Research progress on image-based calculation of coronary artery fractional flow reserve_Journal of Biomedical Engineering

Authors：

 SUN Zheng ^1,2 , JIAO Wenbin ¹

1. Department of Electronic and Communication Engineering, North China Electric Power University, Baoding, Hebei 071003, P. R. China;
2. Hebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University, Baoding, Hebei 071003, P. R. China;

Corresponding?author：

Keywords：

Coronary artery; Fractional flow reserve; Coronary artery imaging; Computational physiology; Image-derived fractional flow reserve

DOI：

10.7507/1001-5515.202206044

Video：

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Coronary artery fractional flow reserve (FFR) is a critical physiological indicator for assessment of impaired blood flow caused by coronary artery stenosis. The wire-based invasive measurement of blood flow pressure gradient across stenosis is the gold standard for clinical measurement of FFR. However, it has the risk of vascular injury and requires the use of vasodilators, increasing the time and overall cost of interventional examination. Coronary imaging is playing an important role in clinical diagnosis of stenotic lesions, evaluation of severity of lesions, and planning of therapies. In recent years, the computation of FFR based on the physiological information of blood flow obtained from routinely collected coronary image data has become a research focus in this field. This technique reduces the cost of physiological assessment of coronary lesions and the use of pressure wires. It is beneficial to strengthen the physiological guidance in interventional therapy. In order to better understand this emerging technique, this paper highlights its implementation principle and diagnostic performance, analyzes practical problems and current challenges in clinical applications, and discusses possible future development.

Citation： SUN Zheng, JIAO Wenbin. Research progress on image-based calculation of coronary artery fractional flow reserve. Journal of Biomedical Engineering, 2023, 40(1): 171-179. doi: 10.7507/1001-5515.202206044 Copy

