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DIAGNOSTIC IMAGE QUALITY ASSESSMENT AND CLASSIFICATION IN MEDICAL IMAGING: OPPORTUNITIES AND CHALLENGES

Jeffrey J Ma1,2, Ukash Nakarmi2, Cedric Yue Sik Kin2

  • 1Department of Computing and Mathematical Sciences, California Institute of Technology.

Proceedings. IEEE International Symposium on Biomedical Imaging
|December 4, 2020
PubMed

Insights

Motion artifacts in Magnetic Resonance Imaging (MRI) degrade image quality. This study explores automated convolutional neural network frameworks to detect non-diagnostic MRI scans, improving efficiency and reducing rescans.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Magnetic Resonance Imaging (MRI) is susceptible to artifacts, primarily motion artifacts, which frequently compromise diagnostic image quality.
  • Current methods for assessing MRI scan quality rely on manual expert evaluation, leading to inefficiencies like patient revisits and rescans for non-diagnostic images.

Purpose of the Study:

  • To develop an automated framework for assessing medical image quality.
  • To detect diagnostic and non-diagnostic Magnetic Resonance Imaging scans using artificial intelligence.

Main Methods:

  • Exploration of various convolutional neural network (CNN)-based frameworks for medical image quality assessment.
  • Investigation of challenges associated with implementing automated image quality detection in MRI.

Main Results:

  • Convolutional neural network frameworks show promise for automated medical image quality assessment.
  • Identified key challenges in developing robust automated systems for detecting non-diagnostic MRI scans.

Conclusions:

  • Automated assessment of MRI quality using CNNs can potentially streamline the diagnostic process.
  • Further research is needed to overcome challenges and fully realize the potential of AI in identifying non-diagnostic MRI scans, reducing the need for rescans.
Keywords:
Image qualitydeep learningmedical imaging

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