Ioannis Valasakis1, Haikun Qi1, Kerstin Hammernik2, Gastao Lima da Cruz1, Daniel Rueckert2,3, Claudia Prieto1, and Rene Botnar1
1King's College London, London, United Kingdom, 2Technical University of Munich, Munich, Germany, 3Imperial College London, London, United Kingdom
1King's College London, London, United Kingdom, 2Technical University of Munich, Munich, Germany, 3Imperial College London, London, United Kingdom
A 3D variational deep neural network (3D-VNN) for the reconstruction of 3D whole-heart coronary MR angiography (CMRA) to fully capture the spatial redundancies in CMRA images.
(A) The CMRA data acquisition and motion correction pipeline using a VD-CASPR trajectory and performing translational motion correction estimated from 2D iNAVs. (B) CSMs and the undersampled k-space data are used as network inputs. The variational network structure for one gradient step: the filters k are learned for the real and complex plane and a linear activation function combines the responses of the filters on those planes. The loss function is the MSE of the 3D-VNN reconstruction and the fully sampled.
CMRA reconstructions for 5-fold undersampling for two representative subjects. 3D-VNN reconstruction is compared against the CS, iterative SENSE, CS and 3D CG MoDL-U-Net for a representative subject. Fully sampled and zero-filled reconstructions are also included for comparison.
