Extracting Tractosemas from a Displacement Probability Field for Tractography in DW-MRI
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
HARDI Denoising: Variational Regularization of the Spherical Apparent Diffusion Coefficient sADC
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Multi-fiber reconstruction from diffusion MRI using mixture of wisharts and sparse deconvolution
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
International Journal of Computer Vision
Fiber enhancement in diffusion-weighted MRI
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
Morphological and Linear Scale Spaces for Fiber Enhancement in DW-MRI
Journal of Mathematical Imaging and Vision
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High angular resolution diffusion imaging (HARDI) is a MRI imaging technique that is able to better capture the intra-voxel diffusion pattern compared to its simpler predecessor diffusion tensor imaging (DTI). However, HARDI in general produces very noisy diffusion patterns due to the low SNR from the scanners at high b-values. Furthermore, it still exhibits limitations in areas where the diffusion pattern is asymmetrical (bifurcations, splaying fibers, etc.). To overcome these limitations, enhancement and denoising of the data based on context information is a crucial step. In order to achieve it, convolutions are performed in the coupled spatial and angular domain. Therefore the kernels applied become also HARDI data. However, these approaches have high computational complexity of an already complex HARDI data processing. In this work, we present an accelerated framework for HARDI data regularizaton and enhancement. The convolution operators are optimized by: pre-calculating the kernels, analysing kernels shape and utilizing look-up-tables. We provide an increase of speed, compared to previous brute force approaches of simpler kernels. These methods can be used as a preprocessing for tractography and lead to new ways for investigation of brain white matter.