Literature DB >> 26736676

Registration of coronary arteries in computed tomography angiography images using Hidden Markov Model.

Yuxuan Luo, Jianjiang Feng, Miao Xu, Jie Zhou, James K Min, Guanglei Xiong.   

Abstract

Computed tomography angiography (CTA) allows for not only diagnosis of coronary artery disease (CAD) with high spatial resolution but also monitoring the remodeling of vessel walls in the progression of CAD. Alignment of coronary arteries in CTA images acquired at different times (with a 3-7 years interval) is required to visualize and analyze the geometric and structural changes quantitatively. Previous work in image registration primarily focused on large anatomical structures and leads to suboptimal results when applying to registration of coronary arteries. In this paper, we develop a novel method to directly align the straightened coronary arteries in the cylindrical coordinate system guided by the extracted centerlines. By using a Hidden Markov Model (HMM), image intensity information from CTA and geometric information of extracted coronary arteries are combined to align coronary arteries. After registration, the pathological features in two straightened coronary arteries can be directly visualized side by side by synchronizing the corresponding cross-sectional slices and circumferential rotation angles. By evaluating with manually labeled landmarks, the average distance error is 1.6 mm.

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Year:  2015        PMID: 26736676      PMCID: PMC4790093          DOI: 10.1109/EMBC.2015.7318776

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  4 in total

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Authors:  Aristeidis Sotiras; Christos Davatzikos; Nikos Paragios
Journal:  IEEE Trans Med Imaging       Date:  2013-05-31       Impact factor: 10.048

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Authors:  James K Min; Leslee J Shaw; Daniel S Berman
Journal:  J Am Coll Cardiol       Date:  2010-03-09       Impact factor: 24.094

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Journal:  IEEE Trans Med Imaging       Date:  1997-12       Impact factor: 10.048

4.  Alternative projections of mortality and disability by cause 1990-2020: Global Burden of Disease Study.

Authors:  C J Murray; A D Lopez
Journal:  Lancet       Date:  1997-05-24       Impact factor: 79.321

  4 in total

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