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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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Lashgari M, Ravikumar N, Teh I, Li JR, Buckley DL, Schneider JE, Frangi AF. Three-dimensional micro-structurally informed in silico myocardium-Towards virtual imaging trials in cardiac diffusion weighted MRI. Med Image Anal 2022; 82:102592. [PMID: 36095906 DOI: 10.1016/j.media.2022.102592] [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: 08/27/2021] [Revised: 08/14/2022] [Accepted: 08/18/2022] [Indexed: 11/24/2022]
Abstract
In silico tissue models (viz. numerical phantoms) provide a mechanism for evaluating quantitative models of magnetic resonance imaging. This includes the validation and sensitivity analysis of imaging biomarkers and tissue microstructure parameters. This study proposes a novel method to generate a realistic numerical phantom of myocardial microstructure. The proposed method extends previous studies by accounting for the variability of the cardiomyocyte shape, water exchange between the cardiomyocytes (intercalated discs), disorder class of myocardial microstructure, and four sheetlet orientations. In the first stage of the method, cardiomyocytes and sheetlets are generated by considering the shape variability and intercalated discs in cardiomyocyte-cardiomyocyte connections. Sheetlets are then aggregated and oriented in the directions of interest. The morphometric study demonstrates no significant difference (p>0.01) between the distribution of volume, length, and primary and secondary axes of the numerical and real (literature) cardiomyocyte data. Moreover, structural correlation analysis validates that the in-silico tissue is in the same class of disorderliness as the real tissue. Additionally, the absolute angle differences between the simulated helical angle (HA) and input HA (reference value) of the cardiomyocytes (4.3°±3.1°) demonstrate a good agreement with the absolute angle difference between the measured HA using experimental cardiac diffusion tensor imaging (cDTI) and histology (reference value) reported by (Holmes et al., 2000) (3.7°±6.4°) and (Scollan et al. 1998) (4.9°±14.6°). Furthermore, the angular distance between eigenvectors and sheetlet angles of the input and simulated cDTI is much smaller than those between measured angles using structural tensor imaging (as a gold standard) and experimental cDTI. Combined with the qualitative results, these results confirm that the proposed method can generate richer numerical phantoms for the myocardium than previous studies.
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Affiliation(s)
- Mojtaba Lashgari
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK; Biomedical Imaging Science Department, Leeds Institute for Cardiovascular and Metabolic Medicine (LICAMM), School of Medicine, University of Leeds, Leeds, UK.
| | - Nishant Ravikumar
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK; Biomedical Imaging Science Department, Leeds Institute for Cardiovascular and Metabolic Medicine (LICAMM), School of Medicine, University of Leeds, Leeds, UK
| | - Irvin Teh
- Biomedical Imaging Science Department, Leeds Institute for Cardiovascular and Metabolic Medicine (LICAMM), School of Medicine, University of Leeds, Leeds, UK
| | - Jing-Rebecca Li
- INRIA Saclay, Equipe DEFI, CMAP, Ecole Polytechnique, Route de Saclay, 91128 Palaiseau Cedex, France
| | - David L Buckley
- Biomedical Imaging Science Department, Leeds Institute for Cardiovascular and Metabolic Medicine (LICAMM), School of Medicine, University of Leeds, Leeds, UK
| | - Jurgen E Schneider
- Biomedical Imaging Science Department, Leeds Institute for Cardiovascular and Metabolic Medicine (LICAMM), School of Medicine, University of Leeds, Leeds, UK
| | - Alejandro F Frangi
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK; Biomedical Imaging Science Department, Leeds Institute for Cardiovascular and Metabolic Medicine (LICAMM), School of Medicine, University of Leeds, Leeds, UK; INRIA Saclay, Equipe DEFI, CMAP, Ecole Polytechnique, Route de Saclay, 91128 Palaiseau Cedex, France; Medical Imaging Research Center (MIRC), Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium; Medical Imaging Research Center (MIRC), Department of Electrical Engineering, KU Leuven, Leuven, Belgium; Alan Turing Institute, London, UK.
