Variational Frameworks for DT-MRI Estimation, Regularization and Visualization
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Regularizing Flows for Constrained Matrix-Valued Images
Journal of Mathematical Imaging and Vision
A Riemannian Framework for Tensor Computing
International Journal of Computer Vision
A Riemannian approach to anisotropic filtering of tensor fields
Signal Processing
Impact of Rician Adapted Non-Local Means Filtering on HARDI
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Bayesian non-local means filter, image redundancy and adaptive dictionaries for noise removal
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Non-local means variants for denoising of diffusion-weighted and diffusion tensor MRI
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Rician noise removal in diffusion tensor MRI
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Fast and simple calculus on tensors in the log-euclidean framework
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Impact of Rician Adapted Non-Local Means Filtering on HARDI
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Bias of Least Squares Approaches for Diffusion Tensor Estimation from Array Coils in DT---MRI
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Design and Construction of a Realistic DWI Phantom for Filtering Performance Assessment
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Higher Order Positive Semidefinite Diffusion Tensor Imaging
SIAM Journal on Imaging Sciences
MRI superresolution using self-similarity and image priors
Journal of Biomedical Imaging
Adaptive medical image denoising using support vector regression
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
A variational model for the restoration of MR images corrupted by blur and Rician noise
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Nonlocal filters for removing multiplicative noise
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
A new similarity measure for nonlocal filtering in the presence of multiplicative noise
Computational Statistics & Data Analysis
Groupwise segmentation improves neuroimaging classification accuracy
MBIA'12 Proceedings of the Second international conference on Multimodal Brain Image Analysis
DWI denoising using spatial, angular, and radiometric filtering
MBIA'12 Proceedings of the Second international conference on Multimodal Brain Image Analysis
A new similarity measure for non-local means filtering of MRI images
Journal of Visual Communication and Image Representation
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Diffusion-Weighted MRI (DW-MRI) is subject to random noise yielding measures that are different from their real values, and thus biasing the subsequently estimated tensors. The Non-Local Means (NLMeans) filter has recently been proposed to denoise MRI with high signal-to-noise ratio (SNR). This filter has been shown to allow the best restoration of image intensities for the estimation of diffusion tensors (DT) compared to state-of-the-art methods. However, for DW-MR images with high b-values (and thus low SNR), the noise, which is strictly Rician-distributed, can no longer be approximated as additive white Gaussian, as implicitly assumed in the classical formulation of the NLMeans. High b-values are typically used in high angular resolution diffusion imaging (HARDI) or q-space imaging (QSI), for which an optimal restoration is critical. In this paper, we propose to adapt the NLMeans filter to Rician noise corrupted data. Validation is performed on synthetic data and on real data for both conventional MR images and DT images. Our adaptation outperforms the original NLMeans filter in terms of peak-signal-to-noise ratio (PSNR) for DW-MRI.