Shayan Guhaniyogi

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PURPOSE We report a series of techniques to reliably eliminate artifacts in interleaved echo-planar imaging (EPI) based diffusion-weighted imaging (DWI). METHODS First, we integrate the previously reported multiplexed sensitivity encoding (MUSE) algorithm with a new adaptive Homodyne partial-Fourier reconstruction algorithm, so that images reconstructed(More)
PURPOSE To develop new techniques for reducing the effects of microscopic and macroscopic patient motion in diffusion imaging acquired with high-resolution multishot echo-planar imaging. THEORY The previously reported multiplexed sensitivity encoding (MUSE) algorithm is extended to account for macroscopic pixel misregistrations, as well as motion-induced(More)
The advantages of high-resolution diffusion tensor imaging (DTI) have been demonstrated in a recent post-mortem human brain study (Miller et al., NeuroImage 2011;57(1):167-181), showing that white matter fiber tracts can be much more accurately detected in data at a submillimeter isotropic resolution. To our knowledge, in vivo human brain DTI at a(More)
Diffusion-weighted MRI (DWI) is an essential tool in clinical applications such as detecting ischemic stroke, and in research applications studying neuronal connectiv-ity in the brain. Diffusion-weighted imaging with multi-shot echo-planar acquisition (DWEPI) offers several advantages over single-shot EPI, including improved spatial resolution and reduced(More)
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