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European Radiology
Published in: European Radiology 2/2022

01-02-2022 | Computed Tomography | Computed Tomography

Dose reduction potential of vendor-agnostic deep learning model in comparison with deep learning–based image reconstruction algorithm on CT: a phantom study

Authors: Hyunsu Choi, Won Chang, Jong Hyo Kim, Chulkyun Ahn, Heejin Lee, Hae Young Kim, Jungheum Cho, Yoon Jin Lee, Young Hoon Kim

Published in: European Radiology | Issue 2/2022

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Abstract

Objectives

To compare the dose reduction potential (DRP) of a vendor-agnostic deep learning model (DLM, ClariCT.AI) with that of a vendor-specific deep learning–based image reconstruction algorithm (DLR, TrueFidelity™).

Methods

Computed tomography (CT) images of a multi-sized image quality phantom (Mercury v4.0) were acquired under six radiation dose levels (0.48/0.97/1.93/3.87/7.74/15.47 mGy) and were reconstructed using filtered back projection (FBP) and three strength levels of the DLR (low/medium/high). The FBP images were denoised using the DLM. For all DLM and DLR images, the detectability index (d′) (a task-based detection performance metric) was obtained, under various combinations of three target sizes (10/5/1 mm), five inlets (CT value difference with the background; −895/50/90/335/1000 HU), five phantom diameters (36/31/26/21/16 cm), and six radiation dose levels. Dose reduction potential (DRP) measures the dose reduction made by using DLM or DLR, while yielding d′ equivalent to that of FBP at full dose.

Results

The DRPs of the DLM, DLR-low, DLR-medium, and DLR-high were 86% (81–88%), 60% (46–67%), 76% (60–81%), and 87% (78–92%), respectively. For 10-mm targets, the DRP of the DLM (87%) was higher than that of all DLR algorithms (58–86%). However, for smaller targets (5 mm/1 mm), the DRPs of the DLR-high (89/88%) were greater than those of the DLM (87/84%).

Conclusion

The dose reduction potential of the vendor-agnostic DLM was shown to be comparable to that of the vendor-specific DLR at high strength and superior to those of the DLRs at medium and low strengths.

