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Sen S, Singh S, Pye H, Moore CM, Whitaker HC, Punwani S, Atkinson D, Panagiotaki E, Slator PJ. ssVERDICT: Self-supervised VERDICT-MRI for enhanced prostate tumor characterization. Magn Reson Med 2024; 92:2181-2192. [PMID: 38852195 DOI: 10.1002/mrm.30186] [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: 12/15/2023] [Revised: 05/17/2024] [Accepted: 05/17/2024] [Indexed: 06/11/2024]
Abstract
PURPOSE Demonstrating and assessing self-supervised machine-learning fitting of the VERDICT (vascular, extracellular and restricted diffusion for cytometry in tumors) model for prostate cancer. METHODS We derive a self-supervised neural network for fitting VERDICT (ssVERDICT) that estimates parameter maps without training data. We compare the performance of ssVERDICT to two established baseline methods for fitting diffusion MRI models: conventional nonlinear least squares and supervised deep learning. We do this quantitatively on simulated data by comparing the Pearson's correlation coefficient, mean-squared error, bias, and variance with respect to the simulated ground truth. We also calculate in vivo parameter maps on a cohort of 20 prostate cancer patients and compare the methods' performance in discriminating benign from cancerous tissue via Wilcoxon's signed-rank test. RESULTS In simulations, ssVERDICT outperforms the baseline methods (nonlinear least squares and supervised deep learning) in estimating all the parameters from the VERDICT prostate model in terms of Pearson's correlation coefficient, bias, and mean-squared error. In vivo, ssVERDICT shows stronger lesion conspicuity across all parameter maps, and improves discrimination between benign and cancerous tissue over the baseline methods. CONCLUSION ssVERDICT significantly outperforms state-of-the-art methods for VERDICT model fitting and shows, for the first time, fitting of a detailed multicompartment biophysical diffusion MRI model with machine learning without the requirement of explicit training labels.
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Affiliation(s)
- Snigdha Sen
- Center for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Saurabh Singh
- Center for Medical Imaging, Division of Medicine, University College London, London, UK
| | - Hayley Pye
- Department of Targeted Intervention, Division of Surgery and Interventional Science, University College London, London, UK
| | - Caroline M Moore
- Department of Targeted Intervention, Division of Surgery and Interventional Science, University College London, London, UK
| | - Hayley C Whitaker
- Department of Targeted Intervention, Division of Surgery and Interventional Science, University College London, London, UK
| | - Shonit Punwani
- Center for Medical Imaging, Division of Medicine, University College London, London, UK
| | - David Atkinson
- Center for Medical Imaging, Division of Medicine, University College London, London, UK
| | - Eleftheria Panagiotaki
- Center for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Paddy J Slator
- Center for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Cardiff University Brain Research Imaging Center, School of Psychology, Cardiff University, Cardiff, UK
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK
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Wang Q, Yu G, Qiu J, Lu W. Application of Intravoxel Incoherent Motion in Clinical Liver Imaging: A Literature Review. J Magn Reson Imaging 2024; 60:417-440. [PMID: 37908165 DOI: 10.1002/jmri.29086] [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: 07/15/2023] [Revised: 10/01/2023] [Accepted: 10/02/2023] [Indexed: 11/02/2023] Open
Abstract
Intravoxel incoherent motion (IVIM) modeling is a widely used double-exponential model for describing diffusion-weighted imaging (DWI) signal, with a slow component related to pure molecular diffusion and a fast component associated with microcirculatory perfusion, which compensates for the limitations of traditional DWI. IVIM is a noninvasive technique for obtaining liver pathological information and characterizing liver lesions, and has potential applications in the initial diagnosis and treatment monitoring of liver diseases. Recent studies have demonstrated that IVIM-derived parameters are useful for evaluating liver lesions, including nonalcoholic fatty liver disease (NAFLD), liver fibrosis and liver tumors. However, the results are not stable. Therefore, it is necessary to summarize the current applications of IVIM in liver disease research, identify existing shortcomings, and point out the future development direction. In this review, we searched for studies related to hepatic IVIM-DWI applications over the past two decades in the PubMed database. We first introduce the fundamental principles and influential factors of IVIM, and then discuss its application in NAFLD, liver fibrosis, and focal hepatic lesions. It has been found that IVIM is still unstable in ensuring the robustness and reproducibility of measurements in the assessment of liver fibrosis grade and liver tumors differentiation, due to inconsistent and substantial overlap in the range of IVIM-derived parameters for different fibrotic stages. In the end, the future direction of IVIM-DWI in the assessment of liver diseases is discussed, emphasizing the need for further research on the stability of IVIM-derived parameters, particularly perfusion-related parameters, in order to promote the clinical practice of IVIM-DWI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Qi Wang
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Taian, China
| | - Guanghui Yu
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Taian, China
| | - Jianfeng Qiu
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Taian, China
| | - Weizhao Lu
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Taian, China
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Jalnefjord O, Björkman-Burtscher IM. Comparison of methods for intravoxel incoherent motion parameter estimation in the brain from flow-compensated and non-flow-compensated diffusion-encoded data. Magn Reson Med 2024; 92:303-318. [PMID: 38321596 DOI: 10.1002/mrm.30042] [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: 12/08/2023] [Revised: 01/12/2024] [Accepted: 01/22/2024] [Indexed: 02/08/2024]
Abstract
PURPOSE Joint analysis of flow-compensated (FC) and non-flow-compensated (NC) diffusion MRI (dMRI) data has been suggested for increased robustness of intravoxel incoherent motion (IVIM) parameter estimation. For this purpose, a set of methods commonly used or previously found useful for IVIM analysis of dMRI data obtained with conventional diffusion encoding were evaluated in healthy human brain. METHODS Five methods for joint IVIM analysis of FC and NC dMRI data were compared: (1) direct non-linear least squares fitting, (2) a segmented fitting algorithm with estimation of the diffusion coefficient from higher b-values of NC data, (3) a Bayesian algorithm with uniform prior distributions, (4) a Bayesian algorithm with spatial prior distributions, and (5) a deep learning-based algorithm. Methods were evaluated on brain dMRI data from healthy subjects and simulated data at multiple noise levels. Bipolar diffusion encoding gradients were used with b-values 0-200 s/mm2 and corresponding flow weighting factors 0-2.35 s/mm for NC data and by design 0 for FC data. Data were acquired twice for repeatability analysis. RESULTS Measurement repeatability as well as estimation bias and variability were at similar levels or better with the Bayesian algorithm with spatial prior distributions and the deep learning-based algorithm for IVIM parametersD $$ D $$ andf $$ f $$ , and for the Bayesian algorithm only forv d $$ {v}_d $$ , relative to the other methods. CONCLUSION A Bayesian algorithm with spatial prior distributions is preferable for joint IVIM analysis of FC and NC dMRI data in the healthy human brain, but deep learning-based algorithms appear promising.
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Affiliation(s)
- Oscar Jalnefjord
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden
| | - Isabella M Björkman-Burtscher
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Radiology, Section of Neuroradiology, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden
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Huang HM. Calculation of intravoxel incoherent motion parameter maps using a kernelized total difference-based method. NMR IN BIOMEDICINE 2024:e5201. [PMID: 38863271 DOI: 10.1002/nbm.5201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 05/13/2024] [Accepted: 05/23/2024] [Indexed: 06/13/2024]
Abstract
Quantitative analysis of diffusion-weighted magnetic resonance imaging (DW-MRI) has been explored for many clinical applications since its development. In particular, the intravoxel incoherent motion (IVIM) model for DW-MRI has been commonly utilized in various organs. However, because of the presence of excessive noise, the IVIM parameter maps obtained from pixel-wise fitting are often unreliable. In this study, we propose a kernelized total difference-based curve-fitting method to estimate the IVIM parameters. Simulated DW-MRI data at five signal-to-noise ratios (i.e., 10, 20, 30, 50, and 100) and real abdominal DW-MRI data acquired on a 1.5-T MRI scanner with nine b-values (i.e., 0, 10, 25, 50, 100, 200, 300, 400, and 500 s/mm2) and six diffusion-encoding gradient directions were used to evaluate the performance of the proposed method. The results were compared with those obtained by three existing methods: trust-region reflective (TRR) algorithm, Bayesian probability (BP), and deep neural network (DNN). Our simulation results showed that the proposed method outperformed the other three comparing methods in terms of root-mean-square error. Moreover, the proposed method could preserve small details in the estimated IVIM parameter maps. The experimental results showed that, compared with the TRR method, the proposed method as well as the BP (and DNN) method could reduce the overestimation of the pseudodiffusion coefficient and improve the quality of IVIM parameter maps. For all studied abdominal organs except the pancreas, both the proposed method and the BP method could provide IVIM parameter estimates close to the reference values; the former had higher precision. The kernelized total difference-based curve-fitting method has the potential to improve the reliability of IVIM parametric imaging.
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Affiliation(s)
- Hsuan-Ming Huang
- Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, Taipei City, Taiwan
- Program for Precision Health and Intelligent Medicine, Graduate School of Advanced Technology, National Taiwan University, Taipei City, Taiwan
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Mesny E, Leporq B, Chapet O, Beuf O. Intravoxel incoherent motion magnetic resonance imaging to assess early tumor response to radiation therapy: Review and future directions. Magn Reson Imaging 2024; 108:129-137. [PMID: 38354843 DOI: 10.1016/j.mri.2024.02.008] [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: 04/20/2023] [Revised: 02/08/2024] [Accepted: 02/10/2024] [Indexed: 02/16/2024]
Abstract
Early prediction of radiation response by imaging is a dynamic field of research and it can be obtained using a variety of noninvasive magnetic resonance imaging methods. Recently, intravoxel incoherent motion (IVIM) has gained interest in cancer imaging. IVIM carries both diffusion and perfusion information, making it a promising tool to assess tumor response. Here, we briefly introduced the basics of IVIM, reviewed existing studies of IVIM in various type of tumors during radiotherapy in order to show whether IVIM is a useful technique for an early assessment of radiation response. 31/40 studies reported an increase of IVIM parameters during radiotherapy compared to baseline. In 27 studies, this increase was higher in patients with good response to radiotherapy. Future directions including implementation of IVIM on MR-Linac and its limitation are discussed. Obtaining new radiologic biomarkers of radiotherapy response could open the way for a more personalized, biology-guided radiation therapy.
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Affiliation(s)
- Emmanuel Mesny
- Radiation Oncology Department, Center Hospitalier Lyon Sud, Pierre Benite, France; Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon F-69100, France.
| | - Benjamin Leporq
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon F-69100, France
| | - Olivier Chapet
- Radiation Oncology Department, Center Hospitalier Lyon Sud, Pierre Benite, France
| | - Olivier Beuf
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon F-69100, France
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Li XM, Ma FZ, Quan XY, Zhang XC, Xiao BH, Wáng YXJ. Repeatability and reproducibility comparisons of liver IVIM imaging with free-breathing or respiratory-triggered sequences. NMR IN BIOMEDICINE 2024; 37:e5080. [PMID: 38113878 DOI: 10.1002/nbm.5080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 10/26/2023] [Accepted: 11/07/2023] [Indexed: 12/21/2023]
Abstract
For liver intravoxel incoherent motion (IVIM) data acquisition, respiratory-triggering (RT) MRI is commonly used, and there are strong motivations to shorten the scan duration. For the same scan duration, more b values or higher numbers of excitations can be allowed for free-breathing (FB) imaging than for RT. We studied whether FB can be used to replace RT when careful IVIM image acquisition and image processing are conducted. MRI data of 22 healthy participants were acquired using a 3.0 T scanner. Diffusion imaging was based on a single-shot spin-echo-type echo-planar sequence and 16 b values of 0, 2, 4, 7, 10, 15, 20, 30, 46, 60, 72, 100, 150, 200, 400, and 600 s/mm2 . Each subject attended two scan sessions with an interval of 10-20 days. For each scan session, a subject was scanned twice, first with RT and then with FB. The mean image acquisition time was 5.4 min for FB and 10.8 min for RT. IVIM parameters were calculated with bi-exponential model segmented fitting with a threshold b value of 60 s/mm2 , and fitting started from b = 2 s/mm2 . There was no statistically significant difference between IVIM parameters measured with FB imaging or RT imaging. Perfusion fraction ICC (intraclass correlation coefficient) for FB imaging and RT imaging in the same scan session was 0.824. For perfusion fraction, wSD (within-subject standard deviation), BA (Bland-Altman) difference, BA 95% limit, and ICC were 0.022, 0.0001, -0.0635~0.0637, and 0.687 for FB and 0.031, 0.0122, -0.0723~0.0967, and 0.611 for RT. For Dslow (×10-3 s/mm2 ), wSD, BA difference, BA 95% limit, and ICC were 0.057, 0.0268, -0.1258~0.1793, and 0.471 for FB and 0.073, -0.0078, -0.2170-0.2014, and <0.4 for RT. The Dfast coefficient of variation was 0.20 for FB imaging and 0.28 for RT imaging. All reproducibility indicators slightly favored FB imaging.
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Affiliation(s)
- Xin-Ming Li
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Fu-Zhao Ma
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Xian-Yue Quan
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Xu-Chang Zhang
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Ben-Heng Xiao
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Yì Xiáng J Wáng
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
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Baidya Kayal E, Kandasamy D, Yadav R, Khare K, Bakhshi S, Sharma R, Mehndiratta A. Radiologists' Rating for Comparative Qualitative Assessment of Intravoxel Incoherent Motion Using Novel Analysis Methods. J Comput Assist Tomogr 2024; 48:263-272. [PMID: 37657076 DOI: 10.1097/rct.0000000000001540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/03/2023]
Abstract
OBJECTIVE The objective was to assess qualitative interpretability and quantitative precision and reproducibility of intravoxel incoherent motion ( IVIM) parametric images evaluated using novel IVIM analysis methods for diagnostic accuracy. METHODS Intravoxel incoherent motion datasets of 55 patients (male/female = 41:14; age = 17.8 ± 5.5 years) with histopathology-proven osteosarcoma were analyzed. Intravoxel incoherent motion parameters-diffusion coefficient ( D ), perfusion fraction ( f ), and perfusion coefficient ( D* )-were estimated using 5 IVIM analysis methods-(i) biexponential (BE) model, (ii) BE-segmented fitting 2-parameter (BESeg-2), (iii) BE-segmented fitting 1-parameter (BESeg-1), (iv) BE model with total variation penalty function (BE + TV), and (v) BE model with Huber penalty function (BE + HPF). Qualitative scoring in a 5-point Likert scale (uninterpretable: 1; poor: 2; fair: 3; good: 4; excellent: 5) was performed by 2 radiologists for 4 criteria: (a) tumor shape and margin, (b) morphologic correlation, (c) noise suppression, and (d) overall interpretability. Interobserver agreement was evaluated using Spearman rank-order correlation ( rs ). Precision and reproducibility were evaluated using within-subject coefficient of variation (wCV) and between-subject coefficient of variation (bCV). RESULTS BE + TV and BE + HPF produced significantly ( P < 10 -3 ) higher qualitative scores for D (fair-good [3.3-3.8]) than BE (poor [2.3]) and for D* (poor-fair [2.2-2.7]) and f (fair-good [3.2-3.8]) than BE, BESeg-2, and BESeg-1 ( D* : uninterpretable-poor [1.3-1.9] and f : poor-fair [1.5-3]). Interobserver agreement for qualitative scoring was rs = 0.48-0.59, P < 0.009. BE + TV and BE + HPF showed significantly ( P < 0.05) improved reproducibility in estimating D (wCV: 24%-31%, bCV: 21%-31% improvement) than the BE method and D* (wCV: 4%-19%, bCV: 5%-19% improvement) and f (wCV: 25%-49%, bCV: 25%-47% improvement) than BE, BESeg-2, and BESeg-1 methods. CONCLUSIONS BE + TV and BE + HPF demonstrated qualitatively and quantitatively improved IVIM parameter estimation and may be considered for clinical use further.
