Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 May 1;95(1133):20210380.
doi: 10.1259/bjr.20210380. Epub 2022 Jan 31.

Application of deep learning image reconstruction in low-dose chest CT scan

Huang Wang  1 , Lu-Lu Li  1 , Jin Shang  1 , Jian Song  1 , Bin Liu  1
Affiliations

Application of deep learning image reconstruction in low-dose chest CT scan

Huang Wang et al. Br J Radiol. .

Abstract

Objective: Deep learning image reconstruction (DLIR) is a new reconstruction method for maintaining image quality at reduced radiation dose. The purpose of this study was to compare image quality of reduced-dose DLIR images with the standard-dose adaptive statistical iterative reconstruction (ASIR-V) images in chest CT.

Methods: Our prospective study included 48 adult patients (30 women and 18 men, mean age ±SD, 49.8 ± 14.3 years) who underwent both the standard-dose CT (SDCT) and low-dose CT (LDCT) on a GE Revolution CT scanner. All patients gave written informed consent. All scans were reconstructed with ASIR-V40%. Additionally, LDCT scans were reconstructed with DLIR with high-setting (DLIR-H) and medium-setting (DLIR-M). Image noise and contrast-noise-ratio (CNR) of thoracic aorta with different reconstruction modes were measured and compared.

Results: LDCT reduced radiation dose by 96% compared with SDCT (CTDIvol: 0.54mGy vs 12.46mGy). In LDCT, DLIR significantly reduced image noise compared with the state-of-the-art ASIR-V40% with DLIR-H provided the lowest image noise and highest image quality score. In addition, the image noise, CNR of aorta and overall image quality of the low-dose DLIR-H images did not have significant difference compared with the SDCT ASIR-V40% images (all p > 0.05).

Conclusion: DLIR significantly reduces image noise in LDCT chest scans and provides similar image quality as the SDCT ASIR-V images at 4% of the radiation dose.

Advances in knowledge: DLIR uses high-quality FBP data to train deep neural networks to learn how to distinguish between signal and noise, and effectively suppresses noise without affecting anatomical and pathological structures. It opens a new era of CT image reconstruction. DLIR significantly reduces image noise and improves image quality compared with ASIR-V40% under same radiation dose condition. DLIR-H achieves similar image quality at 4% radiation dose as ASIR-V40% at standard-dose level in non-contrast chest CT.

PubMed Disclaimer

Figures

Figure 1.
Figure 1.
Comparison of chest CT scan in axial soft tissue window images of mediastinum in 43-year-old male (A), ASIR-V40% at SDCT; (B), ASIR-V40% at LDCT; (C), DLIR-M at LDCT; and (D), DLIR-H at LDCT. In different reconstructions, the image attenuation values (CT numbers) did not have statistically significant difference. For image noise, the SD value did not have significantly difference between SDCT and DLIR-H (p = 1.000), while there were statistically significant differences between any other reconstruction pairs: DLIR-M vs LDCT, p = 0.006, and SDCT vs DLIR-M, SDCT vs LDCT, DLIR-H vs DL-M, DLIR-H vs LDCT, all p < 0.001. ASIR, adaptive statistical iterative reconstruction; DLIR, deep learning image reconstruction; LDCT, low-dose CT; SDCT, standard-dose CT; SD, standard deviation.
Figure 2.
Figure 2.
Comparison of chest CT scan in axial soft tissue window images of lung in 42-year-old male. Images were (A), ASIR-V40% at SDCT; (B), ASIR-V40% at LDCT; (C), DLIR-M at LDCT; and (D), DLIR-H at LDCT. There was no significant difference in the detection rate of nodules among the different reconstructions. The difference was in the appearance of the nodules. ASIR, adaptive statistical iterative reconstruction; DLIR, deep learning image reconstruction; LDCT, low-dose CT; SDCT, standard-dose CT; SD, standard deviation.
See this image and copyright information in PMC

References

    1. Ciompi F, Chung K, van Riel SJ, Setio AAA, Gerke PK, et al. . Towards automatic pulmonary nodule management in lung cancer screening with deep learning . Sci Rep 2017. ; 7: 46479 . doi: 10.1038/srep46479 - DOI - PMC - PubMed
    1. Jensen CT, Liu X, Tamm EP, Chandler AG, Sun J, et al. . Image quality assessment of abdominal ct by use of new deep learning image reconstruction: initial experience . AJR Am J Roentgenol 2020. ; 215: 50 – 57 . doi: 10.2214/AJR.19.22332 - DOI - PubMed
    1. Kim JH, Yoon HJ, Lee E, Kim I, Cha YK, et al. . Validation of deep-learning image reconstruction for low-dose chest computed tomography scan: emphasis on image quality and noise . Korean J Radiol 2021. ; 22: 131 – 38 . doi: 10.3348/kjr.2020.0116 - DOI - PMC - PubMed
    1. Greffier J, Hamard A, Pereira F, Barrau C, Pasquier H, et al. . Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for ct: a phantom study . Eur Radiol 2020. ; 30: 3951 – 59 . doi: 10.1007/s00330-020-06724-w - DOI - PubMed
    1. Du Y, Li Q, Sidorenkov G, Vonder M, Cai J, et al. . Computed tomography screening for early lung cancer, copd and cardiovascular disease in shanghai: rationale and design of a population-based comparative study . Acad Radiol 2021. ; 28: 36 – 45 . doi: 10.1016/j.acra.2020.01.020 - DOI - PubMed