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深度学习重建胸部低剂量 CT 对肺窗图像质量改善和肺实质评估的价值。

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

机构信息

Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No. 1, Wangfujing Street, Dongcheng District, Beijing, 100730, China.

Canon Medical System (China), No. 10, Jiuxianqiao North Road, Chaoyang District, Beijing, 100024, China.

出版信息

Eur Radiol. 2024 Feb;34(2):1053-1064. doi: 10.1007/s00330-023-10087-3. Epub 2023 Aug 15.

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.

摘要

目的

探讨低剂量计算机断层扫描(LDCT)联合深度学习重建(DLR)在提高图像质量和评估肺实质方面的性能。

方法

60 例患者在同一次检查中先后进行胸部常规剂量 CT(RDCT)和 LDCT。RDCT 图像采用混合迭代重建(HIR)重建,LDCT 图像采用 HIR 和 DLR 重建,均采用肺算法。记录辐射剂量。采用 Kruskal-Wallis 检验和 Bonferroni 校正比较正常和异常 CT 特征的图像噪声、信噪比和主观图像质量。

结果

LDCT 的有效辐射剂量明显低于 RDCT(0.29 ± 0.03 比 2.05 ± 0.65 mSv,p < 0.001)。RDCT、LDCT HIR-Strong 和 LDCT DLR-Strong 的平均图像噪声分别为 33.9 ± 4.7、39.6 ± 4.3 和 31.1 ± 3.2 HU(p < 0.001)。LDCT DLR-Strong 的整体图像质量明显优于 LDCT HIR-Strong(p < 0.001),与 RDCT 相当(p > 0.05)。LDCT DLR-Strong 在评估实性结节、密度增高、线性不透明度和气道病变方面与 RDCT-HIR 相当(均 p > 0.05)。在 LDCT 中,DLR 较 HIR 更有利于亚实性结节和衰减降低的显示,但不如 RDCT(均 p < 0.05)。

结论

LDCT DLR 可有效降低图像噪声,提高图像质量。与 RDCT-HIR 相比,除亚实性结节和肺衰减降低外,LDCT DLR 对肺病变的评估具有较好的性能。

临床相关性声明

本研究前瞻性评估了 DLR 应用于胸部低剂量 CT 对图像质量改善和肺实质评估的作用。DLR 可用于降低辐射剂量并保持多种适应证下的图像质量。

关键点

• DLR 可使 LDCT 在非常低的辐射剂量下保持图像质量。• 胸部 LDCT 联合 DLR 可用于评估肺实质病变,除亚实性结节和肺衰减降低外。• 诊断肺气肿或亚实性结节可能需要更高的辐射剂量。

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