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Affiliation(s)
- Esha Baidya Kayal
- From the Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | | | - Richa Yadav
- Department of RadioDiagnosis, All India Institute of Medical Sciences
| | - Kedar Khare
- Department of Physics, Indian Institute of Technology Delhi
| | - Sameer Bakhshi
- Department of Medical Oncology, Dr. B.R. Ambedkar Institute-Rotary Cancer Hospital (IRCH), All India Institute of Medical Sciences
| | - Raju Sharma
- Department of RadioDiagnosis, All India Institute of Medical Sciences
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Chen S, Chu ML, Liang L, Liu YJ, Chen NK, Wang H, Juan CJ, Chang HC. Highly accelerated multi-shot intravoxel incoherent motion diffusion-weighted imaging in brain enabled by parametric POCS-based multiplexed sensitivity encoding. NMR IN BIOMEDICINE 2024; 37:e5063. [PMID: 37871617 DOI: 10.1002/nbm.5063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 09/25/2023] [Accepted: 09/27/2023] [Indexed: 10/25/2023]
Abstract
Recently, intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) has also been demonstrated as an imaging tool for applications in neurological and neurovascular diseases. However, the use of single-shot diffusion-weighted echo-planar imaging for IVIM DWI acquisition leads to suboptimal data quality: for instance, geometric distortion and deteriorated image quality at high spatial resolution. Although the recently commercialized multi-shot acquisition methods, such as multiplexed sensitivity encoding (MUSE), can attain high-resolution and high-quality DWI with signal-to-noise ratio (SNR) performance superior to that of the conventional parallel imaging method, the prolonged scan time associated with multi-shot acquisition is impractical for routine IVIM DWI. This study proposes an acquisition and reconstruction framework based on parametric-POCSMUSE to accelerate the four-shot IVIM DWI with 70% reduction of total scan time (13 min 8 s versus 4 min 8 s). First, the four-shot IVIM DWI scan with 17 b values was accelerated by acquiring only one segment per b value except for b values of 0 and 600 s/mm2 . Second, an IVIM-estimation scheme was integrated into the parametric-POCSMUSE to enable joint reconstruction of multi-b images from under-sampled four-shot IVIM DWI data. In vivo experiments on both healthy subjects and patients show that the proposed framework successfully produced multi-b DW images with significantly higher SNRs and lower reconstruction errors than did the conventional acceleration method based on parallel imaging. In addition, the IVIM quantitative maps estimated from the data produced by the proposed framework showed quality comparable to that of fully sampled MUSE-reconstructed images, suggesting that the proposed framework can enable highly accelerated multi-shot IVIM DWI without sacrificing data quality. In summary, the proposed framework can make multi-shot IVIM DWI feasible in a routine MRI examination, with reasonable scan time and improved geometric fidelity.
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Affiliation(s)
- Shihui Chen
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong
| | - Mei-Lan Chu
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
| | - Liyuan Liang
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong
| | - Yi-Jui Liu
- Department of Automatic Control Engineering, Feng Chia University, Taichung, Taiwan
| | - Nan-Kuei Chen
- Department of Biomedical Engineering, University of Arizona, Tucson, Arizona, USA
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, North Carolina, USA
| | - He Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- Human Phenome Institute, Fudan University, Shanghai, China
| | - Chun-Jung Juan
- Department of Medical Imaging, China Medical University Hsinchu Hospital, Hsinchu, Taiwan
- Department of Radiology, School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan
- Department of Medical Imaging, China Medical University Hospital, Taichung, Taiwan
| | - Hing-Chiu Chang
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong
- Multi-Scale Medical Robotics Center, Shatin, Hong Kong
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Cheung SM, Wu WS, Senn N, Sharma R, McGoldrick T, Gagliardi T, Husain E, Masannat Y, He J. Towards detection of early response in neoadjuvant chemotherapy of breast cancer using Bayesian intravoxel incoherent motion. Front Oncol 2023; 13:1277556. [PMID: 38125950 PMCID: PMC10731248 DOI: 10.3389/fonc.2023.1277556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 11/21/2023] [Indexed: 12/23/2023] Open
Abstract
Introduction The early identification of good responders to neoadjuvant chemotherapy (NACT) holds a significant potential in the optimal treatment of breast cancer. A recent Bayesian approach has been postulated to improve the accuracy of the intravoxel incoherent motion (IVIM) model for clinical translation. This study examined the prediction and early sensitivity of Bayesian IVIM to NACT response. Materials and methods Seventeen female patients with breast cancer were scanned at baseline and 16 patients were scanned after Cycle 1. Tissue diffusion and perfusion from Bayesian IVIM were calculated at baseline with percentage change at Cycle 1 computed with reference to baseline. Cellular proliferative activity marker Ki-67 was obtained semi-quantitatively with percentage change at excision computed with reference to core biopsy. Results The perfusion fraction showed a significant difference (p = 0.042) in percentage change between responder groups at Cycle 1, with a decrease in good responders [-7.98% (-19.47-1.73), n = 7] and an increase in poor responders [10.04% (5.09-28.93), n = 9]. There was a significant correlation between percentage change in perfusion fraction and percentage change in Ki-67 (p = 0.042). Tissue diffusion and pseudodiffusion showed no significant difference in percentage change between groups at Cycle 1, nor was there a significant correlation against percentage change in Ki-67. Perfusion fraction, tissue diffusion, and pseudodiffusion showed no significant difference between groups at baseline, nor was there a significant correlation against Ki-67 from core biopsy. Conclusion The alteration in tumour perfusion fraction from the Bayesian IVIM model, in association with cellular proliferation, showed early sensitivity to good responders in NACT. Clinical trial registration https://clinicaltrials.gov/ct2/show/NCT03501394, identifier NCT03501394.
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Affiliation(s)
- Sai Man Cheung
- Institute of Medical Sciences, School of Medicine, University of Aberdeen, Aberdeen, United Kingdom
| | - Wing-Shan Wu
- Institute of Medical Sciences, School of Medicine, University of Aberdeen, Aberdeen, United Kingdom
| | - Nicholas Senn
- Institute of Medical Sciences, School of Medicine, University of Aberdeen, Aberdeen, United Kingdom
| | - Ravi Sharma
- Department of Oncology, Aberdeen Royal Infirmary, Aberdeen, United Kingdom
| | - Trevor McGoldrick
- Department of Oncology, Aberdeen Royal Infirmary, Aberdeen, United Kingdom
| | - Tanja Gagliardi
- Institute of Medical Sciences, School of Medicine, University of Aberdeen, Aberdeen, United Kingdom
- Department of Radiology, Royal Marsden Hospital, London, United Kingdom
| | - Ehab Husain
- Department of Pathology, Aberdeen Royal Infirmary, Aberdeen, United Kingdom
| | - Yazan Masannat
- Breast Unit, Aberdeen Royal Infirmary, Aberdeen, United Kingdom
| | - Jiabao He
- Institute of Medical Sciences, School of Medicine, University of Aberdeen, Aberdeen, United Kingdom
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
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Sharifzadeh Javidi S, Shirazinodeh A, Saligheh Rad H. Intravoxel Incoherent Motion Quantification Dependent on Measurement SNR and Tissue Perfusion: A Simulation Study. J Biomed Phys Eng 2023; 13:555-562. [PMID: 38148961 PMCID: PMC10749416 DOI: 10.31661/jbpe.v0i0.2102-1281] [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: 02/12/2020] [Accepted: 03/28/2021] [Indexed: 12/28/2023]
Abstract
Background The intravoxel incoherent motion (IVIM) model extracts both functional and structural information of a tissue using motion-sensitizing gradients. Objective The Objective of the present work is to investigate the impact of signal to noise ratio (SNR) and physiologic conditions on the validity of IVIM parameters. Material and Methods This study is a simulation study, modeling IVIM at a voxel, and also done 10,000 times for every single simulation. Complex noises with various standard deviations were added to signal in-silico to investigate SNR effects on output validity. Besides, some blood perfusion situations for different tissues were considered based on their physiological range to explore the impacts of blood fraction at each voxel on the validity of the IVIM outputs. Coefficient variation (CV) and bias of the estimations were computed to assess the validity of the IVIM parameters. Results This study has shown that the validity of IVIM output parameters highly depends on measurement SNR and physiologic characteristics of the studied organ. Conclusion IVIM imaging could be useful if imaging parameters are correctly selected for each specific organ, considering hardware limitations.
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Affiliation(s)
- Sam Sharifzadeh Javidi
- Department of Medical Physics and Biomedical Engineering, Medicine School, Tehran University of Medical Sciences, Tehran, Iran
- Quantitative Medical Imaging Systems Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Alireza Shirazinodeh
- Department of Medical Physics and Biomedical Engineering, Medicine School, Tehran University of Medical Sciences, Tehran, Iran
| | - Hamidreza Saligheh Rad
- Department of Medical Physics and Biomedical Engineering, Medicine School, Tehran University of Medical Sciences, Tehran, Iran
- Quantitative Medical Imaging Systems Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
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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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12
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Stabinska J, Wittsack HJ, Lerman LO, Ljimani A, Sigmund EE. Probing Renal Microstructure and Function with Advanced Diffusion MRI: Concepts, Applications, Challenges, and Future Directions. J Magn Reson Imaging 2023:10.1002/jmri.29127. [PMID: 37991093 PMCID: PMC11117411 DOI: 10.1002/jmri.29127] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 10/26/2023] [Accepted: 10/27/2023] [Indexed: 11/23/2023] Open
Abstract
Diffusion measurements in the kidney are affected not only by renal microstructure but also by physiological processes (i.e., glomerular filtration, water reabsorption, and urine formation). Because of the superposition of passive tissue diffusion, blood perfusion, and tubular pre-urine flow, the limitations of the monoexponential apparent diffusion coefficient (ADC) model in assessing pathophysiological changes in renal tissue are becoming apparent and motivate the development of more advanced diffusion-weighted imaging (DWI) variants. These approaches take advantage of the fact that the length scale probed in DWI measurements can be adjusted by experimental parameters, including diffusion-weighting, diffusion gradient directions and diffusion time. This forms the basis by which advanced DWI models can be used to capture not only passive diffusion effects, but also microcirculation, compartmentalization, tissue anisotropy. In this review, we provide a comprehensive overview of the recent advancements in the field of renal DWI. Following a short introduction on renal structure and physiology, we present the key methodological approaches for the acquisition and analysis of renal DWI data, including intravoxel incoherent motion (IVIM), diffusion tensor imaging (DTI), non-Gaussian diffusion, and hybrid IVIM-DTI. We then briefly summarize the applications of these methods in chronic kidney disease and renal allograft dysfunction. Finally, we discuss the challenges and potential avenues for further development of renal DWI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Julia Stabinska
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Hans-Jörg Wittsack
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich Heine University Düsseldorf, Dusseldorf, Germany
| | - Lilach O. Lerman
- Division of Nephrology and Hypertension and Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Alexandra Ljimani
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich Heine University Düsseldorf, Dusseldorf, Germany
| | - Eric E. Sigmund
- Bernard and Irene Schwartz Center for Biomedical Imaging Center for Advanced Imaging Innovation and Research (CAI2R), New York University Langone Health, New York City, New York, USA
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Liu J, Karfoul A, Marage L, Shu H, Gambarota G. Estimation of intravoxel incoherent motion (IVIM) parameters in vertebral bone marrow: a comparative study of five algorithms. MAGMA (NEW YORK, N.Y.) 2023; 36:837-847. [PMID: 36715885 DOI: 10.1007/s10334-023-01064-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 01/15/2023] [Accepted: 01/16/2023] [Indexed: 01/31/2023]
Abstract
OBJECTIVES To access the performances of different algorithms for quantification of Intravoxel incoherent motion (IVIM) parameters D, f, [Formula: see text] in Vertebral Bone Marrow (VBM). MATERIALS AND METHODS Five algorithms were studied: four deterministic algorithms (the One-Step and three segmented methods: Two-Step, Three-Step, and Fixed-[Formula: see text] algorithm) based on the least-squares (LSQ) method and a Bayesian probabilistic algorithm. Numerical simulations and quantification of IVIM parameters D, f, [Formula: see text] in vivo in vertebral bone marrow, were done on six healthy volunteers. The One-way repeated-measures analysis of variance (ANOVA) followed by Bonferroni's multiple comparison test (p value = 0.05) was applied. RESULTS In numerical simulations, the Bayesian algorithm provided the best estimation of D, f, [Formula: see text] compared to the deterministic algorithms. In vivo VBM-IVIM, the values of D and f estimated by the Bayesian algorithm were close to those of the One-Step method, in contrast to the three segmented methods. DISCUSSION The comparison of the five algorithms indicates that the Bayesian algorithm provides the best estimation of VBM-IVIM parameters, in both numerical simulations and in vivo data.
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Affiliation(s)
- Jie Liu
- Laboratory of Image Science and Technology (LIST), School of Computer Science and Engineering, Southeast University, Nanjing, 210096, China.
- College of Information Science and Technology, Nanjing Forestry University, Nanjing, 210037, China.
