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Magnetic Resonance Imaging (MRI) suffers from several artifacts, the mostcommon of which are motion artifacts. These artifacts often yield images thatare of non-diagnostic quality. To detect such artifacts, images areprospectively evaluated by experts for their diagnostic quality, whichnecessitates patient-revisits and rescans whenever non-diagnostic quality scansare encountered. This motivates the need to develop an automated frameworkcapable of accessing medical image quality and detecting diagnostic andnon-diagnostic images. In this paper, we explore several convolutional neuralnetwork-based frameworks for medical image quality assessment and investigateseveral challenges therein.

Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges