Literature DB >> 18603408

Robust semi-automated path extraction for visualising stenosis of the coronary arteries.

Daniel Mueller1, Anthony Maeder.   

Abstract

Computed tomography angiography (CTA) is useful for diagnosing and planning treatment of heart disease. However, contrast agent in surrounding structures (such as the aorta and left ventricle) makes 3D visualisation of the coronary arteries difficult. This paper presents a composite method employing segmentation and volume rendering to overcome this issue. A key contribution is a novel Fast Marching minimal path cost function for vessel centreline extraction. The resultant centreline is used to compute a measure of vessel lumen, which indicates the degree of stenosis (narrowing of a vessel). Two volume visualisation techniques are presented which utilise the segmented arteries and lumen measure. The system is evaluated and demonstrated using synthetic and clinically obtained datasets.

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Year:  2008        PMID: 18603408     DOI: 10.1016/j.compmedimag.2008.05.003

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  2 in total

1.  A Semi-Automatic Coronary Artery Segmentation Framework Using Mechanical Simulation.

Authors:  Ken Cai; Rongqian Yang; Lihua Li; Shanxing Ou; Yuke Chen; Jianhong Dou
Journal:  J Med Syst       Date:  2015-08-27       Impact factor: 4.460

2.  Angiography-Based Machine Learning for Predicting Fractional Flow Reserve in Intermediate Coronary Artery Lesions.

Authors:  Hyungjoo Cho; June-Goo Lee; Soo-Jin Kang; Won-Jang Kim; So-Yeon Choi; Jiyuon Ko; Hyun-Seok Min; Gun-Ho Choi; Do-Yoon Kang; Pil Hyung Lee; Jung-Min Ahn; Duk-Woo Park; Seung-Whan Lee; Young-Hak Kim; Cheol Whan Lee; Seong-Wook Park; Seung-Jung Park
Journal:  J Am Heart Assoc       Date:  2019-02-19       Impact factor: 5.501

  2 in total

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