Meeting Banner
Abstract #2404

Task-Based Assessment for Neural Networks: Evaluating Undersampled MRI Reconstructions based on Signal Detection

Joshua D Herman1, Rachel E Roca1, Alexandra G O'Neill1, Sajan G Lingala2, and Angel R Pineda1
1Mathematics Department, Manhattan College, Riverdale, NY, United States, 2Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA, United States

Artifacts from neural network reconstructions are difficult to characterize. It is important to assess the image quality in terms of the task for which the images will be used. In this work, we evaluated the effect of undersampling on detection of signals in images reconstructed with a neural network by both human and ideal observers. We compared these results to standard metrics (SSIM and NRMSE). Our results suggest that the undersampling level chosen by SSIM, NRMSE and ideal observer would likely be different than that of a human observer on a detection task for a small signal.

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

Join Here