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European Radiology

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31-03-2022 | Computed Tomography | Urogenital

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

Authors: Gumuyang Zhang, Xiaoxiao Zhang, Lili Xu, Xin Bai, Ru Jin, Min Xu, Jing Yan, Zhengyu Jin, Hao Sun

Published in: European Radiology | Issue 9/2022

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

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

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

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

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

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) 42:2127–2134CrossRef

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 29:2107–2116CrossRef

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 212:808–814CrossRef

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 276:156–166CrossRef

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 72:11–15CrossRef

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 100:375–385CrossRef

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 293:491–503CrossRef

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 29:4016–4025CrossRef

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 58:1085–1093CrossRef

15.

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

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 84:1715–1723CrossRef

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 59:4047–4064CrossRef

18.

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

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:e180011CrossRef

20.

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

21.

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

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 30:3951–3959CrossRef

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 298:180–188CrossRef

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 31:4700–4709CrossRef

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 214:566–573CrossRef

Title: Value of deep learning reconstruction at ultra-low-dose CT for evaluation of urolithiasis
Authors: Gumuyang Zhang
Xiaoxiao Zhang
Lili Xu
Xin Bai
Ru Jin
Min Xu
Jing Yan
Zhengyu Jin
Hao Sun
Publication date: 31-03-2022
Publisher: Springer Berlin Heidelberg
Keywords: Computed Tomography
Computed Tomography
Urolithiasis
Published in: European Radiology / Issue 9/2022
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-022-08739-x

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

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

Abstract

Objectives

Methods

Results

Conclusions

Key Points

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusions

Key Points

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