Deep Learning-Based Reconstruction Algorithm With Lung Enhancement Filter for Chest CT: Effect on Image Quality and Ground Glass Nodule Sharpness.

IF 4.4 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Korean Journal of Radiology Pub Date : 2024-09-01 DOI:10.3348/kjr.2024.0472
Min-Hee Hwang, Shinhyung Kang, Ji Won Lee, Geewon Lee
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Abstract

Objective: To assess the effect of a new lung enhancement filter combined with deep learning image reconstruction (DLIR) algorithm on image quality and ground-glass nodule (GGN) sharpness compared to hybrid iterative reconstruction or DLIR alone.

Materials and methods: Five artificial spherical GGNs with various densities (-250, -350, -450, -550, and -630 Hounsfield units) and 10 mm in diameter were placed in a thorax anthropomorphic phantom. Four scans at four different radiation dose levels were performed using a 256-slice CT (Revolution Apex CT, GE Healthcare). Each scan was reconstructed using three different reconstruction algorithms: adaptive statistical iterative reconstruction-V at a level of 50% (AR50), Truefidelity (TF), which is a DLIR method, and TF with a lung enhancement filter (TF + Lu). Thus, 12 sets of reconstructed images were obtained and analyzed. Image noise, signal-to-noise ratio, and contrast-to-noise ratio were compared among the three reconstruction algorithms. Nodule sharpness was compared among the three reconstruction algorithms using the full-width at half-maximum value. Furthermore, subjective image quality analysis was performed.

Results: AR50 demonstrated the highest level of noise, which was decreased by using TF + Lu and TF alone (P = 0.001). TF + Lu significantly improved nodule sharpness at all radiation doses compared to TF alone (P = 0.001). The nodule sharpness of TF + Lu was similar to that of AR50. Using TF alone resulted in the lowest nodule sharpness.

Conclusion: Adding a lung enhancement filter to DLIR (TF + Lu) significantly improved the nodule sharpness compared to DLIR alone (TF). TF + Lu can be an effective reconstruction technique to enhance image quality and GGN evaluation in ultralow-dose chest CT scans.

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基于深度学习的胸部 CT 重构算法与肺部增强滤波器:对图像质量和磨玻璃结节清晰度的影响
目的与混合迭代重建或单独使用深度学习图像重建(DLIR)算法相比,评估新型肺增强滤波器与深度学习图像重建(DLIR)算法相结合对图像质量和磨玻璃结节(GGN)清晰度的影响:将五个不同密度(-250、-350、-450、-550 和 -630 Hounsfield 单位)、直径为 10 毫米的人造球形 GGN 放入胸腔拟人模型中。使用 256 排 CT(Revolution Apex CT,GE Healthcare)在四种不同辐射剂量水平下进行了四次扫描。每次扫描都使用三种不同的重建算法进行重建:50% 水平的自适应统计迭代重建-V(AR50)、DLIR 方法 Truefidelity(TF)和带肺增强滤波器的 TF(TF + Lu)。因此,共获得并分析了 12 组重建图像。比较了三种重建算法的图像噪声、信噪比和对比度-噪声比。使用半最大全宽值比较了三种重建算法的结节清晰度。此外,还进行了主观图像质量分析:结果:AR50 的噪音水平最高,而使用 TF + Lu 和单独使用 TF 时噪音水平均有所下降(P = 0.001)。与单独使用 TF 相比,TF + Lu 在所有辐射剂量下都能明显提高结节清晰度(P = 0.001)。TF + Lu 的结节锐利度与 AR50 相似。单独使用 TF 的结节锐利度最低:结论:与单独使用 DLIR(TF)相比,在 DLIR 中添加肺增强滤波器(TF + Lu)可显著提高结节锐利度。TF + Lu 是一种有效的重建技术,可提高超低剂量胸部 CT 扫描的图像质量和 GGN 评估。
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来源期刊
Korean Journal of Radiology
Korean Journal of Radiology 医学-核医学
CiteScore
10.60
自引率
12.50%
发文量
141
审稿时长
1.3 months
期刊介绍: The inaugural issue of the Korean J Radiol came out in March 2000. Our journal aims to produce and propagate knowledge on radiologic imaging and related sciences. A unique feature of the articles published in the Journal will be their reflection of global trends in radiology combined with an East-Asian perspective. Geographic differences in disease prevalence will be reflected in the contents of papers, and this will serve to enrich our body of knowledge. World''s outstanding radiologists from many countries are serving as editorial board of our journal.
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