A motion-corrected deep-learning reconstruction framework for accelerating whole-heart magnetic resonance imaging in patients with congenital heart disease

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dc.article.number101039
dc.catalogadoryvc
dc.contributor.authorPhair, Andrew
dc.contributor.authorFotaki, Anastasia
dc.contributor.authorFelsner, Lina
dc.contributor.authorFletcher, Thomas J.
dc.contributor.authorQi, Haikun
dc.contributor.authorBotnar, Rene Michael
dc.contributor.authorPrieto Vásquez, Claudia del Carmen
dc.date.accessioned2024-07-17T21:51:00Z
dc.date.available2024-07-17T21:51:00Z
dc.date.issued2024
dc.description.abstractBackground: Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment and management of adult patients with congenital heart disease (CHD). However, conventional techniques for three-dimensional (3D) whole-heart acquisition involve long and unpredictable scan times and methods that accelerate scans via k-space undersampling often rely on long iterative reconstructions. Deep-learning-based reconstruction methods have recently attracted much interest due to their capacity to provide fast reconstructions while often outperforming existing state-of-the-art methods. In this study, we sought to adapt and validate a non-rigid motion-corrected model-based deep learning (MoCo-MoDL) reconstruction framework for 3D whole-heart MRI in a CHD patient cohort. Methods: The previously proposed deep-learning reconstruction framework MoCo-MoDL, which incorporates a non-rigid motion-estimation network and a denoising regularization network within an unrolled iterative reconstruction, was trained in an end-to-end manner using 39 CHD patient datasets. Once trained, the framework was evaluated in eight CHD patient datasets acquired with seven-fold prospective undersampling. Reconstruction quality was compared with the state-of-the-art non-rigid motion-corrected patch-based low-rank reconstruction method (NR-PROST) and against reference images (acquired with three-or-four-fold undersampling and reconstructed with NR-PROST). Results: Seven-fold undersampled scan times were 2.1 ± 0.3 minutes and reconstruction times were ∼30 seconds, approximately 240 times faster than an NR-PROST reconstruction. Image quality comparable to the reference images was achieved using the proposed MoCo-MoDL framework, with no statistically significant differences found in any of the assessed quantitative or qualitative image quality measures. Additionally, expert image quality scores indicated the MoCo-MoDL reconstructions were consistently of a higher quality than the NR-PROST reconstructions of the same data, with the differences in 12 of the 22 scores measured for individual vascular structures found to be statistically significant. Conclusion: The MoCo-MoDL framework was applied to an adult CHD patient cohort, achieving good quality 3D whole-heart images from ∼2-minute scans with reconstruction times of ∼30 seconds.
dc.format.extent12 páginas
dc.fuente.origenSCOPUS
dc.identifier.doi10.1016/j.jocmr.2024.101039
dc.identifier.issn1532429X 10976647
dc.identifier.scopusidScopus_ID:85189706782
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/87095
dc.information.autorucInstituto de Ingeniería Biológica y Médica; Botnar, Rene Michael; 0000-0003-2811-2509; 1015313
dc.information.autorucEscuela de Ingeniería;Prieto Vásquez, Claudia del Carmen;0000-0003-4602-2523;14195
dc.language.isoen
dc.nota.accesocontenido completo
dc.publisherElsevier B.V.
dc.revistaJournal of Cardiovascular Magnetic Resonance
dc.rightsacceso abierto
dc.rights.licenseCC BY Atribución 4.0 Internacional
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject3D whole-heart
dc.subjectCardiac MRI
dc.subjectCongenital heart disease
dc.subjectConvolutional neural network
dc.subjectImage reconstruction
dc.subjectMotion correction
dc.subject.ddc610
dc.titleA motion-corrected deep-learning reconstruction framework for accelerating whole-heart magnetic resonance imaging in patients with congenital heart disease
dc.typeartículo
dc.volumen26
sipa.codpersvinculados1015313
sipa.codpersvinculados14195
sipa.trazabilidadSCOPUS;2024-04-14
sipa.trazabilidadORCID;2024-07-14
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