Improved Sampling Patterns for Accelerated Diffusion Spectrum Imaging using Compressed Sensing

Marion Irene Menzel¹, Jonathan Immanuel Sperl¹, Ek Tsoon Tan², Kedar Khare², Kevin F. King³, Xiaodong Tao², Christopher J. Hardy², Luca Marinelli²

The combination of undersampled acquisition and reconstruction using compressed sensing enables the acceleration of diffusion spectrum imaging, bringing this application closer to clinical practice. This work evaluates the performance of compressed sensing as a function of data reconstruction size, pattern pa-rameter and specific realization of the random pattern. Among the sampling pattern distributions we tested both in simulations and in vivo data from brains of healthy volunteers, our results demonstrate that Gaussian undersampling performed best. Especially for higher acceleration factors and small matrix sizes the appropriate realization of the sampling pattern from the random distribution has to be chosen carefully.