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Diffusion MRI signal cumulants and hepatocyte microstructure at fixed diffusion time: Insights from simulations, 9.4T imaging, and histology

PURPOSE: Relationships between diffusion‐weighted MRI signals and hepatocyte microstructure were investigated to inform liver diffusion MRI modeling, focusing on the following question: Can cell size and diffusivity be estimated at fixed diffusion time, realistic SNR, and negligible contribution fro...

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Autores principales: Grussu, Francesco, Bernatowicz, Kinga, Casanova‐Salas, Irene, Castro, Natalia, Nuciforo, Paolo, Mateo, Joaquin, Barba, Ignasi, Perez‐Lopez, Raquel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9303340/
https://www.ncbi.nlm.nih.gov/pubmed/35181943
http://dx.doi.org/10.1002/mrm.29174
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author Grussu, Francesco
Bernatowicz, Kinga
Casanova‐Salas, Irene
Castro, Natalia
Nuciforo, Paolo
Mateo, Joaquin
Barba, Ignasi
Perez‐Lopez, Raquel
author_facet Grussu, Francesco
Bernatowicz, Kinga
Casanova‐Salas, Irene
Castro, Natalia
Nuciforo, Paolo
Mateo, Joaquin
Barba, Ignasi
Perez‐Lopez, Raquel
author_sort Grussu, Francesco
collection PubMed
description PURPOSE: Relationships between diffusion‐weighted MRI signals and hepatocyte microstructure were investigated to inform liver diffusion MRI modeling, focusing on the following question: Can cell size and diffusivity be estimated at fixed diffusion time, realistic SNR, and negligible contribution from extracellular/extravascular water and exchange? METHODS: Monte Carlo simulations were performed within synthetic hepatocytes for varying cell size/diffusivity [Formula: see text] / [Formula: see text] , and clinical protocols (single diffusion encoding; maximum b‐value: {1000, 1500, 2000} s/mm(2); 5 unique gradient duration/separation pairs; SNR = {[Formula: see text] , 100, 80, 40, 20}), accounting for heterogeneity in [Formula: see text] and perfusion contamination. Diffusion ([Formula: see text]) and kurtosis ([Formula: see text]) coefficients were calculated, and relationships between [Formula: see text] and [Formula: see text] were visualized. Functions mapping [Formula: see text] to [Formula: see text] were computed to predict unseen [Formula: see text] values, tested for their ability to classify discrete cell‐size contrasts, and deployed on 9.4T ex vivo MRI‐histology data of fixed mouse livers RESULTS: Relationships between [Formula: see text] and [Formula: see text] are complex and depend on the diffusion encoding. Functions mapping [Formula: see text] to [Formula: see text] captures salient characteristics of [Formula: see text] and [Formula: see text] dependencies. Mappings are not always accurate, but they enable just under 70% accuracy in a three‐class cell‐size classification task (for SNR = 20, [Formula: see text] = 1500 s/mm(2), [Formula: see text] = 20 ms, and [Formula: see text] = 75 ms). MRI detects cell‐size contrasts in the mouse livers that are confirmed by histology, but overestimates the largest cell sizes. CONCLUSION: Salient information about liver cell size and diffusivity may be retrieved from minimal diffusion encodings at fixed diffusion time, in experimental conditions and pathological scenarios for which extracellular, extravascular water and exchange are negligible.
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spelling pubmed-93033402022-07-22 Diffusion MRI signal cumulants and hepatocyte microstructure at fixed diffusion time: Insights from simulations, 9.4T imaging, and histology Grussu, Francesco Bernatowicz, Kinga Casanova‐Salas, Irene Castro, Natalia Nuciforo, Paolo Mateo, Joaquin Barba, Ignasi Perez‐Lopez, Raquel Magn Reson Med Technical Notes—Biophysics and Basic Biomedical Research PURPOSE: Relationships between diffusion‐weighted MRI signals and hepatocyte microstructure were investigated to inform liver diffusion MRI modeling, focusing on the following question: Can cell size and diffusivity be estimated at fixed diffusion time, realistic SNR, and negligible contribution from extracellular/extravascular water and exchange? METHODS: Monte Carlo simulations were performed within synthetic hepatocytes for varying cell size/diffusivity [Formula: see text] / [Formula: see text] , and clinical protocols (single diffusion encoding; maximum b‐value: {1000, 1500, 2000} s/mm(2); 5 unique gradient duration/separation pairs; SNR = {[Formula: see text] , 100, 80, 40, 20}), accounting for heterogeneity in [Formula: see text] and perfusion contamination. Diffusion ([Formula: see text]) and kurtosis ([Formula: see text]) coefficients were calculated, and relationships between [Formula: see text] and [Formula: see text] were visualized. Functions mapping [Formula: see text] to [Formula: see text] were computed to predict unseen [Formula: see text] values, tested for their ability to classify discrete cell‐size contrasts, and deployed on 9.4T ex vivo MRI‐histology data of fixed mouse livers RESULTS: Relationships between [Formula: see text] and [Formula: see text] are complex and depend on the diffusion encoding. Functions mapping [Formula: see text] to [Formula: see text] captures salient characteristics of [Formula: see text] and [Formula: see text] dependencies. Mappings are not always accurate, but they enable just under 70% accuracy in a three‐class cell‐size classification task (for SNR = 20, [Formula: see text] = 1500 s/mm(2), [Formula: see text] = 20 ms, and [Formula: see text] = 75 ms). MRI detects cell‐size contrasts in the mouse livers that are confirmed by histology, but overestimates the largest cell sizes. CONCLUSION: Salient information about liver cell size and diffusivity may be retrieved from minimal diffusion encodings at fixed diffusion time, in experimental conditions and pathological scenarios for which extracellular, extravascular water and exchange are negligible. John Wiley and Sons Inc. 2022-02-18 2022-07 /pmc/articles/PMC9303340/ /pubmed/35181943 http://dx.doi.org/10.1002/mrm.29174 Text en © 2022 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Technical Notes—Biophysics and Basic Biomedical Research
Grussu, Francesco
Bernatowicz, Kinga
Casanova‐Salas, Irene
Castro, Natalia
Nuciforo, Paolo
Mateo, Joaquin
Barba, Ignasi
Perez‐Lopez, Raquel
Diffusion MRI signal cumulants and hepatocyte microstructure at fixed diffusion time: Insights from simulations, 9.4T imaging, and histology
title Diffusion MRI signal cumulants and hepatocyte microstructure at fixed diffusion time: Insights from simulations, 9.4T imaging, and histology
title_full Diffusion MRI signal cumulants and hepatocyte microstructure at fixed diffusion time: Insights from simulations, 9.4T imaging, and histology
title_fullStr Diffusion MRI signal cumulants and hepatocyte microstructure at fixed diffusion time: Insights from simulations, 9.4T imaging, and histology
title_full_unstemmed Diffusion MRI signal cumulants and hepatocyte microstructure at fixed diffusion time: Insights from simulations, 9.4T imaging, and histology
title_short Diffusion MRI signal cumulants and hepatocyte microstructure at fixed diffusion time: Insights from simulations, 9.4T imaging, and histology
title_sort diffusion mri signal cumulants and hepatocyte microstructure at fixed diffusion time: insights from simulations, 9.4t imaging, and histology
topic Technical Notes—Biophysics and Basic Biomedical Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9303340/
https://www.ncbi.nlm.nih.gov/pubmed/35181943
http://dx.doi.org/10.1002/mrm.29174
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