1.	安夢楠,李悅,薛竟宜. 冠狀動脈狹窄病變功能學評估新進展. 中國介入心臟病學雜志, 2018, 26(11): 638-640.
2.	丁誠,吳永健. 瞬時無波形比值評價冠狀動脈狹窄的價值. 中華心血管病雜志, 2013, 41(1): 81-82.
3.	Tu Shengxian, Westra J, Adjedj J, et al. Fractional flow reserve in clinical practice: from wire-based invasive measurement to image-based computation. Eur Heart J, 2020, 41(34): 3271-3279.
4.	Mackle E C, Coote J M, Carr E, et al. Fibre optic intravascular measurements of blood flow: a review. Sensor Actuat A-Phys, 2021, 332: 113162.
5.	Jiang Wenbing, Pan Yibin, Hu Yumeng, et al. Diagnostic accuracy of coronary computed tomography angiography-derived fractional flow reserve. BioMed Eng OnLine, 2021, 20(1): 77.
6.	Baumann S, Renker M, Akin I, et al. FFR-derived from coronary CT angiography using workstation-based approaches. JACC Cardiovasc Imaging, 2017, 10(4): 486-499.
7.	N?rgaard B L, Gaur S, Fairbairn T A, et al. Prognostic value of coronary computed tomography angiographic derived fractional flow reserve: a systematic review and meta-analysis. Heart, 2022, 108(3): 194-202.
8.	Tesche C, de Cecco C N, Albrecht M H, et al. Coronary CT angiography–derived fractional flow reserve. Radiology, 2017, 285(1): 17-33.
9.	Coenen A, Lubbers M M, Kurata A, et al. Fractional flow reserve computed from noninvasive CT angiography data: diagnostic performance of an on-siteclinician-operated computational fluid dynamics algorithm. Radiology, 2015, 274(3): 674-683.
10.	Tang Chunxiang, Liu Chunyu, Lu Mengjie, et al. CT FFR for ischemia-specific CAD with a new computational fluid dynamics algorithm: a Chinese multicenter study. JACC Cardiovasc Imaging, 2020, 13(4): 980-990.
11.	Gould K L, Johnson N P, Kirkeeide R. TAG, you're out. JACC Cardiovasc Imaging, 2019, 12(2): 334-337.
12.	Ko B S. An onsite CT-FFR technique based on TAG: a 1-Hit wonder or can we expect more to come?. JACC Cardiovasc Imaging, 2020, 13(4): 991-993.
13.	Baumann S, Hirt M, Schoepf U J, et al. Correlation of machine learning computed tomography-based fractional flow reserve with instantaneous wave free ratio to detect hemodynamically significant coronary stenosis. Clin Res Cardiol, 2020, 109(6): 735-745.
14.	Gao Zhifan, Wang Xin, Sun Shanhui, et al. Learning physical properties in complex visual scenes: an intelligent machine for perceiving blood flow dynamics from static CT angiography imaging. Neural Networks, 2020, 123: 82-93.
15.	Yoon S, Yoon C H, Lee D. Topological recovery for non-rigid 2D/3D registration of coronary artery models. Comput Methods Programs Biomed, 2021, 200: 105922.
16.	Ramasamy A, Jin C, Tufaro V, et al. Computerised methodologies for non-invasive angiography-derived fractional flow reserve assessment: a critical review. J Interv Cardiol, 2020, 2020: 6381637.
17.	Gosling R C, Morris P D, Soto D A S, et al. Virtual coronary intervention: a treatment planning tool based upon the angiogram. JACC-Cardiovasc Imaging, 2019, 12(5): 865-872.
18.	Papafaklis M I, Muramatsu T, Ishibashi Y, et al. Fast virtual functional assessment of intermediate coronary lesions using routine angiographic data and blood flow simulation in humans: comparison with pressure wire-fractional flow reserve. EuroIntervention, 2014, 10(5): 574-583.
19.	Tr?bs M, Achenbach S, R?ther J, et al. Comparison of fractional flow reserve based on computational fluid dynamics modeling using coronary angiographic vessel morphology versus invasively measured fractional flow reserve. Am J Cardiol, 2016, 117(1): 29-35.
20.	Xu Bo, Tu Shengxian, Song Lei, et al. Angiographic quantitative flow ratio-guided coronary intervention (FAVOR III China): a multicentre, randomised, sham-controlled trial. Lancet, 2021, 398(10317): 2149-2159.
21.	Tu Shengxian, Ding Daixin, Chang Yunxiao, et al. Diagnostic accuracy of quantitative flow ratio for assessment of coronary stenosis significance from a single angiographic view: a novel method based on bifurcation fractal law. Catheter Cardiovasc Interv, 2021, 97(S2): 1040-1047.
