| Literature DB >> 33274013 |
Jeffrey J Ma1,2, Ukash Nakarmi2, Cedric Yue Sik Kin2, Christopher M Sandino3, Joseph Y Cheng2, Ali B Syed2, Peter Wei2, John M Pauly3, Shreyas S Vasanawala2.
Abstract
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.Entities:
Keywords: Image quality; deep learning; medical imaging
Year: 2020 PMID： 33274013 PMCID： PMC7710391 DOI： 10.1109/isbi45749.2020.9098735
Source DB: PubMed Journal: Proc IEEE Int Symp Biomed Imaging ISSN： 1945-7928