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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
In general, the ML aided k-t SENSE generated flow curves that were visually sharper. There were no statistical differences in peak velocities and stroke volumes. The technique enabled ~3.6x faster processing than the CS reconstruction making it suitable for the clinical use.

Fig. 2. The ML aided k-t SENSE processing.

Stage I – the $$$M_{x,f}^2$$$ estimation. Both flow encoded ($$$y_{k,t}^{'}$$$) and compensated ($$$y_{k,t}^{''}$$$) data were processed as described [2]. The u-net results were combined for the final x-f signal estimation. Stage II – k-t SENSE: the linear conjugate gradient solver was used to minimise [1] and produce the final PCMR results.

Fig. 3. Imaging results.

$$$U_w$$$ reconstructions presented with smaller or larger artefacts: visible reconstruction patch boundary, signal removal. These are not visible on the $$$U_w^M$$$ results. In two cases $$$U_w$$$ removed heart structures (i.e. the bottom row). In these hard cases temporal blurring can be observed in the $$$U_w^M$$$ results. This had a small effect on the k-t SENSE magnitude results. However, it resulted in blurring of the extracted phase data Fig. 4.