MRtrix3 provides a set of tools to perform various advanced diffusion MRI analyses, including constrained spherical deconvolution (CSD), probabilistic tractography, track-density imaging, and apparent fibre density
There have been multiple potential enhancements to dwidenoise discussed. While there may be additional ideas for improving denoising that may be kept private as research developments, this list is items that are hopefully acceptable for public listing for the purpose of participant recruitment.
[ ] Evaluate demeaning: #2363
Use of single-precision floating-point with an imprecise decomposition algorithm can lead to artifacts in noise estimation & rank selection. One potential solution proposed some time ago was to explicitly demean the signal, storing that mean as a signal component to be included in the output series. Doing so however necessitates ensuring that the rank estimation does not become biased.
[ ] Alternative decomposition: #2906
Simply changing the decomposition used from the Eigen library may improve precision, though at the cost of computation time. This should ideally be evaluated across multiple use cases.
[x] Modify rank estimation: #2591
May be possible to directly translate the modified estimators from externally published work to the MRtrix3 implementation. Would need to contrast against the existing modification of @jelleveraart's estimator as "Exp2".
[ ] Facilitate two-pass approach: #2274
This could be used to stabilise against outliers in noise level estimation. It could also be used in pipelines that explicitly separate between the estimation of corrections to be applied vs. the application of those corrections.
This would be expedited for higher-level workflows by #3035.
[x] Spherical kernel: #2742
Already published; having implemented would reduce issues around kernel size selection, and could also be used to mitigate against issues of kernel size / independence at FoV extremities.
[ ] Handle >4D data: #3021
Would make the method more amenable in particular to multi-echo fMRI data.
[x] Soft component thresholding: #3022
Would mitigate quantisation effects due to a component being just above vs. just below the MP distribution cutoff. May improve denoising of datasets with a very small number of volumes.
[x] Overcomplete local PCA: #3024
Makes output reconstructed DWI volumes a weighted sum of the results of multiple decompositions, rather than being dependent on the single PCA rank reduction at that voxel.
[ ] Subsampling: #3034
Reduce the total number of SVDs to be performed by reconstructing the low-rank signal for multiple output image voxels from a single patch.
[ ] Linear phase ramp removal: #3037
Might be more crucial that originally recognised for complex data denoising.
[ ] Variance-stabilising transform: #3041
[ ] Alternative noise level estimation: #3040
If there are potential IP issues around imposition of the MP distribution, this approach may serve as a suitable alternative.
Also:
[ ] Build dataset of exemplar series for denoising efficacy evaluation
Sometimes we hear about a particular dataset where the denoising performs poorly. We're also now using this method for multi-echo fMRI data in addition to DWI data. So it would be good to start building a dataset of different acquisitions of different quality, so that methods and heuristics can be refined to try to make the implementation robust against such variation, or to provide instructions on recommended settings in different scenarios.
Edit: New Issue: #3036.
There have been multiple potential enhancements to
dwidenoisediscussed. While there may be additional ideas for improving denoising that may be kept private as research developments, this list is items that are hopefully acceptable for public listing for the purpose of participant recruitment.[ ] Evaluate demeaning: #2363 Use of single-precision floating-point with an imprecise decomposition algorithm can lead to artifacts in noise estimation & rank selection. One potential solution proposed some time ago was to explicitly demean the signal, storing that mean as a signal component to be included in the output series. Doing so however necessitates ensuring that the rank estimation does not become biased.
[ ] Alternative decomposition: #2906 Simply changing the decomposition used from the Eigen library may improve precision, though at the cost of computation time. This should ideally be evaluated across multiple use cases.
[x] Modify rank estimation: #2591 May be possible to directly translate the modified estimators from externally published work to the MRtrix3 implementation. Would need to contrast against the existing modification of @jelleveraart's estimator as "
Exp2".[ ] Facilitate two-pass approach: #2274 This could be used to stabilise against outliers in noise level estimation. It could also be used in pipelines that explicitly separate between the estimation of corrections to be applied vs. the application of those corrections. This would be expedited for higher-level workflows by #3035.
[x] Spherical kernel: #2742 Already published; having implemented would reduce issues around kernel size selection, and could also be used to mitigate against issues of kernel size / independence at FoV extremities.
[ ] Handle >4D data: #3021 Would make the method more amenable in particular to multi-echo fMRI data.
[x] Soft component thresholding: #3022 Would mitigate quantisation effects due to a component being just above vs. just below the MP distribution cutoff. May improve denoising of datasets with a very small number of volumes.
[x] Overcomplete local PCA: #3024 Makes output reconstructed DWI volumes a weighted sum of the results of multiple decompositions, rather than being dependent on the single PCA rank reduction at that voxel.
[ ] Subsampling: #3034 Reduce the total number of SVDs to be performed by reconstructing the low-rank signal for multiple output image voxels from a single patch.
[ ] Linear phase ramp removal: #3037 Might be more crucial that originally recognised for complex data denoising.
[ ] Variance-stabilising transform: #3041
[ ] Alternative noise level estimation: #3040 If there are potential IP issues around imposition of the MP distribution, this approach may serve as a suitable alternative.
Also: