 0.65 ± 0.13 × 10-3 mm2 /s, P = 0.0008) and ADCpulsed decreased in the group of progressed hepatocellular carcinomas (ADCpulsed : 0.60 ± 0.08 × 10-3 mm2 /s, P = 0.042; ADCoscillating : 0.68 ± 0.08 × 10-3 mm2 /s, P = 0.1). DATA CONCLUSION ADC during hepatocarcinogenesis in rats increased in nodules with intermediate malignancy and decreased in progressed hepatocellular carcinomas. Our results suggest that oscillating gradient DWI is more sensitive to the early steps of hepatocarcinogenesis and might be useful for differentiating between high-grade dysplastic nodules / early hepatocellular carcinomas and regenerating nodules / low-grade dysplastic nodules. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:1065-1074.
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Affiliation(s)
- Mathilde Wagner
- Laboratory of Imaging Biomarkers, Center for Research on Inflammation, UMR 1149, Inserm - University of Paris, Paris, France.,Department of Radiology, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
| | - Sabrina Doblas
- Laboratory of Imaging Biomarkers, Center for Research on Inflammation, UMR 1149, Inserm - University of Paris, Paris, France
| | - Nicolas Poté
- Department of Pathology, Beaujon University Hospital Paris Nord, AP-HP, Clichy, France.,Center for Research on Inflammation, UMR 1149, Inserm - University of Paris, Paris, France
| | - Simon A Lambert
- Laboratory of Imaging Biomarkers, Center for Research on Inflammation, UMR 1149, Inserm - University of Paris, Paris, France.,CREATIS, CNRS UMR 5220 - Inserm U1206, University of Lyon, Villeurbanne, France
| | - Maxime Ronot
- Laboratory of Imaging Biomarkers, Center for Research on Inflammation, UMR 1149, Inserm - University of Paris, Paris, France.,Department of Radiology, Beaujon University Hospital Paris Nord, AP-HP, Clichy, France
| | - Philippe Garteiser
- Laboratory of Imaging Biomarkers, Center for Research on Inflammation, UMR 1149, Inserm - University of Paris, Paris, France
| | - Valérie Paradis
- Department of Pathology, Beaujon University Hospital Paris Nord, AP-HP, Clichy, France.,Center for Research on Inflammation, UMR 1149, Inserm - University of Paris, Paris, France
| | - Valérie Vilgrain
- Laboratory of Imaging Biomarkers, Center for Research on Inflammation, UMR 1149, Inserm - University of Paris, Paris, France.,Department of Radiology, Beaujon University Hospital Paris Nord, AP-HP, Clichy, France
| | - Bernard E Van Beers
- Laboratory of Imaging Biomarkers, Center for Research on Inflammation, UMR 1149, Inserm - University of Paris, Paris, France.,Department of Radiology, Beaujon University Hospital Paris Nord, AP-HP, Clichy, France
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van Zijl P, Knutsson L. In vivo magnetic resonance imaging and spectroscopy. Technological advances and opportunities for applications continue to abound. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2019; 306:55-65. [PMID: 31377150 PMCID: PMC6703925 DOI: 10.1016/j.jmr.2019.07.034] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Revised: 06/19/2019] [Accepted: 07/08/2019] [Indexed: 05/07/2023]
Abstract
Over the past decades, the field of in vivo magnetic resonance (MR) has built up an impressive repertoire of data acquisition and analysis technologies for anatomical, functional, physiological, and molecular imaging, the description of which requires many book volumes. As such it is impossible for a few authors to have an authoritative overview of the field and for a brief article to be inclusive. We will therefore focus mainly on data acquisition and attempt to give some insight into the principles underlying current advanced methods in the field and the potential for further innovation. In our view, the foreseeable future is expected to show continued rapid progress, for instance in imaging of microscopic tissue properties in vivo, assessment of functional and anatomical connectivity, higher resolution physiologic and metabolic imaging, and even imaging of receptor binding. In addition, acquisition speed and information content will continue to increase due to the continuous development of approaches for parallel imaging (including simultaneous multi-slice imaging), compressed sensing, and MRI fingerprinting. Finally, artificial intelligence approaches are becoming more realistic and will have a tremendous effect on both acquisition and analysis strategies. Together, these developments will continue to provide opportunity for scientific discovery and, in combination with large data sets from other fields such as genomics, allow the ultimate realization of precision medicine in the clinic.
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Affiliation(s)
- Peter van Zijl
- Department of Radiology, Johns Hopkins University, F.M. Kirby Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
| | - Linda Knutsson
- Department of Medical Radiation Physics, Lund University, Lund, Sweden
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Sigmund EE, Rosenkrantz AB. Diffusion-weighted Imaging of Prostate Cancer: Revisiting Occam’s Razor. Radiology 2019; 291:398-399. [DOI: 10.1148/radiol.2019190371] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Eric E. Sigmund
- From the Department of Radiology, New York University School of Medicine, NYU Langone Health, 660 1st Ave, New York, NY 10016
| | - Andrew B. Rosenkrantz
- From the Department of Radiology, New York University School of Medicine, NYU Langone Health, 660 1st Ave, New York, NY 10016
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