Simultaneous Imaging of Ultrasonic Backscatter and Attenuation Coefficients for Liver Steatosis Detection in a Murine Animal Model
| dc.contributor.affiliation | Pontificia Universidad Católica del Perú | |
| dc.contributor.author | Timana, J. | |
| dc.contributor.author | Chahuara, H. | |
| dc.contributor.author | Basavarajappa, L. | |
| dc.contributor.author | Basarab, A. | |
| dc.contributor.author | Hoyt, K. | |
| dc.contributor.author | Lavarello Montero, R. | |
| dc.date.accessioned | 2026-03-13T16:58:42Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Non-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.sponsorship | Funding: 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.doi | https://doi.org/10.1109/ISBI52829.2022.9761657 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.14657/206025 | |
| dc.language.iso | eng | |
| dc.publisher | IEEE Computer Society | |
| dc.relation.conferencename | Proceedings - InterNational Symposium on Biomedical Imaging; Vol. 2022-March (2022) | |
| dc.relation.ispartof | urn:isbn:978-1-6654-0539-2 | |
| dc.rights | info:eu-repo/semantics/closedAccess | |
| dc.subject | Fatty liver | |
| dc.subject | Steatosis | |
| dc.subject | Artificial intelligence | |
| dc.subject | Population | |
| dc.subject | Computer science | |
| dc.subject | Medicine | |
| dc.subject | Pathology | |
| dc.subject | Internal medicine | |
| dc.subject | Disease | |
| dc.subject.ocde | https://purl.org/pe-repo/ocde/ford#3.02.19 | |
| dc.title | Simultaneous Imaging of Ultrasonic Backscatter and Attenuation Coefficients for Liver Steatosis Detection in a Murine Animal Model | |
| dc.type | http://purl.org/coar/resource_type/c_5794 | |
| dc.type.other | Comunicación de congreso | |
| dc.type.version | https://vocabularies.coar-repositories.org/version_types/c_970fb48d4fbd8a85/ |
