Abstract #4954

High-resolution single-shot spiral diffusion-weighted imaging at 7T using the expanded encoding model and compressed sensing

Paul I. Dubovan*^1,2, Gabriel Varela-Mattatall*^1,2, Tales Santini^1,2, Kyle M. Gilbert^1,2, Ravi S. Menon^1,2, and Corey A. Baron^1,2

¹Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute, Western University, London, ON, Canada, ²Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada

Synopsis

Keywords: Image Reconstruction, Image ReconstructionReconstruction problems involving the expanded encoding model and field monitoring are typically solved using least squares optimization with the conjugate gradient method and early stopping as an implicit form of regularization. However, this is likely a suboptimal strategy for low SNR acquisitions, such as accelerated or high-resolution diffusion MRI. Hence, in this work we present an extension to the matMRI reconstruction toolbox that incorporates compressed sensing regularization. Results demonstrate that the expanded encoding model and compressed sensing regularization are complementary tools that mitigate artifacts from phase perturbations while permitting lower SNR conditions for high-resolution single-shot spiral diffusion-weighted imaging.

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