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超分辨率的CBCT在新一代平面成像仪上的C臂廊线性加速器

Licheng Kuo¹, Feifei Li¹, Yabo Fu¹

¹Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.

Medical physics

|July 16, 2025

概括

一个深度学习超分辨率模型提高了千伏圆束计算机断层扫描 (kV CBCT) 投影质量. 这种方法提高了重建的CBCT图像的空间分辨率,为临床应用提供了潜在的好处.

科学领域:

医疗成像医学成像
放射治疗物理物理
医疗保健中的人工智能

背景情况:

千伏圆束计算断层扫描 (kV CBCT) 对于图像引导放射治疗 (IGRT) 是至关重要的.
新的探测器面板提供更高的分辨率,但需要包装,导致投影分辨率损失.
现有的超分辨率 (SR) 方法主要在图像域中运行,限制了对投射域分辨率损失的有效性.

研究的目的:

评估使用条件生成对抗网络 (cGANs) 架构的深度学习 (DL) SR模型.
提高CBCT的空间分辨率,在投影领域使用新的RTI4343iL面板获得.
评估SR增强投影对重建的CBCT图像质量的影响.

主要方法:

开发了一个基于cGANs框架的DL SR模型,使用U-Net生成器进行投影域增强.
使用Varian TrueBeam系统与RTI4343iL面板以及Rando,Longman和Steeve幻影一起获得了576个CBCT投影对.
使用PSNR,MSE,SSIM,FSIM和MAPE进行定量评估的图像增强.

主要成果:

DL SR模型有效地提高了投影分辨率,提高了结构清晰度.
与上调的低分辨率预测相比,SR增强的预测显示出更高的PSNR (44.4比43.7) 和更低的MSE (187,083.7比205,364.4).
从SR增强投影中重建的CBCT图像显示空间分辨率有所改善 (0.9 lp/mm与0.6 lp/mm对比).

结论:

投射域DL SR方法对于CBCT增强是有效的.
开发的模型显示了实现高分辨率CBCT的前景.
这种方法对各种临床应用具有潜在的好处.

关键词:

圆光束CT CT 的情况.有条件的GANs.深度学习是一种深度学习.超级分辨率的超级分辨率

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