Locally weighted regression for estimating and moothing ODF field simultaneously

Authors:
Xiaozheng Liu;Guang Yang;Bradley S. Peterson;Dongrong Xu
Affiliations:
Ministry of Edu., China and Shanghai Key Lab. of Brain Functional Genomics, East China Normal Univ., Shanghai Key Lab. of Magnetic Resonance, East China Normal Univ., Shanghai, China and Columbia ...;Key Laboratory of Brain Functional Genomics, Ministry of Education, China & Shanghai Key Laboratory of Brain Functional Genomics, East China Normal University, Shanghai Key Laboratory of Magne ...;Columbia University Dept of Psychiatry, & New York State Psychiatric Institute, New York, NY;Ministry of Edu., China and Shanghai Key Lab. of Brain Functional Genomics, East China Normal Univ., Shanghai Key Lab. of Magnetic Resonance, East China Normal Univ., Shanghai, China and Columbia ...
Venue:
MIAR'10 Proceedings of the 5th international conference on Medical imaging and augmented reality
Year:
2010

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Abstract

High angular resolution diffusion imaging (HARDI) has become an important tool for resolving neural architecture in regions with complex patterns of fiber crossing. A popular method for estimating the diffusion orientation distribution function (ODF) employs a least square (LS) approach by modeling the raw HARDI data on a spherical harmonic basis. We propose herein a novel approach for reconstruction of ODF fields from raw HARDI data that combines into one step the smoothing of raw HARDI data and the estimation of ODF field using correlated information in a local neighborhood. Based on the most popular method of least square for estimating ODF, we incorporated into it local weights that are determined by a special weighting function, making it a locally weighted linear least square method (LWLLS). The method thus can efficiently perform the smoothing of HARDI data and estimating the ODF field simultaneously. We evaluated the effectiveness of this method using both simulated and real-world HARDI data.