Skip to main content
SpringerLink
Log in

Super-resolution deep-learning reconstruction for cardiac CT: impact of radiation dose and focal spot size on task-based image quality

  • Scientific Paper
  • Published:
Physical and Engineering Sciences in Medicine Aims and scope Submit manuscript

Abstract

This study aimed to evaluate the impact of radiation dose and focal spot size on the image quality of super-resolution deep-learning reconstruction (SR-DLR) in comparison with iterative reconstruction (IR) and normal-resolution DLR (NR-DLR) algorithms for cardiac CT. Catphan-700 phantom was scanned on a 320-row scanner at six radiation doses (small and large focal spots at 1.4–4.3 and 5.8–8.8 mGy, respectively). Images were reconstructed using hybrid-IR, model-based-IR, NR-DLR, and SR-DLR algorithms. Noise properties were evaluated through plotting noise power spectrum (NPS). Spatial resolution was quantified with task-based transfer function (TTF); Polystyrene, Delrin, and Bone-50% inserts were used for low-, intermediate, and high-contrast spatial resolution. The detectability index (d′) was calculated. Image noise, noise texture, edge sharpness of low- and intermediate-contrast objects, delineation of fine high-contrast objects, and overall quality of four reconstructions were visually ranked. Results indicated that among four reconstructions, SR-DLR yielded the lowest noise magnitude and NPS peak, as well as the highest average NPS frequency, TTF50%, d′ values, and visual rank at each radiation dose. For all reconstructions, the intermediate- to high-contrast spatial resolution was maximized at 4.3 mGy, while the lowest noise magnitude and highest d′ were attained at 8.8 mGy. SR-DLR at 4.3 mGy exhibited superior noise performance, intermediate- to high-contrast spatial resolution, d′ values, and visual rank compared to the other reconstructions at 8.8 mGy. Therefore, SR-DLR may yield superior diagnostic image quality and facilitate radiation dose reduction compared to the other reconstructions, particularly when combined with small focal spot scanning.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

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(3):407–477

    Article  PubMed  Google Scholar 

  2. Mancini GBJ, Leipsic J, Budoff MJ et al (2021) CT angiography followed by invasive angiography in patients with moderate or severe ischemia-insights from the ISCHEMIA trial. JACC Cardiovasc Imaging 14(7):1384–1393

    Article  PubMed  PubMed Central  Google Scholar 

  3. Kang EJ (2019) Clinical applications of wide-detector CT scanners for cardiothoracic imaging: an update. Korean J Radiol 20(12):1583–1596

    Article  PubMed  PubMed Central  Google Scholar 

  4. de Graaf FR, Schuijf JD, van Velzen JE et al (2010) Diagnostic accuracy of 320-row multidetector computed tomography coronary angiography in the non-invasive evaluation of significant coronary artery disease. Eur Heart J 31(15):1908–1915

    Article  PubMed  Google Scholar 

  5. Arbab-Zadeh A, Miller JM, Rochitte CE et al (2012) Diagnostic accuracy of computed tomography coronary angiography according to pre-test probability of coronary artery disease and severity of coronary arterial calcification. The CORE-64 (Coronary artery evaluation using 64-row multidetector computed tomography angiography) international multicenter study. J Am Coll Cardiol 59(4):379–387

    Article  PubMed  PubMed Central  Google Scholar 

  6. Song YB, Arbab-Zadeh A, Matheson MB et al (2019) Contemporary discrepancies of stenosis assessment by computed tomography and invasive coronary angiography. Circ Cardiovasc Imaging 12(2):e007720

    Article  PubMed  Google Scholar 

  7. Falck CV, Galanski M, Shin HO (2010) Informatics in radiology: sliding-thin-slab averaging for improved depiction of low-contrast lesions with radiation dose savings at thin-section CT. Radiographics 30(2):317–326

    Article  Google Scholar 

  8. Pugliese F, Cademartiri F, van Mieghem C et al (2006) Multidetector CT for visualization of coronary stents. Radiographics 26(3):887–904

    Article  PubMed  Google Scholar 

  9. Oostveen LJ, Boedeker KL, Brink M, Prokop M, de Lange F, Sechopoulos I (2020) Physical evaluation of an ultra-high-resolution CT scanner. Eur Radiol 30(5):2552–2560

    Article  PubMed  PubMed Central  Google Scholar 

  10. Motoyama S, Ito H, Sarai M et al (2018) Ultra-high-resolution computed tomography angiography for assessment of coronary artery stenosis. Circ J 82(7):1844–1851

    Article  PubMed  Google Scholar 

  11. Takagi H, Tanaka R, Nagata K et al (2018) Diagnostic performance of coronary CT angiography with ultra-high-resolution CT: comparison with invasive coronary angiography. Eur J Radiol 101:30–37