- Univ Rennes, Southeast University, INSERM, Centre de Recherche en Information Biomèdicale sino-français (CRIBs)-LIA, 35000, Rennes, France.
| | - Ahmad Karfoul
- Univ Rennes, Southeast University, INSERM, Centre de Recherche en Information Biomèdicale sino-français (CRIBs)-LIA, 35000, Rennes, France
- Univ Rennes, INSERM, LTSI-UMR 1099, 35000, Rennes, France
| | - Louis Marage
- Department of Medical Physics, Georges François Leclerc Cancer Center, 21000, Dijon, France
| | - Huazhong Shu
- Laboratory of Image Science and Technology (LIST), School of Computer Science and Engineering, Southeast University, Nanjing, 210096, China
- Univ Rennes, Southeast University, INSERM, Centre de Recherche en Information Biomèdicale sino-français (CRIBs)-LIA, 35000, Rennes, France
| | - Giulio Gambarota
- Univ Rennes, Southeast University, INSERM, Centre de Recherche en Information Biomèdicale sino-français (CRIBs)-LIA, 35000, Rennes, France
- Univ Rennes, INSERM, LTSI-UMR 1099, 35000, Rennes, France
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14
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Bian W, Zhang J, Huang Q, Niu W, Li J, Song X, Cui S, Zheng Q, Niu J, Zhou XJ. Quantitative tumor burden imaging parameters of the spleen at MRI for predicting treatment response in patients with acute leukemia. Heliyon 2023; 9:e20348. [PMID: 37810872 PMCID: PMC10550618 DOI: 10.1016/j.heliyon.2023.e20348] [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: 05/05/2023] [Revised: 09/15/2023] [Accepted: 09/19/2023] [Indexed: 10/10/2023] Open
Abstract
Objectives To study the value of standardized volume and intravoxel incoherent motion (IVIM) parameters of the spleen based on tumor burden for predicting treatment response in newly diagnosed acute leukemia (AL). Methods Patients with newly diagnosed AL were recruited and underwent abdominal IVIM diffusion-weighted imaging within one week before the first induction chemotherapy. Quantitative parameters of magnetic resonance imaging (MRI) included the standardized volume (representing volumetric tumor burden) and IVIM parameters (standard apparent diffusion coefficient [sADC]; pure diffusion coefficient [D]; pseudo-diffusion coefficient [D∗]; and pseudo-perfusion fraction [f], representing functional tumor burden) of the spleen. Clinical biomarkers of tumor burden were collected. Patients were divided into complete remission (CR) and non-CR groups according to the treatment response after the first standardized induction chemotherapy, and the MRI and clinical parameters were compared between the two groups. The correlations of MRI parameters with clinical biomarkers were analyzed. Multivariate logistic regression was performed to determine the independent predictors for treatment response. Receiver operating characteristic curves were used to analyze the predicted performance. Results 76 AL patients (CR: n = 43; non-CR: n = 33) were evaluated. Standardized spleen volume, sADC, D, f, white blood cell counts, and lactate dehydrogenase were significantly different between CR and non-CR groups (all p < 0.05). Standardized spleen volume, sADC, and D were correlated with white blood cell and lactate dehydrogenase, and f was correlated with lactate dehydrogenase (all p < 0.05). Standardized spleen volume (hazard ratio = 4.055, p = 0.042), D (hazard ratio = 0.991, p = 0.027), and f (hazard ratio = 1.142, p = 0.008) were independent predictors for treatment response, and the combination of standardized spleen volume, D, and f showed more favorable discrimination (area under the curve = 0.856) than individual predictors. Conclusion Standardized volume, D, and f of the spleen could be used to predict treatment response in newly diagnosed AL, and the combination of morphological and functional parameters would further improve the predicted performance. IVIM parameters of the spleen may be viable indicators for evaluating functional tumor burden in AL.
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Affiliation(s)
- Wenjin Bian
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
- Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Jianling Zhang
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Qianqian Huang
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Weiran Niu
- Department of Mental Health, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Jianting Li
- Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Xiaoli Song
- Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Sha Cui
- Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Qian Zheng
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
- Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Jinliang Niu
- Department of Radiology, The Second Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Xiaohong Joe Zhou
- Center for MR Research and Departments of Radiology, Neurosurgery, And Biomedical Engineering, University of Illinois at Chicago, Chicago, 60612, Illinois, USA
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15
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Kaandorp MPT, Zijlstra F, Federau C, While PT. Deep learning intravoxel incoherent motion modeling: Exploring the impact of training features and learning strategies. Magn Reson Med 2023; 90:312-328. [PMID: 36912473 DOI: 10.1002/mrm.29628] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 02/14/2023] [Accepted: 02/16/2023] [Indexed: 03/14/2023]
Abstract
PURPOSE The development of advanced estimators for intravoxel incoherent motion (IVIM) modeling is often motivated by a desire to produce smoother parameter maps than least squares (LSQ). Deep neural networks show promise to this end, yet performance may be conditional on a myriad of choices regarding the learning strategy. In this work, we have explored potential impacts of key training features in unsupervised and supervised learning for IVIM model fitting. METHODS Two synthetic data sets and one in-vivo data set from glioma patients were used in training of unsupervised and supervised networks for assessing generalizability. Network stability for different learning rates and network sizes was assessed in terms of loss convergence. Accuracy, precision, and bias were assessed by comparing estimations against ground truth after using different training data (synthetic and in vivo). RESULTS A high learning rate, small network size, and early stopping resulted in sub-optimal solutions and correlations in fitted IVIM parameters. Extending training beyond early stopping resolved these correlations and reduced parameter error. However, extensive training resulted in increased noise sensitivity, where unsupervised estimates displayed variability similar to LSQ. In contrast, supervised estimates demonstrated improved precision but were strongly biased toward the mean of the training distribution, resulting in relatively smooth, yet possibly deceptive parameter maps. Extensive training also reduced the impact of individual hyperparameters. CONCLUSION Voxel-wise deep learning for IVIM fitting demands sufficiently extensive training to minimize parameter correlation and bias for unsupervised learning, or demands a close correspondence between the training and test sets for supervised learning.
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Affiliation(s)
- Misha P T Kaandorp
- Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway.,Department of Circulation and Medical Imaging, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Frank Zijlstra
- Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway.,Department of Circulation and Medical Imaging, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Christian Federau
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland.,AI Medical, Zurich, Switzerland
| | - Peter T While
- Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway.,Department of Circulation and Medical Imaging, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
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16
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Nai YH, Wang X, Gan J, Lian CPL, Kirwan RF, Tan FSL, Hausenloy DJ. Effects of fitting methods, high b-values and image quality on diffusion and perfusion quantification and reproducibility in the calf. Comput Biol Med 2023; 157:106746. [PMID: 36924736 DOI: 10.1016/j.compbiomed.2023.106746] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 02/17/2023] [Accepted: 03/04/2023] [Indexed: 03/08/2023]
Abstract
PURPOSES The study aimed to optimize diffusion-weighted imaging (DWI) image acquisition and analysis protocols in calf muscles by investigating the effects of different model-fitting methods, image quality, and use of high b-value and constraints on parameters of interest (POIs). The optimized modeling methods were used to select the optimal combinations of b-values, which will allow shorter acquisition time while achieving the same reliability as that obtained using 16 b-values. METHODS Test-retest baseline and high-quality DWI images of ten healthy volunteers were acquired on a 3T MR scanner, using 16 b-values, including a high b-value of 1200 s/mm2, and structural T1-weighted images for calf muscle delineation. Three and six different fitting methods were used to derive ADC from monoexponential (ME) model and Dd, fp, and Dp from intravoxel incoherent motion (IVIM) model, with or without the high b-value. The optimized ME and IVIM models were then used to determine the optimal combinations of b-values, obtainable with the least number of b-values, using the selection criteria of coefficient of variance (CV) ≤10% for all POIs. RESULTS The find minimum multivariate algorithm was more flexible and yielded smaller fitting errors. The 2-steps fitting method, with fixed Dd, performed the best for IVIM model. The inclusion of high b-value reduced outliers, while constraints improved 2-steps fitting only. CONCLUSIONS The optimal numbers of b-values for ME and IVIM models were nine and six b-values respectively. Test-retest reliability analyses showed that only ADC and Dd were reliable for calf diffusion evaluation, with CVs of 7.22% and 4.09%.
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Affiliation(s)
- Ying-Hwey Nai
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Xiaomeng Wang
- Cardiovascular & Metabolic Disorders Program, Duke-National University of Singapore Medical School, Singapore
| | | | - Cheryl Pei Ling Lian
- Health and Social Sciences Cluster, Singapore Institute of Technology, Singapore
| | - Ryan Fraser Kirwan
- Infocomm Technology Cluster, Singapore Institute of Technology, Singapore
| | - Forest Su Lim Tan
- Infocomm Technology Cluster, Singapore Institute of Technology, Singapore
| | - Derek J Hausenloy
- Cardiovascular & Metabolic Disorders Program, Duke-National University of Singapore Medical School, Singapore; National Heart Research Institute Singapore, National Heart Centre, Singapore; Yong Loo Lin School of Medicine, National University Singapore, Singapore; The Hatter Cardiovascular Institute, University College London, London, UK
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17
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Echeverria‐Chasco R, Martin‐Moreno PL, Garcia‐Fernandez N, Vidorreta M, Aramendia‐Vidaurreta V, Cano D, Villanueva A, Bastarrika G, Fernández‐Seara MA. Multiparametric renal magnetic resonance imaging: A reproducibility study in renal allografts with stable function. NMR IN BIOMEDICINE 2023; 36:e4832. [PMID: 36115029 PMCID: PMC10078573 DOI: 10.1002/nbm.4832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 09/13/2022] [Accepted: 09/15/2022] [Indexed: 06/15/2023]
Abstract
Monitoring renal allograft function after transplantation is key for the early detection of allograft impairment, which in turn can contribute to preventing the loss of the allograft. Multiparametric renal MRI (mpMRI) is a promising noninvasive technique to assess and characterize renal physiopathology; however, few studies have employed mpMRI in renal allografts with stable function (maintained function over a long time period). The purposes of the current study were to evaluate the reproducibility of mpMRI in transplant patients and to characterize normal values of the measured parameters, and to estimate the labeling efficiency of Pseudo-Continuous Arterial Spin Labeling (PCASL) in the infrarenal aorta using numerical simulations considering experimental measurements of aortic blood flow profiles. The subjects were 20 transplant patients with stable kidney function, maintained over 1 year. The MRI protocol consisted of PCASL, intravoxel incoherent motion, and T1 inversion recovery. Phase contrast was used to measure aortic blood flow. Renal blood flow (RBF), diffusion coefficient (D), pseudo-diffusion coefficient (D*), flowing fraction ( f ), and T1 maps were calculated and mean values were measured in the cortex and medulla. The labeling efficiency of PCASL was estimated from simulation of Bloch equations. Reproducibility was assessed with the within-subject coefficient of variation, intraclass correlation coefficient, and Bland-Altman analysis. Correlations were evaluated using the Pearson correlation coefficient. The significance level was p less than 0.05. Cortical reproducibility was very good for T1, D, and RBF, moderate for f , and low for D*, while medullary reproducibility was good for T1 and D. Significant correlations in the cortex between RBF and f (r = 0.66), RBF and eGFR (r = 0.64), and D* and eGFR (r = -0.57) were found. Normal values of the measured parameters employing the mpMRI protocol in kidney transplant patients with stable function were characterized and the results showed good reproducibility of the techniques.
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Affiliation(s)
- Rebeca Echeverria‐Chasco
- Department of RadiologyClínica Universidad de NavarraPamplonaSpain
- IdiSNA, Instituto de Investigación Sanitaria de NavarraPamplonaSpain
| | - Paloma L. Martin‐Moreno
- IdiSNA, Instituto de Investigación Sanitaria de NavarraPamplonaSpain
- Department of NephrologyClínica Universidad de NavarraPamplonaSpain
| | - Nuria Garcia‐Fernandez
- IdiSNA, Instituto de Investigación Sanitaria de NavarraPamplonaSpain
- Department of NephrologyClínica Universidad de NavarraPamplonaSpain
| | | | - Verónica Aramendia‐Vidaurreta
- Department of RadiologyClínica Universidad de NavarraPamplonaSpain
- IdiSNA, Instituto de Investigación Sanitaria de NavarraPamplonaSpain
| | - David Cano
- Department of RadiologyClínica Universidad de NavarraPamplonaSpain
| | - Arantxa Villanueva
- IdiSNA, Instituto de Investigación Sanitaria de NavarraPamplonaSpain
- Electrical Electronics and Communications Engineering Department and Smart Cities InstitutePublic University of NavarrePamplonaSpain
| | - Gorka Bastarrika
- Department of RadiologyClínica Universidad de NavarraPamplonaSpain
- IdiSNA, Instituto de Investigación Sanitaria de NavarraPamplonaSpain
| | - Maria A. Fernández‐Seara
- Department of RadiologyClínica Universidad de NavarraPamplonaSpain
- IdiSNA, Instituto de Investigación Sanitaria de NavarraPamplonaSpain
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18
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Huang HM. An unsupervised convolutional neural network method for estimation of intravoxel incoherent motion parameters. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac9a1f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 10/13/2022] [Indexed: 11/07/2022]
Abstract
Abstract
Objective. Intravoxel incoherent motion (IVIM) imaging obtained by fitting a biexponential model to multiple b-value diffusion-weighted magnetic resonance imaging (DW-MRI) has been shown to be a promising tool for different clinical applications. Recently, several deep neural network (DNN) methods were proposed to generate IVIM imaging. Approach. In this study, we proposed an unsupervised convolutional neural network (CNN) method for estimation of IVIM parameters. We used both simulated and real abdominal DW-MRI data to evaluate the performance of the proposed CNN-based method, and compared the results with those obtained from a non-linear least-squares fit (TRR, trust-region reflective algorithm) and a feed-forward backward-propagation DNN-based method. Main results. The simulation results showed that both the DNN- and CNN-based methods had lower coefficients of variation than the TRR method, but the CNN-based method provided more accurate parameter estimates. The results obtained from real DW-MRI data showed that the TRR method produced many biased IVIM parameter estimates that hit the upper and lower parameter bounds. In contrast, both the DNN- and CNN-based methods yielded less biased IVIM parameter estimates. Overall, the perfusion fraction and diffusion coefficient obtained from the DNN- and CNN-based methods were close to literature values. However, compared with the CNN-based method, both the TRR and DNN-based methods tended to yield increased pseudodiffusion coefficients (55%–180%). Significance. Our preliminary results suggest that it is feasible to estimate IVIM parameters using CNN.
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Hoang-Dinh A, Nguyen-Quang T, Bui-Van L, Gonindard-Melodelima C, Souchon R, Rouvière O. Reproducibility of apparent diffusion coefficient measurement in normal prostate peripheral zone at 1.5T MRI. Diagn Interv Imaging 2022; 103:545-554. [PMID: 35773099 DOI: 10.1016/j.diii.2022.06.001] [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: 04/25/2022] [Revised: 06/05/2022] [Accepted: 06/06/2022] [Indexed: 11/17/2022]
Abstract
PURPOSE The purpose of this study was to quantify the influence of factors of variability on apparent diffusion coefficient (ADC) estimation in the normal prostate peripheral zone (PZ). MATERIALS AND METHODS Fifty healthy volunteers underwent in 2017 (n = 17) or 2020 (n = 33) two-point (0, 800 s/mm²) prostate diffusion-weighted imaging in the morning on 1.5 T scanners A and B from different manufacturers. Additional five-point (50, 150, 300, 500, 800 s/mm²) acquisitions were performed on scanner B in the morning and evening. ADC was measured in PZ at midgland using ADC maps reconstructed with various b-value combinations. ADC distributions from 2017 and 2020 were compared using Wilcoxon rank sum test. ADC obtained in the same volunteers were compared using Bland Altman methodology. The 95% confidence interval upper limit of the repeatability/reproducibility coefficient defined the lowest detectable ADC difference. RESULTS Forty-nine participants with a mean age of 24.6 ± 3.8 [SD] years (range: 21-37 years) were finally included. ADC distributions from 2017 and 2020 were not significantly different and were combined. Despite high individual variability, there was no significant bias (10 × 10-6 mm²/s, P = 0.58) between ADC measurements made on both scanners. On scanner B, differences in lowest b-values chosen within the 0-500 s/mm² range for two-point ADC computation induced significant biases (56-109 × 10-6 mm²/s, P < 0.0001). ADC was significantly lower in the morning (bias: 33 × 10-6 mm²/s, P = 0.006). The number of b-values had little influence on ADC values. The lowest detectable ADC difference varied from 85 × 10-6 to 311 × 10-6 mm²/s across scanners, b-value combinations and periods of the day. CONCLUSIONS The MRI scanner, the lowest b-value used and the period of the day induce substantial variability in ADC computation.
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Affiliation(s)
- Au Hoang-Dinh
- Hanoï Medical University Hospital, Dong Da, Hanoi, Viet Nam
| | | | - Lenh Bui-Van
- Hanoï Medical University Hospital, Dong Da, Hanoi, Viet Nam
| | | | | | - Olivier Rouvière
- LabTAU, INSERM, U1032, 69000, Lyon, France; Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, 69000, Lyon, France; Université de Lyon, Lyon 69003, France; Université Lyon 1, Lyon France; Faculté de Médecine, Lyon Est, 69003, Lyon, France.
