Skip to main content

Advertisement

SpringerLink
Log in

Is it possible to use low-dose deep learning reconstruction for the detection of liver metastases on CT routinely?

  • Computed Tomography
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To compare the image quality and hepatic metastasis detection of low-dose deep learning image reconstruction (DLIR) with full-dose filtered back projection (FBP)/iterative reconstruction (IR).

Methods

A contrast-detail phantom consisting of low-contrast objects was scanned at five CT dose index levels (10, 6, 3, 2, and 1 mGy). A total of 154 participants with 305 hepatic lesions who underwent abdominal CT were enrolled in a prospective non-inferiority trial with a three-arm design based on phantom results. Data sets with full dosage (13.6 mGy) and low dosages (9.5, 6.8, or 4.1 mGy) were acquired from two consecutive portal venous acquisitions, respectively. All images were reconstructed with FBP (reference), IR (control), and DLIR (test). Eleven readers evaluated phantom data sets for object detectability using a two-alternative forced-choice approach. Non-inferiority analyses were performed to interpret the differences in image quality and metastasis detection of low-dose DLIR relative to full-dose FBP/IR.

Results

The phantom experiment showed the dose reduction potential from DLIR was up to 57% based on the reference FBP dose index. Radiation decreases of 30% and 50% resulted in non-inferior image quality and hepatic metastasis detection with DLIR compared to full-dose FBP/IR. Radiation reduction of 70% by DLIR performed inferiorly in detecting small metastases (< 1 cm) compared to full-dose FBP (difference: −0.112; 95% confidence interval [CI]: −0.178 to 0.047) and full-dose IR (difference: −0.123; 95% CI: −0.182 to 0.053) (p < 0.001).

Conclusion

DLIR enables a 50% dose reduction for detecting low-contrast hepatic metastases while maintaining comparable image quality to full-dose FBP and IR.

Key Points

• Non-inferiority study showed that deep learning image reconstruction (DLIR) can reduce the dose to oncological patients with low-contrast lesions without compromising the diagnostic information.

• Radiation dose levels for DLIR can be reduced to 50% of full-dose FBP and IR for detecting low-contrast hepatic metastases, while maintaining comparable image quality.

• The reduction of radiation by 70% by DLIR is clinically acceptable but insufficient for detecting small low-contrast hepatic metastases (< 1 cm).

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
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Abbreviations

2AFC:

Two-alternative forced choice

CI:

Confidence interval

CNR:

Contrast-to-noise ratio

CT:

Computed tomography

CTDIvol :

Volumetric CT dose index

DLIR:

Deep learning image reconstruction

FBP:

Filtered back projection

FOM:

Figure of merit

GEE:

Generalized estimating equations

IQR:

Interquartile range

IR:

Iterative reconstruction

JAFROC:

Jackknife alternative free-response receiver operating characteristic

References

  1. Geyer LL, Schoepf UJ, Meinel FG et al (2015) State of the art: iterative CT reconstruction techniques. Radiology 276:339–357

    Article  PubMed  Google Scholar 

  2. 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 293:491–503

    Article  PubMed  Google Scholar 

  3. 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 47:3961–3971

    Article  PubMed  Google Scholar 

  4. Chen B, Christianson O, Wilson JM, Samei E (2014) Assessment of volumetric noise and resolution performance for linear and nonlinear CT reconstruction methods. Med Phys 41:071909

    Article  PubMed  Google Scholar 

  5. 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 41:1936–1953

    Article  PubMed  Google Scholar 

  6. Fletcher JG, Fidler JL, Venkatesh SK et al (2018) Observer performance with varying radiation dose and reconstruction methods for detection of hepatic metastases. Radiology 289:455–464

    Article  PubMed  Google Scholar 

  7. McCollough CH, Yu L, Kofler JM et al (2015) Degradation of CT low-contrast spatial resolution due to the use of iterative reconstruction and reduced dose levels. Radiology 276:499–506

    Article  PubMed  Google Scholar 

  8. Choi H, Chang W, Kim JH et al (2022) Dose reduction potential of vendor-agnostic deep learning model in comparison with deep learning-based image reconstruction algorithm on CT: a phantom study. Eur Radiol 32:1247–1255

