Skip to content
2000
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
file format pdf download PDF
side by side viewer icon HTML

Abstract

Objective

Challenging HR conditions, such as elevated Heart Rate (HR) and Heart Rate Variability (HRV), are major contributors to motion artifacts in Coronary Computed Tomography Angiography (CCTA). This study aims to assess the impact of a deep learning-based motion correction algorithm (MCA) on motion artifacts in patients with challenging HR conditions, focusing on image quality and diagnostic performance of CCTA.

Materials and Methods

This retrospective study included 240 patients (mean HR: 88.1 ± 14.5 bpm; mean HRV: 32.6 ± 45.5 bpm) who underwent CCTA between June, 2020 and December, 2020. CCTA images were reconstructed with and without the MCA. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured to assess objective image quality. Subjective image quality was evaluated by two radiologists using a 5-point scale regarding vessel visualization, diagnostic confidence, and overall image quality. Moreover, all vessels with scores ≥ 3 were considered clinically interpretable. The diagnostic performance of CCTA with and without MCA for detecting significant stenosis (≥ 50%) was assessed in 34 patients at both per-vessel and per-patient levels, using invasive coronary angiography as the reference standard.

Results

The MCA significantly improved subjective image quality, increasing the vessel interpretability from 89.9% (CI: 0.88-0.92) to 98.8% (CI: 0.98-0.99) ( < 0.001). The use of MCA resulted in significantly higher diagnostic performance in both patient-based (AUC: 0.83 . 0.58, = 0.04) and vessel-based (AUC: 0.92 . 0.81, < 0.001) analyses, with the vessel-based accuracy notably increased from 79.4% (CI: 0.72-0.86) to 91.2% (CI: 0.85-0.95) ( = 0.01). There were no significant differences in objective image quality between the two reconstructions. The mean effective dose in this study was 2.8 ± 1.1 mSv.

Conclusion

The use of MCA allows for obtaining high-quality CCTA images and superior diagnostic performance with low radiation exposure in patients with elevated HR and HRV.

© 2024 The Author(s). Published by Bentham Science Publisher.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Loading

Article metrics loading...

/content/journals/cmir/10.2174/0115734056315753240827114209
2024-01-01
2025-07-24

  • From This Site

    /content/journals/cmir/10.2174/0115734056315753240827114209
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
The full text of this item is not currently available.

