Simultaneous Imaging of Ultrasonic Backscatter and Attenuation Coefficients for Liver Steatosis Detection in a Murine Animal Model

dc.contributor.affiliationPontificia Universidad Católica del Perú
dc.contributor.authorTimana, J.
dc.contributor.authorChahuara, H.
dc.contributor.authorBasavarajappa, L.
dc.contributor.authorBasarab, A.
dc.contributor.authorHoyt, K.
dc.contributor.authorLavarello Montero, R.
dc.date.accessioned2026-03-13T16:58:42Z
dc.date.issued2022
dc.description.abstractNon-alcoholic fatty liver disease (NAFLD) is one of the most prevalent chronic liver diseases. While early diagnosis is the most effective course of action, NAFLD diagnosis procedures are still limited since they are invasive and have a heavy component of subjectivity. In this paper, we present an approach based on Quantitative ultrasound (QUS) and Support Vector Machines (SVM) to detect liver steatosis based on the estimation of backscatter (BSC) and attenuation coefficients (AC) in a murine animal model. We tested our proposed method with data acquired from a population of 21 rats that were randomly divided into two groups subjected to two different diets. The results yielded by the estimation method at 15 MHz show a clear difference in the estimated QUS modalities in healthy liver, where BSC and AC mean and standard deviation values were found to be 0.22 ± 0.28 cm −1 • sr −1 and 0.54 ± 0.03 dB MHz −1 • cm −1 , respectively, with respect to fatty liver, where BSC• and AC mean values were found to be 0.74 ± 0.80 cm −1 • sr −1 and 0.64 ± 0.06 dB • MHz −1 • cm −1 , respectively. Furthermore, the SVM achieved an accuracy of 97.6% when discriminating between healthy and steatotic liver, thus constituting a promising alternative for non-invasive NAFLD diagnosis.
dc.description.sponsorshipFunding: This research was partially supported by National Institute of Health (NIH) Grants R01DK126833, R01EB025841, and R21EB025290, Cancer Preventión and Research Institute of Texas (CPRIT) award RP180670T, and by the Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica (CONCYTEC) under the research grant 150-2020-FONDECYT. The authors have no relevant financial or non-financial interests to disclose.
dc.identifier.doihttps://doi.org/10.1109/ISBI52829.2022.9761657
dc.identifier.urihttp://hdl.handle.net/20.500.14657/206025
dc.language.isoeng
dc.publisherIEEE Computer Society
dc.relation.conferencenameProceedings - InterNational Symposium on Biomedical Imaging; Vol. 2022-March (2022)
dc.relation.ispartofurn:isbn:978-1-6654-0539-2
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectFatty liver
dc.subjectSteatosis
dc.subjectArtificial intelligence
dc.subjectPopulation
dc.subjectComputer science
dc.subjectMedicine
dc.subjectPathology
dc.subjectInternal medicine
dc.subjectDisease
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#3.02.19
dc.titleSimultaneous Imaging of Ultrasonic Backscatter and Attenuation Coefficients for Liver Steatosis Detection in a Murine Animal Model
dc.typehttp://purl.org/coar/resource_type/c_5794
dc.type.otherComunicación de congreso
dc.type.versionhttps://vocabularies.coar-repositories.org/version_types/c_970fb48d4fbd8a85/

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