A heat kernel based cortical thickness estimation algorithm

Gang Wang, Xiaofeng Zhang, Qingtang Su, Jiannong Chen, Lili Wang, Yunyan Ma, Qiming Liu, Liang Xu, Jie Shi, Yalin Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

Cortical thickness estimation in magnetic resonance imaging (MRI) is an important technique for research on brain development and neurodegenerative diseases. This paper presents a heat kernel based cortical thickness estimation algorithm, which is driven by the graph spectrum and the heat kernel theory, to capture the grey matter geometry information in the in vivo brain MR images. First, we use the harmonic energy function to establish the tetrahedral mesh matching with the MR images and generate the Laplace-Beltrami operator matrix which includes the inherent geometric characteristics of the tetrahedral mesh. Second, the isothermal surfaces are computed by the finite element method with the volumetric Laplace-Beltrami operator and the direction of the steamline is obtained by tracing the maximum heat transfer probability based on the heat kernel diffusion. Thereby we can calculate the cerebral cortex thickness information between the point on the outer surface and the corresponding point on the inner surface. The method relies on intrinsic brain geometry structure and the computation is robust and accurate. To validate our algorithm, we apply it to study the thickness differences associated with Alzheimer's disease (AD) and mild cognitive impairment (MCI) on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Our preliminary experimental results in 151 subjects (51 AD, 45 MCI, 55 controls) show that the new algorithm successfully detects statistically significant difference among patients of AD, MCI and healthy control subjects. The results also indicate that the new method may have better performance than the Freesurfer software.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages233-245
Number of pages13
Volume8159 LNCS
DOIs
StatePublished - 2013
Event3rd International Workshop on Multimodal Brain Image Analysis, MBIA 2013, Held in Conjunction with the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan
Duration: Sep 22 2013Sep 22 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8159 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other3rd International Workshop on Multimodal Brain Image Analysis, MBIA 2013, Held in Conjunction with the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
CountryJapan
CityNagoya
Period9/22/139/22/13

Fingerprint

Alzheimer's Disease
Heat Kernel
Estimation Algorithms
Brain
Tetrahedral Mesh
Laplace-Beltrami Operator
Graph Spectra
Neurodegenerative diseases
Neuroimaging
Information Geometry
Operator Matrix
Geometry
Magnetic Resonance Imaging
Magnetic resonance
Cortex
Tracing
Energy Function
Harmonic Functions
Mathematical operators
Heat Transfer

Keywords

  • Cortical thickness
  • False Discovery Rate
  • Heat Kernel
  • Streamline
  • Tetrahedral Mesh

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Wang, G., Zhang, X., Su, Q., Chen, J., Wang, L., Ma, Y., ... Wang, Y. (2013). A heat kernel based cortical thickness estimation algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8159 LNCS, pp. 233-245). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8159 LNCS). https://doi.org/10.1007/978-3-319-02126-3_23

A heat kernel based cortical thickness estimation algorithm. / Wang, Gang; Zhang, Xiaofeng; Su, Qingtang; Chen, Jiannong; Wang, Lili; Ma, Yunyan; Liu, Qiming; Xu, Liang; Shi, Jie; Wang, Yalin.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8159 LNCS 2013. p. 233-245 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8159 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wang, G, Zhang, X, Su, Q, Chen, J, Wang, L, Ma, Y, Liu, Q, Xu, L, Shi, J & Wang, Y 2013, A heat kernel based cortical thickness estimation algorithm. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 8159 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8159 LNCS, pp. 233-245, 3rd International Workshop on Multimodal Brain Image Analysis, MBIA 2013, Held in Conjunction with the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013, Nagoya, Japan, 9/22/13. https://doi.org/10.1007/978-3-319-02126-3_23
Wang G, Zhang X, Su Q, Chen J, Wang L, Ma Y et al. A heat kernel based cortical thickness estimation algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8159 LNCS. 2013. p. 233-245. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-02126-3_23
Wang, Gang ; Zhang, Xiaofeng ; Su, Qingtang ; Chen, Jiannong ; Wang, Lili ; Ma, Yunyan ; Liu, Qiming ; Xu, Liang ; Shi, Jie ; Wang, Yalin. / A heat kernel based cortical thickness estimation algorithm. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8159 LNCS 2013. pp. 233-245 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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