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通过使用深度学习重建,提高计算机断层扫描引导的活检中的图像质量

Kazutoshi Tsunou¹, Hiroaki Ishii²

¹Department of Central Radiology, Japanese Red Cross Okayama Hospital, Okayama City, JPN.

|August 4, 2025

概括

计算机断层扫描 (CT) 中的深度学习重建 (DLR) 显著降低了活检的图像噪声和辐射剂量. 然而,它的更长的重建时间限制在实时程序中使用.

科学领域:

医疗成像医学成像
放射学放射学是一门学科.
图像重建图像的重建

背景情况:

计算机断层扫描 (CT) 导向活检是一种安全且常见的诊断程序.
深度学习重建 (DLR) 在CT成像中提供了改进的降噪和潜在的辐射剂量降低.
目前的DLR技术不支持实时成像,这阻碍了其在干预过程中在CT光镜 (CTF) 中的应用.

研究的目的:

用传统的非实时方法评估DLR的图像质量和重建时间,用于CT引导的活检.
将DLR与过后投影 (FBP) 和混合代重建 (HIR) 等传统的重建技术进行比较.

主要方法:

在一个标准检查幻影上使用不同的标准偏差 (SD) 设置 (20-50 HU) 进行成像.
使用SD量化图像噪声,并测量FBP,HIR和DLR的重建时间.
该研究的重点是非CT光学 (CTF) 应用.

主要成果:

DLR显示出最小的图像噪声,优于HIR和FBP,噪声降低在更高的SD设置上变得更加明显.
DLR实现了卓越的图像质量,保持了诊断标准,同时提供了减少辐射暴露的潜力.
对于六张图像来说,DLR的重建时间最长,超过10秒,这对于实时应用来说是一个显著的延迟.

结论:

在CT导向活检中,DLR显著提高图像质量并降低辐射剂量,当实时成像不必时,DLR显著提高图像质量并降低CT导向活检中的辐射剂量.
目前DLR的重建延迟限制了其用于非干预或非实时程序.
在非实时场景中将DLR与传统方法集成,可以在CT导向活检期间优化患者的辐射暴露.

关键词:

产品的剂量长度产品的剂量长度过的反向投影.混合代重建重建的复杂化图像噪音图像噪音干预性放射学是干预性的放射学.暴露于辐射的辐射暴露.量 ct 剂量指数

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