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Proceedings Paper

Initial simulated FFR investigation using flow measurements in patient-specific 3D printed coronary phantoms
Author(s): Lauren Shepard; Kelsey Sommer; Richard Izzo; Alexander Podgorsak; Michael Wilson; Zaid Said; Frank J. Rybicki; Dimitrios Mitsouras; Stephen Rudin; Erin Angel; Ciprian N. Ionita
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Paper Abstract

Purpose: Accurate patient-specific phantoms for device testing or endovascular treatment planning can be 3D printed. We expand the applicability of this approach for cardiovascular disease, in particular, for CT-geometry derived benchtop measurements of Fractional Flow Reserve, the reference standard for determination of significant individual coronary artery atherosclerotic lesions. Materials and Methods: Coronary CT Angiography (CTA) images during a single heartbeat were acquired with a 320x0.5mm detector row scanner (Toshiba Aquilion ONE). These coronary CTA images were used to create 4 patientspecific cardiovascular models with various grades of stenosis: severe, <75% (n=1); moderate, 50-70% (n=1); and mild, <50% (n=2). DICOM volumetric images were segmented using a 3D workstation (Vitrea, Vital Images); the output was used to generate STL files (using AutoDesk Meshmixer), and further processed to create 3D printable geometries for flow experiments. Multi-material printed models (Stratasys Connex3) were connected to a programmable pulsatile pump, and the pressure was measured proximal and distal to the stenosis using pressure transducers. Compliance chambers were used before and after the model to modulate the pressure wave. A flow sensor was used to ensure flow rates within physiological reported values. Results: 3D model based FFR measurements correlated well with stenosis severity. FFR measurements for each stenosis grade were: 0.8 severe, 0.7 moderate and 0.88 mild. Conclusions: 3D printed models of patient-specific coronary arteries allows for accurate benchtop diagnosis of FFR. This approach can be used as a future diagnostic tool or for testing CT image-based FFR methods.

Paper Details

Date Published: 13 March 2017
PDF: 12 pages
Proc. SPIE 10138, Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications, 101380S (13 March 2017); doi: 10.1117/12.2253889
Show Author Affiliations
Lauren Shepard, Univ. at Buffalo (United States)
Toshiba Stroke and Vascular Research Ctr. (United States)
Kelsey Sommer, Univ. at Buffalo (United States)
Toshiba Stroke and Vascular Research Ctr. (United States)
Richard Izzo, Univ. at Buffalo (United States)
Toshiba Stroke and Vascular Research Ctr. (United States)
The Jacobs Institute, Inc. (United States)
Alexander Podgorsak, Univ. at Buffalo (United States)
Toshiba Stroke and Vascular Research Ctr. (United States)
Michael Wilson, Univ. at Buffalo (United States)
Zaid Said, Univ. at Buffalo (United States)
Frank J. Rybicki, Univ. of Ottawa (Canada)
Dimitrios Mitsouras, Brigham and Women's Hospital (United States)
Stephen Rudin, Univ. at Buffalo (United States)
Toshiba Stroke and Vascular Research Ctr. (United States)
Erin Angel, Toshiba America Medical Systems, Inc. (United States)
Ciprian N. Ionita, Univ. at Buffalo (United States)
Toshiba Stroke and Vascular Research Ctr. (United States)


Published in SPIE Proceedings Vol. 10138:
Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications
Tessa S. Cook; Jianguo Zhang, Editor(s)

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