In silico analysis reveals the prospects of renal anisotropy in improving chronic kidney disease detection using ultrasound shear wave elastography

Renal anisotropy is a complex property of the kidney and often poses a challenge in obtaining consistent measurements when using shear wave elastography to detect chronic kidney disease. To circumvent the challenge posed by renal anisotropy in clinical settings, a dimensionless biomarker termed the...

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Published in:International Journal for Numerical Methods in Biomedical Engineering
Main Author: Lim W.T.H.; Ooi E.H.; Foo J.J.; Ng K.H.; Wong J.H.D.; Leong S.S.
Format: Article
Language:English
Published: John Wiley and Sons Inc 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199981611&doi=10.1002%2fcnm.3857&partnerID=40&md5=195f448b534b6ec35a5be4885e7231a4
id 2-s2.0-85199981611
spelling 2-s2.0-85199981611
Lim W.T.H.; Ooi E.H.; Foo J.J.; Ng K.H.; Wong J.H.D.; Leong S.S.
In silico analysis reveals the prospects of renal anisotropy in improving chronic kidney disease detection using ultrasound shear wave elastography
2024
International Journal for Numerical Methods in Biomedical Engineering
40
9
10.1002/cnm.3857
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199981611&doi=10.1002%2fcnm.3857&partnerID=40&md5=195f448b534b6ec35a5be4885e7231a4
Renal anisotropy is a complex property of the kidney and often poses a challenge in obtaining consistent measurements when using shear wave elastography to detect chronic kidney disease. To circumvent the challenge posed by renal anisotropy in clinical settings, a dimensionless biomarker termed the ‘anisotropic ratio’ was introduced to establish a correlation between changes in degree of renal anisotropy and progression of chronic kidney disease through an in silico perspective. To achieve this, an efficient model reduction approach was developed to model the anisotropic property of kidneys. Good agreement between the numerical and experimental data were obtained, as percentage errors of less than 5.5% were reported when compared against experimental phantom measurement from the literature. To demonstrate the applicability of the model to clinical measurements, the anisotropic ratio of sheep kidneys was quantified, with both numerical and derived experimental results reporting a value of.667. Analysis of the anisotropic ratio with progression of chronic kidney disease demonstrated that patients with normal kidneys would have a lower anisotropic ratio of.872 as opposed to patients suffering from renal impairment, in which the anisotropic ratio may increase to.904, as determined from this study. The findings demonstrate the potential of the anisotropic ratio in improving the detection of chronic kidney disease using shear wave elastography. © 2024 The Author(s). International Journal for Numerical Methods in Biomedical Engineering published by John Wiley & Sons Ltd.
John Wiley and Sons Inc
20407939
English
Article
All Open Access; Hybrid Gold Open Access
author Lim W.T.H.; Ooi E.H.; Foo J.J.; Ng K.H.; Wong J.H.D.; Leong S.S.
spellingShingle Lim W.T.H.; Ooi E.H.; Foo J.J.; Ng K.H.; Wong J.H.D.; Leong S.S.
In silico analysis reveals the prospects of renal anisotropy in improving chronic kidney disease detection using ultrasound shear wave elastography
author_facet Lim W.T.H.; Ooi E.H.; Foo J.J.; Ng K.H.; Wong J.H.D.; Leong S.S.
author_sort Lim W.T.H.; Ooi E.H.; Foo J.J.; Ng K.H.; Wong J.H.D.; Leong S.S.
title In silico analysis reveals the prospects of renal anisotropy in improving chronic kidney disease detection using ultrasound shear wave elastography
title_short In silico analysis reveals the prospects of renal anisotropy in improving chronic kidney disease detection using ultrasound shear wave elastography
title_full In silico analysis reveals the prospects of renal anisotropy in improving chronic kidney disease detection using ultrasound shear wave elastography
title_fullStr In silico analysis reveals the prospects of renal anisotropy in improving chronic kidney disease detection using ultrasound shear wave elastography
title_full_unstemmed In silico analysis reveals the prospects of renal anisotropy in improving chronic kidney disease detection using ultrasound shear wave elastography
title_sort In silico analysis reveals the prospects of renal anisotropy in improving chronic kidney disease detection using ultrasound shear wave elastography
publishDate 2024
container_title International Journal for Numerical Methods in Biomedical Engineering
container_volume 40
container_issue 9
doi_str_mv 10.1002/cnm.3857
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199981611&doi=10.1002%2fcnm.3857&partnerID=40&md5=195f448b534b6ec35a5be4885e7231a4
description Renal anisotropy is a complex property of the kidney and often poses a challenge in obtaining consistent measurements when using shear wave elastography to detect chronic kidney disease. To circumvent the challenge posed by renal anisotropy in clinical settings, a dimensionless biomarker termed the ‘anisotropic ratio’ was introduced to establish a correlation between changes in degree of renal anisotropy and progression of chronic kidney disease through an in silico perspective. To achieve this, an efficient model reduction approach was developed to model the anisotropic property of kidneys. Good agreement between the numerical and experimental data were obtained, as percentage errors of less than 5.5% were reported when compared against experimental phantom measurement from the literature. To demonstrate the applicability of the model to clinical measurements, the anisotropic ratio of sheep kidneys was quantified, with both numerical and derived experimental results reporting a value of.667. Analysis of the anisotropic ratio with progression of chronic kidney disease demonstrated that patients with normal kidneys would have a lower anisotropic ratio of.872 as opposed to patients suffering from renal impairment, in which the anisotropic ratio may increase to.904, as determined from this study. The findings demonstrate the potential of the anisotropic ratio in improving the detection of chronic kidney disease using shear wave elastography. © 2024 The Author(s). International Journal for Numerical Methods in Biomedical Engineering published by John Wiley & Sons Ltd.
publisher John Wiley and Sons Inc
issn 20407939
language English
format Article
accesstype All Open Access; Hybrid Gold Open Access
record_format scopus
collection Scopus
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