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Abstract #1923

Compressed Sensing Based Diffusion Spectrum Imaging (CS-DSI) Tractography

Namgyun Lee1, Bryce Wilkins1, Manbir Singh1

1Radiology and Biomedical Engineering, University of Southern California, Los Angeles, CA, United States

A novel compressed sensing approach for Diffusion Spectrum Imaging incorporating a unique sampling pattern is presented. This approach relies on minimizing the l1 norm of spatial finite-differences of an ensemble average propagator. Results of simulation studies show significant improvements with our approach compared to partial Fourier approach for accurately estimating underlying crossing fibers and reducing false positives. Human tractography studies show recovery of the cingulum, fornix, and other tracts in crossing-fiber areas with quality comparable to a fully-sampled reference (DSI203).

Keywords

accelerating accuracy accurate accurately additional anal appear array artifacts better biomedical blue brain caused causing challenging circles clear coil commonly comparable complicates compressed conjugate conjunction connectivity considering constraint crossing decreased decreases difficulty diffusion dimensions effective employed energy ensemble equipped erroneous false fewer fiber fibers filtered finding finite fractions frequency fully generate gradient gradually grid hemisphere hemispheric highlight human identical imported imposing improvements incoherent indicated leading limiting local measures middle minimizing missing mixed mixing mixture near noisy novel optimal orange orientations origin outperforms partial pattern pixel positive possess properties proposed pulse purple quality radius real reconstructed reconstruction reconstructions recovering reduce reduces reducing reprocess ringing sampled samples sampling scanner selected sensing severe shot significantly simulated simulation since slice slow southern space spacing spectrum sphere spurious step streamline strength studies studio suggesting superior symmetric synthesis synthesizing synthetic task terms theory thinning threshold toward tracks tract tracts twice unequally universal universality unknown variation vectors whole