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Zheng CJ, Xiao BH, Huang H, Zhou N, Yan TY, Wáng YXJ. Bi-exponential fitting excluding b=0 data improves the scan-rescan stability of liver IVIM parameter measures and particularly so for the perfusion fraction. Quant Imaging Med Surg 2022; 12:3288-3299. [PMID: 35655827 PMCID: PMC9131351 DOI: 10.21037/qims-2022-02] [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: 02/08/2022] [Accepted: 03/11/2022] [Indexed: 08/30/2023]
Abstract
BACKGROUND A prerequisite to translating intravoxel incoherent motion (IVIM) imaging into meaningful clinical applications is sufficient scan-rescan reproducibility. This study aims to confirm the hypothesis that IVIM data fitting by not using b=0 images will improve the stability of liver IVIM measurement. METHODS Healthy volunteers' liver IVIM images were prospectively acquired using a 1.5-T magnet or a 3.0 T with 16 b-values. Repeatability study subjects were scanned twice during the same session, resulted in 35 paired scans for 35 subjects (11 men, mean age: 41.82 years, range: 32-60 years; 24 women, mean age: 42.67 years, range: 20-71 years). IVIM analysis was performed with full-fitting and segmented-fitting with a threshold b-value of 60 s/mm2, and fitting started from b=0 s/mm2 or from b=2 s/mm2. Reproducibility study subjects were scanned and then rescanned with an interval of 5-18 days, resulted in 20 paired scans for 11 subjects (4 men, mean age: 26.25 years, range: 25-27 years; 7 women, mean age: 25.57 years, range: 24-27 years). IVIM analysis was performed with segmented-fitting with a threshold b-value of 50 s/mm2, and fitting started from b=0 s/mm2 or from b=3 s/mm2. RESULTS Fitting without b=0 data generally improved the repeatability and reproducibility for both PF and Dslow, and particularly so for PF. For with b=0 data segmented fitting repeatability, PF had within-subject standard deviation of 0.019, bland-Atman 75% agreement limit of -31.52% to 28.35%, and ICC of 0.647, while these values were 0.009, -20.78% to 16.86%, and 0.837 for without b=0 analysis. Though the repeatability and reproducibility for Dfast generally also improved, they remained suboptimal. Measurement stability was better for repeatability than for reproducibility. CONCLUSIONS Scan-rescan repeatability and reproducibility of liver IVIM parameters can be improved by fitting without b=0 data, which is particularly so for PF.
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Affiliation(s)
- Cun-Jing Zheng
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Ben-Heng Xiao
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Hua Huang
- Department of Radiology, The Third People’s Hospital of Shenzhen, The Second Affiliated Hospital of Southern University of Science and Technology, National Clinical Research Center for Infectious Diseases, Shenzhen, China
| | - Nan Zhou
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Tai-Yu Yan
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Yì Xiáng J. Wáng
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
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21
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Perfusion-Diffusion Ratio: A New IVIM Approach in Differentiating Solid Benign and Malignant Primary Lesions of the Liver. BIOMED RESEARCH INTERNATIONAL 2022; 2022:2957759. [PMID: 35075424 PMCID: PMC8783718 DOI: 10.1155/2022/2957759] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 11/02/2021] [Accepted: 11/30/2021] [Indexed: 12/14/2022]
Abstract
Introduction In order to improve the efficacy of intravoxel incoherent motion (IVIM) parameters in characterising specific tissues, a new concept is introduced: the perfusion–diffusion ratio (PDR), which expresses the relationship between the signal S(b) decline rate as a result of IVIM and the rate of signal S(b) decline due to diffusion. The aim of this study was to investigate this novel approach in the differentiation of solid primary liver lesions. Material and Methods. Eighty-three patients referred for liver MRI between August 2017 and January 2020 with a suspected liver tumour were prospectively examined with the standard liver MRI protocol extended by DWI-IVIM sequence. Patients with no liver lesions, haemangiomas, or metastases were excluded. The final study population consisted of 34 patients with primary solid liver masses, 9 with FNH, 4 with regenerative nodules, 10 with HCC, and 11 with CCC. The PDR coefficient was introduced, defined as the ratio of the rate of signal S(b) decrease due to the IVIM effect to the rate of signal S(b) decrease due to the diffusion process, for b = 0. Results No significant differences were found between benign and malignant lesions in the case of IVIM parameters (f, D, or D∗) and ADC. Significant differences were observed only for PDR, with lower values for malignant lesions (p = 0.03). The ROC analysis yielded an AUC value for PDR equal to 0.74, with a cut-off value of 5.06, sensitivity of 81%, specificity of 77%, and accuracy of 79%. Conclusion PDR proved to be more effective than IVIM parameters and ADC in the differentiation of solid benign and malignant primary liver lesions.
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22
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Andersson M, Jalnefjord O, Montelius M, Rizell M, Sternby Eilard M, Ljungberg M. Evaluation of response in patients with hepatocellular carcinoma treated with intratumoral dendritic cell vaccination using intravoxel incoherent motion (IVIM) MRI and histogram analysis. Acta Radiol 2021; 64:32-41. [PMID: 34904868 DOI: 10.1177/02841851211065935] [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] [Indexed: 12/16/2022]
Abstract
BACKGROUND Immunotherapy of hepatocellular carcinoma (HCC) is an emerging method with promising results. Immunotherapy can have an antitumor effect without affecting tumor size, calling for functional imaging methods for response evaluation. PURPOSE To evaluate the response to intratumoral injections with the immune primer ilixadencel in HCCs with diffusion-weighted magnetic resonance imaging (DW-MRI) using intravoxel incoherent motion (IVIM) and histogram analysis. MATERIAL AND METHODS A total of 17 patients with advanced HCC were treated with intratumoral injections with ilixadencel on three occasions 2-5 weeks apart. The patients were examined with IVIM before each injection as well as approximately three months after the first injection. RESULTS The 10th percentile of perfusion-related parameter D* decreased significantly after the first and second intratumoral injections of ilixadencel compared to baseline (P < 0.05). There was a non-significant trend of lower median region of interest f (perfusion fraction) before injection 2 compared to baseline (P = 0.07). There were significant correlations between the 10th percentile and median of D at baseline and change in tumor size after three months (r = 0.79, P < 0.01 and r = 0.72, P < 0.05, respectively). CONCLUSION DW-MRI with IVIM and histogram analysis revealed significant reductions of D* early after treatment as well as an association between D at baseline and smaller tumor growth at three months. The lower percentiles (10th and 50th) were found more important. Further research is needed to confirm our preliminary findings of reduced perfusion after ilixadencel vaccinations, suggesting a treatment effect on HCC.
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Affiliation(s)
- Mats Andersson
- Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden
- Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institute and Department of Radiology, Karolinska University Hospital, Stockholm, Sweden
| | - Oscar Jalnefjord
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Mikael Montelius
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Magnus Rizell
- Department of Surgery, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Malin Sternby Eilard
- Department of Surgery, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Maria Ljungberg
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
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Lee YJ, Kim SH, Kang BJ, Son YH, Grimm R. Associations between angiogenic factors and intravoxel incoherent motion-derived parameters in diffusion-weighted magnetic resonance imaging of breast cancer. Medicine (Baltimore) 2021; 100:e27495. [PMID: 34731130 PMCID: PMC8519258 DOI: 10.1097/md.0000000000027495] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 09/23/2021] [Indexed: 01/05/2023] Open
Abstract
Intravoxel incoherent motion (IVIM) diffusion-weighted magnetic resonance imaging (MRI) can be used to estimate perfusion-related parameters, but these parameters may differ, based on the curve-fitting algorithm used for IVIM. Microvessel density (MVD) and vascular endothelial growth factor (VEGF) status are used as angiogenic factors in breast cancer. We aimed to investigate the relationship between MVD, VEGF, and intravoxel incoherent motion (IVIM)-derived parameters, obtained by 4 curve-fitting algorithms, in patients with invasive breast cancers.This retrospective study investigated IVIM-derived parameters, D (ie, tissue diffusivity), D∗ (ie, pseudodiffusivity), and f (ie, perfusion fraction), of 55 breast cancers, using 10 b values (range, 0-800 s/mm2) and 4 curve-fitting algorithms: algorithm 1, linear fitting of D and f first, followed by D∗; algorithm 2, linear fitting of D and f and nonlinear fitting of D∗; algorithm 3, linear fitting of D and f, linear fitting of D∗, and ignoring D contribution for low b values; and algorithm 4, full nonlinear fitting of D, f, and D∗. We evaluated whole-tumor histograms of D, f, and D∗ for their association with MVD and VEGF.D∗10, D∗25, D∗50, D∗mean, D∗75, D∗90, f10, and f25, derived using algorithm 3, were associated with VEGF expression (P = .043, P = 0.012, P = .019, P = .024, P = .044, P = .041, P = .010, and P = .005, respectively). However, no correlation existed between MVD and IVIM-derived parameters.Perfusion-related IVIM parameters obtained by curve-fitting algorithm 3 may reflect VEGF expression.
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Affiliation(s)
- Youn Joo Lee
- Department of Radiology, Daejeon St. Mary's Hospital, Daejeon
| | - Sung Hun Kim
- Seoul St. Mary's Hospital, The Catholic University of Korea, Republic of Korea
| | - Bong Joo Kang
- Seoul St. Mary's Hospital, The Catholic University of Korea, Republic of Korea
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Usefulness of diffusion derived vessel density computed from a simplified IVIM imaging protocol: An experimental study with rat biliary duct blockage induced liver fibrosis. Magn Reson Imaging 2021; 84:115-123. [PMID: 34619291 DOI: 10.1016/j.mri.2021.09.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 08/23/2021] [Accepted: 09/30/2021] [Indexed: 01/10/2023]
Abstract
OBJECTIVES Liver vessel density can be evaluated by DDVD (diffusion derived vessel density): DDVD(b0b1) = Sb0/ROIarea0 - Sb1/ROIarea1, where Sb0 and Sb1 refer to the liver signal when b is 0 or 1 s/mm2. Sb1 and ROIarea1 may be replaced by other b-values. With a rat biliary duct ligation (BDL) model, this study assesses the usefulness of liver DDVD computed from a simplified IVIM imaging protocol using b = 25 and b = 50 to replace b = 1 s/mm2, alone and in combination with other IVIM parameters. METHODS Male Sprague-Dawley rats were used. The rat number was 5, 5, 5, and 3 respectively, for the timepoints of 7, 14, 21, 28 days post-BDL surgery. 12 rats had partial biliary duct recanalization performed after the rats had BDL for 7 days and then again followed-up for a mean of 14 days. Liver diffusion MRIs were acquired at 3.0 T with a b-value distribution of 0, 25, 50, 75, 100, 150, 300, 700, 1000 s/mm2. DDVDmean (control rats n = 6) was the mean of DDVD(b0b25) and DDVD(b0b50). IVIM fitting started from b = 0 s/mm2 with segmented fitting and a threshold b of 50 s/mm2 (n = 5 for control rats). Three 3-D spaces were constructed using a combination of the four diffusion parameters. RESULTS The control rats and BDL rats (n = 18) had a liver DDVDmean of 84.0 ± 26.2 and 44.7 ± 14.4 au/pixel (p < 0.001). All 3-D spaces totally separated healthy livers and all fibrotic livers (n = 30, BDL rats and recanalization rats). The mean relative distance between healthy liver cluster and fibrotic liver cluster was 0.331 for PF, Dslow, and Dfast; 0.381 for PF, Dfast, and DDVDmean; and 0.384 for PF, Dslow, and DDVDmean. CONCLUSION A combination of PF, Dslow, and Dfast allows total separation of healthy livers and fibrotic livers and the integration of DDVD improved the separation.
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Kaandorp MPT, Barbieri S, Klaassen R, van Laarhoven HWM, Crezee H, While PT, Nederveen AJ, Gurney‐Champion OJ. Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients. Magn Reson Med 2021; 86:2250-2265. [PMID: 34105184 PMCID: PMC8362093 DOI: 10.1002/mrm.28852] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 04/30/2021] [Accepted: 05/03/2021] [Indexed: 12/12/2022]
Abstract
PURPOSE Earlier work showed that IVIM-NETorig , an unsupervised physics-informed deep neural network, was faster and more accurate than other state-of-the-art intravoxel-incoherent motion (IVIM) fitting approaches to diffusion-weighted imaging (DWI). This study presents a substantially improved version, IVIM-NEToptim , and characterizes its superior performance in pancreatic cancer patients. METHOD In simulations (signal-to-noise ratio [SNR] = 20), the accuracy, independence, and consistency of IVIM-NET were evaluated for combinations of hyperparameters (fit S0, constraints, network architecture, number of hidden layers, dropout, batch normalization, learning rate), by calculating the normalized root-mean-square error (NRMSE), Spearman's ρ, and the coefficient of variation (CVNET ), respectively. The best performing network, IVIM-NEToptim was compared to least squares (LS) and a Bayesian approach at different SNRs. IVIM-NEToptim 's performance was evaluated in an independent dataset of 23 patients with pancreatic ductal adenocarcinoma. Fourteen of the patients received no treatment between two repeated scan sessions and nine received chemoradiotherapy between the repeated sessions. Intersession within-subject standard deviations (wSD) and treatment-induced changes were assessed. RESULTS In simulations (SNR = 20), IVIM-NEToptim outperformed IVIM-NETorig in accuracy (NRMSE(D) = 0.177 vs 0.196; NMRSE(f) = 0.220 vs 0.267; NMRSE(D*) = 0.386 vs 0.393), independence (ρ(D*, f) = 0.22 vs 0.74), and consistency (CVNET (D) = 0.013 vs 0.104; CVNET (f) = 0.020 vs 0.054; CVNET (D*) = 0.036 vs 0.110). IVIM-NEToptim showed superior performance to the LS and Bayesian approaches at SNRs < 50. In vivo, IVIM-NEToptim showed significantly less noisy parameter maps with lower wSD for D and f than the alternatives. In the treated cohort, IVIM-NEToptim detected the most individual patients with significant parameter changes compared to day-to-day variations. CONCLUSION IVIM-NEToptim is recommended for accurate, informative, and consistent IVIM fitting to DWI data.
