The Effects of Simulated SAR Data Processing Methods and Network Parameter Tuning on Gridding Artifacts and Network Estimation Accuracy

Motivation: Gridding artifacts in neural network estimated images are common and could inhibit neural network estimation accuracy. Goal(s): The goal is to discover which parameters are responsible for gridding artifacts, and whether the artifacts inhibit estimation quality. Approach: We test the effects of simulated body models, neural network parameters, and postprocessing methods on gridding artifacts, and their effect on overall neural network estimation accuracy in the context of local specific energy absorption rate (SAR) matrices, a patient safety concern for MRI scanning. Results: Altering neural network parameters affects the presentation of gridding artifacts the most. Eliminating gridding artifacts improves network estimation accuracy. Impact: Researchers working with computer vision whose images experience a gridding artifact can inform their neural network parameter tuning efforts with the results of this exploratory study.