3D Curve Inference for Diffusion MRI Regularization

Abstract. We develop a differential geometric framework for regularizing diffusion MRI data. The key idea is to model white matter fibers as 3D space curves and to then extend Parent and Zucker's 2D curve inference approach [8] by using a notion of co-helicity to indicate compatibility between fibre orientation estimates at each voxel with those in a local neighborhood. We argue that this provides several advantages over earlier regularization methods. We validate the approach quantitatively on a biological phantom and on synthetic data, and qualitatively on data acquired in vivo from a human brain.

• Type: Conference
• Year: 2005
• Where: MICCAI
• Authors: Peter Savadjiev, Jennifer S. W. Campbell, G. Bruce Pike, Kaleem Siddiqi
• Keywords: Curve Inference Approach, Diffusion Mri Data, Earlier Regularization Methods, Medical Imaging, MICCAI 2005