Abstract
Purpose: The purpose of this study was to compare the performance of deep learning
networks trained with complex-valued and magnitude images in suppressing the aliasing
artifact for highly accelerated real-time cine MRI.
Methods: Two3DU-netmodels(Complex-Valued-NetandMagnitude-Net)wereimplemented
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
withfullysampledandsynthetizedradialreal-timecinepairsgeneratedfromhighlyundersampled
(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 those of 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 those of 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