Non-Negative Spherical Deconvolution for Fiber Orientation Distribution Estimation

Jian Cheng (1) , Dinggang Shen (1) , Pew-Thian Yap (1)

1 BRIC - Biomedical Research Imaging Center [North Carolina]

Introduction: In diffusion MRI, Spherical Deconvolution (SD) was proposed to estimate the fiber Orientation Distribution Function (fODF) based on spherical deconvolution using a single-fiber response function [1,2]. The peaks or the shape of fODFs can be used to infer local fiber directions. Constrained Spherical Deconvolution (CSD) [1], which takes into consideration the non-negative of the fODF, is the most widely used method among SD variants. Although CSD is capable of accurately determining fiber directions, it is susceptible to false positive peaks especially in the regions with lowanisotropy. This is a common drawback of all existing SD-based methods. Moreover, in practice the fODF estimated using CSD still has significant negative values. We propose a method called Non-Negative Spherical Deconvolution (NNSD) to solve the above two problems. Based on a Riemannian framework of ODFs [3] and Square Root Parameterized Estimation for non-negative definite Ensemble Average Propagator [4], NNSD is formulated such that the non-negativity of the fODF is guaranteed with largely reduced false positive peaks.

Domaines: Traitement des images [eess.IV]; Imagerie médicale

Jian Cheng, Dinggang Shen, Pew-Thian Yap. Non-Negative Spherical Deconvolution for Fiber Orientation Distribution Estimation. Scientific Meeting and Exhibition of the (ISMRM), Apr 2013, United States. pp.1. ⟨hal-00967831⟩