Automated Quality Assurance for Magnetic Resonance Imaging with Extensions to Diffusion Tensor Imaging

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Virginia Tech

Since its inception, Magnetic Resonance Imaging (MRI) has largely been used for qualitative diagnosis. Radiologists and physicians are increasingly becoming interested in quantitative assessments. The American College of Radiology (ACR) developed an accreditation program that incorporates tests pertaining to quantitative and qualitative analyses. As a result, sites often use the ACR procedure for daily quality assurance (QA) testing.

The ACR accreditation program uses information obtained from clinical and phantom images to assess overall image quality of a scanner. For the phantom assessment, a human observer performs manual tests on T1 and T2-weighted volumes of the provided phantom. As these tests are tedious and time consuming, the primary goal of this research was to fully automate the procedure for QA purposes. The performance of the automated procedure was assessed by comparing the test results with the decisions made by human observers. The test results of the automated ACR QA procedure were well correlated with that of human observers. The automated ACR QA procedure takes approximately 5 minutes to complete. Upon program completion, the test results are logged in multiple text files.

To this date, no QA procedure has been reported for Diffusion Tensor Imaging (DTI). Therefore, the secondary goal of this thesis was to develop a DTI QA procedure that assess two of the associated features used most in diagnosis, namely, diffusion anisotropy and the direction of primary diffusion. To this end, a physical phantom was constructed to model restricted diffusion, relative to axon size, using water-filled polytetrafluoroethylene (PTFE) microbore capillary tubes. Automated procedures were developed to test fractional anisotropy (FA) map contrast and capillary bundle (axon) orientation accuracy.

Magnetic Resonance Imaging, Phantom, American College of Radiology Accreditation Progra, Automated Quality Assurance, Diffusion Tensor Imaging