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Improved functional asssessment of ischemic severity using 3D printed models
Author(s): Kranthi K. Kolli; Shalmli Joshi; Eva Romito; Alexandre Caprio; Amir Ali Amiri Moghadam; Seyedhamidreza Alaie; Patricia Xu; Robert Shepherd; Bobak Mosadegh; Simon Dunham
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Paper Abstract

Purpose: To develop a novel in vitro method of evaluating coronary artery ischemia using a combination of noninvasive coronary CT angiograms (CCTA) and 3D printing. Materials and Methods: Five patients with varying degrees of coronary artery disease who underwent non-invasive CCTA scans and invasive fractional flow reserve (FFR) of their left anterior descending coronary artery (LAD) were included in this study. The LAD artery was segmented and reconstructed using Mimics (Materialise). The segmented models were then 3D printed using a Carbon (Carbon Inc.,) and Objet260 Connex (Stratasys) printers with urethane methacrylate (UMA) family of rigid resins and Veroclear, respectively. An in vitro flow circulation system representative of invasive measurements in a cardiac catheterization laboratory was developed to experimentally evaluate the hemodynamic parameters of pressure and flow. Physiological coronary circulation was modelled in vitro as flow‐dependent stenosis resistance in series with variable downstream resistance. A range of physiological flow rates was applied by a peristaltic steady flow pump and titrated by a flow sensor. The pressure drop and the pressure ratio (Pd/Pa) were assessed for patient-specific aortic pressure and differing flow rates to evaluate FFR in vitro. Results: For these five models, there was a good positive correlation (r = 0.78) between the in vitro and invasive FFR. The mean differences, as assessed by Bland-Altman analysis, between in vitro and invasively measured FFR was at 0.01±0.1 (95% limit of agreement -0.1169 to 0.1369). Conclusions: 3D printed patient-specific models can be used in a non-invasive in vitro environment to quantify coronary artery ischemia as assessed by invasive FFR.

Paper Details

Date Published: 15 March 2019
PDF: 6 pages
Proc. SPIE 10954, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications, 109540B (15 March 2019); doi: 10.1117/12.2512779
Show Author Affiliations
Kranthi K. Kolli, Weill Cornell Medicine (United States)
Shalmli Joshi, Univ. of Connecticut School of Medicine (United States)
Eva Romito, Weill Cornell Medicine (United States)
Alexandre Caprio, Weill Cornell Medicine (United States)
Amir Ali Amiri Moghadam, Weill Cornell Medicine (United States)
Seyedhamidreza Alaie, Weill Cornell Medicine (United States)
Patricia Xu, Cornell Univ. (United States)
Robert Shepherd, Cornell Univ. (United States)
Bobak Mosadegh, Weill Cornell Medicine (United States)
Simon Dunham, Weill Cornell Medicine (United States)


Published in SPIE Proceedings Vol. 10954:
Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications
Po-Hao Chen; Peter R. Bak, Editor(s)

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