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Abstract #1669

Quantitative Improvement of Diffusion Spectrum Imaging Tractography Using Statistical Denoising

Li-Wei Kuo1, Justin P. Haldar2, Yu-Chun Lo3, Cheng-Liang Liu1, Zhi-Pei Liang2, Wen-Yih Isaac Tseng1,4

1Center for Optoelectronic Biomedicine, National Taiwan University College of Medicine, Taipei, Taiwan; 2Department of Electrical and Computer Engineering, Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, United States; 3Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan; 4Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan

Noise contamination is a significant problem in diffusion spectrum imaging (DSI) tractography, and previous work has proposed a statistical denoising algorithm to mitigate the effects of low signal-to-noise ratio. In this work, improvements to fiber orientation accuracy due to denoising were quantified using a systematic analysis of angular precision and dispersion metrics. Results show that the proposed denoising method significantly improves angular precision and dispersion. Furthermore, the tractography results demonstrate better reconstruction of white-matter structures using the denoised data. Future work will use the proposed denoising algorithm to improve spatial resolution and reduce scan time.