Skip to main content

Advertisement

SpringerLink
Log in

Can 1.25 mm thin-section images generated with Deep Learning Image Reconstruction technique replace standard-of-care 5 mm images in abdominal CT?

  • Technical
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Background

CT image reconstruction has evolved from filtered back projection to hybrid- and model-based iterative reconstruction. Deep learning-based image reconstruction is a relatively new technique that uses deep convolutional neural networks to improve image quality.

Objective

To evaluate and compare 1.25 mm thin-section abdominal CT images reconstructed with deep learning image reconstruction (DLIR) with 5 mm thick images reconstructed with adaptive statistical iterative reconstruction (ASIR-V).

Methods

This retrospective study included 52 patients (31 F; 56.9±16.9 years) who underwent abdominal CT scans between August-October 2019. Image reconstruction was performed to generate 5 mm images at 40% ASIR-V and 1.25 mm DLIR images at three strengths (low [DLIR-L], medium [DLIR-M], and high [DLIR-H]). Qualitative assessment was performed to determine image noise, contrast, visibility of small structures, sharpness, and artifact based on a 5-point-scale. Image preference determination was based on a 3-point-scale. Quantitative assessment included measurement of attenuation, image noise, and contrast-to-noise ratios (CNR).

Results

Thin-section images reconstructed with DLIR-M and DLIR-H yielded better image quality scores than 5 mm ASIR-V reconstructed images. Mean qualitative scores of DLIR-H for noise (1.77 ± 0.71), contrast (1.6 ± 0.68), small structure visibility (1.42 ± 0.66), sharpness (1.34 ± 0.55), and image preference (1.11 ± 0.34) were the best (p<0.05). DLIR-M yielded intermediate scores. All DLIR reconstructions showed superior ratings for artifacts compared to ASIR-V (p<0.05), whereas each DLIR group performed comparably (p>0.05, 0.405-0.763). In the quantitative assessment, there were no significant differences in attenuation values between all reconstructions (p>0.05). However, DLIR-H demonstrated the lowest noise (9.17 ± 3.11) and the highest CNR (CNRliver = 26.88 ± 6.54 and CNRportal vein = 7.92 ± 3.85) (all p<0.001).

Conclusion

DLIR allows generation of thin-section (1.25 mm) abdominal CT images, which provide improved image quality with higher inter-reader agreement compared to 5 mm thick images reconstructed with ASIR-V.

Clinical Impact

Improved image quality of thin-section CT images reconstructed with DLIR has several benefits in clinical practice, such as improved diagnostic performance without radiation dose penalties.

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

References

  1. Patino M, Fuentes JM, Singh S, Hahn PF, Sahani DV. Iterative Reconstruction Techniques in Abdominopelvic CT: Technical Concepts and Clinical Implementation. AJR Am J Roentgenol. 2015 Jul;205(1):W19-31. https://doi.org/10.2214/AJR.14.13402.

    Article  PubMed  Google Scholar 

  2. Kuo Y, Lin YY, Lee RC, Lin CJ, Chiou YY, Guo WY. Comparison of image quality from filtered back projection, statistical iterative reconstruction, and model-based iterative reconstruction algorithms in abdominal computed tomography. Medicine (Baltimore). 2016;95(31):e4456. https://doi.org/10.1097/MD.0000000000004456

    Article  PubMed  PubMed Central  Google Scholar 

  3. Hara AK, Paden RG, Silva AC, Kujak JL, Lawder HJ, Pavlicek W. Iterative reconstruction technique for reducing body radiation dose at CT: feasibility study. AJR Am J Roentgenol. 2009;193(3):764-771. https://doi.org/10.2214/AJR.09.2397

    Article  PubMed  Google Scholar 

  4. Silva AC, Lawder HJ, Hara A, Kujak J, Pavlicek W. Innovations in CT dose reduction strategy: application of the adaptive statistical iterative reconstruction algorithm. AJR Am J Roentgenol. 2010;194(1):191-199. https://doi.org/10.2214/AJR.09.2953

