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Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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基于CBCT的合成CT生成,使用改进的视觉变压器CycleGANAN.

Yuxin Hu¹, Han Zhou^2,3, Ning Cao¹

¹School of Computer and Software, Hohai University, Nanjing, 211100, China.

Scientific reports

|May 20, 2024

概括

改进的视觉变压器CycleGAN (IViT-CycleGAN) 通过解决文物和噪音,改善临床规划,提高了适应性辐射疗法的圆束计算机断层扫描 (CBCT) 图像质量.

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科学领域:

医疗成像医学成像
医疗保健中的人工智能
辐射瘤学辐射瘤学

背景情况:

圆束计算断层扫描 (CBCT) 对于自适应性辐射疗法至关重要,但受到人工制造物和噪声的影响,限制了其临床使用.
现有的CT图像合成的CycleGAN模型在有效捕捉全球特征方面扎.

研究的目的:

开发一个改进的无监督学习模型,从CBCT数据中合成高质量的CT图像.
通过优异的图像合成,增强CBCT在适应性辐射治疗中的临床效用.

主要方法:

引入了一个改进的视觉变压器CycleGAN (IViT-CycleGAN) 模型.
整合了U-net框架与Vision Transformer (ViT) 和增强的前网络与深度卷积网络.
使用梯度处罚和额外的发电机损失期,提高了训练稳定性.

主要成果:

IViT-CycleGAN模型生成合成CT (sCT) 图像,与其他无监督学习模型相比,具有显著的优势.
实验结果证明了拟议模型的临床适用性和稳定性.
合成的CT图像显示质量有所改善,减少了文物和噪音.

结论:

该IViT-CycleGAN模型有效地解决了CBCT图像合成以前方法的局限性.
这种先进的模型显示出在临床实践中协助精确的放射治疗规划的巨大潜力.
改进的图像质量对于提高适应性辐射治疗的准确性和有效性至关重要.