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.
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