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
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.
Lauren Shepard, Kelsey Sommer, Richard Izzo, Alexander Podgorsak, Michael Wilson, Zaid Said, Frank J. Rybicki, Dimitrios Mitsouras, Stephen Rudin, Erin Angel, and Ciprian N. Ionita, "Initial simulated FFR investigation using flow measurements in patient-specific 3D printed coronary phantoms," Proc. SPIE 10138, Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications, 101380S (Presented at SPIE Medical Imaging: February 16, 2017; Published: 13 March 2017); https://doi.org/10.1117/12.2253889.
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