Abstract #4631

Concatenated multi-contrast wavelet-based compressed sensing reconstruction.

Gabriel Varela-Mattatall^1,2, Jaejin Cho^3,4, Omer Oran⁵, Corey A. Baron^1,2, Berkin Bilgic^3,4, and Ravi S. Menon^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, ³Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital & Harvard Medical School, Boston, MA, United States, ⁴Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Boston, MA, United States, ⁵Siemens Healthcare Limited, Oakville, ON, Canada

Synopsis

Keywords: Image Reconstruction, Image ReconstructionThere is a family of multi-contrast sequences such as MEGRE, MP2RAGE and 3D-QALAS which produce a limited number of contrasts, and their corresponding reconstructions in highly accelerated acquisitions are not optimally represented by either compressed sensing, low rank, or both reconstruction styles together. In this work, we explore a novel way to perform compressed sensing for simultaneous multi-contrast reconstruction. By using a concatenation operator in the regularization term, we can perform a single compressed sensing reconstruction with automatic parameter selection to reconstruct all contrasts at once and provide an implicit form of parallelization for multi-contrast reconstruction.

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