Denoising of diffusion MRI in the cervical spinal cord - effects of denoising strategy and acquisition on intra-cord contrast, signal modeling, and feature conspicuity

Abstract

Quantitative diffusion MRI (dMRI) is a promising technique for evaluating the spinal cord in health and disease. However, low signal-to-noise ratio (SNR) can impede interpretation and quantification of these images. The purpose of this study is to evaluate several dMRI denoising approaches on their ability to improve the quality, reliability, and accuracy of quantitative diffusion MRI of the spinal cord. We evaluate three denoising approaches (Non-Local Means, Marchenko-Pastur PCA, and a newly proposed Patch2Self algorithm) and conduct five experiments to validate the denoising performance on clinical-quality and commonly-acquired dMRI acquisitions: 1) a phantom experiment to assess denoising error and bias; 2) a multi-vendor, multi-acquisition open experiment for both qualitative and quantitative evaluation of noise residuals; 3) a bootstrapping experiment to estimate uncertainty of parametric maps; 4) an assessment of spinal cord lesion conspicuity in a multiple sclerosis group; and 5) an evaluation of denoising for advanced parametric multi-compartment modeling. We find that all methods improve signal-to-noise ratio and conspicuity of MS lesions in individual diffusion weighted images (DWIs), but MPPCA and Patch2Self excel at improving the quality and intra-cord contrast of diffusion weighted images - removing signal fluctuations due to thermal noise while improving precision of estimation of diffusion parameters even with very few DWIs (i.e., 16-32) typical of clinical acquisitions. These denoising approaches hold promise for facilitating reliable diffusion observations and measurements in the spinal cord to investigate biological and pathological processes.

Keywords: Diffusion MRI; Diffusion tensor imaging; Image denoising; Multiple sclerosis; Spinal cord.

Fig. 1.

Experimental datasets and designs. This study includes 5 datasets, including (1) a digital phantom to investigate error and bias in denoising, (2) empirical datasets from multiple vendors and acquisitions to assess noise statistics, (3) a multi-repeat dataset to study precision and bias with bootstrapping, (4) MS datasets to assess lesion conspicuity, and (5) a multi-shell high angular resolution dataset for multi-compartment biophysical modeling.

Fig. 2.

Denoising reduces noise levels, even with low SNR and with few diffusion directions. For SNR = 10 and SNR = 20, DWIs are shown after denoising for NLM, MPPCA, and P2S, along with error from ground truth phantom, and a plot of denoised versus true signal with mean-squared error (MSE) given in plots. These results are shown for a DWI most aligned with cord (greatest signal attenuation), and an example DWI most perpendicular to cord is shown in Supplementary Figure 1.

Fig. 3.

Denoising improves image quality from different vendors and with different acquisition parameters. Results are shown for seven datasets, from 3 vendors, with/without interpolation and signal averaging on the scanner, for diffusion directions against (left) and along (right) the cord.

Fig. 4.

Noise properties estimated from denoising algorithms suggest MPPCA and P2S preserve signal while removing noise. Sigma-normalized residual maps between denoised DWIs and the original data for a single image (left) and averaged over all DWIs (right) are shown, where the presence of anatomical structure indicates interference of algorithms with the signal. Plots at the right show the distribution of normalized residuals with a standard normal distribution (solid black line) as reference. All algorithms have similar magnitude of denoising (standard deviation ~1) although NLM has negative shift of signal.

Fig. 5.

FA maps from acquired and denoised data show small, but visible, improvements in intra-cord quantitative contrast.

Fig. 6.

All denoising techniques improve precision in estimated FA (top, unitless) and MD (bottom, units mm²/s). Bootstrapping experiments enable quantitative estimates of variability shown as maps and as histograms (including voxels in the cord only).

Fig. 7.

Effects of denoising on lesion conspicuity in mean DWIs. Lesions are visible as hyperintense regions on the mean DWI, however, denoising does not improve visibility on these images. Anatomical mFFE images are shown in the first column for reference.

Fig. 8.

Effects of denoising on lesion conspicuity in individual DWIs. Lesions are visible as hyperintense regions in DWIs, and denoising improves conspicuity of lesions as well as white/gray matter contrast. Anatomical mFFE images are shown in the first column for reference.