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Value of deep learning reconstruction at ultra-low-dose CT for evaluation of urolithiasis

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Abstract

Objectives

To determine the diagnostic accuracy and image quality of ultra-low-dose computed tomography (ULDCT) with deep learning reconstruction (DLR) to evaluate patients with suspected urolithiasis, compared with ULDCT with hybrid iterative reconstruction (HIR) by using low-dose CT (LDCT) with HIR as the reference standard.

Methods

Patients with suspected urolithiasis were prospectively enrolled and underwent abdominopelvic LDCT, followed by ULDCT if any urinary stone was observed. Radiation exposure, stone characteristics, image noise, signal-to-noise ratio (SNR), and subjective image quality on a 5-point Likert scale were evaluated and compared.

Results

The average effective radiation dose of ULDCT was significantly lower than that of LDCT (1.28 ± 0.34 vs. 5.49 ± 1.00 mSv, p < 0.001). According to the reference standard (LDCT-HIR), 148 urinary stones were observed in 85.0% (51/60) of patients. ULDCT-DLR detected 143 stones with a rate of 96.6%, and ULDCT-HIR detected 142 stones with a rate of 95.9%. The urinary stones that were not observed with ULDCT-DLR or ULDCT-HIR were renal calculi smaller than 3 mm. There were no significant differences in the detection of clinically significant calculi (≥ 3 mm) or stone size estimation among ULDCT-DLR, ULDCT-HIR, and LDCT-HIR. The image quality of ULDCT-DLR was better than that of ULDCT-HIR and LDCT-HIR with lower image noise, higher SNR, and higher average subjective score.

Conclusions

ULDCT-DLR performed comparably to LDCT-HIR in urinary stone detection and size estimation with better image quality and decreased radiation exposure. ULDCT-DLR may have potential to be considered the first-line choice to evaluate urolithiasis in practice.

Key Points

• Ultra-low-dose computed tomography (ULDCT) has been investigated for diagnosis of urolithiasis, but stone evaluation may be adversely impacted by compromised image quality.

• This study evaluated the value of novel deep learning reconstruction (DLR) at ULDCT by comparing the stone evaluation and image quality of ULDCT-DLR to the reference standard of low-dose CT (LDCT) with hybrid iterative reconstruction (HIR).

• ULDCT-DLR performed comparably to LDCT-HIR in urinary stone detection and size estimation with better image quality and reduced radiation exposure.

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Abbreviations

AEC:

Automatic exposure control

CT:

Computed tomography

CTDIvol :

CT dose index

DLP:

Dose-length product

ED:

Effective dose

HIR:

Hybrid iterative reconstruction

LDCT:

Low-dose computed tomography

MBIR:

Model-based iterative reconstruction

ROI :

Regions of interest

SD :

Standard deviation

SNR :

Signal-to-noise ratio

ULDCT:

Ultra-low-dose computed tomography

References

  1. Raheem OA, Khandwala YS, Sur RL, Ghani KR, Denstedt JD (2017) Burden of urolithiasis: trends in prevalence, treatments, and costs. Eur Urol Focus 3:18–26

    Article  Google Scholar 

  2. Türk C, Petřík A, Sarica K et al (2016) EAU guidelines on diagnosis and conservative management of urolithiasis. Eur Urol 69:468–474

    Article  Google Scholar 

  3. Tzelves L, Türk C, Skolarikos A (2021) European Association of Urology Urolithiasis Guidelines: where are we going? Eur Urol Focus 7:34–38

    Article  Google Scholar 

  4. Lipkin M, Ackerman A (2016) Imaging for urolithiasis: standards, trends, and radiation exposure. Curr Opin Urol 26:56–62

    Article  Google Scholar 

  5. Sahadev R, Maxon V, Srinivasan A (2018) Approaches to eliminate radiation exposure in the management of pediatric urolithiasis. Curr Urol Rep 19:77

    Article  Google Scholar 

  6. Zhang GM, Shi B, Sun H et al (2017) High-pitch low-dose abdominopelvic CT with tin-filtration technique for detecting urinary stones. Abdom Radiol (NY) 42:2127–2134

    Article  Google Scholar 

  7. Leyendecker P, Faucher V, Labani A et al (2019) Prospective evaluation of ultra-low-dose contrast-enhanced 100-kV abdominal computed tomography with tin filter: effect on radiation dose reduction and image quality with a third-generation dual-source CT system. Eur Radiol 29:2107–2116

    Article  Google Scholar 

  8. Mozaffary A, Trabzonlu TA, Kim D, Yaghmai V (2019) Comparison of Tin filter-based spectral shaping CT and low-dose protocol for detection of urinary calculi. AJR Am J Roentgenol 212:808–814

    Article  Google Scholar 

  9. Fontarensky M, Alfidja A, Perignon R et al (2015) Reduced radiation dose with model-based iterative reconstruction versus standard dose with adaptive statistical iterative reconstruction in abdominal CT for diagnosis of acute renal colic. Radiology 276:156–166

    Article  Google Scholar 

  10. Rob S, Bryant T, Wilson I, Somani BK (2017) Ultra-low-dose, low-dose, and standard-dose CT of the kidney, ureters, and bladder: is there a difference? Results from a systematic review of the literature. Clin Radiol 72:11–15

