منابع مشابه
Deconvolution enhanced Generalized Q-Sampling 2 and DSI deconvolution
For the purpose of the ISBI HARDI reconstruction challenge 2013 and for the heavyweight category, we reconstructed the diffusion datasets using two methods: a) Generalized Q-sampling Imaging 2 [1], [2] with spherical deconvolution [3],[4] (GQID), and b) Diffusion Spectrum Imaging with Deconvolution [5] (DSID). GQI2 provides a direct analytical formula to calculate the solid angle ODF (ψGQI2) of...
متن کاملReproducibility of Fiber Bundles from Different Subsampled q-space DSI Data Set
Introduction: Diffusion spectral imaging (DSI) has the potential to resolve crossing fibers. However, acquisition of the DSI data involves long scan times, making it impractical for routine scanning. A simple way to reduce the scan time is to limit the number of q-space points sampled and fill the q-space by exploiting its symmetry. However, the smallest number of q-space points needed to gener...
متن کاملImproved angular resolution at low b-values in Diffusion Spectrum Imaging through Radial acquisition in q-space
Target audience Scientists and clinicians interested in acquiring Diffusion Spectrum MRI at lower b-values. Purpose To demonstrate that radial q-space sampling for Diffusion Spectrum MRI improves ODF sampling and angular resolution at lower b-values as demonstrated by computer simulations and in a clinical scanner in vivo. Diffusion Spectrum MRI (DSI) [1] has been shown to non-invasively image ...
متن کاملQ-space truncation and sampling in diffusion spectrum imaging.
PURPOSE To characterize the q-space truncation and sampling on the spin-displacement probability density function (PDF) in diffusion spectrum imaging (DSI). METHODS DSI data were acquired using the MGH-USC connectome scanner (Gmax = 300 mT/m) with bmax = 30,000 s/mm2 , 17 × 17 × 17, 15 × 15 × 15 and 11 × 11 × 11 grids in ex vivo human brains and bmax = 10,000 s/mm2 , 11 × 11 × 11 grid in v...
متن کاملSparse DSI: Learning DSI Structure for Denoising and Fast Imaging
Diffusion spectrum imaging (DSI) from multiple diffusion-weighted images (DWI) allows to image the complex geometry of water diffusion in biological tissue. To capture the structure of DSI data, we propose to use sparse coding constrained by physical properties of the signal, namely symmetry and positivity, to learn a dictionary of diffusion profiles. Given this estimated model of the signal, w...
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ژورنال
عنوان ژورنال: Magnetic Resonance in Medicine
سال: 2015
ISSN: 0740-3194
DOI: 10.1002/mrm.25917