Metric selection and diffusion tensor swelling

Ofer Pasternak*, Nir Sochen, Peter J. Basser

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations


The measurement of the distance between diffusion tensors is the foundation on which any subsequent analysis or processing of these quantities, such as registration, regularization, interpolation, or statistical inference is based. Euclidean metrics were first used in the context of diffusion tensors; then geometric metrics, having the practical advantage of reducing the “swelling effect,” were proposed instead. In this chapter we explore the physical roots of the swelling effect and relate it to acquisition noise. We find that Johnson noise causes shrinking of tensors, and suggest that in order to account for this shrinking, a metric should support swelling of tensors while averaging or interpolating. This interpretation of the swelling effect leads us to favor the Euclidean metric for diffusion tensor analysis. This is a surprising result considering the recent increase of interest in the geometric metrics.

Original languageEnglish
Title of host publicationNew Developments in the Visualization and Processing of Tensor Fields
EditorsDavid H. Laidlaw, Anna Vilanova
PublisherSpringer Heidelberg
Number of pages14
ISBN (Electronic)978-3-642-27343-8
ISBN (Print)978-3-642-27342-1, 978-3-662-50786-5
StatePublished - 2012
Event3rd Workshop on Visualization and Processing of Tensor Fields, 2009 - Dagstuhl, Germany
Duration: 19 Jul 200924 Jul 2009

Publication series

NameMathematics and Visualization
ISSN (Print)1612-3786
ISSN (Electronic)2197-666X


Conference3rd Workshop on Visualization and Processing of Tensor Fields, 2009


FundersFunder number
Eunice Kennedy Shriver National Institute of Child Health and Human Development
Fulbright Association
Australian-American Fulbright Commission
United States-Israel Binational Science Foundation


    Dive into the research topics of 'Metric selection and diffusion tensor swelling'. Together they form a unique fingerprint.

    Cite this