DenoiseTools

Demonstrations.nb ››
Guide page ›› Code on Github ››

The toobox provides two algorithms that allow denoising of DWI data. The first is based on and LMMSE framework (Aja-Fernandez et al. 2008) and the second is based on a random matrix theory and Principal component analysis framework (Veraart et al. 2016). Furthermore, it provides an anisotropic filters for denoising the estimated diffusion tensor which provides more reliable fiber orientation analysis and fiber tractography (Lee et al. 2006; Damon et al. 2021). Back››

dti data denoising using MP-PCA

comparison of denoising methods

References

  • Veraart, Jelle, Dmitry S. Novikov, Daan Christiaens, Benjamin Ades-aron, Jan Sijbers, and Els Fieremans. 2016. “Denoising of diffusion MRI using random matrix theory.” NeuroImage 142 (November). Elsevier Inc.: 394–406. link››.
  • Aja-Fernandez, Santiago, Marc Niethammer, Marek Kubicki, Martha E. Shenton, and Carl Fredrik Westin. 2008. “Restoration of DWI data using a rician LMMSE estimator.” IEEE Transactions on Medical Imaging 27 (10): 1389–1403. link››.
  • Lee, Jee Eun, M. K. Chung, and A. L. Alexander. 2006. “Evaluation of Anisotropic Filters for Diffusion Tensor Imaging.” In IEEE International Symposium on Biomedical Imaging, 77–80. IEEE. link››.
  • Damon, B. M., Ding, Z., Hooijmans, M. T., Anderson, A. W., Zhou, X., Coolbaugh, C. L., George, M. K., & Landman, B. A. (2021). “A MATLAB toolbox for muscle diffusion-tensor MRI tractography.” Journal of Biomechanics, 124, 110540. link››