Skip to main content
SpringerLink
Log in

Coronary computed tomography angiographic detection of in-stent restenosis via deep learning reconstruction: a feasibility study

  • Cardiac
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

Evaluation of in-stent restenosis (ISR), especially for small stents, remains challenging during computed tomography (CT) angiography. We used deep learning reconstruction to quantify stent strut thickness and lumen vessel diameter at the stent and compared it with values obtained using conventional reconstruction strategies.

Methods

We examined 166 stents in 85 consecutive patients who underwent CT and invasive coronary angiography (ICA) within 3 months of each other from 2019–2021 after percutaneous coronary intervention with coronary stent placement. The presence of ISR was defined as percent diameter stenosis ≥ 50% on ICA. We compared a super-resolution deep learning reconstruction, Precise IQ Engine (PIQE), and a model-based iterative reconstruction, Forward projected model-based Iterative Reconstruction SoluTion (FIRST). All images were reconstructed using PIQE and FIRST and assessed by two blinded cardiovascular radiographers.

Results

PIQE had a larger full width at half maximum of the lumen and smaller strut than FIRST. The image quality score in PIQE was higher than that in FIRST (4.2 ± 1.1 versus 2.7 ± 1.2, p < 0.05). In addition, the specificity and accuracy of ISR detection were better in PIQE than in FIRST (p < 0.05 for both), with particularly pronounced differences for stent diameters < 3.0 mm.

Conclusion

PIQE provides superior image quality and diagnostic accuracy for ISR, even with stents measuring < 3.0 mm in diameter.

Clinical relevance statement

With improvements in the diagnostic accuracy of in-stent stenosis, CT angiography could become a gatekeeper for ICA in post-stenting cases, obviating ICA in many patients after recent stenting with infrequent ISR and allowing non-invasive ISR detection in the late phase.

Key Points

• Despite CT technology advancements, evaluating in-stent stenosis severity, especially in small-diameter stents, remains challenging.

• Compared with conventional methods, the Precise IQ Engine uses deep learning to improve spatial resolution.

• Improved diagnostic accuracy of CT angiography helps avoid invasive coronary angiography after coronary artery stenting.

Graphical Abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Abbreviations

AiCE:

Advanced intelligent clear-IQ engine

FFRCT :

Computed tomography angiography–derived fractional flow reserve

CTA:

Coronary computed tomography angiography

FIRST:

Forward projected model-based Iterative Reconstruction SoluTion

FWHM-Lumen:

Full width at half maximum of lumen

FWHM-Stent:

Full width at half maximum of strut

ISR:

In-stent-restenosis

ICA:

Invasive coronary angiography

LCX:

Left circumflex

LMT-LAD:

Left main trunk-left ascending artery

%DS:

Percent diameter stenosis

PCI:

Percutaneous coronary intervention

PIQE:

Precise IQ Engine

RCA:

Right coronary artery

SR-DLR:

Super-resolution deep learning reconstruction

URCT:

Ultra-high-resolution CT

References

  1. Knuuti J, Wijns W, Saraste A et al (2020) 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J 41:407–477

    Article  PubMed  Google Scholar 

  2. Min JK, Leipsic J, Pencina MJ et al (2012) Diagnostic accuracy of fractional flow reserve from anatomic CT angiography. JAMA 308:1237–1245

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Min JK, Taylor CA, Achenbach S et al (2015) Noninvasive fractional flow reserve derived from coronary CT angiography: clinical data and scientific principles. JACC Cardiovasc Imaging 8:1209–1222

    Article  PubMed  Google Scholar 

  4. Pontone G, Rossi A, Guglielmo M et al (2022) Clinical applications of cardiac computed tomography: a consensus paper of the European Association of Cardiovascular Imaging-part I. Eur Heart J Cardiovasc Imaging 23:299–314

    Article  PubMed  PubMed Central  Google Scholar 

  5. Motoyama S, Ito H, Sarai M et al (2018) Ultra-high-resolution computed tomography angiography for assessment of coronary artery stenosis. Circ J 82:1844–1851

    Article  PubMed  Google Scholar 

  6. Schuijf JD, Lima JAC, Boedeker KL et al (2022) CT imaging with ultra-high-resolution: opportunities for cardiovascular imaging in clinical practice. J Cardiovasc Comput Tomogr 16:388–396

    Article  PubMed  Google Scholar 

  7. Nishii T, Funama Y, Kato S et al (2022) Comparison of visibility of in-stent restenosis between conventional- and ultra-high spatial resolution computed tomography: coronary arterial phantom study. Jpn J Radiol 40:279–288

    Article  PubMed  Google Scholar 

  8. Lee TC, Zhou J, Yu Z, et al (2022) Deep learning enabled wide-coverage high-resolution cardiac CT. In: Medical Imaging 2022: Physics of Medical Imaging vol. 12031, pp. 675–678. SPIE. https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12031/120312O/Deep-learning-enabled-wide-coverage-high-resolution-cardiac-CT/10.1117/12.2611817.short?SSO=1

  9. Boedeker K (2021) Precision-trained deep learning: Redefining cardiac imaging. Available via https://canonmedical.widen.net/content/xyva2vxmfy/original/MWPCT0008EA_fin.pdf?u=vmbupa&

