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DIAGNOSTIC IMAGE QUALITY ASSESSMENT AND CLASSIFICATION IN MEDICAL IMAGING: OPPORTUNITIES AND CHALLENGES

Overview
Journal Proc IEEE Int Symp Biomed Imaging
Publisher IEEE
Date 2020 Dec 4
PMID 33274013
Citations 9
Authors
Jeffrey J Ma
Ukash Nakarmi
Cedric Yue Sik Kin
Christopher M Sandino
Joseph Y Cheng
Ali B Syed
Peter Wei
John M Pauly
Shreyas S Vasanawala
Affiliations
Soon will be listed here.
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.

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