    Article  PubMed  Google Scholar 

  5. Marin D, Nelson RC, Schindera ST, et al. Low-tube-voltage, high-tube-current multidetector abdominal CT: improved image quality and decreased radiation dose with adaptive statistical iterative reconstruction algorithm--initial clinical experience. Radiology. 2010;254(1):145-153. https://doi.org/10.1148/radiol.09090094

    Article  PubMed  Google Scholar 

  6. Han WK, Na JC, Park SY. Low-dose CT angiography using ASiR-V for potential living renal donors: a prospective analysis of image quality and diagnostic accuracy. Eur Radiol. 2020;30(2):798-805. https://doi.org/10.1007/s00330-019-06423-1

    Article  PubMed  Google Scholar 

  7. Yue M, Melnyk R. Benefits of ASiR-V * Reconstruction for Reducing Patient Radiation Dose and Preserving Diagnostic Quality in CT Exams. Published 2014. Accessed July 18, 2022. https://www.semanticscholar.org/paper/Benefits-of-ASiR-V-*-Reconstruction-for-Reducing-in-Yue-Melnyk/185b858e5de4d555d22b42544a6978065494401f

  8. Lim K, Kwon H, Cho J, et al. Initial phantom study comparing image quality in computed tomography using adaptive statistical iterative reconstruction and new adaptive statistical iterative reconstruction v. J Comput Assist Tomogr. 2015;39(3):443-448. https://doi.org/10.1097/RCT.0000000000000216

    Article  PubMed  Google Scholar 

  9. Minamishima K, Sugisawa K, Yamada Y, Jinzaki M. Quantitative and qualitative evaluation of hybrid iterative reconstruction, with and without noise power spectrum models: A phantom study. J Appl Clin Med Phys. 2018;19(3):318-325. https://doi.org/10.1002/acm2.12304

    Article  PubMed  PubMed Central  Google Scholar 

  10. Millon D, Vlassenbroek A, Van Maanen AG, Cambier SE, Coche EE. Low contrast detectability and spatial resolution with model-based Iterative reconstructions of MDCT images: a phantom and cadaveric study. Eur Radiol. 2017;27(3):927-937. https://doi.org/10.1007/s00330-016-4444-x

    Article  PubMed  Google Scholar 

  11. Euler A, Stieltjes B, Szucs-Farkas Z, et al. Impact of model-based iterative reconstruction on low-contrast lesion detection and image quality in abdominal CT: a 12-reader-based comparative phantom study with filtered back projection at different tube voltages. Eur Radiol. 2017;27(12):5252-5259. https://doi.org/10.1007/s00330-017-4825-9

    Article  PubMed  Google Scholar 

  12. Nishizawa M, Tanaka H, Watanabe Y, Kunitomi Y, Tsukabe A, Tomiyama N. Model-based iterative reconstruction for detection of subtle hypoattenuation in early cerebral infarction: a phantom study. Jpn J Radiol. 2015;33(1):26-32. https://doi.org/10.1007/s11604-014-0376-z

    Article  PubMed  Google Scholar 

  13. Chen G, Hong X, Ding Q, et al. AirNet: Fused analytical and iterative reconstruction with deep neural network regularization for sparse-data CT. Med Phys. 2020;47(7):2916-2930. https://doi.org/10.1002/mp.14170

    Article  CAS  PubMed  Google Scholar 

  14. Shin YJ, Chang W, Ye JC, et al. Low-Dose Abdominal CT Using a Deep Learning-Based Denoising Algorithm: A Comparison with CT Reconstructed with Filtered Back Projection or Iterative Reconstruction Algorithm. Korean J Radiol. 2020;21(3):356-364. https://doi.org/10.3348/kjr.2019.0413

