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.
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Keywords
- Orientation Distribution Function
- Orientation Estimate
- High Angular Resolution
- Helix Axis
- Partial Volume Average
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Savadjiev, P., Campbell, J.S.W., Pike, G.B., Siddiqi, K. (2005). 3D Curve Inference for Diffusion MRI Regularization. In: Duncan, J.S., Gerig, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005. MICCAI 2005. Lecture Notes in Computer Science, vol 3749. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11566465_16
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DOI: https://doi.org/10.1007/11566465_16
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