Diagnostic Value of Deep Learning Reconstruction-based Subtraction CT-FFR in Patients with Calcified-related Stenosis or Stent Implantation

Overview

Journal Quant Imaging Med Surg

Specialty Radiology

Date 2025 Feb 25

PMID 39995698

Authors

Cheng Xu

Yan Yi

Min Xu

Li-Miao Zou

Ming Wang

Yun Wang

Zheng-Yu Jin

Yi-Ning Wang

Affiliations

Soon will be listed here.

Abstract

Background: The application of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) is limited due to severe coronary calcium burden or stent implantation. This study aimed to explore the diagnostic value of subtraction CT-FFR with deep learning reconstruction (DLR) or hybrid iterative reconstruction (HIR) in detecting calcified-related hemodynamically significant stenosis, and the feasibility in the application of coronary stents.

Methods: Between March 2020 and January 2022, consecutive patients with calcified-related stenosis or previous stent treatment who had undergone subtraction coronary computed tomography angiography (CTA) and invasive fractional flow reserve (FFR) were included in this prospective study. CT image data were reconstructed using HIR and DLR. The diagnostic performance of CT-FFR, and subtraction CT-FFR were evaluated. An FFR value of 0.8 or less was considered hemodynamically significant.

Results: A total of 30 patients with 52 calcified-related lesions and 14 coronary stents were included in this study. Subtraction CT-FFR outperformed the corresponding CT-FFR in detecting calcified-related hemodynamically significant stenosis and in the application of coronary stents, while there was no significant difference when subtraction CT-FFR was compared with subtraction CT-FFR (P>0.05). Lesion-based analysis showed the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy for subtraction CT-FFR were 100.0%, 71.4%, 63.0%, 100% and 80.8%, respectively in detecting calcified-related hemodynamically significant stenosis, and were 100.0%, 83.3%, 88.9%, 100% and 92.9%, respectively in the application of coronary stents.

Conclusions: Subtraction CT-FFR yielded optimal diagnostic performance for hemodynamically significant calcified-related stenosis, and the application of subtraction CT-FFR in the evaluation of coronary stents was feasible. The diagnostic performance of subtraction CT-FFR was better than that of subtraction CT-FFR, but there was no significant difference.

References

Xiang Tang C, Guo B, Schoepf J, Bayer 2nd R, Liu C, Qiao H . Feasibility and prognostic role of machine learning-based FFR in patients with stent implantation. Eur Radiol. 2021; 31(9):6592-6604. DOI: 10.1007/s00330-021-07922-w. View

Norgaard B, Leipsic J, Gaur S, Seneviratne S, Ko B, Ito H . Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in suspected coronary artery disease: the NXT trial (Analysis of Coronary Blood Flow Using CT Angiography: Next Steps). J Am Coll Cardiol. 2014; 63(12):1145-1155. DOI: 10.1016/j.jacc.2013.11.043. View

Coenen A, Lubbers M, Kurata A, Kono A, Dedic A, Chelu R . Fractional flow reserve computed from noninvasive CT angiography data: diagnostic performance of an on-site clinician-operated computational fluid dynamics algorithm. Radiology. 2014; 274(3):674-83. DOI: 10.1148/radiol.14140992. View

Fuchs A, Kuhl J, Chen M, Helqvist S, Razeto M, Arakita K . Feasibility of coronary calcium and stent image subtraction using 320-detector row CT angiography. J Cardiovasc Comput Tomogr. 2015; 9(5):393-8. PMC: 6290475. DOI: 10.1016/j.jcct.2015.03.016. View

Kha Q, Tran T, Nguyen T, Nguyen V, Than K, Le N . An interpretable deep learning model for classifying adaptor protein complexes from sequence information. Methods. 2022; 207:90-96. DOI: 10.1016/j.ymeth.2022.09.007. View

Agarwal S, Kasula S, Hacioglu Y, Ahmed Z, Uretsky B, Hakeem A . Utilizing Post-Intervention Fractional Flow Reserve to Optimize Acute Results and the Relationship to Long-Term Outcomes. JACC Cardiovasc Interv. 2016; 9(10):1022-31. DOI: 10.1016/j.jcin.2016.01.046. View

Koo B, Erglis A, Doh J, Daniels D, Jegere S, Kim H . Diagnosis of ischemia-causing coronary stenoses by noninvasive fractional flow reserve computed from coronary computed tomographic angiograms. Results from the prospective multicenter DISCOVER-FLOW (Diagnosis of Ischemia-Causing Stenoses Obtained.... J Am Coll Cardiol. 2011; 58(19):1989-97. DOI: 10.1016/j.jacc.2011.06.066. View