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Liu Q, Xu Z, Zhao K, Hoge WS, Zhang X, Mei Y, Lu Q, Niendorf T, Feng Y. Diffusion-weighted magnetic resonance imaging in rat kidney using two-dimensional navigated, interleaved echo-planar imaging at 7.0 T. NMR IN BIOMEDICINE 2022; 35:e4652. [PMID: 34820933 DOI: 10.1002/nbm.4652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 10/29/2021] [Accepted: 10/30/2021] [Indexed: 06/13/2023]
Abstract
The purpose of this study was to investigate the feasibility of two-dimensional (2D) navigated, interleaved multishot echo-planar imaging (EPI) to enhance kidney diffusion-weighted imaging (DWI) in rats at 7.0 T. Fully sampled interleaved four-shot EPI with 2D navigators was tailored for kidney DWI (Sprague-Dawley rats, n = 7) on a 7.0-T small bore preclinical scanner. The image quality of four-shot EPI was compared with T2 -weighted rapid acquisition with relaxation enhancement (RARE) (reference) and single-shot EPI (ss-EPI) without and with parallel imaging (PI). The contrast-to-noise ratio (CNR) was examined to assess the image quality for the EPI approaches. The Dice similarity coefficient and the Hausdorff distance were used for evaluation of image distortion. Mean diffusivity (MD) and fractional anisotropy (FA) were calculated for renal cortex and medulla for all DWI approaches. The corticomedullary difference of MD and FA were assessed by Wilcoxon signed-rank test. Four-shot EPI showed the highest CNR among the three EPI variants and lowest geometric distortion versus T2 -weighted RARE (mean Dice: 0.77 for ss-EPI without PI, 0.88 for ss-EPI with twofold undersampling, and 0.92 for four-shot EPI). The FA map derived from four-shot EPI clearly identified a highly anisotropic region corresponding to the inner stripe of the outer medulla. Four-shot EPI successfully discerned differences in both MD and FA between renal cortex and medulla. In conclusion, 2D navigated, interleaved multishot EPI facilitates high-quality rat kidney DWI with clearly depicted intralayer and interlayer structure and substantially reduced image distortion. This approach enables the anatomic integrity of DWI-MRI in small rodents and has the potential to benefit the characterization of renal microstructure in preclinical studies.
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Affiliation(s)
- Qiang Liu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence & Key Laboratory of Mental Health of the Ministry of Education, Southern Medical University, Guangzhou, China
| | - Zhongbiao Xu
- Department of Radiation Oncology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Kaixuan Zhao
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence & Key Laboratory of Mental Health of the Ministry of Education, Southern Medical University, Guangzhou, China
| | - W Scott Hoge
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Xinyuan Zhang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence & Key Laboratory of Mental Health of the Ministry of Education, Southern Medical University, Guangzhou, China
| | - Yingjie Mei
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence & Key Laboratory of Mental Health of the Ministry of Education, Southern Medical University, Guangzhou, China
| | - Qiqi Lu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence & Key Laboratory of Mental Health of the Ministry of Education, Southern Medical University, Guangzhou, China
| | - Thoralf Niendorf
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrueck Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Yanqiu Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence & Key Laboratory of Mental Health of the Ministry of Education, Southern Medical University, Guangzhou, China
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Periquito JS, Meier M, Niendorf T, Pohlmann A, Jerome NP. Renal MRI Diffusion: Experimental Protocol. Methods Mol Biol 2021; 2216:419-428. [PMID: 33476014 PMCID: PMC9703252 DOI: 10.1007/978-1-0716-0978-1_24] [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: 06/12/2023]
Abstract
Renal diffusion-weighted imaging (DWI) can be used to obtain information on the microstructure of kidney tissue, and has the potential to provide MR-biomarkers for functional renal imaging. Here we describe in a step-by-step experimental protocol the MRI method for measuring renal diffusion coefficients in rodents using ADC or IVIM models. Both methods provide quantification of renal diffusion coefficients; however, IVIM, a more complex model, allows for the calculation of the pseudodiffusion and fraction introduced by tissue vascular and tubular components. DWI provides information of renal microstructure contributing to the understanding of the physiology and the underlying processes that precede the beginning of pathologies.This chapter 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 experimental protocol chapter is complemented by two separate chapters describing the basic concept and data analysis.
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Affiliation(s)
- João S Periquito
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine (MDC) in the Helmholtz Association, Berlin, Germany.
| | - Martin Meier
- ZTL-Imaging Center, Hannover Medical School, Hannover, Germany
| | - Thoralf Niendorf
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine (MDC) in the Helmholtz Association, Berlin, Germany
| | - Andreas Pohlmann
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine (MDC) in the Helmholtz Association, Berlin, Germany
- Siemens Healthcare, Berlin, Germany
| | - Neil Peter Jerome
- Institute for Circulation and Diagnostic Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
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