Consistent reconstruction of cortical surfaces from longitudinal brain MR images

Gang Li, Jingxin Nie, Dinggang Shen

Research output: Contribution to journalArticlepeer-review


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
Publication statusPublished - 2011 Oct 11
Externally publishedYes


  • Cortical surface reconstruction
  • longitudinal cortical surface

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science


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