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Abstract #4487

Novel Applications of Generalized MR Image Reconstruction via Direct Pseudoinversion of the Encoding Matrix (Pinv-Recon)

Kylie Yeung1,2,3, Fergus V Gleeson2,3, Rolf F Schulte4, Anthony McIntyre3, Sébastien Serres5,6, Peter Morris7, Dorothee Auer7,8,9, Damian J Tyler1,10, Florian Wiesinger4,11, and James T Grist1,3,10
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, 5School of Life Sciences, University of Nottingham, Nottingham, United Kingdom, 6The David Greenfield Human Physiology Unit, University of Nottingham, Nottingham, United Kingdom, 7Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom, 8Mental Health and Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, United Kingdom, 9NIHR Nottingham Biomedical Research Centre/ Nottingham Clinical Research Facilities, QMC, Nottingham, United Kingdom, 10Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom, 11Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom

Synopsis

Keywords: Hyperpolarized MR (Non-Gas), Hyperpolarized MR (Non-Gas)

Motivation: MRI reconstruction via direct pseudoinversion of the encoding matrix (Pinv-Recon) is not widely used due to its assumed computational infeasibility compared to FFT-based or iterative methods. However, novel MRI applications with small to medium matrix sizes could benefit from its ease of implementation and flexibility in handling various encoding mechanisms and distortions.

Goal(s): Demonstrate the applicability of Pinv-Recon in novel settings.

Approach: Pinv-Recon was validated on various dataset sizes, including hyperpolarized Xenon-129, hyperpolarized Carbon-13 and proton datasets, with performance comparisons to conventional methods.

Results: By bypassing FFT, Pinv-Recon eliminates gridding artifacts in non-Cartesian datasets, improves B0 correction and simplifies non-Cartesian SENSE reconstruction.

Impact: Highlighting the application of generalized MR image reconstruction via direct pseudoinversion of the encoding matrix (Pinv-Recon) to hyperpolarized MRI and emphasizing its feasibility with modern computational infrastructure, ease of implementation, and advantages over conventional FFT-based approaches.

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Keywords