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Erschienen in:

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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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