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Abstract #0015

Denoising Diffusion-Weighted Images Using x-q Space Non-Local Means

Geng Chen1,2, Yafeng Wu1, Dinggang Shen2, and Pew-Thian Yap2

1Data Processing Center, Northwestern Polytechnical University, Xi'an, China, People's Republic of, 2Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States

In this abstract, we show that improved denoising performance can be attained by extending the non-local means (NLM) algorithm beyond the x-space (i.e., the spatial space) to include the q-space (i.e., the wave-vector space). The advantage afforded by this extension is twofold: (1) Non-local information can now be harnessed not only across space, but also across measurements in q-space; (2) In white matter regions with high curvature, q-space neighborhood matching corrects for such non-linearity so that information from structures oriented in different directions can be used more effectively for denoising without introducing artifacts.

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