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Affiliation(s)
- Misha P. T. Kaandorp
- Department of Radiology and Nuclear MedicineCancer Center Amsterdam, Amsterdam UMC, University of AmsterdamAmsterdamthe Netherlands
- Department of Radiology and Nuclear MedicineSt. Olav’s University HospitalTrondheimNorway
- Department of Circulation and Medical ImagingNTNU – Norwegian University of Science and TechnologyTrondheimNorway
| | | | - Remy Klaassen
- Department of Medical OncologyCancer Center Amsterdam, Amsterdam UMC, University of AmsterdamAmsterdamthe Netherlands
| | - Hanneke W. M. van Laarhoven
- Department of Medical OncologyCancer Center Amsterdam, Amsterdam UMC, University of AmsterdamAmsterdamthe Netherlands
| | - Hans Crezee
- Department of Radiology and Nuclear MedicineCancer Center Amsterdam, Amsterdam UMC, University of AmsterdamAmsterdamthe Netherlands
| | - Peter T. While
- Department of Radiology and Nuclear MedicineSt. Olav’s University HospitalTrondheimNorway
- Department of Circulation and Medical ImagingNTNU – Norwegian University of Science and TechnologyTrondheimNorway
| | - Aart J. Nederveen
- Department of Radiology and Nuclear MedicineCancer Center Amsterdam, Amsterdam UMC, University of AmsterdamAmsterdamthe Netherlands
| | - Oliver J. Gurney‐Champion
- Department of Radiology and Nuclear MedicineCancer Center Amsterdam, Amsterdam UMC, University of AmsterdamAmsterdamthe Netherlands
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Führes T, Riexinger AJ, Loh M, Martin J, Wetscherek A, Kuder TA, Uder M, Hensel B, Laun FB. Echo time dependence of biexponential and triexponential intravoxel incoherent motion parameters in the liver. Magn Reson Med 2021; 87:859-871. [PMID: 34453445 DOI: 10.1002/mrm.28996] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 08/12/2021] [Accepted: 08/12/2021] [Indexed: 11/09/2022]
Abstract
PURPOSE Intravoxel incoherent motion (IVIM) studies are performed with different acquisition protocols. Comparing them requires knowledge of echo time (TE) dependencies. The TE-dependence of the biexponential perfusion fraction f is well-documented, unlike that of its triexponential counterparts f1 and f2 and the biexponential and triexponential pseudodiffusion coefficients D* , D 1 ∗ , and D 2 ∗ . The purpose was to investigate the TE-dependence of these parameters and to check whether the triexponential pseudodiffusion compartments are associated with arterial and venous blood. METHODS Fifteen healthy volunteers (19-58 y; mean: 24.7 y) underwent diffusion-weighted imaging of the abdomen with 24 b-values (0.2-800 s/mm2 ) at TEs of 45, 60, 75, and 90 ms. Regions of interest (ROIs) were manually drawn in the liver. One set of bi- and triexponential IVIM parameters per volunteer and TE was determined. The TE-dependence was assessed with the Kruskal-Wallis test. RESULTS TE-dependence was observed for f (P < .001), f1 (P = .001), and f2 (P < .001). Their median values at the four measured TEs were: f: 0.198/0.240/0.274/0.359, f1 : 0.113/0.139/0.146/0.205, f2 : 0.115/0.155/0.182/0.194. D, D* , D 1 ∗ , and D 2 ∗ showed no significant TE-dependence. Their values were: diffusion coefficient D (10-4 mm2 /s): 9.45/9.63/9.75/9.41, biexponential D* (10-2 mm2 /s): 5.26/5.52/6.13/5.82, triexponential D 1 ∗ (10-2 mm2 /s): 1.73/2.91/2.25/2.51, triexponential D 2 ∗ (mm2 /s): 0.478/1.385/0.616/0.846. CONCLUSION f1 and f2 show similar TE-dependence as f, ie, increase with rising TE; an effect that must be accounted for when comparing different studies. The diffusion and pseudodiffusion coefficients might be compared without TE correction. Because of the similar TE-dependence of f1 and f2 , the triexponential pseudodiffusion compartments are most probably not associated to venous and arterial blood.
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Affiliation(s)
- Tobit Führes
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Andreas Julian Riexinger
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Martin Loh
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | | | - Andreas Wetscherek
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Tristan Anselm Kuder
- Department of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Uder
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Bernhard Hensel
- Center for Medical Physics and Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Frederik Bernd Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
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Periquito JS, Gladytz T, Millward JM, Delgado PR, Cantow K, Grosenick D, Hummel L, Anger A, Zhao K, Seeliger E, Pohlmann A, Waiczies S, Niendorf T. Continuous diffusion spectrum computation for diffusion-weighted magnetic resonance imaging of the kidney tubule system. Quant Imaging Med Surg 2021; 11:3098-3119. [PMID: 34249638 DOI: 10.21037/qims-20-1360] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 03/08/2021] [Indexed: 12/24/2022]
Abstract
Background The use of rigid multi-exponential models (with a priori predefined numbers of components) is common practice for diffusion-weighted MRI (DWI) analysis of the kidney. This approach may not accurately reflect renal microstructure, as the data are forced to conform to the a priori assumptions of simplified models. This work examines the feasibility of less constrained, data-driven non-negative least squares (NNLS) continuum modelling for DWI of the kidney tubule system in simulations that include emulations of pathophysiological conditions. Methods Non-linear least squares (LS) fitting was used as reference for the simulations. For performance assessment, a threshold of 5% or 10% for the mean absolute percentage error (MAPE) of NNLS and LS results was used. As ground truth, a tri-exponential model using defined volume fractions and diffusion coefficients for each renal compartment (tubule system: Dtubules , ftubules ; renal tissue: Dtissue , ftissue ; renal blood: Dblood , fblood ;) was applied. The impact of: (I) signal-to-noise ratio (SNR) =40-1,000, (II) number of b-values (n=10-50), (III) diffusion weighting (b-rangesmall =0-800 up to b-rangelarge =0-2,180 s/mm2), and (IV) fixation of the diffusion coefficients Dtissue and Dblood was examined. NNLS was evaluated for baseline and pathophysiological conditions, namely increased tubular volume fraction (ITV) and renal fibrosis (10%: grade I, mild) and 30% (grade II, moderate). Results NNLS showed the same high degree of reliability as the non-linear LS. MAPE of the tubular volume fraction (ftubules ) decreased with increasing SNR. Increasing the number of b-values was beneficial for ftubules precision. Using the b-rangelarge led to a decrease in MAPE ftubules compared to b-rangesmall. The use of a medium b-value range of b=0-1,380 s/mm2 improved ftubules precision, and further bmax increases beyond this range yielded diminishing improvements. Fixing Dblood and Dtissue significantly reduced MAPE ftubules and provided near perfect distinction between baseline and ITV conditions. Without constraining the number of renal compartments in advance, NNLS was able to detect the (fourth) fibrotic compartment, to differentiate it from the other three diffusion components, and to distinguish between 10% vs. 30% fibrosis. Conclusions This work demonstrates the feasibility of NNLS modelling for DWI of the kidney tubule system and shows its potential for examining diffusion compartments associated with renal pathophysiology including ITV fraction and different degrees of fibrosis.
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Affiliation(s)
- Joāo S Periquito
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.,Institute of Physiology, Charité - Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany.,Experimental and Clinical Research Center, a Joint Cooperation between the Charité Medical Faculty and the Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Thomas Gladytz
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Jason M Millward
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Paula Ramos Delgado
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.,Experimental and Clinical Research Center, a Joint Cooperation between the Charité Medical Faculty and the Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Kathleen Cantow
- Institute of Physiology, Charité - Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
| | - Dirk Grosenick
- Physikalisch-Technische Bundesanstalt (PTB), Berlin, Germany
| | - Luis Hummel
- Institute of Physiology, Charité - Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
| | - Ariane Anger
- Institute of Physiology, Charité - Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
| | - Kaixuan Zhao
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Erdmann Seeliger
- Institute of Physiology, Charité - Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
| | - Andreas Pohlmann
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Sonia Waiczies
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Thoralf Niendorf
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.,Experimental and Clinical Research Center, a Joint Cooperation between the Charité Medical Faculty and the Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
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Spinner GR, Federau C, Kozerke S. Bayesian inference using hierarchical and spatial priors for intravoxel incoherent motion MR imaging in the brain: Analysis of cancer and acute stroke. Med Image Anal 2021; 73:102144. [PMID: 34261009 DOI: 10.1016/j.media.2021.102144] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Revised: 06/12/2021] [Accepted: 06/21/2021] [Indexed: 12/24/2022]
Abstract
The intravoxel incoherent motion (IVIM) model allows to map diffusion (D) and perfusion-related parameters (F and D*). Parameter estimation is, however, error-prone due to the non-linearity of the signal model, the limited signal-to-noise ratio (SNR) and the small volume fraction of perfusion in the in-vivo brain. In the present work, the performance of Bayesian inference was examined in the presence of brain pathologies characterized by hypo- and hyperperfusion. In particular, a hierarchical and a spatial prior were combined. Performance was compared relative to conventional segmented least squares regression, hierarchical prior only (non-segmented and segmented data likelihoods) and a deep learning approach. Realistic numerical brain IVIM simulations were conducted to assess errors relative to ground truth. In-vivo, data of 11 central nervous system cancer patients and 9 patients with acute stroke were acquired. The proposed method yielded reduced error in simulations for both the cancer and acute stroke scenarios compared to other methods across the whole investigated SNR range. The contrast-to-noise ratio of the proposed method was better or on par compared to the other techniques in-vivo. The proposed Bayesian approach hence improves IVIM parameter estimation in brain cancer and acute stroke.
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Affiliation(s)
- Georg Ralph Spinner
- Institute for Biomedical Engineering, University and ETH Zurich, Gloriastrasse 35, Zurich 8092, Switzerland
| | - Christian Federau
- Institute for Biomedical Engineering, University and ETH Zurich, Gloriastrasse 35, Zurich 8092, Switzerland
| | - Sebastian Kozerke
- Institute for Biomedical Engineering, University and ETH Zurich, Gloriastrasse 35, Zurich 8092, Switzerland.
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Troelstra MA, Witjes JJ, van Dijk AM, Mak AL, Gurney-Champion O, Runge JH, Zwirs D, Stols-Gonçalves D, Zwinderman AH, Ten Wolde M, Monajemi H, Ramsoekh S, Sinkus R, van Delden OM, Beuers UH, Verheij J, Nieuwdorp M, Nederveen AJ, Holleboom AG. Assessment of Imaging Modalities Against Liver Biopsy in Nonalcoholic Fatty Liver Disease: The Amsterdam NAFLD-NASH Cohort. J Magn Reson Imaging 2021; 54:1937-1949. [PMID: 33991378 PMCID: PMC9290703 DOI: 10.1002/jmri.27703] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 05/04/2021] [Accepted: 05/06/2021] [Indexed: 12/17/2022] Open
Abstract
Background Noninvasive diagnostic methods are urgently required in disease stratification and monitoring in nonalcoholic fatty liver disease (NAFLD). Multiparametric magnetic resonance imaging (MRI) is a promising technique to assess hepatic steatosis, inflammation, and fibrosis, potentially enabling noninvasive identification of individuals with active and advanced stages of NAFLD. Purpose To examine the diagnostic performance of multiparametric MRI for the assessment of disease severity along the NAFLD disease spectrum with comparison to histological scores. Study Type Prospective, cohort. Population Thirty‐seven patients with NAFLD. Field Strength/Sequence Multiparametric MRI at 3.0 T consisted of magnetic resonance (MR) spectroscopy (MRS) with multi‐echo stimulated‐echo acquisition mode, magnitude‐based and three‐point Dixon using a two‐dimensional multi‐echo gradient echo, MR elastography (MRE) using a generalized multishot gradient‐recalled echo sequence and intravoxel incoherent motion (IVIM) using a multislice diffusion weighted single‐shot echo‐planar sequence. Assessment Histological steatosis grades were compared to proton density fat fraction measured by MRS (PDFFMRS), magnitude‐based MRI (PDFFMRI‐M), and three‐point Dixon (PDFFDixon), as well as FibroScan® controlled attenuation parameter (CAP). Fibrosis and disease activity were compared to IVIM and MRE. FibroScan® liver stiffness measurements were compared to fibrosis levels. Diagnostic performance of all imaging parameters was determined for distinction between simple steatosis and nonalcoholic steatohepatitis (NASH). Statistical Tests Spearman's rank test, Kruskal–Wallis test, Dunn's post‐hoc test with Holm‐Bonferroni P‐value adjustment, receiver operating characteristic curve analysis. A P‐value <0.05 was considered statistically significant. Results Histological steatosis grade correlated significantly with PDFFMRS (rs = 0.66, P < 0.001), PDFFMRI‐M (rs = 0.68, P < 0.001), and PDFFDixon (rs = 0.67, P < 0.001), whereas no correlation was found with CAP. MRE and IVIM diffusion and perfusion significantly correlated with disease activity (rs = 0.55, P < 0.001, rs = −0.40, P = 0.016, rs = −0.37, P = 0.027, respectively) and fibrosis (rs = 0.55, P < 0.001, rs = −0.46, P = 0.0051; rs = −0.53, P < 0.001, respectively). MRE and IVIM diffusion had the highest area‐under‐the‐curve for distinction between simple steatosis and NASH (0.79 and 0.73, respectively). Data Conclusion Multiparametric MRI is a promising method for noninvasive, accurate, and sensitive distinction between simple hepatic steatosis and NASH, as well as for the assessment of steatosis and fibrosis severity. Level of Evidence 2 Technical Efficacy 2
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Affiliation(s)
- Marian A Troelstra
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centres, Amsterdam, The Netherlands
| | - Julia J Witjes
- Department of Internal and Vascular Medicine, Amsterdam University Medical Centres, Amsterdam, The Netherlands
| | - Anne-Marieke van Dijk
- Department of Internal and Vascular Medicine, Amsterdam University Medical Centres, Amsterdam, The Netherlands
| | - Anne L Mak
- Department of Internal and Vascular Medicine, Amsterdam University Medical Centres, Amsterdam, The Netherlands
| | - Oliver Gurney-Champion
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centres, Amsterdam, The Netherlands
| | - Jurgen H Runge
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centres, Amsterdam, The Netherlands
| | - Diona Zwirs
- Department of Internal and Vascular Medicine, Amsterdam University Medical Centres, Amsterdam, The Netherlands
| | - Daniela Stols-Gonçalves
- Department of Internal and Vascular Medicine, Amsterdam University Medical Centres, Amsterdam, The Netherlands
| | - Aelko H Zwinderman
- Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam University Medical Centres, Amsterdam, The Netherlands
| | - Marije Ten Wolde
- Department of Internal Medicine, Flevoziekenhuis, Almere, The Netherlands
| | - Houshang Monajemi
- Department of Internal Medicine, Rijnstate Ziekenhuis, Arnhem, The Netherlands
| | - Sandjai Ramsoekh
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Centres, Amsterdam, The Netherlands
| | - Ralph Sinkus
- Inserm U1148, LVTS, University Paris Diderot, University Paris 13, Paris, France.,School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Otto M van Delden
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centres, Amsterdam, The Netherlands
| | - Ulrich H Beuers
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Centres, Amsterdam, The Netherlands
| | - Joanne Verheij
- Department of Pathology, Amsterdam University Medical Centres, Amsterdam, The Netherlands
| | - Max Nieuwdorp
- Department of Internal and Vascular Medicine, Amsterdam University Medical Centres, Amsterdam, The Netherlands
| | - Aart J Nederveen
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centres, Amsterdam, The Netherlands
| | - Adriaan G Holleboom
- Department of Internal and Vascular Medicine, Amsterdam University Medical Centres, Amsterdam, The Netherlands
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Liu N, Yang X, Lei L, Pan K, Liu Q, Huang X. Intravoxel Incoherent Motion Model in Differentiating the Pathological Grades of Esophageal Carcinoma: Comparison of Mono-Exponential and Bi-Exponential Fit Model. Front Oncol 2021; 11:625891. [PMID: 33912449 PMCID: PMC8071935 DOI: 10.3389/fonc.2021.625891] [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] [Received: 11/04/2020] [Accepted: 03/15/2021] [Indexed: 02/03/2023] Open
Abstract
PURPOSE To compare the diagnostic efficiency of the mono-exponential model and bi-exponential model deriving from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in differentiating the pathological grade of esophageal squamous cell carcinoma (ESCC). METHODS Fifty-four patients with ESCC were divided into three groups of poorly-differentiated (PD), moderately-differentiated (MD), and well-differentiated (WD), and underwent the IVIM-DWI scan. Mono-exponential (Dmono, D*mono, and fmono) and bi-exponential fit parameters (Dbi, D*bi, and fbi) were calculated using the IVIM data for the tumors. Mean parameter values of three groups were compared using a one-way ANOVA followed by post hoc tests. The receiver operating characteristic curve was drawn for differentiating pathological grade of ESCC. Correlations between pathological grades and IVIM parameters were analyzed. RESULTS There were significant differences in fmono and fbi among the PD, MD and WD ESCC groups (all p<0.05). The fmono were 0.32 ± 0.07, 0.23 ± 0.08, and 0.16 ± 0.05, respectively, and the fbi were 0.35 ± 0.08, 0.26 ± 0.10, and 0.18 ± 0.07, respectively. There was a significant difference in the Dmono between the WD and the PD group (1.48 ± 0.51* 10-3 mm2/s versus 1.05 ± 0.44*10-3 mm2/s, p<0.05), but there was no significant difference between the WD and MD groups, MD and PD groups (all p>0.05). The D*mono, Dbi, and D*bi showed no significant difference among the three groups (all p>0.05). The area under the curve (AUC) of Dmono, fmono and fbi in differentiating WD from PD ESCC were 0.764, 0.961 and 0.932, and the sensitivity and specificity were 92.9% and 60%, 92.9% and 90%, 85.7% and 100%, respectively. The AUC of fmono and fbi in differentiating MD from PD ESCC were 0.839 and 0.757, and the sensitivity and specificity were 78.6% and 80%, 85.7% and 70%, respectively. The AUC of fmono and fbi in differentiating MD from WD ESCC were 0.746 and 0.740, and the sensitivity and specificity were 65% and 85%, 80% and 60%, respectively. The pathologically differentiated grade was correlated with all IVIM parameters (all p<0.05). CONCLUSIONS The mono-exponential IVIM model is superior to the bi-exponential IVIM model in differentiating pathological grades of ESCC, which may be a promising imaging method to predict pathological grades of ESCC.
