Estimation of Kurtosis in Accelerated Diffusion Spectrum Imaging using Compressed Sensing

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

Diffusion spectrum imaging provides radial information of diffusivity in the brain, such as diffusional kurtosis. This work presents a two step quadratic programming framework for fitting the diffusion and kurtosis tensors. Furthermore, the effects of using undersampled data and compressed sensing recon-structions are investigated. Various derived scalar measures for kurtosis are compared for a human brain data set. The results show the robustness of the fitting procedure as compared to standard linear fitting. Compressed sensing allows for either faster acquisitions or improved image quality, while providing some degree of denoising.