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Multi‐domain convolutional neural network (MD‐CNN) for radial reconstruction of dynamic cardiac MRI


El‐Rewaidy, Hossam; Fahmy, Ahmed S; Pashakhanloo, Farhad; Cai, Xiaoying; Kucukseymen, Selcuk; Csecs, Ibolya; Neisius, Ulf; Haji‐Valizadeh, Hassan; Menze, Bjoern; Nezafat, Reza (2021). Multi‐domain convolutional neural network (MD‐CNN) for radial reconstruction of dynamic cardiac MRI. Magnetic Resonance in Medicine, 85(3):1195-1208.

Abstract

Purpose

Cardiac MR cine imaging allows accurate and reproducible assessment of cardiac function. However, its long scan time not only limits the spatial and temporal resolutions but is challenging in patients with breath‐holding difficulty or non‐sinus rhythms. To reduce scan time, we propose a multi‐domain convolutional neural network (MD‐CNN) for fast reconstruction of highly undersampled radial cine images.
Methods

MD‐CNN is a complex‐valued network that processes MR data in k‐space and image domains via k‐space interpolation and image‐domain subnetworks for residual artifact suppression. MD‐CNN exploits spatio‐temporal correlations across timeframes and multi‐coil redundancies to enable high acceleration. Radial cine data were prospectively collected in 108 subjects (50 ± 17 y, 72 males) using retrospective‐gated acquisition with 80%:20% split for training/testing. Images were reconstructed by MD‐CNN and k‐t Radial Sparse‐Sense(kt‐RASPS) using an undersampled dataset (14 of 196 acquired views; relative acceleration rate = 14). MD‐CNN images were evaluated quantitatively using mean‐squared‐error (MSE) and structural similarity index (SSIM) relative to reference images, and qualitatively by three independent readers for left ventricular (LV) border sharpness and temporal fidelity using 5‐point Likert‐scale (1‐non‐diagnostic, 2‐poor, 3‐fair, 4‐good, and 5‐excellent).
Results

MD‐CNN showed improved MSE and SSIM compared to kt‐RASPS (0.11 ± 0.10 vs. 0.61 ± 0.51, and 0.87 ± 0.07 vs. 0.72 ± 0.07, respectively; P < .01). Qualitatively, MD‐CCN significantly outperformed kt‐RASPS in LV border sharpness (3.87 ± 0.66 vs. 2.71 ± 0.58 at end‐diastole, and 3.57 ± 0.6 vs. 2.56 ± 0.6 at end‐systole, respectively; P < .01) and temporal fidelity (3.27 ± 0.65 vs. 2.59 ± 0.59; P < .01).
Conclusion

MD‐CNN reduces the scan time of cine imaging by a factor of 23.3 and provides superior image quality compared to kt‐RASPS.

Abstract

Purpose

Cardiac MR cine imaging allows accurate and reproducible assessment of cardiac function. However, its long scan time not only limits the spatial and temporal resolutions but is challenging in patients with breath‐holding difficulty or non‐sinus rhythms. To reduce scan time, we propose a multi‐domain convolutional neural network (MD‐CNN) for fast reconstruction of highly undersampled radial cine images.
Methods

MD‐CNN is a complex‐valued network that processes MR data in k‐space and image domains via k‐space interpolation and image‐domain subnetworks for residual artifact suppression. MD‐CNN exploits spatio‐temporal correlations across timeframes and multi‐coil redundancies to enable high acceleration. Radial cine data were prospectively collected in 108 subjects (50 ± 17 y, 72 males) using retrospective‐gated acquisition with 80%:20% split for training/testing. Images were reconstructed by MD‐CNN and k‐t Radial Sparse‐Sense(kt‐RASPS) using an undersampled dataset (14 of 196 acquired views; relative acceleration rate = 14). MD‐CNN images were evaluated quantitatively using mean‐squared‐error (MSE) and structural similarity index (SSIM) relative to reference images, and qualitatively by three independent readers for left ventricular (LV) border sharpness and temporal fidelity using 5‐point Likert‐scale (1‐non‐diagnostic, 2‐poor, 3‐fair, 4‐good, and 5‐excellent).
Results

MD‐CNN showed improved MSE and SSIM compared to kt‐RASPS (0.11 ± 0.10 vs. 0.61 ± 0.51, and 0.87 ± 0.07 vs. 0.72 ± 0.07, respectively; P < .01). Qualitatively, MD‐CCN significantly outperformed kt‐RASPS in LV border sharpness (3.87 ± 0.66 vs. 2.71 ± 0.58 at end‐diastole, and 3.57 ± 0.6 vs. 2.56 ± 0.6 at end‐systole, respectively; P < .01) and temporal fidelity (3.27 ± 0.65 vs. 2.59 ± 0.59; P < .01).
Conclusion

MD‐CNN reduces the scan time of cine imaging by a factor of 23.3 and provides superior image quality compared to kt‐RASPS.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Department of Quantitative Biomedicine
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Health Sciences > Radiology, Nuclear Medicine and Imaging
Uncontrolled Keywords:Radiology Nuclear Medicine and imaging
Language:English
Date:1 March 2021
Deposited On:29 Jan 2021 15:59
Last Modified:30 Jan 2021 21:01
Publisher:Wiley-Blackwell Publishing, Inc.
ISSN:0740-3194
OA Status:Hybrid
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1002/mrm.28485
PubMed ID:32924188

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