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Affiliation(s)
- Nian Liu
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xiongxiong Yang
- Department of Radiology, Nanchong Hospital of Traditional Chinese Medicine, Nanchong, China
| | - Lixing Lei
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Ke Pan
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Qianqian Liu
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xiaohua Huang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
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Chevallier O, Wáng YXJ, Guillen K, Pellegrinelli J, Cercueil JP, Loffroy R. Evidence of Tri-Exponential Decay for Liver Intravoxel Incoherent Motion MRI: A Review of Published Results and Limitations. Diagnostics (Basel) 2021; 11:diagnostics11020379. [PMID: 33672277 PMCID: PMC7926368 DOI: 10.3390/diagnostics11020379] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 02/14/2021] [Accepted: 02/20/2021] [Indexed: 12/11/2022] Open
Abstract
Diffusion weighted imaging (DWI) and intravoxel incoherent motion (IVIM) have been explored to assess liver tumors and diffused liver diseases. IVIM reflects the microscopic translational motions that occur in voxels in magnetic resonance (MR) DWI. In biologic tissues, molecular diffusion of water and microcirculation of blood in the capillary network can be assessed using IVIM DWI. The most commonly applied model to describe the DWI signal is a bi-exponential model, with a slow compartment of diffusion linked to pure molecular diffusion (represented by the coefficient Dslow), and a fast compartment of diffusion, related to microperfusion (represented by the coefficient Dfast). However, high variance in Dfast estimates has been consistently shown in literature for liver IVIM, restricting its application in clinical practice. This variation could be explained by the presence of another very fast compartment of diffusion in the liver. Therefore, a tri-exponential model would be more suitable to describe the DWI signal. This article reviews the published evidence of the existence of this additional very fast diffusion compartment and discusses the performance and limitations of the tri-exponential model for liver IVIM in current clinical settings.
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Affiliation(s)
- Olivier Chevallier
- Image-Guided Therapy Center, Department of Vascular and Interventional Radiology, François-Mitterrand University Hospital, 14 Rue Paul Gaffarel, BP 77908, 21079 Dijon, France; (O.C.); (K.G.); (J.P.); (J.-P.C.)
| | - Yì Xiáng J. Wáng
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, New Territories, Hong Kong, China;
| | - Kévin Guillen
- Image-Guided Therapy Center, Department of Vascular and Interventional Radiology, François-Mitterrand University Hospital, 14 Rue Paul Gaffarel, BP 77908, 21079 Dijon, France; (O.C.); (K.G.); (J.P.); (J.-P.C.)
| | - Julie Pellegrinelli
- Image-Guided Therapy Center, Department of Vascular and Interventional Radiology, François-Mitterrand University Hospital, 14 Rue Paul Gaffarel, BP 77908, 21079 Dijon, France; (O.C.); (K.G.); (J.P.); (J.-P.C.)
| | - Jean-Pierre Cercueil
- Image-Guided Therapy Center, Department of Vascular and Interventional Radiology, François-Mitterrand University Hospital, 14 Rue Paul Gaffarel, BP 77908, 21079 Dijon, France; (O.C.); (K.G.); (J.P.); (J.-P.C.)
| | - Romaric Loffroy
- Image-Guided Therapy Center, Department of Vascular and Interventional Radiology, François-Mitterrand University Hospital, 14 Rue Paul Gaffarel, BP 77908, 21079 Dijon, France; (O.C.); (K.G.); (J.P.); (J.-P.C.)
- Correspondence: ; Tel.: +33-380-293-677
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Lee W, Kim B, Park H. Quantification of intravoxel incoherent motion with optimized b-values using deep neural network. Magn Reson Med 2021; 86:230-244. [PMID: 33594783 DOI: 10.1002/mrm.28708] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 01/12/2021] [Accepted: 01/13/2021] [Indexed: 12/17/2022]
Abstract
PURPOSE To develop a framework for quantifying intravoxel incoherent motion (IVIM) parameters, where a neural network for quantification and b-values for diffusion-weighted imaging are simultaneously optimized. METHOD A deep neural network (DNN) method is proposed for accurate quantification of IVIM parameters from multiple diffusion-weighted images. In addition, optimal b-values are selected to acquire the multiple diffusion-weighted images. The proposed framework consists of an MRI signal generation part and an IVIM parameter quantification part. Monte-Carlo (MC) simulations were performed to evaluate the accuracy of the IVIM parameter quantification and the efficacy of b-value optimization. In order to analyze the effect of noise on the optimized b-values, simulations were performed with five different noise levels. For in vivo data, diffusion images were acquired with the b-values from four b-values selection methods for five healthy volunteers at 3T MRI system. RESULTS Experiment results showed that both the optimization of b-values and the training of DNN were simultaneously performed to quantify IVIM parameters. We found that the accuracies of the perfusion coefficient (Dp ) and perfusion fraction (f) were more sensitive to b-values than the diffusion coefficient (D) was. Furthermore, when the noise level changed, the optimized b-values also changed. Therefore, noise level has to be considered when optimizing b-values for IVIM quantification. CONCLUSION The proposed scheme can simultaneously optimize b-values and train DNN to minimize quantification errors of IVIM parameters. The trained DNN can quantify IVIM parameters from the diffusion-weighted images obtained with the optimized b-values.
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Affiliation(s)
- Wonil Lee
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Byungjai Kim
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - HyunWook Park
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
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Koopman T, Martens R, Gurney‐Champion OJ, Yaqub M, Lavini C, de Graaf P, Castelijns J, Boellaard R, Marcus JT. Repeatability of IVIM biomarkers from diffusion-weighted MRI in head and neck: Bayesian probability versus neural network. Magn Reson Med 2021; 85:3394-3402. [PMID: 33501657 PMCID: PMC7986193 DOI: 10.1002/mrm.28671] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 12/14/2020] [Accepted: 12/17/2020] [Indexed: 12/16/2022]
Abstract
Purpose The intravoxel incoherent motion (IVIM) model for DWI might provide useful biomarkers for disease management in head and neck cancer. This study compared the repeatability of three IVIM fitting methods to the conventional nonlinear least‐squares regression: Bayesian probability estimation, a recently introduced neural network approach, IVIM‐NET, and a version of the neural network modified to increase consistency, IVIM‐NETmod. Methods Ten healthy volunteers underwent two imaging sessions of the neck, two weeks apart, with two DWI acquisitions per session. Model parameters (ADC, diffusion coefficient Dt, perfusion fraction fp, and pseudo‐diffusion coefficient Dp) from each fit method were determined in the tonsils and in the pterygoid muscles. Within‐subject coefficients of variation (wCV) were calculated to assess repeatability. Training of the neural network was repeated 100 times with random initialization to investigate consistency, quantified by the coefficient of variance. Results The Bayesian and neural network approaches outperformed nonlinear regression in terms of wCV. Intersession wCV of Dt in the tonsils was 23.4% for nonlinear regression, 9.7% for Bayesian estimation, 9.4% for IVIM‐NET, and 11.2% for IVIM‐NETmod. However, results from repeated training of the neural network on the same data set showed differences in parameter estimates: The coefficient of variances over the 100 repetitions for IVIM‐NET were 15% for both Dt and fp, and 94% for Dp; for IVIM‐NETmod, these values improved to 5%, 9%, and 62%, respectively. Conclusion Repeatabilities from the Bayesian and neural network approaches are superior to that of nonlinear regression for estimating IVIM parameters in the head and neck.
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Affiliation(s)
- Thomas Koopman
- Department of Radiology and Nuclear MedicineAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamthe Netherlands
| | - Roland Martens
- Department of Radiology and Nuclear MedicineAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamthe Netherlands
| | | | - Maqsood Yaqub
- Department of Radiology and Nuclear MedicineAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamthe Netherlands
| | - Cristina Lavini
- Department of RadiologyAmsterdam UMC, University of AmsterdamAmsterdamthe Netherlands
| | - Pim de Graaf
- Department of Radiology and Nuclear MedicineAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamthe Netherlands
| | - Jonas Castelijns
- Department of Radiology and Nuclear MedicineAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamthe Netherlands
- Department of Radiologythe Netherlands Cancer Institute–Antoni van LeeuwenhoekAmsterdamthe Netherlands
| | - Ronald Boellaard
- Department of Radiology and Nuclear MedicineAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamthe Netherlands
- Department of Nuclear Medicine and Molecular ImagingUniversity Medical Center GroningenGroningenthe Netherlands
| | - J. Tim Marcus
- Department of Radiology and Nuclear MedicineAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamthe Netherlands
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Jerome NP, Periquito JS. Analysis of Renal Diffusion-Weighted Imaging (DWI) Using Apparent Diffusion Coefficient (ADC) and Intravoxel Incoherent Motion (IVIM) Models. Methods Mol Biol 2021; 2216:611-635. [PMID: 33476027 DOI: 10.1007/978-1-0716-0978-1_37] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2023]
Abstract
Analysis of renal diffusion-weighted imaging (DWI) data to derive markers of tissue properties requires careful consideration of the type, extent, and limitations of the acquired data. Alongside data quality and general suitability for quantitative analysis, choice of diffusion model, fitting algorithm, and processing steps can have consequences for the precision, accuracy, and reliability of derived diffusion parameters. Here we introduce and discuss important steps for diffusion-weighted image processing, and in particular give example analysis protocols and pseudo-code for analysis using the apparent diffusion coefficient (ADC) and intravoxel incoherent motion (IVIM) models. Following an overview of general principles, we provide details of optional steps, and steps for validation of results. Illustrative examples are provided, together with extensive notes discussing wider context of individual steps, and notes on potential pitfalls.This publication is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This analysis protocol chapter is complemented by two separate chapters describing the basic concepts and experimental procedure.
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Affiliation(s)
- Neil Peter Jerome
- Institute for Circulation and Diagnostic Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
- Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway.
| | - João S Periquito
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine (MDC) in the Helmholtz Association, Berlin, Germany
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Marage L, Gambarota G, Lasbleiz J, Lederlin M, Saint-Jalmes H. Confounding factors in multi-parametric q-MRI protocol: A study of bone marrow biomarkers at 1.5 T. Magn Reson Imaging 2020; 74:96-104. [PMID: 32858181 DOI: 10.1016/j.mri.2020.08.011] [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: 12/13/2019] [Revised: 07/23/2020] [Accepted: 08/20/2020] [Indexed: 10/23/2022]
Abstract
OBJECT The MRI tissue characterization of vertebral bone marrow includes the measurement of proton density fat fraction (PDFF), T1 and T2* relaxation times of the water and fat components (T1W, T1F, T2*W, T2*F), IVIM diffusion D, perfusion fraction f and pseudo-diffusion coefficient D*. However, the measurement of these vertebral bone marrow biomarkers (VBMBs) is affected with several confounding factors. In the current study, we investigated these confounding factors including the regional variation taking the example of variation between the anterior and posterior area in lumbar vertebrae, B1 inhomogeneity and the effect of fat suppression on f. MATERIALS AND METHODS A fat suppressed diffusion-weighted sequence and two 3D gradient multi-echo sequences were used for the measurements of the seven VBMBs. A turbo flash B1 map sequence was used to estimate B1 inhomogeneities and thus, to correct flip angle for T1 quantification. We introduced a correction to perfusion fraction f measured with fat suppression, namely fPDFF. RESULTS A significant difference in the values of PDFF, f and fPDFF, T1F, T2*W and D was observed between the anterior and posterior region. Although, little variations of flip angle were observed in this anterior-posterior direction in one vertebra but larger variations were observed in head-feet direction from L1 to L5 vertebrae. DISCUSSION The regional difference in PDFF, fPDFF and T2*W can be ascribed to differences in the trabecular bone density and vascular network within vertebrae. The regional variation of VBMBs shows that care should be taken in reproducing the same region-of-interest location along a longitudinal study. The same attention should be taken while measuring f in fatty environment, and measuring T1. Furthermore, the MRI-protocol presented here allows for measurements of seven VBMBs in less than 6 min and is of interest for longitudinal studies of bone marrow diseases.
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Affiliation(s)
- Louis Marage
- Univ Rennes, CHU Rennes, CLCC Eugène Marquis, Inserm, LTSI - UMR 1099, F-35000 Rennes, France.
| | - Giulio Gambarota
- Univ Rennes, CHU Rennes, CLCC Eugène Marquis, Inserm, LTSI - UMR 1099, F-35000 Rennes, France
| | - Jeremy Lasbleiz
- Univ Rennes, CHU Rennes, CLCC Eugène Marquis, Inserm, LTSI - UMR 1099, F-35000 Rennes, France
| | - Mathieu Lederlin
- Univ Rennes, CHU Rennes, CLCC Eugène Marquis, Inserm, LTSI - UMR 1099, F-35000 Rennes, France
| | - Hervé Saint-Jalmes
- Univ Rennes, CHU Rennes, CLCC Eugène Marquis, Inserm, LTSI - UMR 1099, F-35000 Rennes, France
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Papp D, Breda S, Oei E, Poot D, Kotek G, Hernandez-Tamames J. Fractional order vs. exponential fitting in UTE MR imaging of the patellar tendon. Magn Reson Imaging 2020; 70:91-97. [DOI: 10.1016/j.mri.2020.04.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 03/09/2020] [Accepted: 04/11/2020] [Indexed: 01/18/2023]
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Paulson ES, Ahunbay E, Chen X, Mickevicius NJ, Chen GP, Schultz C, Erickson B, Straza M, Hall WA, Li XA. 4D-MRI driven MR-guided online adaptive radiotherapy for abdominal stereotactic body radiation therapy on a high field MR-Linac: Implementation and initial clinical experience. Clin Transl Radiat Oncol 2020; 23:72-79. [PMID: 32490218 PMCID: PMC7256110 DOI: 10.1016/j.ctro.2020.05.002] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 04/30/2020] [Accepted: 05/03/2020] [Indexed: 12/21/2022] Open
Abstract
This is the first clinical use of 4D-MRI in an online radiation therapy workflow. Eleven patients received SBRT on an Elekta MR-Linac using ATP and ATS workflows. A parallel contour editing approach was successfully utilized in the ATS workflow.
