A multi-scale variational neural network for accelerating motion-compensated whole-heart 3D coronary MR angiography
| Autor: | Julia A. Schnabel, James R. Clough, René M. Botnar, Ilkay Oksuz, Niccolo Fuin, Andrew P. King, Thomas Küstner, Claudia Prieto, Aurelien Bustin |
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| Rok vydání: | 2019 |
| Předmět: |
Adult
Male Computer science Image quality Movement Biomedical Engineering Biophysics Coronary Angiography Regularization (mathematics) law.invention Acceleration Wavelet Imaging Three-Dimensional Sampling (signal processing) law Humans Radiology Nuclear Medicine and imaging Cartesian coordinate system Computer vision Artificial neural network business.industry Respiration Reproducibility of Results Heart Coronary Vessels Undersampling Female Artificial intelligence Neural Networks Computer business Magnetic Resonance Angiography |
| Zdroj: | Magnetic resonance imaging. 70 |
| ISSN: | 1873-5894 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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