    Article  PubMed  PubMed Central  Google Scholar 

  15. Parakh A, Cao J, Pierce TT, Blake MA, Savage CA, Kambadakone AR. Sinogram-based deep learning image reconstruction technique in abdominal CT: image quality considerations. Eur Radiol. 2021;31(11):8342-8353. https://doi.org/10.1007/s00330-021-07952-4

    Article  PubMed  Google Scholar 

  16. Tamm EP, Rong XJ, Cody DD, Ernst RD, Fitzgerald NE, Kundra V. Quality initiatives: CT radiation dose reduction: how to implement change without sacrificing diagnostic quality. Radiogr Rev Publ Radiol Soc N Am Inc. 2011;31(7):1823-1832. https://doi.org/10.1148/rg.317115027

  17. Nakaura T, Iyama Y, Kidoh M, et al. Comparison of iterative model, hybrid iterative, and filtered back projection reconstruction techniques in low-dose brain CT: impact of thin-slice imaging. Neuroradiology. 2016;58(3):245-251. https://doi.org/10.1007/s00234-015-1631-4

    Article  PubMed  Google Scholar 

  18. Sprawls P. AAPM tutorial. CT image detail and noise. Radiogr Rev Publ Radiol Soc N Am Inc. 1992;12(5):1041-1046. https://doi.org/10.1148/radiographics.12.5.1529128

  19. Christianson O, Winslow J, Frush DP, Samei E. Automated Technique to Measure Noise in Clinical CT Examinations. AJR Am J Roentgenol. 2015;205(1):W93-99. https://doi.org/10.2214/AJR.14.13613

    Article  PubMed  Google Scholar 

  20. Funama Y, Awai K, Miyazaki O, et al. Improvement of low-contrast detectability in low-dose hepatic multidetector computed tomography using a novel adaptive filter: evaluation with a computer-simulated liver including tumors. Invest Radiol. 2006;41(1):1-7. https://doi.org/10.1097/01.rli.0000188026.20172.5d

    Article  PubMed  Google Scholar 

  21. Benson AB, Abrams TA, Ben-Josef E, et al. NCCN clinical practice guidelines in oncology: hepatobiliary cancers. J Natl Compr Cancer Netw JNCCN. 2009;7(4):350-391. https://doi.org/10.6004/jnccn.2009.0027

    Article  CAS  PubMed  Google Scholar 

  22. Tempero MA, Malafa MP, Al-Hawary M, et al. Pancreatic Adenocarcinoma, Version 2.2021, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Cancer Netw JNCCN. 2021;19(4):439-457. https://doi.org/10.6004/jnccn.2021.0017

    Article  CAS  PubMed  Google Scholar 

  23. Soo G, Lau KK, Yik T, Kutschera P. Optimal reconstructed section thickness for the detection of liver lesions with multidetector CT. Clin Radiol. 2010;65(3):193-197. https://doi.org/10.1016/j.crad.2009.10.009

    Article  CAS  PubMed  Google Scholar 

  24. Leonardi M, Bongartz G, Geleijns DJ, et al. (1999) European guidelines on quality criteria for computed tomography. European Commission. http://www.drs.dk/guidelines/ct/quality/htmlindex.htm. Accessed 18 June 2020.

  25. National Research Council. Health Risks from Exposure to Low Levels of Ionizing Radiation: BEIR VII Phase 2. The National Academies Press; 2006. https://doi.org/10.17226/11340

  26. Jensen CT, Liu X, Tamm EP, et al. Image Quality Assessment of Abdominal CT by Use of New Deep Learning Image Reconstruction: Initial Experience. AJR Am J Roentgenol. 2020;215(1):50-57. https://doi.org/10.2214/AJR.19.22332

    Article  PubMed  Google Scholar 

  27. Akagi M, Nakamura Y, Higaki T, et al. Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT. Eur Radiol. 2019;29(11):6163-6171. https://doi.org/10.1007/s00330-019-06170-3