Background and purpose In this report, we describe our implementation and initial clinical experience using 4D-MRI driven MR-guided online adaptive radiotherapy (MRgOART) for abdominal stereotactic body radiotherapy (SBRT) on the Elekta Unity MR-Linac. Materials and methods Eleven patients with abdominal malignancies were treated with free-breathing SBRT in three to five fractions on a 1.5 T MR-Linac. Online adaptive plans were generated using Adapt-To-Position (ATP) or Adapt-To-Shape (ATS) workflows based on motion averaged or mid-position images derived from a pre-beam 4D-MRI. A high performance server positioned on the local MR-Linac machine network was utilized for 4D-MR image reconstruction. A parallel contour editing approach was employed in the ATS workflow. Intravoxel incoherent motion (IVIM) and T2 mapping sequences were acquired during adaptive planning in both ATP and ATS workflows for treatment response monitoring. Adaptive plans were delivered under real-time cine image motion monitoring. Results The shortest 4D-MRI time-to-image was the motion averaged image, followed by mid position and respiratory binned images. In this cohert of patients, 50% of treatments utilized the ATS workflow; the remaining treatments utilized the ATP workflow. Mid-position images were utilized as daily planning images for two of the eleven patients. The mean daily adaptive plan secondary dose calculation and ArcCheck 3D Gamma passing rates were 97.5% (92.1–100.0%) and 99.3% (96.2–100.0%), respectively. The median overall treatment times for abdominal SBRT was 46 and 62 min for ATP and ATS workflows, respectively. Conclusion We have successfully implemented and utilized a 4D-MRI driven MRgOART process with ATP and ATS workflows for free-breathing abdominal SBRT on a 1.5 T Elekta Unity MR-Linac.
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Affiliation(s)
- Eric S Paulson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States.,Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, United States.,Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Ergun Ahunbay
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Xinfeng Chen
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Nikolai J Mickevicius
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Guang-Pei Chen
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Christopher Schultz
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Beth Erickson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Michael Straza
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - William A Hall
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - X Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
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Huang HM. Reliable estimation of brain intravoxel incoherent motion parameters using denoised diffusion-weighted MRI. NMR IN BIOMEDICINE 2020; 33:e4249. [PMID: 31922646 DOI: 10.1002/nbm.4249] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 12/03/2019] [Accepted: 12/04/2019] [Indexed: 06/10/2023]
Abstract
In this study, we evaluate whether diffusion-weighted magnetic resonance imaging (DW-MRI) data after denoising can provide a reliable estimation of brain intravoxel incoherent motion (IVIM) perfusion parameters. Brain DW-MRI was performed in five healthy volunteers on a 3 T clinical scanner with 12 different b-values ranging from 0 to 1000 s/mm2 . DW-MRI data denoised using the proposed method were fitted with a biexponential model to extract perfusion fraction (PF), diffusion coefficient (D) and pseudo-diffusion coefficient (D*). To further evaluate the accuracy and precision of parameter estimation, IVIM parametric images obtained from one volunteer were used to resimulate the DW-MRI data using the biexponential model with the same b-values. Rician noise was added to generate DW-MRI data with various signal-to-noise ratio (SNR) levels. The experimental results showed that the denoised DW-MRI data yielded precise estimates for all IVIM parameters. We also found that IVIM parameters were significantly different between gray matter and white matter (P < 0.05), except for D* (P = 0.6). Our simulation results show that the proposed image denoising method displays good performance in estimating IVIM parameters (both bias and coefficient of variation were <12% for PF, D and D*) in the presence of different levels of simulated Rician noise (SNRb=0 = 20-40). Simulations and experiments show that brain DW-MRI data after denoising can provide a reliable estimation of IVIM parameters.
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Affiliation(s)
- Hsuan-Ming Huang
- Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, Taipei City, Taiwan
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39
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He R, Ding Y, Mohamed ASR, Ng SP, Ger RB, Elhalawani H, Elgohari BA, Young KH, Hutcheson KA, Fuller CD, Lai SY. Simultaneously spatial and temporal Higher-Order Total Variations for noise suppression and motion reduction in DCE and IVIM. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2020; 11313. [PMID: 32753776 DOI: 10.1117/12.2549625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
In many applications based on kinetic evaluation analysis and model fitting, quantitative mapping retrieved on data series from modalites such as MRI is completed on a voxel-by-voxel basis, where motion and low signal to noise ratio (SNR) would considerably degenerate the reliability of estimations. The coherence of image series in space and time can be used as prior knowledge to mitigate this occurrence. In this study, spatial and temporal higher-order total variations (HOTVs) are applied on a data series of MRI signal (e.g. dynamic contrast-enhanced (DCE) MRI and intravoxel incoherent motion (IVIM) MRI) to exploit the coherence of signal in space and time to minimize the variabilities caused by motion as well as improving quality of images with low SNR while retaining the physical details of original data properly. Simultaneously applying spatial and temporal HOTVs on images is non-trivial in implementation since it is a non-smooth optimization problem with multiple regularizers. Therefore, we use the proximal gradient method as well as a primal-dual split proximal mechanism to address the problem properly. In addition to increase the reliability of quantitative parametric map estimations, this preprocessing procedure can be included into many existing map estimation algorithms and pipelines effortlessly. We demonstrate our method on the parametric maps estimation for DCE MRI and IVIM MRI.
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Affiliation(s)
- Renjie He
- University of Texas, MD Anderson Cancer Cente, Houston, TX 77030
| | - Yao Ding
- University of Texas, MD Anderson Cancer Cente, Houston, TX 77030
| | | | - Sweet Ping Ng
- University of Texas, MD Anderson Cancer Cente, Houston, TX 77030
| | - Rachel B Ger
- University of Texas, MD Anderson Cancer Cente, Houston, TX 77030
| | | | - Baher A Elgohari
- University of Texas, MD Anderson Cancer Cente, Houston, TX 77030
| | - Kristina H Young
- Earle A. Chiles Research Institute, Providence Cancer Center, Portland, OR 97213.,Radiation Oncology Division, The Oregon Clinic, Portland, OR 97213
| | | | - Clifton D Fuller
- University of Texas, MD Anderson Cancer Cente, Houston, TX 77030
| | - Stephen Y Lai
- University of Texas, MD Anderson Cancer Cente, Houston, TX 77030
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Lanzarone E, Mastropietro A, Scalco E, Vidiri A, Rizzo G. A novel bayesian approach with conditional autoregressive specification for intravoxel incoherent motion diffusion-weighted MRI. NMR IN BIOMEDICINE 2020; 33:e4201. [PMID: 31884712 DOI: 10.1002/nbm.4201] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 08/28/2019] [Accepted: 09/13/2019] [Indexed: 06/10/2023]
Abstract
The Intra-Voxel Incoherent Motion (IVIM) model is largely adopted to estimate slow and fast diffusion coefficients of water molecules in biological tissues, which are used in cancer applications. The most reported fitting approach is a voxel-wise segmented non-linear least square, whereas Bayesian approaches with a direct fit, also considering spatial regularization, were proposed too. In this work a novel segmented Bayesian method was proposed, also in combination with a spatial regularization through a Conditional Autoregressive (CAR) prior specification. The two segmented Bayesian approaches, with and without CAR specification, were compared with two standard least-square and a direct Bayesian fitting methods. All approaches were tested on simulated images and real data of patients with head-and-neck and rectal cancer. Estimation accuracy and maps noisiness were quantified on simulated images, whereas the coefficient of variation and the goodness of fit were evaluated for real data. Both versions of the segmented Bayesian approach outperformed the standard methods on simulated images for pseudo-diffusion (D∗ ) and perfusion fraction (f), whilst the segmented least-square fitting remained the less biased for the diffusion coefficient (D). On real data, Bayesian approaches provided the less noisy maps, and the two Bayesian methods without CAR generally estimated lower values for f and D∗ coefficients with respect to the other approaches. The proposed segmented Bayesian approaches were superior, in terms of estimation accuracy and maps quality, to the direct Bayesian model and the least-square fittings. The CAR method improved the estimation accuracy, especially for D∗ .
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Affiliation(s)
- Ettore Lanzarone
- Institute for Applied Mathematics and Information Technologies (IMATI-CNR), Milan, Italy
| | - Alfonso Mastropietro
- Institute of Biomedical Technologies (ITB-CNR), Segrate (MI), Italy
- Institute of Molecular Bioimaging and Physiology (IBFM-CNR), Segrate (MI), Italy
| | - Elisa Scalco
- Institute of Biomedical Technologies (ITB-CNR), Segrate (MI), Italy
- Institute of Molecular Bioimaging and Physiology (IBFM-CNR), Segrate (MI), Italy
| | - Antonello Vidiri
- Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Giovanna Rizzo
- Institute of Biomedical Technologies (ITB-CNR), Segrate (MI), Italy
- Institute of Molecular Bioimaging and Physiology (IBFM-CNR), Segrate (MI), Italy
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41
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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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Lévy S, Rapacchi S, Massire A, Troalen T, Feiweier T, Guye M, Callot V. Intravoxel Incoherent Motion at 7 Tesla to quantify human spinal cord perfusion: limitations and promises. Magn Reson Med 2020; 84:1198-1217. [DOI: 10.1002/mrm.28195] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Revised: 12/16/2019] [Accepted: 01/10/2020] [Indexed: 12/15/2022]
Affiliation(s)
- Simon Lévy
- Aix‐Marseille Univ, CNRS, CRMBM Marseille France
- APHM, Hopital Universitaire Timone, CEMEREM Marseille France
- Aix‐Marseille Univ, IFSTTAR, LBA Marseille France
- iLab‐Spine International Associated Laboratory Marseille‐Montreal France‐Canada
| | - Stanislas Rapacchi
- Aix‐Marseille Univ, CNRS, CRMBM Marseille France
- APHM, Hopital Universitaire Timone, CEMEREM Marseille France
| | - Aurélien Massire
- Aix‐Marseille Univ, CNRS, CRMBM Marseille France
- APHM, Hopital Universitaire Timone, CEMEREM Marseille France
- iLab‐Spine International Associated Laboratory Marseille‐Montreal France‐Canada
| | | | | | - Maxime Guye
- Aix‐Marseille Univ, CNRS, CRMBM Marseille France
- APHM, Hopital Universitaire Timone, CEMEREM Marseille France
| | - Virginie Callot
- Aix‐Marseille Univ, CNRS, CRMBM Marseille France
- APHM, Hopital Universitaire Timone, CEMEREM Marseille France
- iLab‐Spine International Associated Laboratory Marseille‐Montreal France‐Canada
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43
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On the identification of the blood vessel confounding effect in intravoxel incoherent motion (IVIM) Diffusion-Weighted (DW)-MRI in liver: An efficient sparsity based algorithm. Med Image Anal 2020; 61:101637. [PMID: 32014805 DOI: 10.1016/j.media.2020.101637] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Revised: 12/27/2019] [Accepted: 01/08/2020] [Indexed: 01/16/2023]
Abstract
IntraVoxel Incoherent Motion (IVIM) Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) is of great interest for evaluating tissue diffusion and perfusion and producing parametric maps in clinical applications for liver pathologies. However, the presence of macroscopic blood vessels (not capillaries) in a given Region of Interest (ROI) results in a confounding effect that bias the quantification of tissue perfusion. Therefore, it is necessary to identify those voxels affected by blood vessels. In this paper, an efficient algorithm for an automatic identification of blood vessels in a given ROI is proposed. It relies on the sparsity of the spatial distribution of blood vessels. This sparsity prior can be easily incorporated using the all-voxel IVIM-MRI model introduced in this paper. In addition to the identification of blood vessels, the proposed algorithm provides a quantification of blood vessels, tissue diffusion and tissue perfusion of all voxels in a given ROI, in one single step. Besides, two strategies are proposed in this paper to deal with the nonnegativity of the model parameters. The efficiency of the proposed algorithm compared to the Non-Negative Least Square (NNLS)-based method, recently introduced to deal with the confounding blood vessel effect in the IVIM-MRI model, is confirmed using both realistic and real DW-MR images.
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44
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Tosun M, Onal T, Uslu H, Alparslan B, Çetin Akhan S. Intravoxel incoherent motion imaging for diagnosing and staging the liver fibrosis and inflammation. Abdom Radiol (NY) 2020; 45:15-23. [PMID: 31705248 DOI: 10.1007/s00261-019-02300-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
PURPOSE To evaluate the diagnostic accuracy of intravoxel incoherent motion (IVIM) model parameters for the diagnosis and staging of liver fibrosis and inflammation in patients with chronic hepatitis B. METHODS Fifty-four patients with chronic hepatitis B and 42 healthy volunteers were included in the study. All subjects were examined by 3 T magnetic resonance imaging. Diffusion-weighted imaging was undertaken with sixteen b values. IVIM parameters [D (true diffusion coefficient), D* (pseudo-diffusion coefficient), f (perfusion fraction)] were calculated. Histological evaluation of biopsy samples was considered the reference standard for the staging of liver fibrosis and inflammation. Differences in IVIM parameters between patient and control groups were analyzed. In the patient group, fibrosis stage and inflammation grade groups were analyzed with respect to IVIM parameters. The correlation was assessed between IVIM parameters and Ishak-modified scale of fibrosis stages and inflammation grades. RESULTS The D was significantly lower in the patient group than the control group, p = 0.038 with Cohen's d effect size of 0.452. D was significantly different between fibrosis stage levels. D values decreased in fibrosis stages from the minimal to moderate to marked fibrosis. Fibrosis grades significantly negatively correlated with D and D* values, p = 0.001, and 0.021, respectively. In addition, inflammation grades negatively correlated with f values, p = 0.047. CONCLUSION D values measured with IVIM imaging may help to diagnose liver fibrosis. IVIM imaging could be an alternative to liver biopsy for the staging of liver fibrosis.
