Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts. These artifacts often yield images that are of non-diagnostic quality. To detect such artifacts, images are prospectively evaluated by experts for their diagnostic quality, which necessitates patient-revisits and rescans whenever non-diagnostic quality scans are encountered. This motivates the need to develop an automated framework capable of accessing medical image quality and detecting diagnostic and non-diagnostic images. In this paper, we explore several convolutional neural network-based frameworks for medical image quality assessment and investigate several challenges therein.
@article{arxiv.1912.02907,
title = {Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges},
author = {Jeffrey Ma and Ukash Nakarmi and Cedric Yue Sik Kin and Christopher Sandino and Joseph Y. Cheng and Ali B. Syed and Peter Wei and John M. Pauly and Shreyas Vasanawala},
journal= {arXiv preprint arXiv:1912.02907},
year = {2019}
}