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15.08.2023 | Chest

Value of deep learning reconstruction of chest low-dose CT for image quality improvement and lung parenchyma assessment on lung window

verfasst von: Jinhua Wang, Xin Sui, Ruijie Zhao, Huayang Du, Jiaru Wang, Yun Wang, Ruiyao Qin, Xiaoping Lu, Zhuangfei Ma, Yinghao Xu, Zhengyu Jin, Lan Song, Wei Song

Erschienen in: European Radiology | Ausgabe 2/2024

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Abstract

Objectives

To explore the performance of low-dose computed tomography (LDCT) with deep learning reconstruction (DLR) for the improvement of image quality and assessment of lung parenchyma.

Methods

Sixty patients underwent chest regular-dose CT (RDCT) followed by LDCT during the same examination. RDCT images were reconstructed with hybrid iterative reconstruction (HIR) and LDCT images were reconstructed with HIR and DLR, both using lung algorithm. Radiation exposure was recorded. Image noise, signal-to-noise ratio, and subjective image quality of normal and abnormal CT features were evaluated and compared using the Kruskal–Wallis test with Bonferroni correction.

Results

The effective radiation dose of LDCT was significantly lower than that of RDCT (0.29 ± 0.03 vs 2.05 ± 0.65 mSv, p < 0.001). The mean image noise ± standard deviation was 33.9 ± 4.7, 39.6 ± 4.3, and 31.1 ± 3.2 HU in RDCT, LDCT HIR-Strong, and LDCT DLR-Strong, respectively (p < 0.001). The overall image quality of LDCT DLR-Strong was significantly better than that of LDCT HIR-Strong (p < 0.001) and comparable to that of RDCT (p > 0.05). LDCT DLR-Strong was comparable to RDCT in evaluating solid nodules, increased attenuation, linear opacity, and airway lesions (all p > 0.05). The visualization of subsolid nodules and decreased attenuation was better with DLR than with HIR in LDCT but inferior to RDCT (all p < 0.05).

Conclusion

LDCT DLR can effectively reduce image noise and improve image quality. LDCT DLR provides good performance for evaluating pulmonary lesions, except for subsolid nodules and decreased lung attenuation, compared to RDCT-HIR.

Clinical relevance statement

The study prospectively evaluated the contribution of DLR applied to chest low-dose CT for image quality improvement and lung parenchyma assessment. DLR can be used to reduce radiation dose and keep image quality for several indications.

Key Points

• DLR enables LDCT maintaining image quality even with very low radiation doses.

• Chest LDCT with DLR can be used to evaluate lung parenchymal lesions except for subsolid nodules and decreased lung attenuation.

• Diagnosis of pulmonary emphysema or subsolid nodules may require higher radiation doses.

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Smith-Bindman R, Wang Y, Chu P et al (2019) International variation in radiation dose for computed tomography examinations: prospective cohort study. BMJ 364:k4931CrossRefPubMedPubMedCentral

Alsharif W, Qurashi A (2021) Effectiveness of COVID-19 diagnosis and management tools: a review. Radiography (Lond) 27(2):682–687CrossRef

Kim H, Park CM, Chae HD et al (2015) Impact of radiation dose and iterative reconstruction on pulmonary nodule measurements at chest CT: a phantom study. Diagn Interv Radiol 21(6):459–465CrossRefPubMedPubMedCentral

Zhang M, Qi W, Sun Y et al (2018) Screening for lung cancer using sub-millisievert chest CT with iterative reconstruction algorithm: image quality and nodule detectability. Br J Radiol 91(1090):20170658CrossRefPubMed

Ye K, Zhu Q, Li M et al (2019) A feasibility study of pulmonary nodule detection by ultralow-dose CT with adaptive statistical iterative reconstruction-V technique. Eur J Radiol 119:108652CrossRefPubMed

Willemink MJ, Noël PB (2019) The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence. Eur Radiol 29(5):2185–2195CrossRefPubMed

Hata A, Yanagawa M, Yoshida Y et al (2021) The image quality of deep-learning image reconstruction of chest CT images on a mediastinal window setting. Clin Radiol 76(2):155.e15–155.e23CrossRefPubMed

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(3):566–573CrossRefPubMed

Kim JH, Yoon HJ, Lee E et al (2021) Validation of deep-learning image reconstruction for low-dose chest computed tomography scan: emphasis on image quality and noise. Korean J Radiol 22(1):131–138CrossRefPubMed

10.

