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14 February 2012HARDI denoising using nonlocal means on S2
Diffusion MRI (dMRI) is a unique imaging modality for in vivo delineation of the anatomical structure of white
matter in the brain. In particular, high angular resolution diffusion imaging (HARDI) is a specific instance of
dMRI which is known to excel in detection of multiple neural fibers within a single voxel. Unfortunately, the
angular resolution of HARDI is known to be inversely proportional to SNR, which makes the problem of denoising
of HARDI data be of particular practical importance. Since HARDI signals are effectively band-limited, denoising
can be accomplished by means of linear filtering. However, the spatial dependency of diffusivity in brain tissue
makes it impossible to find a single set of linear filter parameters which is optimal for all types of diffusion
signals. Hence, adaptive filtering is required. In this paper, we propose a new type of non-local means (NLM)
filtering which possesses the required adaptivity property. As opposed to similar methods in the field, however,
the proposed NLM filtering is applied in the spherical domain of spatial orientations. Moreover, the filter uses
an original definition of adaptive weights, which are designed to be invariant to both spatial rotations as well
as to a particular sampling scheme in use. As well, we provide a detailed description of the proposed filtering
procedure, its efficient implementation, as well as experimental results with synthetic data. We demonstrate
that our filter has substantially better adaptivity as compared to a number of alternative methods.
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Alan Kuurstra, Sudipto Dolui, Oleg Michailovich, "HARDI denoising using nonlocal means on S2," Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83140I (14 February 2012); https://doi.org/10.1117/12.911770