Image Quality Assessment to Emulate Experts’ Perception in Lumbar MRI Using Machine Learning

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dc.contributor.authorChabert, Steren
dc.contributor.authorCastro, Juan Sebastian
dc.contributor.authorMuñoz, Leonardo
dc.contributor.authorCox, Pablo
dc.contributor.authorRiveros, Rodrigo
dc.contributor.authorVielma, Juan
dc.contributor.authorHuerta, Gamaliel
dc.contributor.authorQuerales, Marvin
dc.contributor.authorSaavedra, Carolina
dc.contributor.authorVeloz, Alejandro
dc.contributor.authorSalas, Rodrigo
dc.date.accessioned2022-11-30T02:46:16Z
dc.date.available2022-11-30T02:46:16Z
dc.date.issued2021
dc.description.abstractMedical image quality is crucial to obtaining reliable diagnostics. Most quality controls rely on routine tests using phantoms, which do not reflect closely the reality of images obtained on patients and do not reflect directly the quality perceived by radiologists. The purpose of this work is to develop a method that classifies the image quality perceived by radiologists in MR images. The focus was set on lumbar images as they are widely used with different challenges. Three neuroradiologists evaluated the image quality of a dataset that included T1-weighting images in axial and sagittal orientation, and sagittal T2-weighting. In parallel, we introduced the computational assessment using a wide range of features extracted from the images, then fed them into a classifier system. A total of 95 exams were used, from our local hospital and a public database, and part of the images was manipulated to broaden the distribution quality of the dataset. Good recall of 82% and an area under curve (AUC) of 77% were obtained on average in testing condition, using a Support Vector Machine. Even though the actual implementation still relies on user interaction to extract features, the results are promising with respect to a potential implementation for monitoring image quality online with the acquisition process.en_ES
dc.facultadFacultad de Ingenieríaen_ES
dc.file.nameChabert_Ima2021.pdf
dc.identifier.citationChabert, S.; Castro, J.S.; Muñoz, L.; Cox, P.; Riveros, R.; Vielma, J.; Huerta, G.; Querales, M.; Saavedra, C.; Veloz, A.; Salas, R. Image Quality Assessment to Emulate Experts’ Perception in Lumbar MRI Using Machine Learning. Appl. Sci. 2021, 11, 6616. https://doi.org/10.3390/app11146616en_ES
dc.identifier.doihttps://doi.org/10.3390/app11146616
dc.identifier.urihttp://repositoriobibliotecas.uv.cl/handle/uvscl/7285
dc.languageen
dc.publisherMDPI
dc.rightsThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
dc.sourceApplied Sciences
dc.subjectAPPLIED SCIENCESen_ES
dc.titleImage Quality Assessment to Emulate Experts’ Perception in Lumbar MRI Using Machine Learning
dc.typeArticulo
uv.departamentoEscuela de Ingenieria Biomedica

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