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Abstract #1212

Highly Accelerated 3D TOF MRA using Deep Learning Reconstruction with Raw K-space Simulation

Hao Li1, Mark Chiew2,3, Iulius Dragonu4, Peter Jezzard1, and Thomas Okell1
1Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, 2Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada, 3Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada, 4Research & Collaborations GB&I, Siemens Healthcare Ltd, Camberley, United Kingdom

Synopsis

Keywords: Blood Vessels, Vascular, MRA, Time-of-Flight Angiography

Motivation: 3D-TOF-MRA suffers from long scan times that increase motion artifacts, limiting its resolution and clinical applicability. Although deep learning (DL) reconstruction shows promise for acceleration, large 3D-TOF-MRA k-space datasets for training are generally unavailable.

Goal(s): To develop a DL-based reconstruction method that enables highly accelerated 3D-TOF-MRA using publicly available data for training.

Approach: We simulated complex-valued multi-coil k-space from the IXI magnitude dataset and used a customized 3D variational network with architectural and data-handling improvements designed for 3D-TOF-MRA.

Results: The proposed method demonstrated superior reconstruction results on in vivo data over existing methods, preserving most fine vessels with minimal artifacts with 8-fold acceleration.

Impact: The proposed method shows promise for highly accelerating 3D-TOF-MRA due to its superior performance. Overcoming data scarcity, this approach holds the potential for advancing research and clinical applications of high-resolution whole-head 3D-TOF-MRA imaging, enhancing cerebrovascular diagnostics.

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