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Affiliation(s)
- Mesude Tosun
- Department of Radiology, Kocaeli University School of Medicine, Kocaeli, Turkey.
| | | | - Hande Uslu
- Department of Radiology, Kocaeli University School of Medicine, Kocaeli, Turkey
| | - Burcu Alparslan
- Department of Radiology, Kocaeli University School of Medicine, Kocaeli, Turkey
| | - Sıla Çetin Akhan
- Department of Infectious Diseases and Clinical Microbiology, Kocaeli University School of Medicine, Kocaeli, Turkey
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Chevallier O, Zhou N, Cercueil JP, He J, Loffroy R, Wáng YXJ. Comparison of tri-exponential decay versus bi-exponential decay and full fitting versus segmented fitting for modeling liver intravoxel incoherent motion diffusion MRI. NMR IN BIOMEDICINE 2019; 32:e4155. [PMID: 31361366 DOI: 10.1002/nbm.4155] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Revised: 05/15/2019] [Accepted: 06/23/2019] [Indexed: 06/10/2023]
Abstract
OBJECTIVES To determine whether bi- or tri-exponential models, and full or segmented fittings, better fit the intravoxel incoherent motion (IVIM) imaging signal of healthy livers. METHODS Diffusion-weighted images were acquired with a 3 T scanner using a respiratory-triggered echo-planar sequence and 16 b-values (0-800 s/mm2 ). Eighteen healthy volunteers had their livers scanned twice in the same session, and then once in another session. Liver parenchyma region-of-interest-based measurements were processed with bi-exponential and tri-exponential models, with both full fitting and segmented fitting (threshold b-value = 200 s/mm2 ). RESULTS With the signal of all scans averaged, bi-exponential model full fitting showed Dslow = 1.14 × 10-3 mm2 /s, Dfast = 193.6 × 10-3 mm2 /s, and perfusion fraction (PF) = 16.9%, and segmented fitting showed Dslow = 0.98 × 10-3 mm2 /s, Dfast = 42.2 × 10-3 mm2 /s, and PF = 23.3%. IVIM parameters derived from the tri-exponential model were similar for full fitting and segmented fitting, with slow (D'slow = 0.98 × 10-3 mm2 /s; F'slow = 76.4 or 76.6%), fast (D'fast = 15.1 or 15.4 × 10-3 mm2 /s; F'fast = 11.8 or 11.7%) and very fast (D'Vfast = 445.0 or 448.8 × 10-3 mm2 /s; F'Vfast = 11.8 or 11.7%) diffusion compartments. The tri-exponential model provided an overall better fit than the bi-exponential model. For the bi-exponential model, full fitting provided a better fit at very low and low b-values compared with segmented fitting, with the latter tending to underestimate Dfast ; however, the segmented method demonstrated lower error in signal prediction for high b-values. Compared with full fitting, tri-exponential segmented fitting offered better scan-rescan reproducibility. CONCLUSION For healthy liver, tri-exponential modeling is preferred to bi-exponential modeling. For the bi-exponential model, segmented fitting underestimates Dfast , but offers a more accurate estimation of Dslow .
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Affiliation(s)
- Olivier Chevallier
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, New Territories, Hong Kong SAR
- Department of Vascular and Interventional Radiology, Image-Guided Therapy Center, François-Mitterrand University Hospital, Dijon Cedex, France
| | - Nan Zhou
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Jean-Pierre Cercueil
- Department of Vascular and Interventional Radiology, Image-Guided Therapy Center, François-Mitterrand University Hospital, Dijon Cedex, France
| | - Jian He
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Romaric Loffroy
- Department of Vascular and Interventional Radiology, Image-Guided Therapy Center, François-Mitterrand University Hospital, Dijon Cedex, France
| | - Yì Xiáng J Wáng
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, New Territories, Hong Kong SAR
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Ljimani A, Caroli A, Laustsen C, Francis S, Mendichovszky IA, Bane O, Nery F, Sharma K, Pohlmann A, Dekkers IA, Vallee JP, Derlin K, Notohamiprodjo M, Lim RP, Palmucci S, Serai SD, Periquito J, Wang ZJ, Froeling M, Thoeny HC, Prasad P, Schneider M, Niendorf T, Pullens P, Sourbron S, Sigmund EE. Consensus-based technical recommendations for clinical translation of renal diffusion-weighted MRI. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2019; 33:177-195. [PMID: 31676990 PMCID: PMC7021760 DOI: 10.1007/s10334-019-00790-y] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Revised: 10/17/2019] [Accepted: 10/19/2019] [Indexed: 12/13/2022]
Abstract
Objectives Standardization is an important milestone in the validation of DWI-based parameters as imaging biomarkers for renal disease. Here, we propose technical recommendations on three variants of renal DWI, monoexponential DWI, IVIM and DTI, as well as associated MRI biomarkers (ADC, D, D*, f, FA and MD) to aid ongoing international efforts on methodological harmonization. Materials and methods Reported DWI biomarkers from 194 prior renal DWI studies were extracted and Pearson correlations between diffusion biomarkers and protocol parameters were computed. Based on the literature review, surveys were designed for the consensus building. Survey data were collected via Delphi consensus process on renal DWI preparation, acquisition, analysis, and reporting. Consensus was defined as ≥ 75% agreement. Results Correlations were observed between reported diffusion biomarkers and protocol parameters. Out of 87 survey questions, 57 achieved consensus resolution, while many of the remaining questions were resolved by preference (65–74% agreement). Summary of the literature and survey data as well as recommendations for the preparation, acquisition, processing and reporting of renal DWI were provided. Discussion The consensus-based technical recommendations for renal DWI aim to facilitate inter-site harmonization and increase clinical impact of the technique on a larger scale by setting a framework for acquisition protocols for future renal DWI studies. We anticipate an iterative process with continuous updating of the recommendations according to progress in the field. Electronic supplementary material The online version of this article (10.1007/s10334-019-00790-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Alexandra Ljimani
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, Moorenstr. 5, 40225, Düsseldorf, Germany.
| | - Anna Caroli
- Department of Biomedical Engineering, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, Italy
| | - Christoffer Laustsen
- MR Research Centre, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Susan Francis
- Sir Peter Mansfield Imaging Centre, University Park, University of Nottingham, Nottingham, NG7 2RD, UK
| | | | - Octavia Bane
- Translational and Molecular Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Fabio Nery
- Developmental Imaging and Biophysics Section, UCL Great Ormond Street Institute of Child Health, London, UK
| | - Kanishka Sharma
- Imaging Biomarkers Group, Department of Biomedical Imaging Sciences, University of Leeds, Leeds, UK
| | - Andreas Pohlmann
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrueck Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Ilona A Dekkers
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Jean-Paul Vallee
- Department of Diagnostic, Geneva University Hospital and University of Geneva, 1211, Geneva-14, Switzerland
| | - Katja Derlin
- Department of Radiology, Hannover Medical School, Hannover, Germany
| | - Mike Notohamiprodjo
- Die Radiologie, Munich, Germany.,Department of Radiology, University Hospital Tuebingen, Tübingen, Germany
| | - Ruth P Lim
- Department of Radiology, Austin Health, The University of Melbourne, Melbourne, Australia
| | - Stefano Palmucci
- Department of Medical Surgical Sciences and Advanced Technologies, Radiology I Unit, University Hospital "Policlinico-Vittorio Emanuele", University of Catania, Catania, Italy
| | - Suraj D Serai
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Joao Periquito
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrueck Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Zhen Jane Wang
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Martijn Froeling
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Harriet C Thoeny
- Department of Radiology, Hôpital Cantonal Fribourgois (HFR), University of Fribourg, 1708, Fribourg, Switzerland
| | - Pottumarthi Prasad
- Department of Radiology, Center for Advanced Imaging, NorthShore University Health System, Evanston, IL, USA
| | - Moritz Schneider
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany.,Comprehensive Pneumology Center, German Center for Lung Research, Munich, Germany
| | - Thoralf Niendorf
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrueck Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Pim Pullens
- Ghent Institute for Functional and Metabolic Imaging, Ghent University, Ghent, Belgium.,Department of Radiology, University Hospital Ghent, Ghent, Belgium
| | - Steven Sourbron
- Imaging Biomarkers Group, Department of Biomedical Imaging Sciences, University of Leeds, Leeds, UK
| | - Eric E Sigmund
- Department of Radiology, Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), NYU Langone Health, New York, NY, USA
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Barbieri S, Gurney‐Champion OJ, Klaassen R, Thoeny HC. Deep learning how to fit an intravoxel incoherent motion model to diffusion‐weighted MRI. Magn Reson Med 2019; 83:312-321. [DOI: 10.1002/mrm.27910] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Revised: 06/22/2019] [Accepted: 06/26/2019] [Indexed: 12/29/2022]
Affiliation(s)
| | - Oliver J. Gurney‐Champion
- Joint Department of Physics The Institute of Cancer Research London United Kingdom
- The Royal Marsden NHS Foundation Trust London United Kingdom
| | - Remy Klaassen
- Cancer Center Amsterdam, Department of Medical Oncology and LEXOR (Laboratory for Experimental Oncology and Radiobiology) Academic Medical Center Amsterdam The Netherlands
| | - Harriet C. Thoeny
- Department of Radiology HFR Fribourg‐Hôpital Cantonal Fribourg Switzerland
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48
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Jalnefjord O, Montelius M, Starck G, Ljungberg M. Optimization of b-value schemes for estimation of the diffusion coefficient and the perfusion fraction with segmented intravoxel incoherent motion model fitting. Magn Reson Med 2019; 82:1541-1552. [PMID: 31148264 PMCID: PMC6772171 DOI: 10.1002/mrm.27826] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 05/03/2019] [Accepted: 05/05/2019] [Indexed: 01/01/2023]
Abstract
PURPOSE Intravoxel incoherent motion (IVIM) modeling for estimation of the diffusion coefficient (D) and perfusion fraction (f) is increasingly popular, but no consensus on standard protocols exists. This study provides a framework for optimization of b-value schemes for reduced estimation uncertainty of D and f from segmented model fitting. THEORY Analytical expressions for uncertainties of D and f from segmented model fitting were derived as Cramer-Rao lower bounds (CRLBs). METHODS Optimized b-value schemes were obtained for 3 to 12 acquisitions and in the limit of infinitely many acquisitions through constrained minimization of the CRLBs, with b-values constrained to be 0 or 200 to 800 s/mm2 . The optimized b-value scheme with eight acquisitions was compared with b-values linearly distributed in the allowed range using simulations and in vivo liver data from seven healthy volunteers. RESULTS All optimized b-value schemes contained exactly three unique b-values regardless of the total number of acquisitions (0, 200, and 800 s/mm2 ) with repeated acquisitions distributed approximately as 1:2:2. Compared with linearly distributed b-values, the variability of estimates of D and f was reduced by approximately 30% as seen both in simulations and in repeated in vivo measurements. CONCLUSION The uncertainty of IVIM D and f estimates can be reduced by the use of optimized b-value schemes.
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Affiliation(s)
- Oscar Jalnefjord
- Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Mikael Montelius
- Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Göran Starck
- Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Maria Ljungberg
- Department of Radiation Physics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
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Gurney-Champion OJ, Collins DJ, Wetscherek A, Rata M, Klaassen R, van Laarhoven HWM, Harrington KJ, Oelfke U, Orton MR. Principal component analysis fosr fast and model-free denoising of multi b-value diffusion-weighted MR images. Phys Med Biol 2019; 64:105015. [PMID: 30965296 PMCID: PMC7655121 DOI: 10.1088/1361-6560/ab1786] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 03/18/2019] [Accepted: 04/09/2019] [Indexed: 02/08/2023]
Abstract
Despite the utility of tumour characterisation using quantitative parameter maps from multi-b-value diffusion-weighted MRI (DWI), clinicians often prefer the use of the image with highest diffusion-weighting (b-value), for instance for defining regions of interest (ROIs). However, these images are typically degraded by noise, as they do not utilize the information from the full acquisition. We present a principal component analysis (PCA) approach for model-free denoising of DWI data. PCA-denoising was compared to synthetic MRI, where a diffusion model is fitted for each voxel and a denoised image at a given b-value is generated from the model fit. A quantitative comparison of systematic and random errors was performed on data simulated using several diffusion models (mono-exponential, bi-exponential, stretched-exponential and kurtosis). A qualitative visual comparison was also performed for in vivo images in six healthy volunteers and three pancreatic cancer patients. In simulations, the reduction in random errors from PCA-denoising was substantial (up to 55%) and similar to synthetic MRI (up to 53%). Model-based synthetic MRI denoising resulted in substantial (up to 29% of signal) systematic errors, whereas PCA-denoising was able to denoise without introducing systematic errors (less than 2%). In vivo, the signal-to-noise ratio (SNR) and sharpness of PCA-denoised images were superior to synthetic MRI, resulting in clearer tumour boundaries. In the presence of motion, PCA-denoising did not cause image blurring, unlike image averaging or synthetic MRI. Multi-b-value MRI can be denoised model-free with our PCA-denoising strategy that reduces noise to a level similar to synthetic MRI, but without introducing systematic errors associated with the synthetic MRI method.
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Affiliation(s)
- Oliver J Gurney-Champion
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden
NHS Foundation Trust, London, United
Kingdom
| | - David J Collins
- Cancer Research UK Cancer Imaging Centre,
The Institute of Cancer Research and The
Royal Marsden NHS Foundation Trust, London, United
Kingdom
| | - Andreas Wetscherek
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden
NHS Foundation Trust, London, United
Kingdom
| | - Mihaela Rata
- Cancer Research UK Cancer Imaging Centre,
The Institute of Cancer Research and The
Royal Marsden NHS Foundation Trust, London, United
Kingdom
| | - Remy Klaassen
- Department of Medical Oncology, Cancer Center
Amsterdam, Amsterdam UMC, University of
Amsterdam, Amsterdam, The Netherlands
| | - Hanneke W M van Laarhoven
- Department of Medical Oncology, Cancer Center
Amsterdam, Amsterdam UMC, University of
Amsterdam, Amsterdam, The Netherlands
| | - Kevin J Harrington
- Targeted Therapy Team, The Institute of Cancer Research and The Royal Marsden
NHS Foundation Trust, London, United
Kingdom
| | - Uwe Oelfke
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden
NHS Foundation Trust, London, United
Kingdom
| | - Matthew R Orton
- Cancer Research UK Cancer Imaging Centre,
The Institute of Cancer Research and The
Royal Marsden NHS Foundation Trust, London, United
Kingdom
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50
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Threshold Isocontouring on High b-Value Diffusion-Weighted Images in Magnetic Resonance Mammography. J Comput Assist Tomogr 2019; 43:434-442. [PMID: 31082949 DOI: 10.1097/rct.0000000000000868] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVES Motivated by the similar appearance of malignant breast lesions in high b-value diffusion-weighted imaging (DWI) and positron emission tomography, the purpose of this work was to evaluate the applicability of a threshold isocontouring approach commonly used in positron emission tomography to analyze DWI data acquired from female human breasts with minimal interobserver variability. METHODS Twenty-three female participants (59.4 ± 10.0 years) with 23 lesions initially classified as suggestive of cancers in x-ray mammography screening were subsequently imaged on a 1.5-T magnetic resonance imaging scanner. Diffusion-weighted imaging was performed prior to biopsy with b values of 0, 100, 750, and 1500 s/mm. Isocontouring with different threshold levels was performed on the highest b-value image to determine the voxels used for subsequent evaluation of diffusion metrics. The coefficient of variation was computed by specifying 4 different regions of interest drawn around the lesion. Additionally, a receiver operating statistical analysis was performed. RESULTS Using a relative threshold level greater than or equal to 0.85 almost completely suppresses the intra-individual and inter-individual variability. Among 4 studied diffusion metrics, the diffusion coefficients from the intravoxel incoherent motion model returned the highest area under curve value of 0.9. The optimal cut-off diffusivity was found to be 0.85 μm/ms with a sensitivity of 87.5% and specificity of 90.9%. CONCLUSION Threshold isocontouring on high b-value maps is a viable approach to reliably evaluate DWI data of suspicious focal lesions in magnetic resonance mammography.
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