CCTA-PET Registration for Quantitative Analysis of Myocardial Infarction

Zhenzhen Xu, Bo Tao, Heng Zhao, Feng Cao, Jimin Liang


Published in:Fully3D 2017 Proceedings


coronary CTA, PET, multimodality registration, quantitative analysis, myocardial infarction
Quantitative analysis of myocardial infarction is important for the prognostic assessment and therapeutic strategy of patients with ischemic heart disease (IHD). 18F-fluorodeoxyglucose (FDG) PET is generally regarded as the gold standard for in vivo assessing myocardial viability. However, currently PET scans can only be interpreted by well trained radiologists because the morphological variation and location of infarct myocardium is hard to reflect visually on 2D slices or polar plot. In this paper, a 3D non-rigid coronary CTA (CCTA)-PET registration strategy is presented to quantitative analysis the extent of myocardial infarction. Whole heart is firstly segmented from both CCTA and PET/CT. Then the myocardium of left ventricle (LV) is separated from CCTA and PET/CT, respectively. After morphological post-processing, the myocardium image is integrated with the whole heart image both for CCTA and PET/CT to conduct the myocardial registration. A random forest classifier is trained to identify the infarct area of LV wall. The myocardial infarction analysis method proposed in this paper is compared with the cardiac tissue histological study. The result demonstrates good agreement with the TTC stain result in both infarct size and location, and suggests a potential value for clinic application in the prognosis of myocardial infarction.
Zhenzhen Xu
Xidian University
Bo Tao
Chinese PLA General Hospital
Heng Zhao
Xidian University
Feng Cao
Chinese PLA General Hospital
Jimin Liang
Xidian University
  1. M. M. Al and Z. H. Sun, "Diagnostic value of 18F-FDG PET in the assessment of myocardial viability in coronary artery disease: A comparative study with 99mTc SPECT and echocardiography," vol. 11, pp. 229-236, 2014.
  2. M. Kobylecka, J. Mączewska, K. Fronczewska-Wieniawska, T. Mazurek, M. T. Płazińska, and L. Królicki, "Myocardial viability assessment in 18FDG PET/CT study (18FDG PET myocardial viability assessment)," Nuclear Medicine Review Central & Eastern Europe, vol. 15, pp. 52-60, 2012.
  3. J. R. Mccrary, L. S. Wann, and R. C. Thompson, "PET imaging with FDG to guide revascularization in patients with systolic heart failure," Egyptian Heart Journal, vol. 65, pp. 123–129, 2013.
  4. M. Sciumbata, S. Critello, and D. Galea, "[Quantitative analysis of myocardial glucose metabolism by using dynamic FDG-PET acquisition]," Recenti Progressi in Medicina, vol. 103, pp. 450-454, 2012.
  5. M. Hacker, "Cardiac PET-CT and CT Angiography," Current Cardiovascular Imaging Reports, vol. 6, pp. 191-196, 2013.
  6. W. Tanis, A. Scholtens, J. Habets, V. D. B. Rb, L. A. van Herwerden, S. A. Chamuleau, et al., "CT angiography and ¹⁸F-FDG-PET fusion imaging for prosthetic heart valve endocarditis," Jacc Cardiovascular Imaging, vol. 6, pp. 1008–1013, 2013.
  7. R. Nakazato, D. Dey, E. Alexánderson, A. Meave, M. Jiménez, E. Romero, et al., "Automatic alignment of myocardial perfusion PET and 64-slice coronary CT angiography on hybrid PET/CT," Journal of Nuclear Cardiology, vol. 19, pp. 482-491, 2012.
  8. B. Tao, H. Gao, M. Zheng, Z. Luo, L. Liu, W. Bai, et al., "Preclinical modeling and multimodality imaging of chronic myocardial infarction in minipigs induced by novel interventional embolization technique," Ejnmmi Research, vol. 6, pp. 1-10, 2016.
  9. H. A. Kirisli, S. Klein, T. V. Walsum, and W. J. Niessen, "Fully automatic cardiac segmentation from 3D CTA data: a multi-atlas based approach," in SPIE Medical Imaging, 2010.
  10. B. B. Avants, N. J. Tustison, M. Stauffer, G. Song, B. Wu, and J. C. Gee, "The Insight ToolKit image registration framework," Frontiers in Neuroinformatics, vol. 8, p. 44, 2014.
  11. X. H. Wang, B. Liu, and Z. Q. Song, "3-Dimensional Brain MRI Segmentation Based on Multi-Layer Background Subtraction and Seed Region Growing Algorithm," Applied Mechanics & Materials, vol. 536-537, pp. 218-221, 2014.
  12. S. Gargiulo, A. Greco, M. Gramanzini, M. P. Petretta, A. Ferro, M. Larobina, et al., "PET/CT imaging in mouse models of myocardial ischemia," Journal of Biomedicine & Biotechnology, vol. 2012, p.: 541872., 2012.
  13. J. Fleming and J. Fleming, Quantitative analysis in nuclear medicine imaging: Springer, 2006.
  14. J. Lønborg, N. Vejlstrup, H. Kelbæk, L. Nepperchristensen, E. Jørgensen, S. Helqvist, et al., "Impact of acute hyperglycemia on myocardial infarct size, area at risk, and salvage in patients with STEMI and the association with exenatide treatment: results from a randomized study," Diabetes, vol. 63, pp. 2474-85, 2014.
  15. D. F. Polan, S. L. Brady, and R. A. Kaufman, "Tissue segmentation of computed tomography images using a Random Forest algorithm: a feasibility study," Physics in Medicine & Biology, vol. 61, pp. 6553-6569, 2016.
  16. A. Galli and F. Lombardi, "Postinfarct Left Ventricular Remodelling: A Prevailing Cause of Heart Failure," Cardiology Research & Practice, vol. 2016, pp. 1-12, 2016.