A multi-scale variational neural network for accelerating motion-compensated whole-heart 3D coronary MR angiography |
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| Institution: | 1. Department of Radiology, Zhongshan Hospital, Fudan University, and Shanghai Institute of Medical Imaging, Shanghai 200032, China;2. Department of Medical Imaging, Shanghai Medical school, Fudan University;3. Department of cardiology, Zhongshan Hospital, Fudan University;4. Department of Radiology, The 5th People''s Hospital of Shanghai, Fudan University, Shanghai, China;1. Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany;2. Department of Radiology, University of Tübingen, Tübingen, Germany;3. Section on Experimental Radiology, University of Tübingen, Germany;4. Department of Electronic Engineering, Chinese University of Hong Kong, Hong Kong;5. INSERM U1101, LaTIM, University of Bretagne, Brest, France;1. Department of Computer Science, University College London, London WC1E 6BT, United Kingdom;2. Centre for Cardiovascular Imaging, Institute of Cardiovascular Science, University College London, London WC1N 1EH, United Kingdom;1. Department of Biomedical Engineering, University of Southern California, Los Angeles, USA;2. Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, USA;3. Division of Imaging Sciences and Biomedical Engineering, King''s College London, London, UK |
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| Abstract: | PurposeTo 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.MethodsEighteen 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.ResultsThe 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.ConclusionThe 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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