Franck C, Zhang G, Deak P et al (2021) Preserving image texture while reducing radiation dose with a deep learning image reconstruction algorithm in chest CT: a phantom study. Phys Med 81:86–93CrossRefPubMed

11.

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(7):3951–3959CrossRefPubMed

12.

Svalkvist A, Fagman E, Vikgren J et al (2023) Evaluation of deep-learning image reconstruction for chest CT examinations at two different dose levels. J Appl Clin Med Phys 24(3):e13871CrossRefPubMed

13.

Tian Q, Li X, Li J et al (2022) Image quality improvement in low-dose chest CT with deep learning image reconstruction. J Appl Clin Med Phys 23(12):e13796CrossRefPubMedPubMedCentral

14.

Wang H, Li LL, Shang J et al (2022) Application of deep learning image reconstruction in low-dose chest CT scan. Br J Radiol 95(1133):20210380CrossRefPubMed

15.

Aberle DR, Berg CD, Black WC et al (2011) The National Lung Screening Trial: overview and study design. Radiology 258(1):243–253CrossRef

16.

Kang H (2021) Sample size determination and power analysis using the G*Power software. J Educ Eval Health Prof 18:17CrossRefPubMedPubMedCentral

17.

Jiang B, Li N, Shi X et al (2022) Deep learning reconstruction shows better lung nodule detection for ultra-low-dose chest CT. Radiology 303(1):202–212CrossRef

18.

Pontana F, Billard AS, Duhamel A et al (2016) Effect of iterative reconstruction on the detection of systemic sclerosis-related interstitial lung disease: clinical experience in 55 patients. Radiol 279(1):297–305CrossRef

19.

Yamada Y, Jinzaki M, Hosokawa T et al (2012) Dose reduction in chest CT: comparison of the adaptive iterative dose reduction 3D, adaptive iterative dose reduction, and filtered back projection reconstruction techniques. Eur J Radiol 81(12):4185–4195CrossRefPubMed

20.

Lee SW, Kim Y, Shim SS et al (2014) Image quality assessment of ultra low-dose chest CT using sinogram-affirmed iterative reconstruction. Eur Radiol 24(4):817–826CrossRefPubMed

21.

Hansell DM, Bankier AA, MacMahon H et al (2008) Fleischner Society: glossary of terms for thoracic imaging. Radiology 246(3):697–722CrossRef

22.

Deak PD, Smal Y, Kalender WA (2010) Multisection CT protocols: sex- and age-specific conversion factors used to determine effective dose from dose-length product. Radiology 257(1):158–166CrossRef

23.

Christner JA, Braun NN, Jacobsen MC et al (2012) Size-specific dose estimates for adult patients at CT of the torso. Radiology 265(3):841–847CrossRef

24.

AAPM Task Group 204 (2011) Size-specific dose estimates (SSDE) in pediatric and adult body CT examinations. Report of AAPM Task Group 204. https://www.aapm.org/pubs/reports/RPT_204.pdf. Accessed 20 May 2021

25.

Sui X, Meinel FG, Song W et al (2016) Detection and size measurements of pulmonary nodules in ultra-low-dose CT with iterative reconstruction compared to low dose CT. Eur J Radiol 85(3):564–570CrossRefPubMed

26.

Christe A, Charimo-Torrente J, Roychoudhury K et al (2013) Accuracy of low-dose computed tomography (CT) for detecting and characterizing the most common CT-patterns of pulmonary disease. Eur J Radiol 82(3):e142–e150CrossRefPubMed

27.