22.	Tar B, Jenei C, Dezsi C A, et al. Less invasive fractional flow reserve measurement from 3-dimensional quantitative coronary angiography and classic fluid dynamic equations. EuroIntervention, 2018, 14(8): 942-950.
23.	Pellicano M, Lavi I, de Bruyne B, et al. Validation study of image-based fractional flow reserve during coronary angiography. Circ-Cardiovasc Interv, 2017, 10(9): e005259.
24.	Masdjedi K, van Zandvoort L J C, Balbi M M, et al. Validation of a three-dimensional quantitative coronary angiography-based software to calculate fractional flow reserve: the FAST study. EuroIntervention, 2020, 16(7): 591-599.
25.	Rogui A, Abu Dogosh A, Feld Y, et al. Early feasibility of automated artificial intelligence angiography based fractional flow reserve estimation. Am J Cardiol, 2021, 139: 8-14.
26.	Zhang Dong, Yang Guang, Zhao Shu, et al. Direct quantification of coronary artery stenosis through hierarchical attentive multi-view learning. IEEE Trans Med Imaging, 2020, 39(12): 4322-4334.
27.	Campo G, Biscaglia S. Functional assessment 3. 0: from wire through angio to OCT. EuroIntervention, 2020, 16(7): 534-535.
28.	Okuya Y, Seike F, Yoneda K, et al. Functional assessment of tandem coronary artery stenosis by intracoronary optical coherence tomography-derived virtual fractional flow reserve: a case series. Eur Heart J, 2019, 3(2): ytz087.
29.	Jang S J, Ahn J M, Kim B, et al. Comparison of accuracy of one use methods for calculating fractional flow reserve by intravascular optical coherence tomography to that determined by the pressure-wire method. Am J Cardiol, 2017, 120(11): 1920-1925.
30.	Huang Jiayue, Yang Fan, Gutiérrez-Chico J L, et al. Optical coherence tomography-derived changes in plaque structural stress over the cardiac cycle: a new method for plaque biomechanical assessment. Front Cardiovasc Med, 2021, 8: 715995.
31.	Cha J J, Son T D, Ha J, et al. Optical coherence tomography-based machine learning for predicting fractional flow reserve in intermediate coronary stenosis: a feasibility study. Sci Rep, 2020, 10(1): 20421.
32.	Seike F, Uetani T, Nishimura K, et al. Intravascular ultrasound-derived virtual fractional flow reserve for the assessment of myocardial ischemia. Circ J, 2018, 82(3): 815-823.
33.	Lee J G, Ko J, Hae H, et al. Intravascular ultrasound-based machine learning for predicting fractional flow reserve in intermediate coronary artery lesions. Atherosclerosis, 2020, 292: 171-177.
34.	Yu Wei, Tanigaki T, Ding Daixin, et al. Accuracy of intravascular ultrasound based fractional flow reserve in identifying hemodynamic significance of coronary stenosis. Circ-Cardiovasc Inte, 2021, 14(2): e009840.
35.	Kilic Y, Safi H, Bajaj R, et al. The evolution of data fusion methodologies developed to reconstruct coronary artery geometry from intravascular imaging and coronary angiography data: a comprehensive review. Front Cardiovasc Med, 2020, 7: 33.
36.	Jiang Jun, Feng Li, Li Changling, et al. Fractional flow reserve for coronary stenosis assessment derived from fusion of intravascular ultrasound and X-ray angiography. Quant Imag Med Surg, 2021, 11(11): 4543-4555.
37.	Gao Zijun, Wang Lu, Soroushmehr R, et al. Vessel segmentation for X-ray coronary angiography using ensemble methods with deep learning and filter-based features. BMC Med Imaging, 2022, 22(1): 10.
38.	Wen X, Lei P, Xiong K, et al. High-robustness intravascular photoacoustic endoscope with a hermetically sealed opto-sono capsule. Opt Express, 2020, 28(13): 19255-19269.
39.	Leng Ji, Zhang Jinke, Li Chenguang, et al. Multi-spectral intravascular photoacoustic/ultrasound/optical coherence tomography tri-modality system with a fully-integrated 0. 9-mm full field-of-view catheter for plaque vulnerability imaging. Biomed Opt Express, 2021, 12(4): 1934-1946.

