Reducing motion artifacts in the aorta: super-resolution deep learning reconstruction with motion reduction algorithm

SR-DLR-M provided significantly better CT images in diagnosing aortic dissection compared to SR-DLR and DLR-M.

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Publication Details

Journal
Japanese Journal of Radiology
Published
2025-08-09
DOI
https://doi.org/10.1007/s11604-025-01849-8
Citations
5
Primary Topic
Cardiac Imaging and Diagnostics
Type
article
Field-Weighted Citation Impact
6.80

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article

Reducing motion artifacts in the aorta: super-resolution deep learning reconstruction with motion reduction algorithm

Osamu Abe, Rintaro Miyo, Koichiro Yasaka, Rin Tsujimoto
5 citations
Japanese Journal of Radiology
Cardiac Imaging and Diagnostics
6.80
article

Reducing motion artifacts in the aorta: super-resolution deep learning reconstruction with motion reduction algorithm

Osamu Abe, Rintaro Miyo, Koichiro Yasaka, Rin Tsujimoto
article en
5 citations

Abstract

SR-DLR-M provided significantly better CT images in diagnosing aortic dissection compared to SR-DLR and DLR-M.

Japanese Journal of RadiologyVol. 43(12)
University of Tokyo Hospital (JP)
University of Tokyo
Sustainable cities and communities
Openalex Percentile: Top 3%
Cardiac Imaging and Diagnostics
6.80
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Reducing motion artifacts in the aorta: super-resolution deep learning reconstruction with motion reduction algorithm — Osamu Abe, Rintaro Miyo, et al. · Japanese Journal of Radiology (2025) | TGRS Research Map | TGRS