Computer-assisted Screening of Motion Artefact for Quality Control in Large-scale MR Imaging Trials
Abstract
As the scale of medical imaging trials increases, manual quality control of the enormous volume of imaging data becomes intractable and costly. Machine learning may provide solutions to reduce the challenge of these large trials through the development of computer-assisted screening tools. The objective of this dissertation was to evaluate the suitability of machine learning for solving scalability problems of manual quality control by training an automated classifier to detect simulated motion artefact on otherwise high-quality magnetic resonance images of healthy human brain. The classifier achieved high accuracy (98.5%) without any performance optimization, and, incidentally, discovered a screening error within the experiment dataset, further demonstrating the power of machine learning in this domain and encouraging further research towards computer-assisted screening tools.
Description
Keywords
Radiology, Artificial Intelligence, Computer Science, Engineering--Biomedical
Citation
Adair, D. (2017). Computer-assisted Screening of Motion Artefact for Quality Control in Large-scale MR Imaging Trials (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/25401