Abstract #4857

Accelerated Compressed Sensing of Diffusion-Inferred Intra-Voxel Structure Through Adaptive Refinement

Bennett Allan Landman^1,2, Hanlin Wan^2,3, John A. Bogovic³, Peter C. M. van Zijl^4,5, Pierre-Louis Bazin⁶, Jerry L. Prince,²³

¹Electrical Engineering, Vanderbilt University, Nashville, TN, United States; ²Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States; ³Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States; ⁴F.M. Kirby Center, Kennedy Krieger Institute, Baltimore, MD, United States; ⁵Biomedical Engineering, Johns Hopkins University, Nashville, TN, United States; ⁶Radiology, Johns Hopkins University, Baltimore, MD, United States

Compressed sensing is a promising technique to estimate intra-voxel structure with traditional DTI data and avoid many of the practical constraints (e.g., long scan times, low signal-to-noise ratio) that plague more detailed, high b-value studies. However, computational complexity is a major limitation of compressed sensing techniques as currently proposed. We demonstrate a novel technique for accelerated compressed sensing of diffusion-inferred intra-voxel structure utilizing adaptive refinement of a multi-resolution basis set. Our approach achieves a tenfold reduction in computational complexity and enables more practical consideration of intra-voxel orientations in time-sensitive settings, routine data analysis, or in large studies.