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Abstract #1888

Effects of Compressed Sensing Reconstruction on Kurtosis Tensor Fitting in Diffusion Spectrum Imaging

Jonathan I. Sperl1, Marion I. Menzel1, Ek T. Tan2, Kedar Khare2, Kevin F. King3, Christopher J. Hardy2, Luca Marinelli2

1GE Global Research, Garching n. Munich, BY, Germany; 2GE Global Research, Niskayuna, NY, United States; 3GE Healthcare, Waukesha, WI, United States

Diffusion spectrum imaging (DSI) not only provides angular information about diffusivity in the brain but also radial information such as diffusional kurtosis. Due to the non-Gaussian noise distribution in DSI, a standard least-squares fitting of diffusion and kurtosis tensor induces bias on the fitted tensor elements and the subsequently derived scalar measures such as mean kurtosis. This work is intended to show that compressed sensing reconstruction in q-space, which is used to accelerate DSI by enabling undersampled acquisitions, also helps to reduce the bias on the data and by this means improve the estimation of kurtosis.