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Can segmented 3D images be used for stenosis evaluation in coronary CT angiography?
Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization, CMIV.ORCID iD: 0000-0002-0442-3524
Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization, CMIV.ORCID iD: 0000-0002-9446-6981
Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Centre, Department of Clinical Physiology UHL.
Linköping University, Faculty of Health Sciences. Linköping University, Department of Medical and Health Sciences, Radiology.
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2012 (English)In: Acta Radiologica, ISSN 0284-1851, E-ISSN 1600-0455, Vol. 53, no 8, 845-851 p.Article in journal (Refereed) Published
Abstract [en]

Purpose: To retrospectively evaluate the diagnostic accuracy of coronary CT angiography (CCTA) using segmented 3D data for the detection of significant stenoses with catheter angiography (CA) as the reference standard.

Method: CCTA data sets from 30 patients were acquired with a 64-slice dual source CT scanner and segmented by an independent observer using the region growing (RG) method and the “virtual contrast injection” (VC) method. For every examination, each of the three types of images was  then reviewed by one of three reviewers in a blinded fashion for the presence of stenoses with diameter reduction of 50% or more. For the original series, the reviewer was allowed to use all the 2D or 3D visualization tools available (mixed method). For the segmented results (from RG and VC), the reviewer only used the 3D maximum intensity projection. Evaluation results were compared with CA for each artery.

Results: Overall, 34 arteries with significant stenosis were identified by CA. The percentage of evaluable arteries, accuracy and negative predictive value (NPV) for detecting stenosis were, respectively, 86%, 74% and 93% for the mixed method, 83%, 71% and 92% for VC, and 64%, 56% and 93% for RG. Accuracy was significantly lower for the RG method than for the other two methods (p<0.01), whereas there was no significant difference in accuracy between the VC method and the mixed method (p = 0.22). Excluding vessels with heavy calcification, all three methods had similar accuracy.

Conclusion: Diagnostic accuracy when using segmented 3D data was lower than with access to 2D images. However, the high NPV of the 3D methods suggests a potential of using them as an initial step, with access to 2D reviewing techniques for suspected lesions and cases with heavy calcification. The VC method, which generates more evaluable arteries and has higher accuracy, seems more promising for this purpose than the RG method.

Place, publisher, year, edition, pages
Informa Healthcare, 2012. Vol. 53, no 8, 845-851 p.
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
URN: urn:nbn:se:liu:diva-68794DOI: 10.1258/ar.2012.120053ISI: 000310820000004OAI: oai:DiVA.org:liu-68794DiVA: diva2:420942
Available from: 2011-06-07 Created: 2011-06-07 Last updated: 2013-10-21Bibliographically approved
In thesis
1. Computer-­Assisted  Coronary  CT  Angiography  Analysis: From  Software  Development  to  Clinical  Application
Open this publication in new window or tab >>Computer-­Assisted  Coronary  CT  Angiography  Analysis: From  Software  Development  to  Clinical  Application
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Advances in coronary Computed Tomography Angiography (CTA) have resulted in a boost in the use of this new technique in recent years, creating a challenge for radiologists due to the increasing number of exams and the large amount of data for each patient. The main goal of this study was to develop a computer tool to facilitate coronary CTA analysis by combining knowledge of medicine and image processing, and to evaluate the performance in clinical settings.

Firstly, a competing fuzzy connectedness tree algorithm was developed to segment the coronary arteries and extract centerlines for each branch. The new algorithm, which is an extension of the “virtual contrast injection” (VC) method, preserves the low-density soft tissue around the artery, and thus reduces the possibility of introducing false positive stenoses during segmentation. Visually reasonable results were obtained in clinical cases.

Secondly, this algorithm was implemented in open source software in which multiple visualization techniques were integrated into an intuitive user interface to facilitate user interaction and provide good over­views of the processing results. An automatic seeding method was introduced into the interactive segmentation workflow to eliminate the requirement of user initialization during post-processing. In 42 clinical cases, all main arteries and more than 85% of visible branches were identified, and testing the centerline extraction in a reference database gave results in good agreement with the gold standard.

Thirdly, the diagnostic accuracy of coronary CTA using the segmented 3D data from the VC method was evaluated on 30 clinical coronary CTA datasets and compared with the conventional reading method and a different 3D reading method, region growing (RG), from a commercial software. As a reference method, catheter angiography was used. The percentage of evaluable arteries, accuracy and negative predictive value (NPV) for detecting stenosis were, respectively, 86%, 74% and 93% for the conventional method, 83%, 71% and 92% for VC, and 64%, 56% and 93% for RG. Accuracy was significantly lower for the RG method than for the other two methods (p<0.01), whereas there was no significant difference in accuracy between the VC method and the conventional method (p = 0.22).

Furthermore, we developed a fast, level set-based algorithm for vessel segmentation, which is 10-20 times faster than the conventional methods without losing segmentation accuracy. It enables quantitative stenosis analysis at interactive speed.

In conclusion, the presented software provides fast and automatic coron­ary artery segmentation and visualization. The NPV of using only segmented 3D data is as good as using conventional 2D viewing techniques, which suggests a potential of using them as an initial step, with access to 2D reviewing techniques for suspected lesions and cases with heavy calcification. Combining the 3D visualization of segmentation data with the clinical workflow could shorten reading time.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. 57 p.
Series
Linköping University Medical Dissertations, ISSN 0345-0082 ; 1237
Keyword
Vessel segmentation, coronary CTA, fuzzy connectedness, level set, coronary artery disease
National Category
Computer Vision and Robotics (Autonomous Systems) Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:liu:diva-68705 (URN)978-91-7393-191-5 (ISBN)
Public defence
2011-06-07, Wrannesalen, CMIV, Universitetssjukhuset, Campus US, Linköpings univeristet, Linköping, 09:00 (English)
Opponent
Supervisors
Available from: 2011-06-07 Created: 2011-05-30 Last updated: 2013-10-21Bibliographically approved

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Wang, ChunliangPersson, AndersEngvall, Jande Geer, JakobFransson, Sven GöranSmedby, Örjan
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