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

Authors :
Sergio Uribe
Hernán Mella
Joaquín Mura
Julio Sotelo
Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile.
Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile.
ANID - Millennium Science Initiative Program - Millennium Nucleus in Cardiovascular Magnetic Resonance, Santiago, Chile
Department of Mechanical Engineering, Universidad Técnica Federico Santa María, Santiago, Chile.
School of Biomedical Engineering, Universidad de Valparaíso, Valparaíso, Chile.
Department of Radiology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.
Source :
Magnetic Resonance Imaging, Magnetic Resonance Imaging, Elsevier, 2021, 83, pp.14-26. ⟨10.1016/j.mri.2021.07.001⟩
Publication Year :
2020

Abstract

International audience; We 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 throughplane 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 2 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.

Details

ISSN :
18735894 and 0730725X
Volume :
83
Database :
OpenAIRE
Journal :
Magnetic resonance imaging
Accession number :
edsair.doi.dedup.....cc5f2baf7f2dfc20b77ba625cf808f4a