Consistent reconstruction of cortical surfaces from longitudinal brain MR images

Gang Li, Jingxin Nie, Dinggang Shen

Research output: Contribution to journalArticle

Abstract

Accurate and consistent reconstruction of cortical surfaces from longitudinal human brain MR images is of great importance in studying subtle morphological changes of the cerebral cortex. This paper presents a new deformable surface method for consistent and accurate reconstruction of inner, central and outer cortical surfaces from longitudinal MR images. Specifically, the cortical surfaces of the group-mean image of all aligned longitudinal images of the same subject are first reconstructed by a deformable surface method driven by a force derived from the Laplace's equation. And then the longitudinal cortical surfaces are consistently reconstructed by jointly deforming the cortical surfaces from the group-mean image to all longitudinal images. The proposed method has been successfully applied to both simulated and real longitudinal images, demonstrating its validity.

Original languageEnglish
Pages (from-to)671-679
Number of pages9
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6892 LNCS
Issue numberPART 2
DOIs
Publication statusPublished - 2011 Oct 11
Externally publishedYes

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Keywords

  • Cortical surface reconstruction
  • longitudinal cortical surface

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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