We examined the quantitative
multi parameter mapping method so called transient state DESPOT (tsDESPOT) which
based on conventional DESPOT sequence. From the acquired data,
low rank approximated (LRA) images which include transient state information were reconstructed, then T1, T2, B1 and PD maps were estimated by dense neural network. In
this study, we proposed fast estimation method of accurate full sampled LRA
images using approximate ADMM (alternating direction method of
multipliers) which optimize Unet estimation and data consistency.
Compared to simple Unet estimation method, the method improved quantitative
accuracy of maps and removed artifact that couldn’t be removed.