    Article  PubMed  Google Scholar 

  9. Racine D, Brat HG, Dufour B et al (2021) Image texture, low contrast liver lesion detectability and impact on dose: deep learning algorithm compared to partial model-based iterative reconstruction. Eur J Radiol 141:109808

    Article  CAS  PubMed  Google Scholar 

  10. Greffier J, Hamard A, Pereira F et al (2020) Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study. Eur Radiol 30:3951–3959

    Article  PubMed  Google Scholar 

  11. Hsieh J, Liu E, Nett B, Tang J, Thibault J-B, Sahney S (2019) A new era of image reconstruction: TrueFidelity™. White Paper (JB68676XX), GE Healthcare

  12. Bornet PA, Villani N, Gillet R et al (2022) Clinical acceptance of deep learning reconstruction for abdominal CT imaging: objective and subjective image quality and low-contrast detectability assessment. Eur Radiol 32:3161–3172

    Article  PubMed  Google Scholar 

  13. Fletcher JG, Yu L, Fidler JL et al (2017) Estimation of observer performance for reduced radiation dose levels in CT: eliminating reduced dose levels that are too low is the first step. Acad Radiol 24:876–890

    Article  PubMed  PubMed Central  Google Scholar 

  14. Jensen CT, Gupta S, Saleh MM et al (2022) Reduced-dose deep learning reconstruction for abdominal CT of liver metastases. Radiology 303:90–98

    Article  PubMed  Google Scholar 

  15. Noda Y, Kaga T, Kawai N et al (2021) Low-dose whole-body CT using deep learning image reconstruction: image quality and lesion detection. Br J Radiol :20201329

  16. Solomon J, Mileto A, Ramirez-Giraldo JC, Samei E (2015) Diagnostic performance of an advanced modeled iterative reconstruction algorithm for low-contrast detectability with a third-generation dual-source multidetector CT scanner: potential for radiation dose reduction in a multireader study. Radiology 275:735–745

    Article  PubMed  Google Scholar 

  17. Jensen CT, Liu X, Tamm EP et al (2020) Image quality assessment of abdominal CT by use of new deep learning image reconstruction: initial experience. AJR Am J Roentgenol 215:50–57

    Article  PubMed  Google Scholar 

  18. Solomon J, Marin D, Roy Choudhury K, Patel B, Samei E (2017) Effect of radiation dose reduction and reconstruction algorithm on image noise, contrast, resolution, and detectability of subtle hypoattenuating liver lesions at multidetector CT: filtered back projection versus a commercial model-based iterative reconstruction algorithm. Radiology 284:777–787

    Article  PubMed  Google Scholar 

  19. Kanal KM, Butler PF, Sengupta D, Bhargavan-Chatfield M, Coombs LP, Morin RL (2017) U.S. diagnostic reference levels and achievable doses for 10 adult CT examinations. Radiology 284:120–133

    Article  PubMed  Google Scholar 

  20. Lv P, Zhou Z, Liu J et al (2019) Can virtual monochromatic images from dual-energy CT replace low-kVp images for abdominal contrast-enhanced CT in small- and medium-sized patients. Eur Radiol 29:2878–2889

    Article  PubMed  Google Scholar 

  21. Lee KH, Lee JM, Moon SK et al (2012) Attenuation-based automatic tube voltage selection and tube current modulation for dose reduction at contrast-enhanced liver CT. Radiology 265:437–447

    Article  PubMed  Google Scholar 

  22. Bellini D, Ramirez-Giraldo JC, Bibbey A et al (2017) Dual-source single-energy multidetector CT used to obtain multiple radiation exposure levels within the same patient: phantom development and clinical validation. Radiology 283:526–537

    Article  PubMed  Google Scholar 

  23. Boone JMSK, Cody DD, McCollough CH, McNitt-Gray MF, Toth TL (2011) Size-specific dose estimates (SSDE) in pediatric and adult body CT examinations. In: Report of Am Assoc Phys Med AAPM Task Group 204. American Association of Physicists in Medicine, College Park

  24. Nam JG, Hong JH, Kim DS, Oh J, Goo JM (2021) Deep learning reconstruction for contrast-enhanced CT of the upper abdomen: similar image quality with lower radiation dose in direct comparison with iterative reconstruction. Eur Radiol 31:5533–5543

