61 research outputs found
Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges
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
Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges
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
Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges
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.Comment: 4 pages, 8 Figures, Conference Submissio
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