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Abstract #1914

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

1GE Global Research, Garching bei Mnchen, Germany; 2GE Global Research, Niskayuna, NY, United States; 3GE Healthcare, Waukesha, WI, United States

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.