References

  1. LuG. YeW. OuJ. LiX. TanZ. LiT. LiuH. Coronary computed tomography angiography assessment of high-risk plaques in predicting acute coronary syndrome.Front. Cardiovasc. Med.2021874353810.3389/fcvm.2021.74353834660742
     [Google Scholar]
  2. JinL. ZhouJ. GaoY. ZhaoW. LiM. WangZ. Reduction of cardiac motion artifact in step-and-shoot coronary CT angiography with third-generation as compared with second-generation dual-source CT scanners.Diagn. Interv. Radiol.202127448248710.5152/dir.2021.1947334313232
     [Google Scholar]
  3. AndreiniD. PontoneG. MushtaqS. ManciniM.E. ConteE. GuglielmoM. VolpatoV. AnnoniA. BaggianoA. FormentiA. DitaliV. PerchinunnoM. FiorentiniC. BartorelliA.L. PepiM. Image quality and radiation dose of coronary CT angiography performed with whole-heart coverage CT scanner with intra-cycle motion correction algorithm in patients with atrial fibrillation.Eur. Radiol.20182841383139210.1007/s00330‑017‑5131‑229164383
     [Google Scholar]
  4. SunJ. OkerlundD. CaoY. LiH. ZhuY. LiJ. PengY. Further improving image quality of cardiovascular computed tomography angiography for children with high heart rates using second-generation motion correction algorithm.J. Comput. Assist. Tomogr.202044579079510.1097/RCT.000000000000103532936580
     [Google Scholar]
  5. QiuS. ShiS. PingH. ZhouS. WangH. YangB. Efficacy of ivabradine versus beta-blockers for heart rate reduction during computed tomography coronary angiography: A meta-analysis of randomized controlled trials.Cardiology2016135313314010.1159/00044723627328446
     [Google Scholar]
  6. GoldbergA. AdamsW.H. ThomsenB. AshrafU. VasilopoulosV. Validation of second-generation motion-correction software for computed tomography coronary angiography with novel quantitative approach.J. Comput. Assist. Tomogr.202145340340710.1097/RCT.000000000000114533797442
     [Google Scholar]
  7. YangC.C. LawW.Y. LuK.M. WuT.H. Relationship between heart rate and optimal reconstruction phase in coronary CT angiography performed on a 256-slice multidetector CT.Br. J. Radiol.20199211012018094510.1259/bjr.2018094531322906
     [Google Scholar]
  8. PontoneG. AndreiniD. BertellaE. BaggianoA. MushtaqS. LoguercioM. SeguriniC. ConteE. BeltramaV. AnnoniA. FormentiA. PetullàM. GuaricciA.I. MontorsiP. TrabattoniD. BartorelliA.L. PepiM. Impact of an intra-cycle motion correction algorithm on overall evaluability and diagnostic accuracy of computed tomography coronary angiography.Eur. Radiol.201626114715610.1007/s00330‑015‑3793‑125953001
     [Google Scholar]
  9. YangR. ChaY. YangW. PengZ. XieB. Research on the feasibility of low-dose coronary CT angiography based on motion correction algorithm with high heart rate.J Clini Radio2017361110731077
     [Google Scholar]
  10. WangB. ChenX. YingX. Application value of coronary artery snapshot freeze technique in improving the image quality of coronary CTA with retrospective ECG-gating technique.Radiol. Prat.2017324427430
     [Google Scholar]
  11. LiangJ. WangH. XuL. DongL. FanZ. WangR. SunZ. Impact of SSF on diagnostic performance of coronary computed tomography angiography within 1 heart beat in patients with high heart rate using a 256-row detector computed tomography.J. Comput. Assist. Tomogr.2018421546110.1097/RCT.000000000000064128708724
     [Google Scholar]
  12. ParkJ.B. JeongY.J. LeeG. LeeN.K. KimJ.Y. LeeJ.W. Influence of heart rate and innovative motion-correction algorithm on coronary artery image quality and measurement accuracy using 256-detector row computed tomography scanner: Phantom study.Korean J. Radiol.20192019410110.3348/kjr.2018.025130627025
     [Google Scholar]
  13. ShetaH.M. EgstrupK. HusicM. HeinsenL.J. NiemanK. LambrechtsenJ. Impact of a motion correction algorithm on image quality in patients undergoing CT angiography: A randomized controlled trial.Clin. Imaging2017421610.1016/j.clinimag.2016.11.00227838576
     [Google Scholar]
  14. CarrascosaP. DeviggianoA. CapunayC. De ZanM.C. GoldsmitA. Rodriguez-GranilloG.A. Effect of intracycle motion correction algorithm on image quality and diagnostic performance of computed tomography coronary angiography in patients with suspected coronary artery disease.Acad. Radiol.2015221818610.1016/j.acra.2014.07.01625281361
     [Google Scholar]
  15. LiangJ. WangH. XuL. Diagnostic performance of 256-row detector coronary CT angiography in patients with high heart rates within a single cardiac cycle: A preliminary study.Clin Radiol2017728694.e7694.e1410.1016/j.crad.2017.03.004.
     [Google Scholar]
  16. LaghariA.A. EstrelaV.V. YinS. How to collect and interpret medical pictures captured in highly challenging environments that range from nanoscale to hyperspectral imaging.Curr. Med. Imaging20222036582065
     [Google Scholar]
  17. LaghariA A. EstrelaV V. LiH. Quality of experience assessment in virtual/augmented reality serious games for healthcare: A systematic literature review.Technol. Disabil.202436411210.3233/TAD‑230035.
     [Google Scholar]
  18. DasS. AdhikaryA. LaghariA.A. MitraS. Eldo-care: EEG with Kinect sensor based telehealthcare for the disabled and the elderly.Neuroscience Informatics20233210013010.1016/j.neuri.2023.100130