    Article  CAS  Google Scholar 

  11. Rodger F, Roditi G, Aboumarzouk OM (2018) Diagnostic accuracy of low and ultra-low dose CT for identification of urinary tract stones: a systematic review. Urol Int 100:375–385

    Article  Google Scholar 

  12. 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  Google Scholar 

  13. 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 29:4016–4025

    Article  Google Scholar 

  14. Yasaka K, Furuta T, Kubo T et al (2017) Full and hybrid iterative reconstruction to reduce artifacts in abdominal CT for patients scanned without arm elevation. Acta Radiol 58:1085–1093

    Article  Google Scholar 

  15. Greffier J, Frandon J, Larbi A, Beregi JP, Pereira F (2020) CT iterative reconstruction algorithms: a task-based image quality assessment. Eur Radiol 30:487–500

    Article  CAS  Google Scholar 

  16. Nakamoto A, Kim T, Hori M et al (2015) Clinical evaluation of image quality and radiation dose reduction in upper abdominal computed tomography using model-based iterative reconstruction; comparison with filtered back projection and adaptive statistical iterative reconstruction. Eur J Radiol 84:1715–1723

    Article  Google Scholar 

  17. Ott JG, Becce F, Monnin P, Schmidt S, Bochud FO, Verdun FR (2014) Update on the non-prewhitening model observer in computed tomography for the assessment of the adaptive statistical and model-based iterative reconstruction algorithms. Phys Med Biol 59:4047–4064

    Article  Google Scholar 

  18. Willemink MJ, Noël PB (2019) The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence. Eur Radiol 29:2185–2195

    Article  Google Scholar 

  19. Nakamura Y, Higaki T, Tatsugami F et al (2019) Deep learning-based CT image reconstruction: initial evaluation targeting hypovascular hepatic metastases. Radiol Artif Intell 1:e180011

    Article  Google Scholar 

  20. Akagi M, Nakamura Y, Higaki T et al (2019) Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT. Eur Radiol 29:6163–6171

    Article  Google Scholar 

  21. 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  Google Scholar 

  22. 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  Google Scholar 

  23. Brady SL, Trout AT, Somasundaram E, Anton CG, Li Y, Dillman JR (2021) Improving image quality and reducing radiation dose for pediatric CT by using deep learning reconstruction. Radiology 298:180–188

    Article  Google Scholar 

  24. 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  Google Scholar 

  25. Delabie A, Bouzerar R, Pichois R, Desdoit X, Vial J, Renard C (2021) Diagnostic performance and image quality of deep learning image reconstruction on unenhanced low-dose abdominal CT for urolithiasis. Acta Radiol. https://doi.org/10.1177/02841851211035896

  26. Thapaliya S, Brady SL, Somasundaram E et al (2021) Detection of urinary tract calculi on CT images reconstructed with deep learning algorithms. Abdom Radiol (NY). https://doi.org/10.1007/s00261-021-03274-7

  27. Singh R, Digumarthy SR, Muse VV et al (2020) Image quality and lesion detection on deep learning reconstruction and iterative reconstruction of submillisievert chest and abdominal CT. AJR Am J Roentgenol 214:566–573

    Article  Google Scholar 

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Acknowledgements

We would like to thank Canon Medical Systems for their technical support in this study.

Funding

This study has received funding by the National Natural Science Foundation of China [81901742]; the Natural Science Foundation of Beijing Municipality [7192176]; and the Clinical and Translational Research Project of Chinese Academy of Medical Sciences [XK320028].

Author information

Author notes

  1. Gumuyang Zhang and Xiaoxiao Zhang have contributed equally to this work and share first authorship.

  2. Zhengyu Jin and Hao Sun have contributed equally to this work and share last authorship.

Authors and Affiliations

  1. Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, No.1 Shuaifuyuan, Wangfujing Street, Dongcheng District, Beijing, 100730, China

    Gumuyang Zhang, Xiaoxiao Zhang, Lili Xu, Xin Bai, Ru Jin, Zhengyu Jin & Hao Sun

  2. Canon Medical Systems, Beijing, China

    Min Xu & Jing Yan

Authors
  1. Gumuyang Zhang
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  2. Xiaoxiao Zhang
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  5. Ru Jin
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  6. Min Xu
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Corresponding authors

Correspondence to Zhengyu Jin or Hao Sun.

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Guarantor

The scientific guarantor of this publication is Hao Sun.

Conflict of Interest

The authors of this manuscript declare relationships with the following companies: Min Xu and Jing Yan are employees of Canon Medical Systems, China, which provided the deep learning reconstruction algorithm used in the study. All remaining authors have declared no conflicts of interest.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was received from each enrolled patient after study process being fully explained.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• prospective

• diagnostic or prognostic study

• performed at one institution

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Zhang, G., Zhang, X., Xu, L. et al. Value of deep learning reconstruction at ultra-low-dose CT for evaluation of urolithiasis. Eur Radiol 32, 5954–5963 (2022). https://doi.org/10.1007/s00330-022-08739-x

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  • DOI: https://doi.org/10.1007/s00330-022-08739-x

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