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Application of Magnetic Resonance Imaging in Liver Biomechanics: A Systematic Review
MRI-based biomechanical studies can provide a deep understanding of the mechanisms governing liver function, its mechanical performance but also liver diseases. In addition, comprehensive modeling of the liver can help improve liver disease treatment. Furthermore, such studies demonstrate the beginn...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8493836/ https://www.ncbi.nlm.nih.gov/pubmed/34630152 http://dx.doi.org/10.3389/fphys.2021.733393 |
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author | Seyedpour, Seyed M. Nabati, Mehdi Lambers, Lena Nafisi, Sara Tautenhahn, Hans-Michael Sack, Ingolf Reichenbach, Jürgen R. Ricken, Tim |
author_facet | Seyedpour, Seyed M. Nabati, Mehdi Lambers, Lena Nafisi, Sara Tautenhahn, Hans-Michael Sack, Ingolf Reichenbach, Jürgen R. Ricken, Tim |
author_sort | Seyedpour, Seyed M. |
collection | PubMed |
description | MRI-based biomechanical studies can provide a deep understanding of the mechanisms governing liver function, its mechanical performance but also liver diseases. In addition, comprehensive modeling of the liver can help improve liver disease treatment. Furthermore, such studies demonstrate the beginning of an engineering-level approach to how the liver disease affects material properties and liver function. Aimed at researchers in the field of MRI-based liver simulation, research articles pertinent to MRI-based liver modeling were identified, reviewed, and summarized systematically. Various MRI applications for liver biomechanics are highlighted, and the limitations of different viscoelastic models used in magnetic resonance elastography are addressed. The clinical application of the simulations and the diseases studied are also discussed. Based on the developed questionnaire, the papers' quality was assessed, and of the 46 reviewed papers, 32 papers were determined to be of high-quality. Due to the lack of the suitable material models for different liver diseases studied by magnetic resonance elastography, researchers may consider the effect of liver diseases on constitutive models. In the future, research groups may incorporate various aspects of machine learning (ML) into constitutive models and MRI data extraction to further refine the study methodology. Moreover, researchers should strive for further reproducibility and rigorous model validation and verification. |
format | Online Article Text |
id | pubmed-8493836 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84938362021-10-07 Application of Magnetic Resonance Imaging in Liver Biomechanics: A Systematic Review Seyedpour, Seyed M. Nabati, Mehdi Lambers, Lena Nafisi, Sara Tautenhahn, Hans-Michael Sack, Ingolf Reichenbach, Jürgen R. Ricken, Tim Front Physiol Physiology MRI-based biomechanical studies can provide a deep understanding of the mechanisms governing liver function, its mechanical performance but also liver diseases. In addition, comprehensive modeling of the liver can help improve liver disease treatment. Furthermore, such studies demonstrate the beginning of an engineering-level approach to how the liver disease affects material properties and liver function. Aimed at researchers in the field of MRI-based liver simulation, research articles pertinent to MRI-based liver modeling were identified, reviewed, and summarized systematically. Various MRI applications for liver biomechanics are highlighted, and the limitations of different viscoelastic models used in magnetic resonance elastography are addressed. The clinical application of the simulations and the diseases studied are also discussed. Based on the developed questionnaire, the papers' quality was assessed, and of the 46 reviewed papers, 32 papers were determined to be of high-quality. Due to the lack of the suitable material models for different liver diseases studied by magnetic resonance elastography, researchers may consider the effect of liver diseases on constitutive models. In the future, research groups may incorporate various aspects of machine learning (ML) into constitutive models and MRI data extraction to further refine the study methodology. Moreover, researchers should strive for further reproducibility and rigorous model validation and verification. Frontiers Media S.A. 2021-09-22 /pmc/articles/PMC8493836/ /pubmed/34630152 http://dx.doi.org/10.3389/fphys.2021.733393 Text en Copyright © 2021 Seyedpour, Nabati, Lambers, Nafisi, Tautenhahn, Sack, Reichenbach and Ricken. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Physiology Seyedpour, Seyed M. Nabati, Mehdi Lambers, Lena Nafisi, Sara Tautenhahn, Hans-Michael Sack, Ingolf Reichenbach, Jürgen R. Ricken, Tim Application of Magnetic Resonance Imaging in Liver Biomechanics: A Systematic Review |
title | Application of Magnetic Resonance Imaging in Liver Biomechanics: A Systematic Review |
title_full | Application of Magnetic Resonance Imaging in Liver Biomechanics: A Systematic Review |
title_fullStr | Application of Magnetic Resonance Imaging in Liver Biomechanics: A Systematic Review |
title_full_unstemmed | Application of Magnetic Resonance Imaging in Liver Biomechanics: A Systematic Review |
title_short | Application of Magnetic Resonance Imaging in Liver Biomechanics: A Systematic Review |
title_sort | application of magnetic resonance imaging in liver biomechanics: a systematic review |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8493836/ https://www.ncbi.nlm.nih.gov/pubmed/34630152 http://dx.doi.org/10.3389/fphys.2021.733393 |
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