1. 安夢楠,李悅,薛竟宜. 冠狀動脈狹窄病變功能學評估新進展. 中國介入心臟病學雜志, 2018, 26(11): 638-640.
2. 丁誠,吳永健. 瞬時無波形比值評價冠狀動脈狹窄的價值. 中華心血管病雜志, 2013, 41(1): 81-82.
3. Tu Shengxian, Westra J, Adjedj J, et al. Fractional flow reserve in clinical practice: from wire-based invasive measurement to image-based computation. Eur Heart J, 2020, 41(34): 3271-3279.
4. Mackle E C, Coote J M, Carr E, et al. Fibre optic intravascular measurements of blood flow: a review. Sensor Actuat A-Phys, 2021, 332: 113162.
5. Jiang Wenbing, Pan Yibin, Hu Yumeng, et al. Diagnostic accuracy of coronary computed tomography angiography-derived fractional flow reserve. BioMed Eng OnLine, 2021, 20(1): 77.
6. Baumann S, Renker M, Akin I, et al. FFR-derived from coronary CT angiography using workstation-based approaches. JACC Cardiovasc Imaging, 2017, 10(4): 486-499.
7. N?rgaard B L, Gaur S, Fairbairn T A, et al. Prognostic value of coronary computed tomography angiographic derived fractional flow reserve: a systematic review and meta-analysis. Heart, 2022, 108(3): 194-202.
8. Tesche C, de Cecco C N, Albrecht M H, et al. Coronary CT angiography–derived fractional flow reserve. Radiology, 2017, 285(1): 17-33.
9. Coenen A, Lubbers M M, Kurata A, et al. Fractional flow reserve computed from noninvasive CT angiography data: diagnostic performance of an on-siteclinician-operated computational fluid dynamics algorithm. Radiology, 2015, 274(3): 674-683.
10. Tang Chunxiang, Liu Chunyu, Lu Mengjie, et al. CT FFR for ischemia-specific CAD with a new computational fluid dynamics algorithm: a Chinese multicenter study. JACC Cardiovasc Imaging, 2020, 13(4): 980-990.
11. Gould K L, Johnson N P, Kirkeeide R. TAG, you're out. JACC Cardiovasc Imaging, 2019, 12(2): 334-337.
12. Ko B S. An onsite CT-FFR technique based on TAG: a 1-Hit wonder or can we expect more to come?. JACC Cardiovasc Imaging, 2020, 13(4): 991-993.
13. Baumann S, Hirt M, Schoepf U J, et al. Correlation of machine learning computed tomography-based fractional flow reserve with instantaneous wave free ratio to detect hemodynamically significant coronary stenosis. Clin Res Cardiol, 2020, 109(6): 735-745.
14. Gao Zhifan, Wang Xin, Sun Shanhui, et al. Learning physical properties in complex visual scenes: an intelligent machine for perceiving blood flow dynamics from static CT angiography imaging. Neural Networks, 2020, 123: 82-93.
15. Yoon S, Yoon C H, Lee D. Topological recovery for non-rigid 2D/3D registration of coronary artery models. Comput Methods Programs Biomed, 2021, 200: 105922.
16. Ramasamy A, Jin C, Tufaro V, et al. Computerised methodologies for non-invasive angiography-derived fractional flow reserve assessment: a critical review. J Interv Cardiol, 2020, 2020: 6381637.
17. Gosling R C, Morris P D, Soto D A S, et al. Virtual coronary intervention: a treatment planning tool based upon the angiogram. JACC-Cardiovasc Imaging, 2019, 12(5): 865-872.
18. Papafaklis M I, Muramatsu T, Ishibashi Y, et al. Fast virtual functional assessment of intermediate coronary lesions using routine angiographic data and blood flow simulation in humans: comparison with pressure wire-fractional flow reserve. EuroIntervention, 2014, 10(5): 574-583.
19. Tr?bs M, Achenbach S, R?ther J, et al. Comparison of fractional flow reserve based on computational fluid dynamics modeling using coronary angiographic vessel morphology versus invasively measured fractional flow reserve. Am J Cardiol, 2016, 117(1): 29-35.
20. Xu Bo, Tu Shengxian, Song Lei, et al. Angiographic quantitative flow ratio-guided coronary intervention (FAVOR III China): a multicentre, randomised, sham-controlled trial. Lancet, 2021, 398(10317): 2149-2159.
21. Tu Shengxian, Ding Daixin, Chang Yunxiao, et al. Diagnostic accuracy of quantitative flow ratio for assessment of coronary stenosis significance from a single angiographic view: a novel method based on bifurcation fractal law. Catheter Cardiovasc Interv, 2021, 97(S2): 1040-1047.
