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

Simple item page
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.fechaingreso.objetodigital2024-07-18
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.subject.deweyMedicina y saludes_ES
dc.subject.ods03 Good health and well-being
dc.subject.odspa03 Salud y bienestar
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
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Full A motion-corrected deep-learning reconstruction framework.pdf
Size:
8.55 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Artículos de revistas