A multi-scale variational neural network for accelerating motion-compensated whole-heart 3D coronary MR angiography
Fuin N, Bustin A, Kustner T, Oksuz I, Clough J, King AP, Schnabel JA, Botnar RM, Prieto C (2020)
Publication Type: Journal article
Publication year: 2020
Journal
Book Volume: 70
Pages Range: 155-167
DOI: 10.1016/j.mri.2020.04.007
Abstract
Purpose: To enable fast reconstruction of undersampled motion-compensated whole-heart 3D coronary magnetic resonance angiography (CMRA) by learning a multi-scale variational neural network (MS-VNN) which allows the acquisition of high-quality 1.2 × 1.2 × 1.2 mm isotropic volumes in a short and predictable scan time. Methods: Eighteen healthy subjects and one patient underwent free-breathing 3D CMRA acquisition with variable density spiral-like Cartesian sampling, combined with 2D image navigators for translational motion estimation/compensation. The proposed MS-VNN learns two sets of kernels and activation functions for the magnitude and phase images of the complex-valued data. For the magnitude, a multi-scale approach is applied to better capture the small calibre of the coronaries. Ten subjects were considered for training and validation. Prospectively undersampled motion-compensated data with 5-fold and 9-fold accelerations, from the remaining 9 subjects, were used to evaluate the framework. The proposed approach was compared to Wavelet-based compressed-sensing (CS), conventional VNN, and to an additional fully-sampled (FS) scan. Results: The average acquisition time (m:s) was 4:11 for 5-fold, 2:34 for 9-fold acceleration and 18:55 for fully-sampled. Reconstruction time with the proposed MS-VNN was ~14 s. The proposed MS-VNN achieves higher image quality than CS and VNN reconstructions, with quantitative right coronary artery sharpness (CS:43.0%, VNN:43.9%, MS-VNN:47.0%, FS:50.67%) and vessel length (CS:7.4 cm, VNN:7.7 cm, MS-VNN:8.8 cm, FS:9.1 cm) comparable to the FS scan. Conclusion: The proposed MS-VNN enables 5-fold and 9-fold undersampled CMRA acquisitions with comparable image quality that the corresponding fully-sampled scan. The proposed framework achieves extremely fast reconstruction time and does not require tuning of regularization parameters, offering easy integration into clinical workflow.
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APA:
Fuin, N., Bustin, A., Kustner, T., Oksuz, I., Clough, J., King, A.P.,... Prieto, C. (2020). A multi-scale variational neural network for accelerating motion-compensated whole-heart 3D coronary MR angiography. Magnetic Resonance Imaging, 70, 155-167. https://doi.org/10.1016/j.mri.2020.04.007
MLA:
Fuin, Niccolo, et al. "A multi-scale variational neural network for accelerating motion-compensated whole-heart 3D coronary MR angiography." Magnetic Resonance Imaging 70 (2020): 155-167.
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