This view provides the user interface to denoise a diffusion weighted image (DWI) with several methods.
Available sections:
Input Data
Mandatory Input:
Optional Input:
- Binary mask to define a denoising area.
Output Data
- Denoised DWI: if a mask is set for denoising, all voxel excluding the masked area are set to 0.
Non-local means filter
Denoise the input DWI using a non local means algorithm. For more details refer to [1] and [2].
Parameters:
- Searchradius: defines the size of the neighborhood V (Fig. 1 b)) in which the voxels will be weighted to reconstruct the center voxel. The resulting neighborhood size is defined as 2x searchradius + 1 (e.g. a searchradius of 1 generates a neighborhood cube with size 3x3x3).
- Comparisonradius: defines the size of the compared neighborhoods N (Fig. 1 b)) around each voxel. The resulting neighborhood size is defined as 2x comaprisonradius + 1 (e.g. a comparisonradius of 1 generates a neighborhood cube with size 3x3x3).
- Noise variance: the variance of the noise need to be set for filtering. An estimation of the noise varinace will be implemented soon.
- Rician adaption: if checked the non-local means uses an adaption for Rician distributed noise.
- Joint information: if checked the whole DWI is seen as a vector image, weighting each voxels complete vector, instead of weighting each channel seperate. (This might be a bit faster, but is less accurate)
Fig. 1: a) View using the Non-local means filter b) 2D illustration of the Non-local means principle [1]
Discrete gaussian filter
Denoise the input DWI using a discrete gaussian filter.
Parameters:
- Variance: defines the varinance of the gaussian distribution to denoise the image.
Fig. 2: View using the discrete gaussian filter
References
[1] Wiest-DaesslĂ© et al. Non-Local Means Variants for Denoising of Diffusion-Weigthed and Diffusion Tensor MRI. MICCAI 2007, Part II, LNCS 4792, pp- 344-351.
[2] Wiest-DaesslĂ© et al. Rician Noise Removal by Non-Local Means Filtering for Low Signal-to-Noise Ratio MRI: Applications to DT-MRI. MICCAI 2008, Part II, LNCS 5242, pp. 171-179.