Comparison of Complex k-Space Data and Magnitude-Only for Training of Deep Learning–Based Artifact Suppression for Real-Time Cine MRI - Haji-Valizadeh, Hassan; Guo, Rui; Kucukseymen, Selcuk; Tuyen, Yankama; Rodriguez, Jennifer; Paskavitz, Amanda; Pierce, Patrick; Goddu, Beth; Ngo, Long H.; Nezafat, Reza (Frontiers in Physics, 2021)
Propose The purpose of this study was to compare the performance of deep-learning networks trained with complex-valued and magnitude images in suppressing aliasing artifact for highly accelerated real-time cine MRI. Methods Two 3D U-net models (Complex-Valued-Net and Magnitude-Net) were implemented to suppress aliasing artifacts in real-time cine images. ECG-segmented cine images (n=503) generated from both complex k-space data and magnitude-only DICOM were used to synthetize radial real-time cine MRI. Complex-Valued-Net and Magnitude-Net were trained with fully-sampled and synthetized radial real-time cine pairs generated from highly undersampled (12-fold) complex k-space and DICOM images, respectively. Real-time cine was prospectively acquired in 29 patients with 12-fold accelerated free-breathing tiny golden-angle radial sequence and reconstructed with both Complex-Valued-Net and Magnitude-Net. Cardiac function, left-ventricular (LV) structure, and subjective image quality (1(non-diagnostic)-5(excellent)) were calculated from Complex-Valued-Net and Magnitude-Net reconstructed real-time cine datasets and compared to ECG-segmented cine (reference). Results Free-breathing real-time cine reconstructed by both networks had high correlation (all R2>0.7) and good agreement (all P>0.05) with standard clinical ECG-segmented cine with respect to LV function and structural parameters. Real-time cine reconstructed by Complex-Valued-Net had superior image quality compared to images from Magnitude-Net in terms of myocardial edge sharpness (Complex-Valued-Net=3.5±0.5; Magnitude-Net=2.6±0.5), temporal fidelity (Complex-Valued-Net=3.1±0.4; Magnitude-Net=2.1±0.4), and artifact suppression (Complex-Valued-Net=3.1±0.5; Magnitude-Net=2.0±0.0), which were all inferior to ECG-segmented cine (4.1±1.4, 3.9±1.0, and 4.0±1.1). Conclusion Compared to Magnitude-Net, Complex-valued-Net produced improved subjective image quality for reconstructed real-time cine images and did not show any difference in quantitative measures of LV function and structure.