Published April 2020
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Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges
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.
Additional Information
© 2020 IEEE. This work is supported in part by the NIH R01EB009690 and NIH R01 EB026136 grants, by GE Healthcare, and by the Caltech SURF program.Attached Files
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Additional details
- Eprint ID
- 106350
- Resolver ID
- CaltechAUTHORS:20201030-080636957
- R01 EB009690
- NIH
- R01 EB026136
- NIH
- GE Healthcare
- Caltech Summer Undergraduate Research Fellowship (SURF)
- Created
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2020-10-30Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field