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Abstract #2604

Computational Feasibility of MR Image Reconstruction via Explicit Construction and Inversion of the Encoding Matrix

Kylie Yeung1,2,3, Fergus V Gleeson2,3, Rolf F Schulte4, Damian J Tyler1,5, James T Grist1,3,5, and Florian Wiesinger4,6
1Oxford Centre for Clinical Magnetic Resonance Research (OCMR), University of Oxford, Oxford, United Kingdom, 2Department of Oncology, University of Oxford, Oxford, United Kingdom, 3Department of Radiology, Oxford University Hospitals NHS Trust, Oxford, United Kingdom, 4GE HealthCare, Munich, Germany, 5Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom, 6Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom

Synopsis

Keywords: Image Reconstruction, Image Reconstruction

Motivation: MR reconstruction using the direct pseudoinverse of the encoding matrix, which is a simple and versatile approach, has not been widely adopted clinically due to its perceived computational intractability.

Goal(s): To demonstrate the computational feasibility of generalized MR image reconstruction via direct pseudoinversion of the encoding matrix using high-end computational systems similar to modern MR reconstruction computers.

Approach: To test computational times required for direct pseudoinversion of various encoding matrices and for calculating relevant image metrics.

Results: SVD computation time scales with the ~1.4th power of the cardinality of the encoding matrix. Spatial-response-functions and noise matrices were also derived.

Impact: Demonstration of the current computational feasibility of generalized MR image reconstruction via direct pseudoinversion of the encoding matrix (Pinv-Recon), which is as a simple and versatile reconstruction approach able to incorporate a variety of encoding mechanisms and distortions.

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Keywords