Diseased region detection of longitudinal knee MRI data.

Journal Article (Journal Article)

Statistical analysis of longitudinal cartilage changes in osteoarthritis (OA) is of great importance and still a challenge in knee MRI data analysis. A major challenge is to establish a reliable correspondence across subjects within the same latent subpopulations. We develop a novel Gaussian hidden Markov model (GHMM) to establish spatial correspondence of cartilage thinning across both time and subjects within the same latent subpopulations and make statistical inference on the detection of diseased regions in each OA patient. A hidden Markov random field (HMRF) is proposed to extract such latent subpopulation structure. The EM algorithm and pseudo-likelihood method are both considered in making statistical inference. The proposed model can effectively detect diseased regions and present a localized analysis of longitudinal cartilage thickness within each latent subpopulation. Simulation studies and diseased region detection on 2D thickness maps extracted from full 3D longitudinal knee MRI Data for Pfizer Longitudinal Dataset are performed, which show that our proposed model outperforms standard voxel-based analysis.

Full Text

Duke Authors

Cited Authors

  • Huang, C; Shan, L; Charles, C; Niethammer, M; Zhu, H

Published Date

  • 2013

Published In

Volume / Issue

  • 23 /

Start / End Page

  • 632 - 643

PubMed ID

  • 24684005

Pubmed Central ID

  • PMC4012563

International Standard Serial Number (ISSN)

  • 1011-2499

Digital Object Identifier (DOI)

  • 10.1007/978-3-642-38868-2_53


  • eng

Conference Location

  • Germany