Machine Learning aided k-t SENSE for fast reconstruction of highly accelerated PCMR data

The Machine Learning aided k-t SENSE for the reconstruction of highly undersampled GAS_perturbed PCMR data is validated. We introduce a modified version of the u-net Convolutional Neural Network (u-net M) that utilises the spatial signal distribution information to improve removal of the MR image magnitude aliases. The high resolution magnitude predictions enable creation of regularisation priors used in the k-t SENSE for the final reconstruction of the PCMR data. 20 patients were scanned in the in-vivo validataion. The technique enabled ~3.6x faster processing than the CS reconstruction with no statistical difference in the measured peak mean velocity and stroke volumes.