Abstract #3800

Unifying Compressed-Sensing Reconstruction Framework for Multidimensional MRI: Combining Novel Dictionary Models with Frame-Based Sparsity and Flexible Undersampling Schemes

Suyash P. Awate¹, Edward V.R. DiBella²

¹Scientific Computing and Imaging (SCI) Institute, University of Utah, Salt Lake City, UT, United States; ²Utah Center for Advanced Imaging Research (UCAIR), University of Utah, Salt Lake City, UT, United States

We propose a novel unified framework for compressed-sensing reconstruction of multidimensional magnetic resonance imaging (MRI) including dynamic MRI and high angular resolution diffusion imaging (HARDI). This brand-new framework incorporates a novel formulation for the compressed-sensing reconstruction problem which makes it very flexible with regards to (i) the kinds of imaging or undersampling strategies that can be exploited as well as (ii) the kinds of sparse models that need to be enforced on the data, allowing a variety of wavelet-frame models, total-variation models, and novel dictionary models.