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2013 Diffusion tensor smoothing through weighted Karcher means
Owen Carmichael, Jun Chen, Debashis Paul, Jie Peng
Electron. J. Statist. 7: 1913-1956 (2013). DOI: 10.1214/13-EJS825

Abstract

Diffusion tensor magnetic resonance imaging (MRI) quantifies the spatial distribution of water diffusion at each voxel on a regular grid of locations in a biological specimen by diffusion tensors– $3\times 3$ positive definite matrices. Removal of noise from DTI is an important problem due to the high scientific relevance of DTI and relatively low signal to noise ratio it provides. Leading approaches to this problem amount to estimation of weighted Karcher means of diffusion tensors within spatial neighborhoods, under various metrics imposed on the space of tensors. However, it is unclear how the behavior of these estimators varies with the magnitude of DTI sensor noise (the noise resulting from the thermal effects of MRI scanning) as well as the geometric structure of the underlying diffusion tensor neighborhoods. In this paper, we combine theoretical analysis, empirical analysis of simulated DTI data, and empirical analysis of real DTI scans to compare the noise removal performance of three kernel-based DTI smoothers that are based on Euclidean, log-Euclidean, and affine-invariant metrics. The results suggest, contrary to conventional wisdom, that imposing a simplistic Euclidean metric may in fact provide comparable or superior noise removal, especially in relatively unstructured regions and/or in the presence of moderate to high levels of sensor noise. On the contrary, log-Euclidean and affine-invariant metrics may lead to better noise removal in highly structured anatomical regions, especially when the sensor noise is of low magnitude. These findings emphasize the importance of considering the interplay of sensor noise magnitude and tensor field geometric structure when assessing diffusion tensor smoothing options. They also point to the necessity for continued development of smoothing methods that perform well across a large range of scenarios.

Citation

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Owen Carmichael. Jun Chen. Debashis Paul. Jie Peng. "Diffusion tensor smoothing through weighted Karcher means." Electron. J. Statist. 7 1913 - 1956, 2013. https://doi.org/10.1214/13-EJS825

Information

Received: 1 October 2012; Published: 2013
First available in Project Euclid: 18 July 2013

zbMATH: 1293.62089
MathSciNet: MR3084676
Digital Object Identifier: 10.1214/13-EJS825

Subjects:
Primary: 62G08
Secondary: 62P10

Rights: Copyright © 2013 The Institute of Mathematical Statistics and the Bernoulli Society

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