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利用深度学习图像重建提高腹部双能CT中病变的显见度:一项针对五名阅片者的前瞻性研究

Improving lesion conspicuity in abdominal dual-energy CT with deep learning image reconstruction: a prospective study with five readers.

作者信息

Zhong Jingyu, Wang Lingyun, Shen Hailin, Li Jianying, Lu Wei, Shi Xiaomeng, Xing Yue, Hu Yangfan, Ge Xiang, Ding Defang, Yan Fuhua, Du Lianjun, Yao Weiwu, Zhang Huan

机构信息

Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.

Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.

出版信息

Eur Radiol. 2023 Aug;33(8):5331-5343. doi: 10.1007/s00330-023-09556-6. Epub 2023 Mar 28.

Abstract

OBJECTIVES

To evaluate image quality, diagnostic acceptability, and lesion conspicuity in abdominal dual-energy CT (DECT) using deep learning image reconstruction (DLIR) compared to those using adaptive statistical iterative reconstruction-V (Asir-V) at 50% blending (AV-50), and to identify potential factors impacting lesion conspicuity.

METHODS

The portal-venous phase scans in abdominal DECT of 47 participants with 84 lesions were prospectively included. The raw data were reconstructed to virtual monoenergetic image (VMI) at 50 keV using filtered back-projection (FBP), AV-50, and DLIR at low (DLIR-L), medium (DLIR-M), and high strength (DLIR-H). A noise power spectrum (NPS) was generated. CT number and standard deviation values of eight anatomical sites were measured. Signal-to-noise (SNR), and contrast-to-noise ratio (CNR) values were calculated. Five radiologists assessed image quality in terms of image contrast, image noise, image sharpness, artificial sensation, and diagnostic acceptability, and evaluated the lesion conspicuity.

RESULTS

DLIR further reduced image noise (p < 0.001) compared to AV-50 while better preserved the average NPS frequency (p < 0.001). DLIR maintained CT number values (p > 0.99) and improved SNR and CNR values compared to AV-50 (p < 0.001). DLIR-H and DLIR-M showed higher ratings in all image quality analyses than AV-50 (p < 0.001). DLIR-H provided significantly better lesion conspicuity than AV-50 and DLIR-M regardless of lesion size, relative CT attenuation to surrounding tissue, or clinical purpose (p < 0.05).

CONCLUSIONS

DLIR-H could be safely recommended for routine low-keV VMI reconstruction in daily contrast-enhanced abdominal DECT to improve image quality, diagnostic acceptability, and lesion conspicuity.

KEY POINTS

• DLIR is superior to AV-50 in noise reduction, with less shifts of the average spatial frequency of NPS towards low frequency, and larger improvements of NPS noise, noise peak, SNR, and CNR values. • DLIR-M and DLIR-H generate better image quality in terms of image contrast, noise, sharpness, artificial sensation, and diagnostic acceptability than AV-50, while DLIR-H provides better lesion conspicuity than AV-50 and DLIR-M. • DLIR-H could be safely recommended as a new standard for routine low-keV VMI reconstruction in contrast-enhanced abdominal DECT to provide better lesion conspicuity and better image quality than the standard AV-50.

摘要

目的

评估与使用50%融合的自适应统计迭代重建-V(Asir-V,AV-50)相比,深度学习图像重建(DLIR)在腹部双能量CT(DECT)中的图像质量、诊断可接受性和病变清晰度,并确定影响病变清晰度的潜在因素。

方法

前瞻性纳入47名参与者腹部DECT门静脉期扫描的84个病变。原始数据使用滤波反投影(FBP)、AV-50以及低强度(DLIR-L)、中等强度(DLIR-M)和高强度(DLIR-H)的DLIR重建为50keV的虚拟单能图像(VMI)。生成噪声功率谱(NPS)。测量八个解剖部位的CT值和标准差。计算信噪比(SNR)和对比噪声比(CNR)。五名放射科医生从图像对比度、图像噪声、图像清晰度、人工伪影感和诊断可接受性方面评估图像质量,并评估病变清晰度。

结果

与AV-50相比,DLIR进一步降低了图像噪声(p<0.001),同时更好地保留了平均NPS频率(p<0.001)。与AV-50相比,DLIR保持了CT值(p>0.99),并提高了SNR和CNR值(p<0.001)。在所有图像质量分析中,DLIR-H和DLIR-M的评分均高于AV-50(p<0.001)。无论病变大小、相对于周围组织的CT衰减或临床目的如何,DLIR-H的病变清晰度均显著优于AV-50和DLIR-M(p<0.05)。

结论

在日常腹部增强DECT中,对于常规低keV VMI重建,DLIR-H可被安全推荐,以提高图像质量、诊断可接受性和病变清晰度。

关键点

• 在降低噪声方面,DLIR优于AV-50,NPS的平均空间频率向低频的偏移更小,NPS噪声、噪声峰值、SNR和CNR值的改善更大。• 在图像对比度、噪声、清晰度、人工伪影感和诊断可接受性方面,DLIR-M和DLIR-H生成的图像质量优于AV-50,而DLIR-H的病变清晰度优于AV-50和DLIR-M。• DLIR-H可被安全推荐作为腹部增强DECT常规低keV VMI重建的新标准,以提供比标准AV-50更好的病变清晰度和图像质量。

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