Han D, Lin A, Gransar H, Dey D, Berman D . Influence of Coronary Artery Calcium Score on Computed Tomography-Derived Fractional Flow Reserve: A Meta-Analysis. JACC Cardiovasc Imaging. 2020; 14(3):702-703. DOI: 10.1016/j.jcmg.2020.09.022. View

Xu C, Yi Y, Xu M, Yan J, Guo Y, Wang J . Coronary Artery Stent Evaluation by CTA: Impact of Deep Learning Reconstruction and Subtraction Technique. AJR Am J Roentgenol. 2022; 220(1):63-72. DOI: 10.2214/AJR.22.27983. View

10.

Andreini D, Mushtaq S, Pontone G, Rogers C, Pepi M, Bartorelli A . Severe in-stent restenosis missed by coronary CT angiography and accurately detected with FFR. Int J Cardiovasc Imaging. 2016; 33(1):119-120. DOI: 10.1007/s10554-016-0971-4. View

11.

Le N . Predicting emerging drug interactions using GNNs. Nat Comput Sci. 2024; 3(12):1007-1008. DOI: 10.1038/s43588-023-00555-7. View

12.

Tesche C, De Cecco C, Baumann S, Renker M, McLaurin T, Duguay T . Coronary CT Angiography-derived Fractional Flow Reserve: Machine Learning Algorithm versus Computational Fluid Dynamics Modeling. Radiology. 2018; 288(1):64-72. DOI: 10.1148/radiol.2018171291. View

13.

Yoshioka K, Tanaka R, Muranaka K . Subtraction coronary CT angiography for calcified lesions. Cardiol Clin. 2012; 30(1):93-102. DOI: 10.1016/j.ccl.2011.10.004. View

14.

Nazir M, Mittal T, Weir-McCall J, Nieman K, Channon K, Nicol E . Opportunities and challenges of implementing computed tomography fractional flow reserve into clinical practice. Heart. 2020; 106(18):1387-1393. DOI: 10.1136/heartjnl-2019-315607. View

15.

Fuchs A, Kuhl J, Chen M, Vilades Medel D, Alomar X, Shanbhag S . Subtraction CT angiography improves evaluation of significant coronary artery disease in patients with severe calcifications or stents-the C-Sub 320 multicenter trial. Eur Radiol. 2018; 28(10):4077-4085. PMC: 6737932. DOI: 10.1007/s00330-018-5418-y. View

16.

Yi Y, Xu C, Xu M, Yan J, Li Y, Wang J . Diagnostic Improvements of Deep Learning-Based Image Reconstruction for Assessing Calcification-Related Obstructive Coronary Artery Disease. Front Cardiovasc Med. 2021; 8:758793. PMC: 8595262. DOI: 10.3389/fcvm.2021.758793. View

17.

Collison D, Copt S, Mizukami T, Collet C, McLaren R, Didagelos M . Angina After Percutaneous Coronary Intervention: Patient and Procedural Predictors. Circ Cardiovasc Interv. 2023; 16(4):e012511. PMC: 10101135. DOI: 10.1161/CIRCINTERVENTIONS.122.012511. View

18.

Kamo Y, Fujimoto S, Nozaki Y, Aoshima C, Kawaguchi Y, Dohi T . Incremental Diagnostic Value of CT Fractional Flow Reserve Using Subtraction Method in Patients with Severe Calcification: A Pilot Study. J Clin Med. 2021; 10(19). PMC: 8509262. DOI: 10.3390/jcm10194398. View

19.

Li S, Ge Z, Kan J, Zhang J, Ye F, Kwan T . Cutoff Value and Long-Term Prediction of Clinical Events by FFR Measured Immediately After Implantation of a Drug-Eluting Stent in Patients With Coronary Artery Disease: 1- to 3-Year Results From the DKCRUSH VII Registry Study. JACC Cardiovasc Interv. 2017; 10(10):986-995. DOI: 10.1016/j.jcin.2017.02.012. View

20.

Tonino P, Fearon W, De Bruyne B, Oldroyd K, Leesar M, Ver Lee P . Angiographic versus functional severity of coronary artery stenoses in the FAME study fractional flow reserve versus angiography in multivessel evaluation. J Am Coll Cardiol. 2010; 55(25):2816-21. DOI: 10.1016/j.jacc.2009.11.096. View