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Abstract #2053

Noise Reduction in Accelerated Diffusion Spectrum Imaging Through Integration of SENSE Reconstruction Into Joint Reconstruction in Combination with Q-Space Compressed Sensing

Vladimir Golkov1, 2, Tim Sprenger1, 3, Marion I. Menzel1, Ek T. Tan4, Kevin F. King5, Christopher J. Hardy4, Luca Marinelli4, Daniel Cremers2, Jonathan I. Sperl1

1GE Global Research, Garching n. Munich, Bavaria, Germany; 2Department of Computer Science, Technical University Munich, Garching n. Munich, Bavaria, Germany; 3IMETUM, Technical University Munich, Garching n. Munich, Bavaria, Germany; 4GE Global Research, Niskayuna, NY, United States; 5GE Healthcare, Waukesha, WI, United States

Sensitivity encoding (SENSE) reconstruction of diffusion weighted images (DWIs) in diffusion MRI is usually done independently for each DWI, without exploiting structural correlations between the DWIs. In this work, SENSE is incorporated into a joint reconstruction framework which models the prior knowledge of common smooth regions and edges in the DWIs. Image quality is improved in comparison to SENSE reconstruction, and even more so in combination with q-space compressed sensing.

Keywords

accelerated acceleration accurate acquisition acquisitions adjustment advanced advances anatomical approaches appropriate arbitrary architecture assumption audience brain calculating channel coil coils combination common commonalities complex compressed compute computer consist consistency context cube density depicts diffusion distribution domain edge edges encoding enhancing equipped estimation every exploiting fast fiber finite fitted formulation framework functions global half head healthy human implies improved improves improving incorporate incorporates incorporating independent independently instead integration inverse involve iterative iteratively joint jointly king knowledge kurtosis least leveraging limited linear mapping math minx modeling models modify moreover multiplicative neighboring noise operator orientation original parallel physicians preserving press principles prior process proposed pure quality randomly reciprocal reconstructed reconstruction reduction regularization represents review scalar scanner science scientists sense sensing sensitivity series several shrinkage similarity slice smoothing smoothness solution solving space sparsity spatial spectrum squares structural structure subsequently superior systems takes target technical tensor tensors term terms thresholding tracking tracts transform volunteers yielded yielding