TY - JOUR
T1 - Comparison of Complex k-Space Data and Magnitude-Only for Training of Deep Learning–Based Artifact Suppression for Real-Time Cine MRI
AU - Haji-Valizadeh, Hassan
AU - Guo, Rui
AU - Kucukseymen, Selcuk
AU - Tuyen, Yankama
AU - Rodriguez, Jennifer
AU - Paskavitz, Amanda
AU - Pierce, Patrick
AU - Goddu, Beth
AU - Ngo, Long H.
AU - Nezafat, Reza
N1 - Publisher Copyright:
© Copyright © 2021 Haji-Valizadeh, Guo, Kucukseymen, Tuyen, Rodriguez, Paskavitz, Pierce, Goddu, Ngo and Nezafat.
PY - 2021/9/8
Y1 - 2021/9/8
N2 - 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.
AB - 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.
KW - artifact suppression
KW - complex
KW - deep learning
KW - magnitude
KW - real-time cine
UR - http://www.scopus.com/inward/record.url?scp=85115353074&partnerID=8YFLogxK
U2 - 10.3389/fphy.2021.684184
DO - 10.3389/fphy.2021.684184
M3 - Article
AN - SCOPUS:85115353074
SN - 2296-424X
VL - 9
JO - Frontiers in Physics
JF - Frontiers in Physics
M1 - 684184
ER -