     [Google Scholar]
  19. LaghariA.A. SunY. AlhusseinM. AurangzebK. AnwarM.S. RashidM. Deep residual-dense network based on bidirectional recurrent neural network for atrial fibrillation detection.Sci. Rep.20231311510910.1038/s41598‑023‑40343‑x37704659
     [Google Scholar]
  20. LaghariA.A. ShahidS. YadavR. KarimS. KhanA. LiH. ShoulinY. The state of art and review on video streaming.J. High Speed Netw202329321123610.3233/JHS‑222087
     [Google Scholar]
  21. SaeedU. KumarK. KhuhroM.A. LaghariA.A. ShaikhA.A. RaiA. DeepLeukNet—A CNN based microscopy adaptation model for acute lymphoblastic leukemia classification.Multimedia Tools Appl.2023837210192104310.1007/s11042‑023‑16191‑2
     [Google Scholar]
  22. YanC. ZhouG. YangX. LuX. ZengM. JiM. Image quality of automatic coronary CT angiography reconstruction for patients with HR ≥ 75 bpm using an AI-assisted 16-cm z-coverage CT scanner.BMC Med. Imaging20212112410.1186/s12880‑021‑00559‑733573625
     [Google Scholar]
  23. ShuaiT. ZhongS. ZhangG. WangZ. ZhangY. LiZ. Deep learning-based motion correction in projection domain for coronary computed tomography angiography: A clinical evaluation.J. Comput. Assist. Tomogr.202347689890510.1097/RCT.000000000000150437948364
     [Google Scholar]
  24. YaoX. ZhongS. XuM. ZhangG. YuanY. ShuaiT. LiZ. Deep learning‐based motion correction algorithm for coronary CT angiography: Lowering the phase requirement for morphological and functional evaluation.J. Appl. Clin. Med. Phys.2023249e1410410.1002/acm2.1410437485892
     [Google Scholar]
  25. FuchsT.A. StehliJ. DougoudS. FiechterM. SahB.R. BuechelR.R. BullS. GaemperliO. KaufmannP.A. Impact of a new motion-correction algorithm on image quality of low-dose coronary CT angiography in patients with insufficient heart rate control.Acad. Radiol.201421331231710.1016/j.acra.2013.10.01424332603
     [Google Scholar]
  26. LiuZ. ZhangZ. HongN. ChenL. CaoC. LiuJ. SunY. Diagnostic performance of free-breathing coronary computed tomography angiography without heart rate control using 16-cm z-coverage CT with motion-correction algorithm.J. Cardiovasc. Comput. Tomogr.201913211311710.1016/j.jcct.2019.01.00530635197
     [Google Scholar]
  27. LiQ. LiP. SuZ. YaoX. WangY. WangC. DuX. LiK. Effect of a novel motion correction algorithm (SSF) on the image quality of coronary CTA with intermediate heart rates: Segment-based and vessel-based analyses.Eur. J. Radiol.201483112024203210.1016/j.ejrad.2014.08.00225193780
     [Google Scholar]
  28. LiangJ. SunY. YeZ. SunY. XuL. ZhouZ. ThomsenB. LiJ. SunZ. FanZ. Second-generation motion correction algorithm improves diagnostic accuracy of single-beat coronary CT angiography in patients with increased heart rate.Eur. Radiol.20192984215422710.1007/s00330‑018‑5929‑630617487
     [Google Scholar]
  29. BudoffM.J. LiD. KazerooniE.A. ThomasG.S. MieresJ.H. ShawL.J. Diagnostic accuracy of noninvasive 64-row computed tomographic coronary angiography (CCTA) compared with myocardial perfusion imaging (MPI): The PICTURE study, a prospective multicenter trial.Acad. Radiol.2017241222910.1016/j.acra.2016.09.00827771227
     [Google Scholar]
  30. MaierJ. LebedevS. ErathJ. EuligE. SawallS. FourniéE. StierstorferK. LellM. KachelrießM. Deep learning‐based coronary artery motion estimation and compensation for short‐scan cardiac CT.Med. Phys.20214873559357110.1002/mp.1492733959983
     [Google Scholar]
  31. LeeD. ChoiJ. KimH. ChoM. LeeK.Y. Validation of a novel cardiac motion correction algorithm for x-ray computed tomography: From phantom experiments to initial clinical experience.PLoS One2020159e023951110.1371/journal.pone.023951132997677
     [Google Scholar]
  32. DobrolińskaM. van der WerfN. GreuterM. JiangB. SlartR. XieX. Classification of moving coronary calcified plaques based on motion artifacts using convolutional neural networks: A robotic simulating study on influential factors.BMC Med. Imaging202121115110.1186/s12880‑021‑00680‑734666714
     [Google Scholar]
  33. DengF. TieC. ZengY. ShiY. WuH. WuY. LiangD. LiuX. ZhengH. ZhangX. HuZ. Correcting motion artifacts in coronary computed tomography angiography images using a dual-zone cycle generative adversarial network.J. XRay Sci. Technol.202129457759510.3233/XST‑21084133935130
     [Google Scholar]
  34. ManderGTW. DobeliK. SteffensenC. MunnZ. Diagnostic accuracy of prospectively gated, 128-slice or greater CTCA at high heart rates: A systematic review and meta-analysis.J Med Radiat Sci202168443544510.1002/jmrs.525.
     [Google Scholar]
/content/journals/cmir/10.2174/0115734056315753240827114209
Loading
Improving Image Quality and Diagnostic Performance of CCTA in Patients with Challenging Heart Rate Conditions using a Deep Learning-based Motion Correction Algorithm
Curr. Med. Imaging 20, e15734056315753 (2024); https://doi.org/10.2174/0115734056315753240827114209
/content/journals/cmir/10.2174/0115734056315753240827114209
/content/journals/cmir/10.2174/0115734056315753240827114209
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): Coronary computed tomography angiography; HR; HRV; Motion artifacts; Motion correction; Signal-to-noise ratio

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test