Evaluating the impact of denoising diffusion MRI data on tractometry metrics of optic tract abnormalities in glaucoma

Diffusion MRI (dMRI)-based tractometry is a non-invasive neuroimaging method for evaluating white matter tracts in living humans, capable of detecting abnormalities caused by disorders. However, measurement noise in dMRI data often compromises the signal quality. Several denoising methods for dMRI have been proposed, but the extent to which denoising affects tractometry metrics of white matter tissue properties associated with disorders remains unclear. We evaluated how denoising affects tractometry along the optic tract (OT) in patients with glaucoma. Because glaucoma damages retinal ganglion cells, the OT in patients with glaucoma is likely to exhibit tissue abnormalities. Therefore, we examined dMRI data from patients with glaucoma to evaluate how two widely used denoising methods (MPPCA and Patch2Self) affect tractometry metrics regarding the expected tissue changes in the OT. We found that denoising affected the appearance of diffusion-weighted images, increased the estimated signal-to-noise ratio, and reduced residuals in voxelwise model fitting. However, denoising had a limited impact on the differences in tractometry metrics of the OT between patients with glaucoma and controls. Moreover, we found no evidence that denoising improved the reproducibility of tractometry. These findings suggest that the current denoising methods have a limited impact when used together with a tractometry framework.