A comprehensive comparison between shortest-path HARP refinement, SinMod, and DENSEanalysis processing tools applied to CSPAMM and DENSE images

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dc.contributor.authorMella, Hernán
dc.contributor.authorMura, Joaquín
dc.contributor.authorSotelo, Julio
dc.contributor.authorUribe, Sergio
dc.date.accessioned2022-11-30T02:46:34Z
dc.date.available2022-11-30T02:46:34Z
dc.date.issued2021
dc.description.abstractWe addressed comprehensively the performance of Shortest-Path HARP Refinement (SP-HR), SinMod, and DENSEanalysis using 2D slices of synthetic CSPAMM and DENSE images with realistic contrasts obtained from 3D phantoms. The three motion estimation techniques were interrogated under ideal and no-ideal conditions (with MR induced artifacts, noise, and through-plane motion), considering several resolutions and noise levels. Under noisy conditions, and for isotropic pixel sizes of 1.5 mm and 3.0 mm in CSPAMM and DENSE images respectively, the nRMSE obtained for the circumferential and radial strain components were 10.7 ± 10.8% and 25.5 ± 14.8% using SP-HR, 11.9 ± 2.5% and 29.3 ± 6.5% using SinMod, and 6.4 ± 2.0% and 18.2 ± 4.6% using DENSEanalysis. Overall, the results showed that SP-HR tends to fail for large tissue motions, whereas SinMod and DENSEanalysis gave accurate displacement and strain field estimations, being the last which performed the best.en_ES
dc.facultadFacultad de Ingenieríaen_ES
dc.file.nameMella_Com2021.pdf
dc.identifier.doihttps://doi.org/10.1016/j.mri.2021.07.001
dc.identifier.urihttp://repositoriobibliotecas.uv.cl/handle/uvscl/7421
dc.languageen
dc.publisherElsevier
dc.rights© 2021 Elsevier Inc. All rights reserved.
dc.sourceMagnetic Resonance Imaging
dc.subjectTAGGING MRIen_ES
dc.subjectCSPAMMen_ES
dc.subjectDENSE MRIen_ES
dc.subjectCARDIAC MRIen_ES
dc.subjectCARDIAC STRAINen_ES
dc.subjectHARPen_ES
dc.subjectSP-HRen_ES
dc.subjectSINMODen_ES
dc.subjectDENSEANALYSISen_ES
dc.titleA comprehensive comparison between shortest-path HARP refinement, SinMod, and DENSEanalysis processing tools applied to CSPAMM and DENSE images
dc.typeArticulo
uv.departamentoEscuela de Ingenieria Biomedica
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