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Abstract
The three-dimensional (3D) representation of the bifurcation anatomy and disease burden is essential for better understanding of the anatomical complexity of bifurcation disease and planning of stenting strategies. We propose a novel methodology for 3D reconstruction of coronary artery bifurcations based on the integration of angiography, which provides the backbone of the bifurcation, with optical coherence tomography (OCT), which provides the vessel shape. Our methodology introduces several technical novelties to tackle the OCT frame misalignment, correct positioning of the OCT frames at the carina, lumen surface reconstruction, and merging of bifurcation lumens. The accuracy and reproducibility of the methodology were tested in n = 5 patient-specific silicone bifurcations compared to contrast-enhanced micro-computed tomography (µCT), which was used as reference. The feasibility and time-efficiency of the method were explored in n = 7 diseased patient bifurcations of varying anatomical complexity. The OCT-based reconstructed bifurcation models were found to have remarkably high agreement compared to the µCT reference models, yielding r2 values between 0.91 and 0.98 for the normalized lumen areas, and mean differences of 0.005 for lumen shape and 0.004 degrees for bifurcation angles. Likewise, the reproducibility of our methodology was remarkably high. Our methodology successfully reconstructed all the patient bifurcations yielding favorable processing times (average lumen reconstruction time < 60 min). Overall, our method is an easily applicable, time-efficient, and user-friendly tool that allows accurate and reproducible 3D reconstruction of coronary bifurcations. Our technique can be used in the clinical setting to provide information about the bifurcation anatomy and plaque burden, thereby enabling planning, education, and decision making on bifurcation stenting.
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Details
1 University of Nebraska Medical Center, Cardiovasclar Biology and Biomechanics Laboratory, Cardiovascular Division, Omaha, USA (GRID:grid.266813.8) (ISNI:0000 0001 0666 4105)
2 StudioGijs, Tilburg, The Netherlands (GRID:grid.266813.8)
3 Politecnico di Milano, Biosignals, Bioimaging and Bioinformatics Laboratory (B3-Lab), Department of Electronics, Information and Bioengineering, Milan, Italy (GRID:grid.4643.5) (ISNI:0000 0004 1937 0327)
4 Teikyo University Hospital, Department of Cardiology, Tokyo, Japan (GRID:grid.412305.1) (ISNI:0000 0004 1769 1397)
5 National Hospital Organization Kyushu Medical Center, Department of Cardiology, Fukuoka, Japan (GRID:grid.415613.4)
6 Fondazione Policlinico Universitario A. Gemelli IRCCS Università Cattolica del Sacro Cuore, Department of Cardiovascular Sciences, Rome, Italy (GRID:grid.8142.f) (ISNI:0000 0001 0941 3192)
7 Minneapolis Heart Institute, Minneapolis, USA (GRID:grid.413195.b) (ISNI:0000 0000 8795 611X)
8 Mount Sinai Hospital, Department of Cardiovascular Medicine, New York City, USA (GRID:grid.416167.3)
9 Institut Cardiovasculaire Paris Sud, Massy, France (GRID:grid.418134.b)
10 Clinical Center of Serbia, Department of Cardiology, Belgrade, Serbia (GRID:grid.418577.8) (ISNI:0000 0000 8743 1110)
11 California Medical Innovation Institute, San Diego, USA (GRID:grid.418577.8)
12 Politecnico di Milano, Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering “Giulio Natta, Milan, Italy (GRID:grid.4643.5) (ISNI:0000 0004 1937 0327)
13 Politecnico di Torino, PoliToBIOMed Lab, Department of Mechanical and Aerospace Engineering, Turin, Italy (GRID:grid.4800.c) (ISNI:0000 0004 1937 0343)