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24.06.2022 | Chest

Can deep learning improve image quality of low-dose CT: a prospective study in interstitial lung disease

verfasst von: Ruijie Zhao, Xin Sui, Ruiyao Qin, Huayang Du, Lan Song, Duxue Tian, Jinhua Wang, Xiaoping Lu, Yun Wang, Wei Song, Zhengyu Jin

Erschienen in: European Radiology | Ausgabe 12/2022

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Abstract

Objectives

To investigate whether deep learning reconstruction (DLR) could keep image quality and reduce radiation dose in interstitial lung disease (ILD) patients compared with HRCT reconstructed with hybrid iterative reconstruction (hybrid-IR).

Methods

Seventy ILD patients were prospectively enrolled and underwent HRCT (120 kVp, automatic tube current) and LDCT (120 kVp, 30 mAs) scans. HRCT images were reconstructed with hybrid-IR (Adaptive Iterative Dose Reduction 3-Dimensional [AIDR3D], standard-setting); LDCT images were reconstructed with DLR (Advanced Intelligence Clear-IQ Engine [AiCE], lung/bone, mild/standard/strong setting). Image noise, streak artifact, overall image quality, and visualization of normal and abnormal features of ILD were evaluated.

Results

The mean radiation dose of LDCT was 38% of HRCT. Objective image noise of reconstructed LDCT images was 33.6 to 111.3% of HRCT, and signal-to-noise ratio (SNR) was 0.9 to 3.1 times of the latter (p < 0.001). LDCT-AiCE was not significantly different from or even better than HRCT in overall image quality and visualization of normal lung structures. LDCT-AiCE (lung, mild/standard/strong) showed progressively better recognition of ground glass opacity than HRCT-AIDR3D (p < 0.05, p < 0.01, p < 0.001), and LDCT-AiCE (lung, mild/standard/strong; bone, mild) was superior to HRCT-AIDR3D in visualization of architectural distortion (p < 0.01, p < 0.01, p < 0.01; p < 0.05). LDCT-AiCE (bone, strong) was better than HRCT-AIDR3D in the assessment of bronchiectasis and/or bronchiolectasis (p < 0.05). LDCT-AiCE (bone, mild/standard/strong) was significantly better at the visualization of honeycombing than HRCT-AIDR3D (p < 0.05, p < 0.05, p < 0.01).

Conclusion

Deep learning reconstruction could effectively reduce radiation dose and keep image quality in ILD patients compared to HRCT with hybrid-IR.

Key Points

• Deep learning reconstruction was a novel image reconstruction algorithm based on deep convolutional neural networks. It was applied in chest CT studies and received auspicious results.

• HRCT plays an essential role in the whole process of diagnosis, treatment efficacy evaluation, and follow-ups for interstitial lung disease patients. However, cumulative radiation exposure could increase the risks of cancer.

• Deep learning reconstruction method could effectively reduce the radiation dose and keep the image quality compared with HRCT reconstructed with hybrid iterative reconstruction in patients with interstitial lung disease.

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Titel: Can deep learning improve image quality of low-dose CT: a prospective study in interstitial lung disease
verfasst von: Ruijie Zhao
Xin Sui
Ruiyao Qin
Huayang Du
Lan Song
Duxue Tian
Jinhua Wang
Xiaoping Lu
Yun Wang
Wei Song
Zhengyu Jin
Publikationsdatum: 24.06.2022
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 12/2022
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-022-08870-9

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Can deep learning improve image quality of low-dose CT: a prospective study in interstitial lung disease

Abstract

Objectives

Methods

Results

Conclusion

Key Points

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