Comparison of Complex k-Space Data and Magnitude-Only for Training of Deep Learning–Based Artifact Suppression for Real-Time Cine MRI

Abstract

Propose: 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: 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 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.

An inline deep learning based free-breathing ECG-free cine for exercise cardiovascular magnetic resonance

“…The U-Net was implemented in PyTorch v1.9.0 (Facebook, Menlo Park, California, USA) and trained for 2,900 iterations. The initial learning rate was set at 0.001 and was reduced by 5% every 100 iterations, using the mean square error loss function and Adam optimizer [ 29 ].…”

Automated T1 and T2 mapping segmentation on cardiovascular magnetic resonance imaging using deep learning

“…at k-space field, when machine learning reconstruction approaches were used to learn non-linear optimization and improve the CMR reconstruction when an MR-based acceleration technique (e.g., Compressed Sensing) was used ( 9 ). In another perspective DL can be applied at k-space to suppress the artifacts before the image reconstruction ( 10 ).…”

DeepFittingNet: A deep neural network‐based approach for simplifying cardiac T1 and T2 estimation with improved robustness

“…9,11 In recent years, various deep learning-based approaches have been proposed for cardiac parametric mapping. [12][13][14][15][16][17][18][19] To minimize cardiac motion artifacts, a convolutional neural network was built for reconstructing T 1 -weighted images of highly accelerated radial data acquired within a short period. 20 Zhang et al, Hamilton et al, and Shao et al sought to use deep-learning methods to speed up the reconstruction of T 1 and T 2 maps for MR fingerprinting and BLESSPC techniques.…”

“…In recent years, various deep learning‐based approaches have been proposed for cardiac parametric mapping 12‐19 . To minimize cardiac motion artifacts, a convolutional neural network was built for reconstructing T 1 ‐weighted images of highly accelerated radial data acquired within a short period 20 .…”