Modelling the liver region as porous media to noninvasively measure portal vein pressure gradient (PPG) with numerical methods.

Computational fluid dynamics (CFD) Liver cirrhosis Porous media model Portal hypertension Portal vein pressure gradient (PPG)

Journal

Journal of biomechanics
ISSN: 1873-2380
Titre abrégé: J Biomech
Pays: United States
ID NLM: 0157375

Informations de publication

Date de publication:
06 2023
Historique:
received: 01 11 2022
revised: 11 05 2023
accepted: 23 05 2023
medline: 15 6 2023
pubmed: 8 6 2023
entrez: 7 6 2023
Statut: ppublish

Résumé

Portal hypertension is the initial and main consequence of liver cirrhosis. Currently the diagnosis relies on invasive and complex operation. This study proposed a new computational method in computational fluid dynamics (CFD) analysis to noninvasively measure the portal pressure gradient (PPG) by considering the liver region as porous media to account for the patient-specific liver resistance. Patient-specific computational models based on the CT scan images and the ultrasound (US) velocity measurement was established. The results show that the PPG derived from CFD analysis is in great agreement with clinical measured data (23.93 mmHg vs 23 mmHg). Validation of the numerical method and was performed by post-TIPS PPG measurement (10.69 mmHg vs 11 mmHg). Then the range of porous media parameters is investigated in a validation group of three patients. The computational method proposed in this study is promising in more accurately measuring the PPG noninvasively.

Identifiants

pubmed: 37285779
pii: S0021-9290(23)00229-4
doi: 10.1016/j.jbiomech.2023.111660
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

111660

Informations de copyright

Copyright © 2023 Elsevier Ltd. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Yue Qiu (Y)

Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, PR China; West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, PR China; Med-X Center for Informatics, Sichuan University, Chengdu, Sichuan 610041, PR China; West China Hospital-SenseTime Joint Lab, Chengdu, Sichuan 610041, PR China.

Yang Tai (Y)

Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, PR China.

Jianguo Lei (J)

Med-X Center for Informatics, Sichuan University, Chengdu, Sichuan 610041, PR China; West China Hospital-SenseTime Joint Lab, Chengdu, Sichuan 610041, PR China.

Yi Zeng (Y)

Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, PR China.

Hao Wu (H)

Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, PR China. Electronic address: hxxhwh@163.com.

Kang Li (K)

West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, PR China; Med-X Center for Informatics, Sichuan University, Chengdu, Sichuan 610041, PR China; West China Hospital-SenseTime Joint Lab, Chengdu, Sichuan 610041, PR China. Electronic address: likang@wchscu.cn.

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Classifications MeSH