    Article  PubMed  Google Scholar 

  28. Cao L, Liu X, Li J, et al. A study of using a deep learning image reconstruction to improve the image quality of extremely low-dose contrast-enhanced abdominal CT for patients with hepatic lesions. Br J Radiol. 2021;94(1118):20201086. https://doi.org/10.1259/bjr.20201086

    Article  PubMed  Google Scholar 

  29. von Falck C, Galanski M, Shin HO. Informatics in radiology: sliding-thin-slab averaging for improved depiction of low-contrast lesions with radiation dose savings at thin-section CT. Radiogr Rev Publ Radiol Soc N Am Inc. 2010;30(2):317-326. https://doi.org/10.1148/rg.302096007

  30. Szczykutowicz TP, Nett B, Cherkezyan L, et al. Protocol Optimization Considerations for Implementing Deep Learning CT Reconstruction. AJR Am J Roentgenol. 2021;216(6):1668-1677. https://doi.org/10.2214/AJR.20.23397

    Article  PubMed  Google Scholar 

  31. Tamura A, Nakayama M, Ota Y, et al. Feasibility of thin-slice abdominal CT in overweight patients using a vendor neutral image-based denoising algorithm: Assessment of image noise, contrast, and quality. PloS One. 2019;14(12):e0226521. https://doi.org/10.1371/journal.pone.0226521

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Bushberg JT, Seibert JA, Leidholdt EM, Boone JM. The Essential Physics of Medical Imaging. Fourth edition. Wolters Kluwer Health; 2020.

  33. Abdelmoumene A, Chevallier P, Chalaron M, et al. Detection of liver metastases under 2 cm: comparison of different acquisition protocols in four row multidetector-CT (MDCT). Eur Radiol. 2005;15(9):1881-1887. https://doi.org/10.1007/s00330-005-2741-x

    Article  PubMed  Google Scholar 

  34. De Marco P, Origgi D. New adaptive statistical iterative reconstruction ASiR-V: Assessment of noise performance in comparison to ASiR. J Appl Clin Med Phys. 2018;19(2):275-286. https://doi.org/10.1002/acm2.12253

    Article  PubMed  PubMed Central  Google Scholar 

  35. Euler A, Solomon J, Marin D, Nelson RC, Samei E. A Third-Generation Adaptive Statistical Iterative Reconstruction Technique: Phantom Study of Image Noise, Spatial Resolution, Lesion Detectability, and Dose Reduction Potential. AJR Am J Roentgenol. 2018;210(6):1301-1308. https://doi.org/10.2214/AJR.17.19102

    Article  PubMed  Google Scholar 

Download references

Funding

This study was funded by GE (Grant number 237908 to Avinash Kambadakone).

Author information

Authors and Affiliations

  1. Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA

    Jinjin Cao, Nayla Mroueh, Nisanard Pisuchpen, Anushri Parakh, Simon Lennartz, Theodore T. Pierce & Avinash R. Kambadakone

  2. Department of Radiology, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand

    Nisanard Pisuchpen

  3. Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Kerpener Straße 62, 50937, Cologne, Germany

    Simon Lennartz

Authors
  1. Jinjin Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nayla Mroueh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nisanard Pisuchpen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anushri Parakh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Simon Lennartz
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Theodore T. Pierce
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Avinash R. Kambadakone
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Avinash R. Kambadakone.

Ethics declarations

Conflict of interest

Avinash Kambadakone: Research grants (GE, Philips Healthcare and PanCAN). Simon Lennartz: Research support (Philips Healthcare).

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

Cao, J., Mroueh, N., Pisuchpen, N. et al. Can 1.25 mm thin-section images generated with Deep Learning Image Reconstruction technique replace standard-of-care 5 mm images in abdominal CT?. Abdom Radiol 48, 3253–3264 (2023). https://doi.org/10.1007/s00261-023-03992-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00261-023-03992-0

Keywords

Search

Navigation