Similarity-driven motion-resolved reconstruction for ferumoxytol-enhanced whole-heart MRI in congenital heart disease

• Published in: PloS one Vol. 19; no. 6; p. e0304612
• Main Authors: Romanin, Ludovica, Milani, Bastien, Roy, Christopher W, Yerly, Jérôme, Bustin, Aurélien, Si-Mohamed, Salim, Prsa, Milan, Rutz, Tobias, Tenisch, Estelle, Schwitter, Juerg, Stuber, Matthias, Piccini, Davide
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 13.06.2024
• Subjects: Adolescent; Adult; Algorithms; Artificial neural networks; Automation; Biology and Life Sciences; Blood; Cardiovascular disease; Cardiovascular diseases; Child; Clustering; Clusters; Compensation; Congenital diseases; Congenital heart disease; Contrast Media; Coronary artery; Coronary vessels; Data acquisition; Female; Ferrosoferric Oxide; Fourier transforms; Genetic disorders; Heart; Heart - diagnostic imaging; Heart - physiopathology; Heart Defects, Congenital - diagnostic imaging; Heart diseases; Humans; Hypotheses; Image contrast; Image processing; Image Processing, Computer-Assisted - methods; Image quality; Image reconstruction; Liver; Lungs; Magnetic resonance; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medical imaging; Medicine and Health Sciences; Motion; Motion compensation; Myocardium; Neural networks; Physical Sciences; Physiology; Regularization; Research and Analysis Methods; Sharpness; Similarity; Young Adult; Germany
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0304612

A similarity-driven multi-dimensional binning algorithm (SIMBA) reconstruction of free-running cardiac magnetic resonance imaging data was previously proposed. While very efficient and fast, the original SIMBA focused only on the reconstruction of a single motion-consistent cluster, discarding the remaining data acquired. However, the redundant data clustered by similarity may be exploited to further improve image quality. In this work, we propose a novel compressed sensing (CS) reconstruction that performs an effective regularization over the clustering dimension, thanks to the integration of inter-cluster motion compensation (XD-MC-SIMBA). This reconstruction was applied to free-running ferumoxytol-enhanced datasets from 24 patients with congenital heart disease, and compared to the original SIMBA, the same XD-MC-SIMBA reconstruction but without motion compensation (XD-SIMBA), and a 5D motion-resolved CS reconstruction using the free-running framework (FRF). The resulting images were compared in terms of lung-liver and blood-myocardium sharpness, blood-myocardium contrast ratio, and visible length and sharpness of the coronary arteries. Moreover, an automated image quality score (IQS) was assigned using a pretrained deep neural network. The lung-liver sharpness and blood-myocardium sharpness were significantly higher in XD-MC-SIMBA and FRF. Consistent with these findings, the IQS analysis revealed that image quality for XD-MC-SIMBA was improved in 18 of 24 cases, compared to SIMBA. We successfully tested the hypothesis that multiple motion-consistent SIMBA clusters can be exploited to improve the quality of ferumoxytol-enhanced cardiac MRI when inter-cluster motion-compensation is integrated as part of a CS reconstruction.