    Article  PubMed  Google Scholar 

  25. Park S, Yoon JH, Joo I et al (2022) Image quality in liver CT: low-dose deep learning vs standard-dose model-based iterative reconstructions. Eur Radiol 32:2865–2874

    Article  CAS  PubMed  Google Scholar 

  26. Lyu P, Neely B, Solomon J et al (2021) Effect of deep learning image reconstruction in the prediction of resectability of pancreatic cancer: diagnostic performance and reader confidence. Eur J Radiol 141:109825

    Article  PubMed  Google Scholar 

  27. Racine D, Becce F, Viry A et al (2020) Task-based characterization of a deep learning image reconstruction and comparison with filtered back-projection and a partial model-based iterative reconstruction in abdominal CT: a phantom study. Phys Med 76:28–37

    Article  PubMed  Google Scholar 

  28. 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 31:4700–4709

    Article  PubMed  Google Scholar 

  29. Wang X, Zheng F, Xiao R et al (2021) Comparison of image quality and lesion diagnosis in abdominopelvic unenhanced CT between reduced-dose CT using deep learning post-processing and standard-dose CT using iterative reconstruction: a prospective study. Eur J Radiol 139:109735

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We are grateful to Dr. Scott Robertson, Dr. Joshua Wilson, Nicole Lafata, Dr. Crystal Green, Dr. Steve Mann and Dr. Jeffrey Ashton from Duke University Medical Center for completing the 2AFC experiment of this work. We also thank Brian Thomsen, a senior research manager from GE Healthcare in USA, for providing technical consultation in this study.

Funding

Dr. Peijie Lyu received funding as research fellowship scholarship from GE Healthcare between 2019 to 2020, and received financial support from Key Scientific Research Project of Higher Education in Henan Province (22A320057).

Author information

Authors and Affiliations

  1. Department of Radiology, The First Affiliated Hospital of Zhengzhou University, No.1 East Jianshe Road, Zhengzhou, 450052, Henan Province, China

    Peijie Lyu, Nana Liu, Huixia Wang, Yan Chen & Jianbo Gao

  2. Department of Radiology, Duke University Medical Center, 2301 Erwin Rd, Box 3808, Durham, NC, 27710, USA

    Peijie Lyu, Yuqin Ding, Fides Regina Schwartz, Hanyu Jiang & Daniele Marin

  3. Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA

    Brian Harrawood, Justin Solomon & Ehsan Samei

  4. Beth Israel Deaconess Medical Center Department of Radiology, Harvard Medical School, 1 Deaconess Rd, 330 Brookline Ave, Boston, MA, 02215, USA

    Francesca Rigiroli

  5. Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 20032, China

    Yuqin Ding

  6. Department of Radiology, West China Hospital of Sichuan University, 37 Guoxue Alley, Chengdu, 610041, Sichuan Province, China

    Hanyu Jiang

  7. Clinical Imaging Physics Group, Duke University Health System, 2424 Erwin Rd, Ste. 302, Durham, NC, 27705, USA

    Carolyn Lowry

  8. CT Imaging Research Center, GE Healthcare China, No.1 Tongji South Road, Beijing, 100176, China

    Luotong Wang

Authors
  1. Peijie Lyu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nana Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Brian Harrawood
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Justin Solomon
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Huixia Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Francesca Rigiroli
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yuqin Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Fides Regina Schwartz
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Hanyu Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Carolyn Lowry
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Luotong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Ehsan Samei
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Jianbo Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Daniele Marin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianbo Gao.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Dr. Jianbo Gao.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: GE Healthcare China for Luotong Wang. The other authors declare that they have no conflict of interest.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

The institutional review board approved this single-institution prospective study (Registry number: ChiCTR-DPD-16010302), and all participants provided written informed consent before enrollment.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• prospective

• diagnostic study

• performed at two institutions (phantom part performed in Duke University Medical Center, clinical part performed in The First Affiliated Hospital of Zhengzhou University)

Additional information

Publisher’s note

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

Supplementary information

ESM 1

(DOCX 1289 kb)

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

Lyu, P., Liu, N., Harrawood, B. et al. Is it possible to use low-dose deep learning reconstruction for the detection of liver metastases on CT routinely?. Eur Radiol 33, 1629–1640 (2023). https://doi.org/10.1007/s00330-022-09206-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-022-09206-3

Keywords

Search

Navigation