A multi-scale variational neural network for accelerating motion-compensated whole-heart 3D coronary MR angiography

Magn Reson Imaging. 2020 Jul:70:155-167. doi: 10.1016/j.mri.2020.04.007. Epub 2020 Apr 27.

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.

Keywords: Cardiac MRI; Coronary imaging; Fast imaging; Undersampling; Variational neural network.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Coronary Angiography*
  • Coronary Vessels / diagnostic imaging*
  • Female
  • Heart / diagnostic imaging*
  • Humans
  • Imaging, Three-Dimensional / methods*
  • Magnetic Resonance Angiography*
  • Male
  • Movement*
  • Neural Networks, Computer*
  • Reproducibility of Results
  • Respiration