Abstract #2174

Accurate quantitative parameter maps reconstruction method for tsDESPOT using Low Rank approximated Unet ADMM

Yuuzo Kamiguchi¹, Sadanori Tomiha², and Masao Yui³

¹Advanced Technology Reserch Dept. Reserch and Development Center, Canon Medical Systems Corporation, Kawasaki, Japan, ²Advanced Technology Reserch Dept. Reserch and Development Center, Canon Medical Systems Corporation, Otawara, Japan, ³Reserch and Development Center, Canon Medical Systems Corporation, Otawara, Japan

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.

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