Martini K, Barth BK, Nguyen-Kim TD et al (2016) Evaluation of pulmonary nodules and infection on chest CT with radiation dose equivalent to chest radiography: prospective intra-individual comparison study to standard dose CT. Eur J Radiol 85(2):360–365CrossRefPubMed

28.

Neroladaki A, Botsikas D, Boudabbous S et al (2013) Computed tomography of the chest with model-based iterative reconstruction using a radiation exposure similar to chest X-ray examination: preliminary observations. Eur Radiol 23(2):360–366CrossRefPubMed

29.

de Margerie-Mellon C, de Bazelaire C, Montlahuc C et al (2016) Reducing radiation dose at chest CT: comparison among model-based type iterative reconstruction, hybrid iterative reconstruction, and filtered back projection. Acad Radiol 23(10):1246–1254CrossRefPubMed

30.

Nagatani Y, Takahashi M, Murata K et al (2015) Lung nodule detection performance in five observers on computed tomography (CT) with adaptive iterative dose reduction using three-dimensional processing (AIDR 3D) in a Japanese multicenter study: comparison between ultra-low-dose CT and low-dose CT by receiver-operating characteristic analysis. Eur J Radiol 84(7):1401–1412CrossRefPubMed

31.

Rampinelli C, Origgi D, Vecchi V et al (2015) Ultra-low-dose CT with model-based iterative reconstruction (MBIR): detection of ground-glass nodules in an anthropomorphic phantom study. Radiol Med 120(7):611–617CrossRefPubMed

32.

Xu X, Sui X, Song L et al (2019) Feasibility of low-dose CT with spectral shaping and third-generation iterative reconstruction in evaluating interstitial lung diseases associated with connective tissue disease: an intra-individual comparison study. Eur Radiol 29(9):4529–4537CrossRefPubMed

33.

Meyer E, Labani A, Schaeffer M et al (2019) Wide-volume versus helical acquisition in unenhanced chest CT: prospective intra-patient comparison of diagnostic accuracy and radiation dose in an ultra-low-dose setting. Eur Radiol 29(12):6858–6866CrossRefPubMed

34.

Higaki T, Nakamura Y, Zhou J et al (2020) Deep learning reconstruction at CT: phantom study of the image characteristics. Acad Radiol 27(1):82–87CrossRefPubMed

35.

Baldwin DR, Duffy SW, Wald NJ et al (2011) UK Lung Screen (UKLS) nodule management protocol: modelling of a single screen randomised controlled trial of low-dose CT screening for lung cancer. Thorax 66(4):308–313CrossRefPubMed

36.

Ababneh ZQ, Almusallam M, Alfajri I et al (2021) Estimation of the organ and effective dose to patients undergoing medical diagnostic X-ray examinations in Saudi Arabia. Radiat Prot Dosim 194(1):1–8CrossRef

37.

Park C, Choo KS, Jung Y et al (2021) CT iterative vs deep learning reconstruction: comparison of noise and sharpness. Eur Radiol 31(5):3156–3164CrossRefPubMed

38.

Azour L, Hu Y, Ko JP et al (2023) Deep learning denoising of low-dose computed tomography chest images: a quantitative and qualitative image analysis. J Comput Assist Tomogr 47(2):212–219CrossRefPubMed

Titel: Value of deep learning reconstruction of chest low-dose CT for image quality improvement and lung parenchyma assessment on lung window
verfasst von: Jinhua Wang
Xin Sui
Ruijie Zhao
Huayang Du
Jiaru Wang
Yun Wang
Ruiyao Qin
Xiaoping Lu
Zhuangfei Ma
Yinghao Xu
Zhengyu Jin
Lan Song
Wei Song
Publikationsdatum: 15.08.2023
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 2/2024
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-023-10087-3

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Value of deep learning reconstruction of chest low-dose CT for image quality improvement and lung parenchyma assessment on lung window