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基于深度学习图像重建的低剂量腹部盆腔 CT 的图像质量和病变检出率。

Image Quality and Lesion Detectability of Lower-Dose Abdominopelvic CT Obtained Using Deep Learning Image Reconstruction.

机构信息

Department of Radiology, Seoul Medical Center, Seoul, Korea.

Department of Radiology, Yonsei University College of Medicine, Seoul, Korea.

出版信息

Korean J Radiol. 2022 Apr;23(4):402-412. doi: 10.3348/kjr.2021.0683. Epub 2022 Jan 27.

Abstract

OBJECTIVE

To evaluate the image quality and lesion detectability of lower-dose CT (LDCT) of the abdomen and pelvis obtained using a deep learning image reconstruction (DLIR) algorithm compared with those of standard-dose CT (SDCT) images.

MATERIALS AND METHODS

This retrospective study included 123 patients (mean age ± standard deviation, 63 ± 11 years; male:female, 70:53) who underwent contrast-enhanced abdominopelvic LDCT between May and August 2020 and had prior SDCT obtained using the same CT scanner within a year. LDCT images were reconstructed with hybrid iterative reconstruction (h-IR) and DLIR at medium and high strengths (DLIR-M and DLIR-H), while SDCT images were reconstructed with h-IR. For quantitative image quality analysis, image noise, signal-to-noise ratio, and contrast-to-noise ratio were measured in the liver, muscle, and aorta. Among the three different LDCT reconstruction algorithms, the one showing the smallest difference in quantitative parameters from those of SDCT images was selected for qualitative image quality analysis and lesion detectability evaluation. For qualitative analysis, overall image quality, image noise, image sharpness, image texture, and lesion conspicuity were graded using a 5-point scale by two radiologists. Observer performance in focal liver lesion detection was evaluated by comparing the jackknife free-response receiver operating characteristic figures-of-merit (FOM).

RESULTS

LDCT (35.1% dose reduction compared with SDCT) images obtained using DLIR-M showed similar quantitative measures to those of SDCT with h-IR images. All qualitative parameters of LDCT with DLIR-M images but image texture were similar to or significantly better than those of SDCT with h-IR images. The lesion detectability on LDCT with DLIR-M images was not significantly different from that of SDCT with h-IR images (reader-averaged FOM, 0.887 vs. 0.874, respectively; = 0.581).

CONCLUSION

Overall image quality and detectability of focal liver lesions is preserved in contrast-enhanced abdominopelvic LDCT obtained with DLIR-M relative to those in SDCT with h-IR.

摘要

目的

评估使用深度学习图像重建(DLIR)算法获得的腹部和骨盆低剂量 CT(LDCT)的图像质量和病变检出率与标准剂量 CT(SDCT)图像相比的情况。

材料和方法

本回顾性研究纳入了 123 例患者(平均年龄±标准差,63±11 岁;男:女,70:53),这些患者于 2020 年 5 月至 8 月期间进行了腹部和骨盆增强 LDCT 检查,且在一年内曾使用同一台 CT 扫描仪进行过 SDCT 检查。LDCT 图像分别使用混合迭代重建(h-IR)和中高强度 DLIR(DLIR-M 和 DLIR-H)进行重建,而 SDCT 图像则使用 h-IR 进行重建。为了进行定量图像质量分析,在肝脏、肌肉和主动脉中测量了图像噪声、信噪比和对比噪声比。在三种不同的 LDCT 重建算法中,选择定量参数与 SDCT 图像差异最小的一种进行定性图像质量分析和病变检出率评估。在定性分析中,两位放射科医生使用 5 分制对整体图像质量、图像噪声、图像锐度、图像纹理和病变显著性进行了评分。通过比较 Jackknife 自由响应接收者操作特征(FOM)图,评估了局灶性肝病变检测中观察者的性能。

结果

与 SDCT 相比,DLIR-M 获得的 LDCT(剂量降低 35.1%)图像的定量测量值与 SDCT 与 h-IR 图像相似。除了图像纹理外,DLIR-M 图像的所有定性参数均与 SDCT 与 h-IR 图像相似或显著更好。与 SDCT 与 h-IR 图像相比,DLIR-M 图像的病变检出率无显著差异(读者平均 FOM 分别为 0.887 和 0.874,P=0.581)。

结论

与 SDCT 与 h-IR 相比,DLIR-M 获得的对比度增强腹部和骨盆 LDCT 的整体图像质量和局灶性肝病变的检出率保持不变。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bfdb/8961013/6c733a89318f/kjr-23-402-g001.jpg

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