Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges
This addresses the need to reduce patient revisits and rescans in medical imaging by automating quality checks, though it appears incremental as it explores existing CNN methods.
The paper tackles the problem of motion artifacts in MRI leading to non-diagnostic images, proposing automated convolutional neural network frameworks for medical image quality assessment and classification.
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.