Deep learning-based reconstruction for 3D coronary MR angiography with a 3D variational neural network (3D-VNN)

3D whole-heart coronary MR angiography (CMRA) is limited by long scan times. Undersampled reconstruction approaches, such as compressed sensing or low-rank methods show promise to significantly accelerate CMRA but are computationally expensive, require careful parameter optimisation and can suffer from residual aliasing artefacts. A 2D multi-scale variational network (VNN) as recently proposed to improve image quality and significantly shorten the reconstruction time. We propose to extend the VNN reconstruction to 3D to fully capture the spatial redundancies in 3D CMRA. The 3D-VNN is compared against conventional and 3D model-based U-Net reconstruction techniques, showing promising results while shortening the reconstruction time.