  10. Tatsugami F, Higaki T, Nakamura Y et al (2019) Deep learning-based image restoration algorithm for coronary CT angiography. Eur Radiol 29:5322–5329

    Article  PubMed  Google Scholar 

  11. Tatsugami F, Higaki T, Sakane H et al (2017) Coronary artery stent evaluation with model-based iterative reconstruction at coronary CT angiography. Acad Radiol 24:975–981

    Article  PubMed  Google Scholar 

  12. Andreini D, Pontone G, Mushtaq S et al (2019) Diagnostic accuracy of coronary CT angiography performed in 100 consecutive patients with coronary stents using a whole-organ high-definition CT scanner. Int J Cardiol 274:382–387

    Article  PubMed  Google Scholar 

  13. Yang J, Yang X, De Cecco CN et al (2017) Iterative reconstruction improves detection of in-stent restenosis by high-pitch dual-source coronary CT angiography. Sci Rep 7:6956

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  14. Tatsugami F, Higaki T, Sakane H et al (2018) Diagnostic accuracy of in-stent restenosis using model-based iterative reconstruction at coronary CT angiography: initial experience. Br J Radiol 91:20170598

    PubMed  PubMed Central  Google Scholar 

  15. Geyer LL, Glenn GR, De Cecco CN et al (2015) CT evaluation of small-diameter coronary artery stents: effect of an integrated circuit detector with iterative reconstruction. Radiology 276:706–714

    Article  PubMed  Google Scholar 

  16. Eisentopf J, Achenbach S, Ulzheimer S et al (2013) Low-dose dual-source CT angiography with iterative reconstruction for coronary artery stent evaluation. JACC Cardiovasc Imaging 6:458–465

    Article  PubMed  Google Scholar 

  17. Narula J, Chandrashekhar Y, Ahmadi A et al (2021) SCCT 2021 Expert consensus document on coronary computed tomographic angiography: a report of the Society of Cardiovascular Computed Tomography. J Cardiovasc Comput Tomogr 15:192–217

    Article  PubMed  Google Scholar 

  18. Dai T, Wang JR, Hu PF (2018) Diagnostic performance of computed tomography angiography in the detection of coronary artery in-stent restenosis: evidence from an updated meta-analysis. Eur Radiol 28:1373–1382

    Article  PubMed  Google Scholar 

Download references

Funding

The authors state that this work has not received any funding.

Author information

Authors and Affiliations

  1. Department of Cardiology, Fujita Health University, 1-98 Dengakugakubo, Kutsukake, Toyoake, Aichi, 470-1192, Japan

    Hideki Kawai, Sadako Motoyama, Masayoshi Sarai, Yoshihiro Sato, Hiroshi Takahashi, Takashi Muramatsu, Yukio Ozaki & Hideo Izawa

  2. Department of Radiology, Fujita Health University, Toyoake, Aichi, Japan

    Takahiro Matsuyama, Yoshiharu Ohno & Hiroshi Toyama

  3. Department of Radiology, Fujita Health University Hospital, Toyoake, Aichi, Japan

    Ryota Matsumoto, Akio Katagata, Yumi Kataoka & Yoshihiro Ida

  4. Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University, Toyoake, Japan

    Yoshiharu Ohno

  5. Icahn School of Medicine at Mount Sinai, Mount Sinai Heart, New York, NY, USA

    Jagat Narula

Authors
  1. Hideki Kawai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sadako Motoyama
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Masayoshi Sarai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yoshihiro Sato
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Takahiro Matsuyama
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ryota Matsumoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hiroshi Takahashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Akio Katagata
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yumi Kataoka
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yoshihiro Ida
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Takashi Muramatsu
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Yoshiharu Ohno
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Yukio Ozaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Hiroshi Toyama
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Jagat Narula
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Hideo Izawa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hideki Kawai.

Ethics declarations

Guarantor

The Scientific guarantor is Hideki Kawai.

Conflict of interest

Yoshiharu Ohno and Hiroshi Toyama have received research grants from Canon Medical Systems. Hideo Izawa has received grant support through his institution from Bayer, Daiichi-Sankyo, Dainihon-Sumitomo, Kowa, Ono, Otsuka, Takeda, and Fuji Film Toyama Kagaku, and honoraria for lectures from Boehringer Ingelheim, Daiichi-Sankyo, Novartis, and Otsuka Corporation. The remaining authors have nothing to disclose.

Statistics and biometry

One of the authors (Hiroshi Takahashi) has significant statistical expertise.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

An opt-out method on our department’s website was used to obtain consent for this study.

Ethical approval

Institutional Review Board approval was obtained.

The study protocol was approved by the Institutional Review Board and ethics committees of the Fujita Health University.

Study subjects or cohorts overlap

None.

Methodology

• Retrospective

• diagnostic study

• performed at one institution

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kawai, H., Motoyama, S., Sarai, M. et al. Coronary computed tomography angiographic detection of in-stent restenosis via deep learning reconstruction: a feasibility study. Eur Radiol 34, 2647–2657 (2024). https://doi.org/10.1007/s00330-023-10110-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-023-10110-7

Keywords

Search

Navigation