@inproceedings{f16006d2860e4076b3e4d6e986d895d8,
title = "Metric selection and diffusion tensor swelling",
abstract = "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.",
author = "Ofer Pasternak and Nir Sochen and Basser, {Peter J.}",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2012.; 3rd Workshop on Visualization and Processing of Tensor Fields, 2009 ; Conference date: 19-07-2009 Through 24-07-2009",
year = "2012",
doi = "10.1007/978-3-642-27343-8_17",
language = "אנגלית",
isbn = "978-3-642-27342-1",
series = "Mathematics and Visualization",
publisher = "Springer Heidelberg",
pages = "323--336",
editor = "Laidlaw, {David H.} and Anna Vilanova",
booktitle = "New Developments in the Visualization and Processing of Tensor Fields",
address = "גרמניה",
edition = "1",
}