Meeting Banner
Abstract #4696

A Validation Approach for Imperfect Training Data Fidelity using Signal + Artifact + Noise-based Neural Net (SAN3)-derived Directionalized Streaking Removal

Nanyque A Boyd1, Yudai Suzuki1,2, Amit R Patel3, Jacob P Goes1, Marcella K Vaicik1, Satoru Hayamizu2, Satoshi Tamura2, and Keigo Kawaji1,3

1Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States, 2Electrical, Electronics, and Information Engineering, Gifu University, Gifu City, Japan, 3Medicine, The University of Chicago, Chicago, IL, United States

While Deep Neural Network (DNN)-based sub-Nyquist reconstruction approaches are well-suited for high-fidelity static imaging targets such as the brain, temporally constrained (i.e. dynamic) sequences may potentially be ill-suited for DNN as these would often embed unresolved MR artifacts into the Training Data. Here, we describe an assessment approach for a generalizable DNN-based dynamic MRI reconstruction method that outputs such artifacts as characterizable and filterable streaks. This work further validates the DNN-model coding process to ensure the desired artifact/noise properties into the DNN output. Using Fourier properties, we demonstrate such validation of streaking directionalization using DNN.

This abstract and the presentation materials are available to members only; a login is required.

Join Here