Key Points

• DRP of the vendor-agnostic model was comparable to that of high-strength vendor-specific model and superior to those of medium- and low-strength models.
• Under various radiation dose levels, the deep learning model shows higher detectability indexes compared to FBP.
Appendix
Available only for authorised users
Literature
1.
Brenner DJ, Hall EJ (2007) Computed tomography—an increasing source of radiation exposure. N Engl J Med 357:2277–2284CrossRef
2.
Smith-Bindman R, Lipson J, Marcus R et al (2009) Radiation dose associated with common computed tomography examinations and the associated lifetime attributable risk of cancer. Arch Intern Med 169:2078–2086CrossRef
3.
Hara AK, Paden RG, Silva AC, Kujak JL, Lawder HJ, Pavlicek W (2009) Iterative reconstruction technique for reducing body radiation dose at CT: feasibility study. AJR Am J Roentgenol 193:764–771CrossRef
4.
Leipsic J, LaBounty TM, Heilbron B et al (2010) Estimated radiation dose reduction using adaptive statistical iterative reconstruction in coronary CT angiography: the ERASIR study. AJR Am J Roentgenol 195:655–660CrossRef
5.
Katsura M, Matsuda I, Akahane M et al (2012) Model-based iterative reconstruction technique for radiation dose reduction in chest CT: comparison with the adaptive statistical iterative reconstruction technique. Eur Radiol 22:1613–1623CrossRef
6.
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
7.
Tatsugami F, Higaki T, Nakamura Y et al (2019) Deep learning–based image restoration algorithm for coronary CT angiography. Eur Radiol 29:5322–5329CrossRef
8.
Shin YJ, Chang W, Ye JC et al (2020) 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 21:356–364CrossRef
9.
Higaki T, Nakamura Y, Zhou J et al (2020) Deep learning reconstruction at ct: phantom study of the image characteristics. Acad Radiol 27:82–87CrossRef
10.
Kim JH, Yoon HJ, Lee E, Kim I, Cha YK, Bak SH (2021) Validation of deep-learning image reconstruction for low-dose chest computed tomography scan: emphasis on image quality and noise. Korean J Radiol 22:131–138CrossRef
11.
Homayounieh F, Holmberg O, Al Umairi R et al (2021) Variations in CT utilization, protocols, and radiation doses in COVID-19 pneumonia: results from 28 countries in the IAEA study. Radiology 298:e141–e151CrossRef
12.
Lim WH, Choi YH, Park JE et al (2019) Application of vendor-neutral iterative reconstruction technique to pediatric abdominal computed tomography. Korean J Radiol 20:1358–1367CrossRef
13.
Hong JH, Park E-A, Lee W, Ahn C, Kim J-H (2020) Incremental image noise reduction in coronary CT angiography using a deep learning-based technique with iterative reconstruction. Korean J Radiol 21:1165–1177CrossRef
14.
Kolb M, Storz C, Kim JH et al (2019) Effect of a novel denoising technique on image quality and diagnostic accuracy in low-dose CT in patients with suspected appendicitis. Eur J Radiol 116:198–204CrossRef
15.
16.
Samei E, Bakalyar D, Boedeker KL et al (2019) Performance evaluation of computed tomography systems: summary of AAPM task group 233. Med Phys 46:e735–e756CrossRef
17.
Kanal KM, Butler PF, Sengupta D, Bhargavan-Chatfield M, Coombs LP, Morin RL (2017) US diagnostic reference levels and achievable doses for 10 adult CT examinations. Radiology 284:120–133CrossRef
18.
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 41:071909CrossRef
19.
20.
Ahn C, Heo C, Kim JH (2019) Combined low-dose simulation and deep learning for CT denoising: application in ultra-low-dose chest CT, International Forum on Medical Imaging in Asia 2019
21.
22.
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
23.
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 42:314–323CrossRef
24.
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 275:725–734CrossRef
25.
Mettler FA Jr, Huda W, Yoshizumi TT, Mahesh M (2008) Effective doses in radiology and diagnostic nuclear medicine: a catalog. Radiology 248:254–263CrossRef
26.
Kim HJ, Jeon BG, Hong CK et al (2017) Low-dose CT for the diagnosis of appendicitis in adolescents and young adults (LOCAT): a pragmatic, multicentre, randomised controlled non-inferiority trial. Lancet Gastroenterol Hepatol 2:793–804CrossRef
27.
Kim K, Kim YH, Kim SY et al (2012) Low-dose abdominal CT for evaluating suspected appendicitis. N Engl J Med 366:1596–1605CrossRef
28.
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 47:3961–3971CrossRef
29.
Yu L, Vrieze TJ, Leng S, Fletcher JG, McCollough CH (2015) Measuring contrast-and noise-dependent spatial resolution of an iterative reconstruction method in CT using ensemble averaging. Med Phys 42:2261–2267CrossRef
30.
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 276:499–506CrossRef
31.
Li K, Garrett J, Ge Y, Chen GH (2014) Statistical model based iterative reconstruction (MBIR) in clinical CT systems. Part II. Experimental assessment of spatial resolution performance. Med Phys 41:071911CrossRef
32.
Yang Q, Yan P, Zhang Y et al (2018) Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss. IEEE Trans Med Imaging 37:1348–1357CrossRef
Metadata
Title
Dose reduction potential of vendor-agnostic deep learning model in comparison with deep learning–based image reconstruction algorithm on CT: a phantom study
Authors
Hyunsu Choi
Won Chang
Jong Hyo Kim
Chulkyun Ahn
Heejin Lee
Hae Young Kim
Jungheum Cho
Yoon Jin Lee
Young Hoon Kim
Publication date
01-02-2022
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 2/2022
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-021-08199-9

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