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Abstract #1762

AutoSON: Automated Sequence Optimization by joint training with a Neural network

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

In MRI, scan parameters are carefully selected for desired results. We propose a new optimization method, AutoSON, that determines optimal scan parameters for flexibly-selected objectives on given tissue properties such as distribution and noises. AutoSON optimizes not only scan parameters but also a neural estimator, which extracts the desired information from MR signals (e.g., quantification mapping). The method successfully optimized the flip angles in DESPOT1 for T1 mapping and classification of white matter and gray matter. The last example does not have a simple analytic equation, and therefore, demonstrates a potential utility of the method.

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