Position-orientation adaptive smoothing of diffusion weighted magnetic resonance data (POAS)

Abstract

We introduce an algorithm for diffusion weighted magnetic resonance imaging data enhancement based on structural adaptive smoothing in both voxel space and diffusion-gradient space. The method, called POAS, does not refer to a specific model for the data, like the diffusion tensor or higher order models. It works by embedding the measurement space into a space with defined metric, in this case the Lie group of three-dimensional Euclidean motion SE(3). Subsequently, pairwise comparisons of the values of the diffusion weighted signal are used for adaptation. POAS preserves the edges of the observed fine and anisotropic structures. It is designed to reduce noise directly in the diffusion weighted images and consequently also to reduce bias and variability of quantities derived from the data for specific models. We evaluate the algorithm on simulated and experimental data and demonstrate that it can be used to reduce the number of applied diffusion gradients and hence acquisition time while achieving a similar quality of data, or to improve the quality of data acquired in a clinically feasible scan time setting.

Analysis of White Matter Damage in Patients with Multiple Sclerosis via a Novel In Vivo MR Method for Measuring Myelin, Axons, and G-Ratio

“…23 Images were then denoised by using multishell position-orientation adaptive smoothing based on the propagation-separation approach. 24,25 This algorithm does not assume a specific model and can be used for any diffusion model including NODDI. Instead of treating data from each shell separately, we treated the 2-shell diffusion data simultaneously for denoising to improve stability.…”

Local estimation of the noise level in MRI using structural adaptation

“…The parameter r i ¼ rðx i Þ generally depends on the reconstruction algorithm and is assumed to be a smooth and slowly varying function of location. The parameter function h i is supposed to be locally constant with x i (Becker et al, 2012(Becker et al, , 2014. This assumption is motivated by the observation that the expected signal intensity or equivalently the non-centrality parameter h i relates to properties of the biological tissue.…”

“…If information from L independent receiver coils is combined as a root sum of squares (RSoS) we get L i L. In case of SENSE (Pruessmann et al, 1999) the image is obtained from a linear combination of complex Gaussian images and hence L i 1. Reconstruction methods like GRAPPA (Griswold et al, 2002) or ZOOPPA (Heidemann et al, 2012) in general lead to a spatially varying L i , see e.g. Aja-Fernández et al (2011).…”

“…Furthermore, estimates of the standard deviation of the MR signal are directly used in a number of data enhancing methods to discriminate noise variation from structural differences, see e.g. Aja-Fernández et al (2008), Coupé et al (2008), Becker et al (2012Becker et al ( , 2014, Rajan et al (2012), Haldar et al (2013) or the citations in the latter paper for a more comprehensive list.…”

Recovery of thalamic microstructural damage after Shiga toxin 2-associated hemolytic–uremic syndrome

“…Necessary smoothing of the DW images typically blurs and distorts the structural and thus the diffusivity information in MR images [24,25]. To account for this problem, Becker et al developed a new algorithm for structural adaptive smoothing of DW MRI data [26,27]. This method called position-orientation adaptive smoothing (POAS) preserves edges of fine and anisotropic structures and thus reduces confounding partial volume effects.…”