Meeting Banner
Abstract #0456

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

Grzegorz Tomasz Kowalik1, Javier Montalt-Tordera1, Jennifer Steeden1, and Vivek Muthurangu1
1Institute of Cardiovascular Science, University College London, London, United Kingdom

The Machine Learning aided k-t SENSE for the reconstruction of highly undersampled GASperturbed 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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here