CaltechAUTHORS
Published April 2020 | Version Submitted
Book Section - Chapter Open

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

Creators

  • 1. ROR icon California Institute of Technology
  • 2. ROR icon Stanford University

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

Submitted - 1912.02907.pdf

Files

1912.02907.pdf

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Additional details

Identifiers

Eprint ID
106350
Resolver ID
CaltechAUTHORS:20201030-080636957

Related works

Describes
https://arxiv.org/abs/1912.02907 (URL)

Funding

NIH
R01 EB009690
NIH
R01 EB026136
GE Healthcare
Caltech Summer Undergraduate Research Fellowship (SURF)

Dates

Created
2020-10-30
Created from EPrint's datestamp field
Updated
2021-11-16
Created from EPrint's last_modified field