Non-local mean denoising in diffusion tensor space
نویسندگان
چکیده
The aim of the present study was to present a novel non-local mean (NLM) method to denoise diffusion tensor imaging (DTI) data in the tensor space. Compared with the original NLM method, which uses intensity similarity to weigh the voxel, the proposed method weighs the voxel using tensor similarity measures in the diffusion tensor space. Euclidean distance with rotational invariance, and Riemannian distance and Log-Euclidean distance with affine invariance were implemented to compare the geometric and orientation features of the diffusion tensor comprehensively. The accuracy and efficacy of the proposed novel NLM method using these three similarity measures in DTI space, along with unbiased novel NLM in diffusion-weighted image space, were compared quantitatively and qualitatively in the present study.
منابع مشابه
Non-Local Means Variants for Denoising of Diffusion-Weighted and Diffusion Tensor MRI
Diffusion tensor imaging (DT-MRI) is very sensitive to corrupting noise due to the non linear relationship between the diffusion-weighted image intensities (DW-MRI) and the resulting diffusion tensor. Denoising is a crucial step to increase the quality of the estimated tensor field. This enhanced quality allows for a better quantification and a better image interpretation. The methods proposed ...
متن کاملEvaluation of Non-Local Means Based Denoising Filters for Diffusion Kurtosis Imaging Using a New Phantom
Image denoising has a profound impact on the precision of estimated parameters in diffusion kurtosis imaging (DKI). This work first proposes an approach to constructing a DKI phantom that can be used to evaluate the performance of denoising algorithms in regard to their abilities of improving the reliability of DKI parameter estimation. The phantom was constructed from a real DKI dataset of a h...
متن کاملSimultaneous Smoothing & Estimation of DTI via Robust Variational Non-local Means
Regularized diffusion tensor estimation is an essential step in DTI analysis. There are many methods proposed in literature for this task but most of them are neither statistically robust nor feature preserving denoising techniques that can simultaneously estimate symmetric positive definite (SPD) diffusion tensors from diffusion MRI. One of the most popular techniques in recent times for featu...
متن کاملA Riemannian Framework for Denoising Diffusion Tensor Images
Diffusion Tensor Imaging (DTI) is a relatively new imaging modality that has been extensively used to study diffusion processes in the brain and has applications ranging from diagnostic to surgical planning. However, DTI imaging systems are highly sensitive to noise, leading to reconstructed images with low SNR. Thus, there is a need for image denoising algorithms specifically designed to regul...
متن کاملStructure-adaptive sparse denoising for diffusion-tensor MRI
Diffusion tensor magnetic resonance imaging (DT-MRI) is becoming a prospective imaging technique in clinical applications because of its potential for in vivo and non-invasive characterization of tissue organization. However, the acquisition of diffusion-weighted images (DWIs) is often corrupted by noise and artifacts, and the intensity of diffusion-weighted signals is weaker than that of class...
متن کامل