Lauren Shepard1,2, Kelsey Sommer1,2, Richard Izzo1,2,3, Alexander Podgorsak1,2, Michael Wilson4, Zaid Said4, Frank J Rybicki5, Dimitrios Mitsouras6, Stephen Rudin1,2, Erin Angel7, Ciprian N Ionita1,2. 1. University Dept. of Biomedical Engineering, University at Buffalo, Buffalo, NY. 2. Toshiba Stroke and Vascular Research Center, Buffalo, NY. 3. The Jacobs Institute, Buffalo, NY. 4. Interventional Cardiology, University at Buffalo Medicine, UBMD, Buffalo, NY. 5. The Ottawa Hospital Research Institute and the Department of Radiology, University of Ottawa, Ottawa, ON, CA. 6. Brigham and Women's Hospital, Boston, MA. 7. Toshiba American Medical Systems, Tustin, CA.
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 320×0.5mm detector row scanner (Toshiba Aquilion ONE). These coronary CTA images were used to create 4 patient-specific 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.
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 320×0.5mm detector row scanner (Toshiba Aquilion ONE). These coronary CTA images were used to create 4 patient-specific 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.
Authors: Dimitris Mitsouras; Peter Liacouras; Amir Imanzadeh; Andreas A Giannopoulos; Tianrun Cai; Kanako K Kumamaru; Elizabeth George; Nicole Wake; Edward J Caterson; Bohdan Pomahac; Vincent B Ho; Gerald T Grant; Frank J Rybicki Journal: Radiographics Date: 2015 Nov-Dec Impact factor: 5.333
Authors: Rine Nakanishi; Suguru Matsumoto; Anas Alani; Dong Li; Pieter H Kitslaar; Alexander Broersen; Bon-Kwon Koo; James K Min; Matthew J Budoff Journal: Int J Cardiovasc Imaging Date: 2015-04-24 Impact factor: 2.357
Authors: Bon-Kwon Koo; Andrejs Erglis; Joon-Hyung Doh; David V Daniels; Sanda Jegere; Hyo-Soo Kim; Allison Dunning; Tony DeFrance; Alexandra Lansky; Jonathan Leipsic; James K Min Journal: J Am Coll Cardiol Date: 2011-11-01 Impact factor: 24.094
Authors: Ciprian N Ionita; Maxim Mokin; Nicole Varble; Daniel R Bednarek; Jianping Xiang; Kenneth V Snyder; Adnan H Siddiqui; Elad I Levy; Hui Meng; Stephen Rudin Journal: Proc SPIE Int Soc Opt Eng Date: 2014-03-13
Authors: Lauren M Shepard; Kelsey N Sommer; Erin Angel; Vijay Iyer; Michael F Wilson; Frank J Rybicki; Dimitrios Mitsouras; Sabee Molloi; Ciprian N Ionita Journal: J Med Imaging (Bellingham) Date: 2019-03-12
Authors: Christopher T Wilke; Mohamed Zaid; Caroline Chung; Clifton D Fuller; Abdallah S R Mohamed; Heath Skinner; Jack Phan; G Brandon Gunn; William H Morrison; Adam S Garden; Steven J Frank; David I Rosenthal; Mark S Chambers; Eugene J Koay Journal: 3D Print Med Date: 2017-11-16
Authors: Kelsey N Sommer; Lauren Shepard; Nitant Vivek Karkhanis; Vijay Iyer; Erin Angel; Michael F Wilson; Frank J Rybicki; Dimitrios Mitsouras; Stephen Rudin; Ciprian N Ionita Journal: Proc SPIE Int Soc Opt Eng Date: 2018-03-12