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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
Summary
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.
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