Variational regularization of multiple diffusion tensor fields

Ofer Pasternak, Nir Sochen, Yaniv Assaf

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

Abstract

Diffusion Tensor Imaging (DTI) became a popular tool for white matter tract visualization in the brain. It provides quantitative measures of water molecule diffusion anisotropy and the ability to delineate major white matter bundles. The diffusion model of DTI was found to be inappropriate in cases of partial volume effect, such as Multiple Fiber Orientations (MFO) ambiguity. Recently, a variety of image processing methods were proposed to enhance DTI results by reducing noise and correcting artifacts, but most techniques were not designed to resolve MFO ambiguity. In this Chapter we describe variational based DTI processing techniques, and show how such techniques can be adapted to the Multiple Tensor (MT) diffusion model via the Multiple Tensor Variational (MTV) framework. We show how the MTV framework can be used in separating differently oriented white matter fiber bundles.

Original languageEnglish
Title of host publicationVisualization and Processing of Tensor Fields
EditorsJoachim Weickert, Hans Hagen
PublisherSpringer Heidelberg
Pages165-176
Number of pages12
ISBN (Electronic)9783540312727
ISBN (Print) 978-3-540-25032-6, 978-3-642-43926-1
DOIs
StatePublished - 2006
EventWorkshop on Visualization and Processing of Tensor Fields, 2004 - Schloss Dagstuhl, Germany
Duration: 18 Apr 200423 Apr 2004

Publication series

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

Conference

ConferenceWorkshop on Visualization and Processing of Tensor Fields, 2004
Country/TerritoryGermany
CitySchloss Dagstuhl
Period18/04/0423/04/04

Fingerprint

Dive into the research topics of 'Variational regularization of multiple diffusion tensor fields'. Together they form a unique fingerprint.

Cite this