    Article  PubMed  Google Scholar 

  12. Latina J, Shabani M, Kapoor K et al (2021) Ultra-high-resolution coronary CT angiography for assessment of patients with severe coronary artery calcification: initial experience. Radiol Cardiothorac Imaging 3(4):e210053

    Article  PubMed  PubMed Central  Google Scholar 

  13. Onishi H, Hori M, Ota T et al (2018) Phantom study of in-stent restenosis at high-spatial-resolution CT. Radiology 289(1):255–260

    Article  PubMed  Google Scholar 

  14. Yanagawa M, Hata A, Honda O et al (2018) Subjective and objective comparisons of image quality between ultra-high-resolution CT and conventional area detector CT in phantoms and cadaveric human lungs. Eur Radiol 28(12):5060–5068

    Article  PubMed  PubMed Central  Google Scholar 

  15. Iwasawa T, Sato M, Yamaya T et al (2020) Ultra-high-resolution computed tomography can demonstrate alveolar collapse in novel coronavirus (COVID-19) pneumonia. Jpn J Radiol 38(5):394–398

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Yamashita K, Hiwatashi A, Togao O et al (2018) Ultrahigh-resolution CT scan of the temporal bone. Eur Arch Otorhinolaryngol 275(11):2797–2803

    Article  PubMed  Google Scholar 

  17. Ohara A, Machida H, Shiga H, Yamamura W, Yokoyama K (2020) Improved image quality of temporal bone CT with an ultrahigh-resolution CT scanner: clinical pilot studies. Jpn J Radiol 38(9):878–883

    Article  PubMed  PubMed Central  Google Scholar 

  18. Boedeker K (2018) AiCE deep learning reconstruction: bringing the power of ultra-high resolution CT to routine imaging. Canon Medical Systems USA. https://global.medical.canon/publication/ct/2019WP_AiCE_Deep_Learning. Accessed 10 Mar 23

  19. Greffier J, Frandon J, Si-Mohamed S et al (2021) Comparison of two deep learning image reconstruction algorithms in chest CT images: a task-based image quality assessment on phantom data. Diagn Interv Imaging 103:21

    Article  PubMed  Google Scholar 

  20. Nakamura Y, Narita K, Higaki T, Akagi M, Honda Y, Awai K (2021) Diagnostic value of deep learning reconstruction for radiation dose reduction at abdominal ultra-high-resolution CT. Eur Radiol 31(7):4700–4709

    Article  PubMed  Google Scholar 

  21. Nagayama Y, Goto M, Sakabe D et al (2022) Radiation dose reduction for 80-kVp pediatric CT using deep learning-based reconstruction: a clinical and phantom study. AJR Am J Roentgenol 219(2):315–324

    Article  PubMed  Google Scholar 

  22. Nagayama Y, Sakabe D, Goto M et al (2021) Deep learning–based reconstruction for lower-dose pediatric CT: technical principles, image characteristics, and clinical implementations. Radiographics 41(7):1936–1953

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  24. Nagayama Y, Goto M, Sakabe D et al (2022) Radiation dose optimization potential of deep learning-based reconstruction for multiphase hepatic CT: a clinical and phantom study. Eur J Radiol 151:110280

    Article  PubMed  Google Scholar 

  25. Higaki T, Nakamura Y, Zhou J et al (2020) Deep learning reconstruction at CT: phantom study of the image characteristics. Acad Radiol 27(1):82–87

    Article  PubMed  Google Scholar 

  26. Narita K, Nakamura Y, Higaki T, Akagi M, Honda Y, Awai K (2020) Deep learning reconstruction of drip-infusion cholangiography acquired with ultra-high-resolution computed tomography. Abdom Radiol 45(9):2698–2704

    Article  Google Scholar 

  27. Kitahara H, Nagatani Y, Otani H et al (2022) A novel strategy to develop deep learning for image super-resolution using original ultra-high-resolution computed tomography images of lung as training dataset. Jpn J Radiol 40(1):38–47

    Article  PubMed  Google Scholar 

  28. Umehara K, Ota J, Ishida T (2018) Application of super-resolution convolutional neural network for enhancing image resolution in chest CT. J Digit Imaging 31(4):441–450

    Article  PubMed  Google Scholar 

  29. Park J, Hwang D, Kim KY, Kang SK, Kim YK, Lee JS (2018) Computed tomography super-resolution using deep convolutional neural network. Phys Med Biol 63(14):145011

    Article  PubMed  Google Scholar 

  30. Boedeker K (2021) Precision-trained deep learning: redefining cardiac imaging. https://canonmedical.widen.net/content/xyva2vxmfy/original/MWPCT0008EA_fin.pdf?u=vmbupa&. Accessed 18 Sept 2022

  31. Nagayama Y, Emoto T, Kato Y et al (2023) Improving image quality with super-resolution deep-learning-based reconstruction in coronary CT angiography. Eur Radiol 33(12):8488–8500

