To generate accurate and realistic models of coronary artery bifurcations before and after percutaneous coronary intervention (PCI), using information from two image modalities. Because bifurcations are regions where atherosclerotic plaque appears frequently and intervention is more challenging, generation of such realistic models could be of high value to predict the risk of restenosis or thrombosis after stent implantation, and to study geometrical and hemodynamical changes.
Two image modalities have been employed to generate the bifurcation models: computer tomography angiography (CTA) to obtain the 3D trajectory of vessels, and 2D conventional coronary angiography (CCA) to obtain radius information of the vessel lumen, due to its better contrast and image resolution. In addition, CCA can be acquired right before and after the intervention in the operation room; therefore, the combination of CTA and CCA allows the generation of realistic preprocedure and postprocedure models of coronary bifurcations. The method proposed is semiautomatic, based on landmarks manually placed on both image modalities.
A comparative study of the models obtained with the proposed method with models manually obtained using only CTA, shows more reliable results when both modalities are used together. The authors show that using preprocedure CTA and postprocedure CCA, realistic postprocedure models can be obtained. Analysis carried out of the Murray's law in all patient bifurcations shows the geometric improvement of PCI in our models, better than using manual models from CTA alone. An experiment using a cardiac phantom also shows the feasibility of the proposed method.
The authors have shown that fusion of CTA and CCA is feasible for realistic generation of coronary bifurcation models before and after PCI. The method proposed is efficient, and relies on minimal user interaction, and therefore is of high value to study geometric and hemodynamic changes of treated patients.
This work was supported by the Spanish Industrial and Technological Development Center (cvREMOD CEN-20091044). R.C. is funded by a Beatriu de Pinós fellowship from AGAUR. The authors want to thank Dr. Marta Sitges, Dr. T. M. De Caralt, Dr. N. Solanes, Dr. M. Rigol, and D. Flores from Hospital Clinic, Barcelona, for their valuable support especially during the image acquisitions.
I. INTRODUCTION AND BACKGROUND
II. METHODS AND MATERIALS
II.A. Semiautomatic model generation from CTA and CCA
II.A.1. 3D centerline extraction
II.A.2. Processing of the 2D angiographic images
II.A.3. Radius mapping
II.A.4. Predictive postprocedure models
II.A.5. Limitations of the method
II.B. Manual model generation from CTA
II.C.1. Patient data
II.C.2. Heart phantom
III.A. Results of semiautomatic models generated from CTA and CCA
III.B. Phantom results
III.C. Model comparison with CTA manual data
III.D. Models comparison with angiography data
III.E. Murray's law analysis
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