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Abstract #3810

Simulation Reveals Evidence for Bias in Parameter Estimates for Compressed Sensing of Temporally Dynamic Systems

Nathan Murtha1,2, James Rioux1,2,3, Olliver Marriott2, Chris Bowen1,2,3, Sharon Clarke1,2,3, and Steven Beyea1,2,3

1Physics and Atmospheric Science, Dalhousie University, Halifax, NS, Canada, 2Biomedical Translational Imaging Centre, QE2 Health Sciences Centre, Halifax, NS, Canada, 3Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada

There exists no objective framework for assessment of acquisition and reconstruction methods in compressed sensing (CS) MRI involving temporal dynamics. We propose a simulation framework to address this gap. Image quality was assessed using two quantitative metrics, and temporal parameters were recovered using least-squares fitting. CS regularization weighting was varied to determine the effect on both image quality and accuracy of recovered temporal dynamic parameters. Image quality metrics displayed distinct optima, though bias, dependent on the underlying temporal dynamics, was introduced to temporal parameter estimates. These results support the need for an objective tool to characterize CS MRI methodologies.

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