End-to-end deep learning-based motion correction and reconstruction for accelerated whole-heart joint T 1/T 2 mapping.

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dc.article.number110396
dc.catalogadoraba
dc.contributor.authorFelsner, Lina
dc.contributor.authorVelasco, Carlos
dc.contributor.authorPhair, Andrew
dc.contributor.authorFletcher, Thomas J.
dc.contributor.authorQi, Haikun
dc.contributor.authorBotnar, René Michael
dc.contributor.authorPrieto Vásquez, Claudia
dc.date.accessioned2025-05-16T20:12:14Z
dc.date.available2025-05-16T20:12:14Z
dc.date.issued2025
dc.description.abstractPURPOSE: To accelerate 3D whole-heart joint T 1/T 2 mapping for myocardial tissue characterization using an end-to-end deep learning algorithm for joint motion estimation and model-based motion-corrected reconstruction of multi-contrast undersampled data.METHODS: A free-breathing high-resolution motion-compensated 3D joint T 1/T 2 water/fat sequence is employed. The sequence consists of the acquisition of four interleaved volumes with 2-echo encoding, resulting in eight volumes with different contrasts. An end-to-end non-rigid motion-corrected reconstruction network is used to estimate high quality motion-corrected reconstructions from the eight multi-contrast undersampled data for subsequent joint T 1/T 2 mapping. Reconstruction with the proposed approach was compared against state-of-the-art motion-corrected HD-PROST reconstruction.RESULTS: The proposed approach yields images with good visual agreement compared to the reference reconstructions. The comparison of the quantitative values in the T 1 and T 2 maps showed the absence of systematic errors, and a small bias of -6.35 ms and -1.8 ms, respectively. The proposed reconstruction time was 24 seconds in comparison to 2.5 hours with motion-corrected HD-PROST, resulting in a reconstruction speed-up of over 370 times.CONCLUSION: In conclusion, this study presents a promising method for efficient whole-heart myocardial tissue characterization. Specifically, the research highlights the potential of the multi-contrast end-to-end deep learning algorithm for joint motion estimation and model-based motion-corrected reconstruction of multi-contrast undersampled data. The findings underscore its ability to compute T 1 and T 2 values with good agreement when compared to the reference motion-corrected HD-PROST method, while substantially reducing reconstruction time.
dc.fuente.origenORCID
dc.identifier.doi10.1016/j.mri.2025.110396
dc.identifier.scopusid2-s2.0-105004600227
dc.identifier.urihttps://doi.org/10.1016/j.mri.2025.110396
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/104357
dc.information.autorucEscuela de Ingeniería; Botnar , Rene Michael; 0000-0003-2811-2509; 1015313
dc.information.autorucEscuela de Ingeniería; Prieto Vásquez, Claudia; 0000-0003-4602-2523; 14195
dc.language.isoen
dc.nota.accesocontenido completo
dc.revistaMagnetic Resonance Imaging
dc.rightsacceso abierto
dc.rights.licenseAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectMyocardial tissue characterization
dc.subjectWhole-heart joint T1/T2 mapping
dc.subjectEnd-to-end deep learning reconstruction
dc.subjectMotion-correction
dc.subject.ddc620
dc.subject.deweyIngenieríaes_ES
dc.titleEnd-to-end deep learning-based motion correction and reconstruction for accelerated whole-heart joint T 1/T 2 mapping.
dc.typeartículo
dc.volumen121
sipa.codpersvinculados1015313
sipa.codpersvinculados1015313
sipa.codpersvinculados14195
sipa.trazabilidadORCID;2025-05-07
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