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End-to-End Deep Learning-Based Motion Correction and Reconstruction for Accelerated Whole-Heart Joint T1/T2 Mapping.

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Bibliographic Details
Title: End-to-End Deep Learning-Based Motion Correction and Reconstruction for Accelerated Whole-Heart Joint T1/T2 Mapping.
Authors: Felsner L; School of Biomedical Engineering and Imaging Science, King's College London, London, United Kingdom., Velasco C; School of Biomedical Engineering and Imaging Science, King's College London, London, United Kingdom., Phair A; School of Biomedical Engineering and Imaging Science, King's College London, London, United Kingdom., Fletcher TJ; School of Biomedical Engineering and Imaging Science, King's College London, London, United Kingdom., Qi H; School of Biomedical Engineering, ShanghaiTech University, Shanghai, China., Botnar RM; School of Biomedical Engineering and Imaging Science, King's College London, London, United Kingdom; School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile; Institute for Biological and Biomedical Engineering, Santiago, Chile; Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile; Institute of Advanced Study, Technical University of Munich, Munich, Germany., Prieto C; School of Biomedical Engineering and Imaging Science, King's College London, London, United Kingdom; School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile; Institute for Biological and Biomedical Engineering, Santiago, Chile; Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile. Electronic address: claudia.prieto@kcl.ac.uk.
Source: Magnetic resonance imaging [Magn Reson Imaging] 2025 Sep; Vol. 121, pp. 110396. Date of Electronic Publication: 2025 Apr 21.
Publication Type: Journal Article
Journal Info: Publisher: Elsevier Country of Publication: Netherlands NLM ID: 8214883 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-5894 (Electronic) Linking ISSN: 0730725X NLM ISO Abbreviation: Magn Reson Imaging Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1873-5894
DOI:10.1016/j.mri.2025.110396