3D Submillimeter-Resolution Reduced-Field-of-View Diffusion Tensor Imaging

Introduction For better delineation of neuronal architecture, higher spatial resolution is always desirable. Challenges in realization of high spatial resolution include but are not limited to long total scan time, low signal-to-noise ratio (SNR), and high sensitivity to T2* blurring, off-resonance, and motion-related artifacts. In the present work, the reduced field-of-view (rFOV) imaging approach is chosen for minimizing the effects of the previously mentioned artifacts at high resolution. Three-dimensional encoding in combination with multiecho acquisition is proposed for increased SNR efficiency (the ratio between SNR and the square root of the total acquisition time) as compared to traditional 2D rFOV acquisitions. Imperfections in realization of rFOV imaging are minimized by the proposed penalized iterative reconstruction algorithm.