22. Tar B, Jenei C, Dezsi C A, et al. Less invasive fractional flow reserve measurement from 3-dimensional quantitative coronary angiography and classic fluid dynamic equations. EuroIntervention, 2018, 14(8): 942-950.
23. Pellicano M, Lavi I, de Bruyne B, et al. Validation study of image-based fractional flow reserve during coronary angiography. Circ-Cardiovasc Interv, 2017, 10(9): e005259.
24. Masdjedi K, van Zandvoort L J C, Balbi M M, et al. Validation of a three-dimensional quantitative coronary angiography-based software to calculate fractional flow reserve: the FAST study. EuroIntervention, 2020, 16(7): 591-599.
25. Rogui A, Abu Dogosh A, Feld Y, et al. Early feasibility of automated artificial intelligence angiography based fractional flow reserve estimation. Am J Cardiol, 2021, 139: 8-14.
26. Zhang Dong, Yang Guang, Zhao Shu, et al. Direct quantification of coronary artery stenosis through hierarchical attentive multi-view learning. IEEE Trans Med Imaging, 2020, 39(12): 4322-4334.
27. Campo G, Biscaglia S. Functional assessment 3. 0: from wire through angio to OCT. EuroIntervention, 2020, 16(7): 534-535.
28. Okuya Y, Seike F, Yoneda K, et al. Functional assessment of tandem coronary artery stenosis by intracoronary optical coherence tomography-derived virtual fractional flow reserve: a case series. Eur Heart J, 2019, 3(2): ytz087.
29. Jang S J, Ahn J M, Kim B, et al. Comparison of accuracy of one use methods for calculating fractional flow reserve by intravascular optical coherence tomography to that determined by the pressure-wire method. Am J Cardiol, 2017, 120(11): 1920-1925.
30. Huang Jiayue, Yang Fan, Gutiérrez-Chico J L, et al. Optical coherence tomography-derived changes in plaque structural stress over the cardiac cycle: a new method for plaque biomechanical assessment. Front Cardiovasc Med, 2021, 8: 715995.
31. Cha J J, Son T D, Ha J, et al. Optical coherence tomography-based machine learning for predicting fractional flow reserve in intermediate coronary stenosis: a feasibility study. Sci Rep, 2020, 10(1): 20421.
32. Seike F, Uetani T, Nishimura K, et al. Intravascular ultrasound-derived virtual fractional flow reserve for the assessment of myocardial ischemia. Circ J, 2018, 82(3): 815-823.
33. Lee J G, Ko J, Hae H, et al. Intravascular ultrasound-based machine learning for predicting fractional flow reserve in intermediate coronary artery lesions. Atherosclerosis, 2020, 292: 171-177.
34. Yu Wei, Tanigaki T, Ding Daixin, et al. Accuracy of intravascular ultrasound based fractional flow reserve in identifying hemodynamic significance of coronary stenosis. Circ-Cardiovasc Inte, 2021, 14(2): e009840.
35. Kilic Y, Safi H, Bajaj R, et al. The evolution of data fusion methodologies developed to reconstruct coronary artery geometry from intravascular imaging and coronary angiography data: a comprehensive review. Front Cardiovasc Med, 2020, 7: 33.
36. Jiang Jun, Feng Li, Li Changling, et al. Fractional flow reserve for coronary stenosis assessment derived from fusion of intravascular ultrasound and X-ray angiography. Quant Imag Med Surg, 2021, 11(11): 4543-4555.
37. Gao Zijun, Wang Lu, Soroushmehr R, et al. Vessel segmentation for X-ray coronary angiography using ensemble methods with deep learning and filter-based features. BMC Med Imaging, 2022, 22(1): 10.
38. Wen X, Lei P, Xiong K, et al. High-robustness intravascular photoacoustic endoscope with a hermetically sealed opto-sono capsule. Opt Express, 2020, 28(13): 19255-19269.
39. Leng Ji, Zhang Jinke, Li Chenguang, et al. Multi-spectral intravascular photoacoustic/ultrasound/optical coherence tomography tri-modality system with a fully-integrated 0. 9-mm full field-of-view catheter for plaque vulnerability imaging. Biomed Opt Express, 2021, 12(4): 1934-1946.

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