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Abstract #1255

Basis function compression for compact representation of high spatial orders of field variation using field monitoring

Paul I. Dubovan1,2, Gabriel Varela-Mattatall1,3, Ravi S. Menon1,2, Adam B. Kerr4,5, and Corey A. Baron1,2
1Medical Biophysics, Western University, London, ON, Canada, 2Centre for Functional and Metabolic Mapping, Western University, London, ON, Canada, 3Lawson Health Research Institute, London, ON, Canada, 4Center for Cognitive and Neurobiological Imaging, Stanford University, Stanford, CA, United States, 5Department of Electrical Engineering, Stanford University, Stanford, CA, United States

Synopsis

Keywords: Artifacts, Artifacts, Field Monitoring

Motivation: Field monitoring using field probes has shown to inaccurately estimate higher order field variations on a high-performance gradient system using the conventional fitting procedure.

Goal(s): To develop and validate a new fitting approach for field monitoring measurements for improved higher order field characterizations on complex MRI systems.

Approach: Perform a calibration scan by moving probes around imaging volume to accurately characterize field variations, then compress this data to preserve important field information, with the purpose of applying this information to new scans.

Results: Quantitative phantom results and qualitative in-vivo diffusion images show significantly improved image quality when using the proposed fitting method.

Impact: This work presents a new method for accurately calculating higher order field monitoring measurements on a head-only MRI scanner, resulting in substantially improved image quality. This may be useful for other research centers that also utilize complex, high-performance MRI systems.

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Keywords