Non Local Spatial and Angular Matching: Enabling higher spatial resolution diffusion MRI datasets through adaptive denoising

Abstract

Diffusion magnetic resonance imaging (MRI) datasets suffer from low Signal-to-Noise Ratio (SNR), especially at high b-values. Acquiring data at high b-values contains relevant information and is now of great interest for microstructural and connectomics studies. High noise levels bias the measurements due to the non-Gaussian nature of the noise, which in turn can lead to a false and biased estimation of the diffusion parameters. Additionally, the usage of in-plane acceleration techniques during the acquisition leads to a spatially varying noise distribution, which depends on the parallel acceleration method implemented on the scanner. This paper proposes a novel diffusion MRI denoising technique that can be used on all existing data, without adding to the scanning time. We first apply a statistical framework to convert both stationary and non stationary Rician and non central Chi distributed noise to Gaussian distributed noise, effectively removing the bias. We then introduce a spatially and angular adaptive denoising technique, the Non Local Spatial and Angular Matching (NLSAM) algorithm. Each volume is first decomposed in small 4D overlapping patches, thus capturing the spatial and angular structure of the diffusion data, and a dictionary of atoms is learned on those patches. A local sparse decomposition is then found by bounding the reconstruction error with the local noise variance. We compare against three other state-of-the-art denoising methods and show quantitative local and connectivity results on a synthetic phantom and on an in-vivo high resolution dataset. Overall, our method restores perceptual information, removes the noise bias in common diffusion metrics, restores the extracted peaks coherence and improves reproducibility of tractography on the synthetic dataset. On the 1.2 mm high resolution in-vivo dataset, our denoising improves the visual quality of the data and reduces the number of spurious tracts when compared to the noisy acquisition. Our work paves the way for higher spatial resolution acquisition of diffusion MRI datasets, which could in turn reveal new anatomical details that are not discernible at the spatial resolution currently used by the diffusion MRI community.

Quantifying cardiac‐induced brain tissue expansion using DENSE

“…Further improvements in SNR through the use of specialized local surface coils may also facilitate a voxel‐wise analysis of peak volumetric strain in superficial brain regions. Also, de‐noising algorithms exist that show considerable improvement in other noisy MRI data such as cardiac motion and diffusion‐weighted imaging . These improvements remain the focus of future work.…”

Building connectomes using diffusion MRI: why, how and but

“…147,151 Another group of post-processing methods boost certain features to improve estimation. These include denoising approaches for improving SNR 152,153 and up-sampling or super-resolution methods for improving spatial/angular resolution. [154][155][156] Nevertheless, we can expect recent technological advances to provide better operating points for all these competing features and improve overall data quality.…”

Neighborhood Matching for Curved Domains with Application to Denoising in Diffusion MRI

“…Our approach extends NLM beyond x -space to include q -space, allowing information from white matter regions with high curvature to be used more effectively for denoising without introducing artifacts. Experiments with synthetic and real data confirm the effectiveness of our method, in comparison with methods such non-local means (NLM) [2], non-local spatial-angular matching (NLSAM) [6], and x-q space non-local means (XQ-NLM) [7]. …”

“…We compared our method with NLM [2], NLSAM [6], and XQ-NLM [7]. Their parameters were set as suggested in [2,6,7].…”

“…Their parameters were set as suggested in [2,6,7]. We used the peak-to-signal ratio (PSNR) as the performance metric.…”