Abstract #3424

Fast, Automatic MRI Quality Assurance: Validation and Comparison with Manual Analysis using the ACR phantom

Hamzeh Ahmad Mohammad Al Masri^1,2,3, Tonima Ali¹, Katie McMahon⁴, and Markus Barth^1,3,5

¹Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia, ²Medical Imaging Department, The Hashemite University, Al-Zarqa, Jordan, ³ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia, ⁴Royal Brisbane & Women's Hospital, Queensland University of Technology, Brisbane, Australia, ⁵School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia

Quality assurance (QA) is mandatory to ensure the stable performance of MR scanners over time. Automated analysis of QA tests can be useful to increase operator efficiency and overcome the manual processing issues such as time constraints and human bias. In this paper, we compare the manual and automated analysis approaches of the QA image datasets that have been collected from 3T MRI scanners using the American College of Radiology (ACR) accreditation phantom. We found that the automated method can significantly reduce the QA analysis time and the results of both methods were in agreement with each other.

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