    Article  PubMed  Google Scholar 

  32. Nagayama Y, Emoto T, Hayashi H et al (2023) Coronary stent evaluation by CTA: image quality comparison between super-resolution deep-learning reconstruction and other reconstruction algorithms. AJR Am J Roentgenol 221(5):599–610

    Article  PubMed  Google Scholar 

  33. Greffier J, Pastor M, Si-Mohamed S et al (2023) Comparison of two deep-learning image reconstruction algorithms on cardiac CT images: a phantom study. Diagn Interv Imaging 105:110

    Article  PubMed  Google Scholar 

  34. Sato H, Fujimoto S, Tomizawa N et al (2023) Impact of a deep learning-based super-resolution image reconstruction technique on high-contrast computed tomography: a phantom study. Acad Radiol 30(11):2657–2665

    Article  PubMed  Google Scholar 

  35. Hernandez AM, Shin DW, Abbey CK et al (2020) Validation of synthesized normal-resolution image data generated from high-resolution acquisitions on a commercial CT scanner. Med Phys 47(10):4775–4785

    Article  PubMed  Google Scholar 

  36. Samei E, Bakalyar D, Boedeker KL et al (2019) Performance evaluation of computed tomography systems: summary of AAPM Task Group 233. Med Phys 46(11):e735–e756

    Article  PubMed  Google Scholar 

  37. Solomon J, Lyu P, Marin D, Samei E (2020) Noise and spatial resolution properties of a commercially available deep learning-based CT reconstruction algorithm. Med Phys 47(9):3961–3971

    Article  PubMed  Google Scholar 

  38. Goto M, Nagayama Y, Sakabe D et al (2023) Lung-optimized deep-learning-based reconstruction for ultralow-dose CT. Acad Radiol 30(3):431–440

    Article  PubMed  Google Scholar 

  39. Samei E, Richard S (2015) Assessment of the dose reduction potential of a model-based iterative reconstruction algorithm using a task-based performance metrology. Med Phys 42(1):314–323

    Article  PubMed  Google Scholar 

  40. Christianson O, Chen JJ, Yang Z et al (2015) An improved index of image quality for task-based performance of CT iterative reconstruction across three commercial implementations. Radiology 275(3):725–734

    Article  PubMed  Google Scholar 

  41. Si-Mohamed SA, Boccalini S, Lacombe H et al (2022) Coronary CT angiography with photon-counting CT: first-in-human results. Radiology 303(2):303–313

    Article  PubMed  Google Scholar 

  42. Tatsugami F, Higaki T, Kawashita I et al (2023) Improvement of spatial resolution on coronary CT angiography by using super-resolution deep learning reconstruction. Acad Radiol 30:2497

    Article  PubMed  Google Scholar 

  43. Laurent G, Villani N, Hossu G et al (2019) Full model-based iterative reconstruction (MBIR) in abdominal CT increases objective image quality, but decreases subjective acceptance. Eur Radiol 29(8):4016–4025

    Article  PubMed  Google Scholar 

  44. Mileto A, Guimaraes LS, McCollough CH, Fletcher JG, Yu L (2019) State of the art in abdominal CT: the limits of iterative reconstruction algorithms. Radiology 293(3):491–503

    Article  PubMed  Google Scholar 

Download references

Funding

Yasunori Nagayama is currently receiving a grant from the Japan Society for the Promotion of Science KAKENHI (Grant Number 23K14873).

Author information

Author notes

  1. Yasunori Nagayama and Takafumi Emoto contributed equally to this work.

Authors and Affiliations

  1. Department of Central Radiology, Kumamoto University Hospital, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan

    Takafumi Emoto, Daisuke Sakabe, Shinsuke Shigematsu, Makoto Goto & Kengo Nakato

  2. Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan

    Yasunori Nagayama, Sentaro Takada, Ryuya Yoshida, Ryota Harai, Masafumi Kidoh, Seitaro Oda, Takeshi Nakaura & Toshinori Hirai

Authors
  1. Takafumi Emoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yasunori Nagayama
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sentaro Takada
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daisuke Sakabe
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shinsuke Shigematsu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Makoto Goto
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kengo Nakato
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ryuya Yoshida
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ryota Harai
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Masafumi Kidoh
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Seitaro Oda
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Takeshi Nakaura
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Toshinori Hirai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yasunori Nagayama.

Ethics declarations

Conflicts of interest

Toshinori Hirai has received research support from Canon Medical Systems. The Canon Medical Systems had no control over the interpretation, writing, or publication of this work. For the remaining authors none were disclosed.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional review board and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

The retrospective review and presentation of clinical image data were approved by the institutional review board, and the requirement for written informed consent from patients was waived.

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

Emoto, T., Nagayama, Y., Takada, S. et al. Super-resolution deep-learning reconstruction for cardiac CT: impact of radiation dose and focal spot size on task-based image quality. Phys Eng Sci Med (2024). https://doi.org/10.1007/s13246-024-01423-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13246-024-01423-y

Keywords

Search

Navigation