Diffusion tensor smoothing through weighted Karcher means

Owen Carmichael, Jun Chen, Debashis Paul, Jie Peng

Research output: Contribution to journalArticle

6 Citations (Scopus)

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- 3×3 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.

Original languageEnglish (US)
Pages (from-to)1913-1956
Number of pages44
JournalElectronic Journal of Statistics
Volume7
Issue number1
DOIs
StatePublished - 2013

Fingerprint

Weighted Mean
Smoothing
Tensor
Noise Removal
Euclidean
Sensor
Affine Invariant
Invariant Metric
Magnetic Resonance Imaging
Empirical Analysis
Geometric Structure
Metric
Smoothing Methods
Positive definite matrix
Thermal Effects
Voxel
Spatial Distribution
Scanning
Theoretical Analysis
Data analysis

Keywords

  • Diffusion MRI
  • Karcher mean
  • Kernel smoothing
  • Perturbation analysis
  • Tensor space

ASJC Scopus subject areas

  • Statistics and Probability

Cite this

Diffusion tensor smoothing through weighted Karcher means. / Carmichael, Owen; Chen, Jun; Paul, Debashis; Peng, Jie.

In: Electronic Journal of Statistics, Vol. 7, No. 1, 2013, p. 1913-1956.

Research output: Contribution to journalArticle

Carmichael, O, Chen, J, Paul, D & Peng, J 2013, 'Diffusion tensor smoothing through weighted Karcher means', Electronic Journal of Statistics, vol. 7, no. 1, pp. 1913-1956. https://doi.org/10.1214/13-EJS825
Carmichael, Owen ; Chen, Jun ; Paul, Debashis ; Peng, Jie. / Diffusion tensor smoothing through weighted Karcher means. In: Electronic Journal of Statistics. 2013 ; Vol. 7, No. 1. pp. 1913-1956.
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