Hongjun An1, Dongmyung Shin1, Hyeong-Geol Shin1, Woojin Jung1, and Jongho Lee1
1Department of Electrical and computer Engineering, Seoul National University, Seoul, Korea, Republic of
1Department of Electrical and computer Engineering, Seoul National University, Seoul, Korea, Republic of
A new optimization method for MRI sequences is proposed. This method jointly optimizes the sequence and a neural estimator that extracts information (e.g. quantitative mapping ) from MR signals. The method shows well-optimized results regardless of problems.
Figure
1. Overview
of AutoSON. (a) Structure of AutoSON. AutoSON contains an MRI simulator for the
generation of training signals and a neural network that extracts target information
for the objective. (b) Joint optimization of AutoSON. The distribution of tissue
properties and noise conditions were utilized to generate training data. Using
this data, joint optimization was performed for scan parameters and weights of
the neural network using a loss function for the objective. (c) After the joint
optimization, the sequence parameters were optimized for the objective.
Figure
4. Partially
spoiled SSFP T2 mapping results of the scan parameters. The AutoSON optimized
parameters yielded better performance than the parameters from Wang et al. in
a computer simulation, although the mapping was
performed by without a neural network.