Diseased Region Detection of Longitudinal Knee Magnetic Resonance Imaging Data.

TitleDiseased Region Detection of Longitudinal Knee Magnetic Resonance Imaging Data.
Publication TypeJournal Article
Year of Publication2015
AuthorsHuang, Chao, Liang Shan, Cecil H Charles, Wolfgang Wirth, Marc Niethammer, and Hongtu Zhu
JournalIEEE Trans Med Imaging
Volume34
Issue9
Pagination1914-27
Date Published2015 Sep
ISSN1558-254X
KeywordsAlgorithms, Cartilage, Articular, Female, Humans, Image Processing, Computer-Assisted, Knee, Knee Joint, Magnetic Resonance Imaging, Osteoarthritis, Knee
Abstract

Magnetic resonance imaging (MRI) has become an important imaging technique for quantifying the spatial location and magnitude/direction of longitudinal cartilage morphology changes in patients with osteoarthritis (OA). Although several analytical methods, such as subregion-based analysis, have been developed to refine and improve quantitative cartilage analyses, they can be suboptimal due to two major issues: the lack of spatial correspondence across subjects and time and the spatial heterogeneity of cartilage progression across subjects. The aim of this paper is to present a statistical method for longitudinal cartilage quantification in OA patients, while addressing these two issues. The 3D knee image data is preprocessed to establish spatial correspondence across subjects and/or time. Then, a Gaussian hidden Markov model (GHMM) is proposed to deal with the spatial heterogeneity of cartilage progression across both time and OA subjects. To estimate unknown parameters in GHMM, we employ a pseudo-likelihood function and optimize it by using an expectation-maximization (EM) algorithm. The proposed model can effectively detect diseased regions in each OA subject and present a localized analysis of longitudinal cartilage thickness within each latent subpopulation. Our GHMM integrates the strengths of two standard statistical methods including the local subregion-based analysis and the ordered value approach. We use simulation studies and the Pfizer longitudinal knee MRI dataset to evaluate the finite sample performance of GHMM in the quantification of longitudinal cartilage morphology changes. Our results indicate that GHMM significantly outperforms several standard analytical methods.

DOI10.1109/TMI.2015.2415675
Alternate JournalIEEE Trans Med Imaging
Original PublicationDiseased region detection of longitudinal knee magnetic resonance imaging data.
PubMed ID25823031
PubMed Central IDPMC4560622
Grant ListR21 AR059890 / AR / NIAMS NIH HHS / United States
UL1 TR001111 / TR / NCATS NIH HHS / United States
R025747-01 / / PHS HHS / United States
CA142538-01 / CA / NCI NIH HHS / United States
5R21AR059890-02 / AR / NIAMS NIH HHS / United States
R01 MH086633 / MH / NIMH NIH HHS / United States
B005149-01 / / PHS HHS / United States
T32 MH106440 / MH / NIMH NIH HHS / United States
MH086633 / MH / NIMH NIH HHS / United States
R01 MH091645 / MH / NIMH NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
R01 EB020426 / EB / NIBIB NIH HHS / United States
R01 MH091645-01A1 / MH / NIMH NIH HHS / United States
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