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Framework for estimating renal function using magnetic resonance imaging

PURPOSE: Nephrologists have empirically predicted renal function from renal morphology. In diagnosing a case of renal dysfunction of unknown course, acute kidney injury and chronic kidney disease are diagnosed from blood tests and an imaging study including magnetic resonance imaging (MRI), and an e...

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Autores principales: Ishikawa, Masahiro, Inoue, Tsutomu, Kozawa, Eito, Okada, Hirokazu, Kobayashi, Naoki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Society of Photo-Optical Instrumentation Engineers 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8923691/
https://www.ncbi.nlm.nih.gov/pubmed/35360418
http://dx.doi.org/10.1117/1.JMI.9.2.024501
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author Ishikawa, Masahiro
Inoue, Tsutomu
Kozawa, Eito
Okada, Hirokazu
Kobayashi, Naoki
author_facet Ishikawa, Masahiro
Inoue, Tsutomu
Kozawa, Eito
Okada, Hirokazu
Kobayashi, Naoki
author_sort Ishikawa, Masahiro
collection PubMed
description PURPOSE: Nephrologists have empirically predicted renal function from renal morphology. In diagnosing a case of renal dysfunction of unknown course, acute kidney injury and chronic kidney disease are diagnosed from blood tests and an imaging study including magnetic resonance imaging (MRI), and an examination/treatment policy is determined. A framework for the estimation of renal function from water images obtained using the Dixon method is proposed to provide information that helps clinicians reach a diagnosis by accurately estimating renal function on the basis of renal MRI. APPROACH: The proposed framework consists of four steps. First, the kidney area is extracted by MRI using the Dixon method with a U-net by deep learning. Second, the extracted renal region is registered with the target mask. Third, the kidney features are calculated based on the target mask classification information created by a specialist. Fourth, the estimated glomerular filtration rate (eGFR) representing the renal function is estimated using a regression support vector machine from the calculated features. RESULTS: For the accuracy evaluation, we conducted an experiment to estimate the eGFR when MRI was performed and the eGFR slope, which is the annual rate of decline in eGFR. When the accuracy was evaluated for 165 subjects, the eGFR was estimated to have a root mean square error (RMSE) of 11.99 and a correlation coefficient of 0.83. Moreover, the eGFR slope was estimated to have an RMSE of 4.8 and a correlation coefficient of 0.5. CONCLUSIONS: Therefore, the proposed method shows the possibility of estimating the prognosis of renal function based on water images obtained by the Dixon method.
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spelling pubmed-89236912023-03-15 Framework for estimating renal function using magnetic resonance imaging Ishikawa, Masahiro Inoue, Tsutomu Kozawa, Eito Okada, Hirokazu Kobayashi, Naoki J Med Imaging (Bellingham) Computer-Aided Diagnosis PURPOSE: Nephrologists have empirically predicted renal function from renal morphology. In diagnosing a case of renal dysfunction of unknown course, acute kidney injury and chronic kidney disease are diagnosed from blood tests and an imaging study including magnetic resonance imaging (MRI), and an examination/treatment policy is determined. A framework for the estimation of renal function from water images obtained using the Dixon method is proposed to provide information that helps clinicians reach a diagnosis by accurately estimating renal function on the basis of renal MRI. APPROACH: The proposed framework consists of four steps. First, the kidney area is extracted by MRI using the Dixon method with a U-net by deep learning. Second, the extracted renal region is registered with the target mask. Third, the kidney features are calculated based on the target mask classification information created by a specialist. Fourth, the estimated glomerular filtration rate (eGFR) representing the renal function is estimated using a regression support vector machine from the calculated features. RESULTS: For the accuracy evaluation, we conducted an experiment to estimate the eGFR when MRI was performed and the eGFR slope, which is the annual rate of decline in eGFR. When the accuracy was evaluated for 165 subjects, the eGFR was estimated to have a root mean square error (RMSE) of 11.99 and a correlation coefficient of 0.83. Moreover, the eGFR slope was estimated to have an RMSE of 4.8 and a correlation coefficient of 0.5. CONCLUSIONS: Therefore, the proposed method shows the possibility of estimating the prognosis of renal function based on water images obtained by the Dixon method. Society of Photo-Optical Instrumentation Engineers 2022-03-15 2022-03 /pmc/articles/PMC8923691/ /pubmed/35360418 http://dx.doi.org/10.1117/1.JMI.9.2.024501 Text en © 2022 The Authors https://creativecommons.org/licenses/by/4.0/Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
spellingShingle Computer-Aided Diagnosis
Ishikawa, Masahiro
Inoue, Tsutomu
Kozawa, Eito
Okada, Hirokazu
Kobayashi, Naoki
Framework for estimating renal function using magnetic resonance imaging
title Framework for estimating renal function using magnetic resonance imaging
title_full Framework for estimating renal function using magnetic resonance imaging
title_fullStr Framework for estimating renal function using magnetic resonance imaging
title_full_unstemmed Framework for estimating renal function using magnetic resonance imaging
title_short Framework for estimating renal function using magnetic resonance imaging
title_sort framework for estimating renal function using magnetic resonance imaging
topic Computer-Aided Diagnosis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8923691/
https://www.ncbi.nlm.nih.gov/pubmed/35360418
http://dx.doi.org/10.1117/1.JMI.9.2.024501
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