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使用合成MRI进行高剂量前列腺支架疗法前列腺细分的精度治疗规划

Hyejoo Kang¹, Alexander R Podgorsak¹, Bhanu Prasad Venkatesulu¹

¹Department of Radiation Oncology, Stritch School of Medicine, Cardinal Bernadin Cancer Center, Loyola University Chicago, 2160 S. 1st Ave, Maywood, IL, United States of America.

Physics in medicine and biology

|July 11, 2023

概括

一个新的生成对抗网络 (GAN),PxCGAN,从CT扫描中创建合成MRI (sMRI),以在高剂量率支架疗法中进行前列腺细分. 这种方法实现了与真实MRI可比的细分精度,即使MRI可用性有限.

科学领域:

放射学放射学是一门学科.
医疗成像医学成像
人工智能的人工智能

背景情况:

计算机断层扫描 (CT) 和磁共振成像 (MRI) 对于高剂量率 (HDR) 前列腺胸膜疗法至关重要.
磁力共振成像对于前列腺细分至关重要,但访问可能是有限的.
从CT生成合成MRI (sMRI) 的方法可以克服MRI访问的限制.

研究的目的:

开发一个新的生成对抗网络 (GAN),PxCGAN,用于从CT扫描生成sMRI.
评估生成的sMRI的图像质量和前列腺细分精度.
将PxCGAN与Pix2Pix和CycleGAN等现有方法进行比较.

主要方法:

一种混合GAN,PxCGAN,被训练在58对的CT-MRI数据集上,来自HDR前列腺支骨疗法患者.
使用20个独立数据集的平均绝对误差 (MAE),平均平方误差 (MSE),峰值信号对噪声比 (PSNR) 和结构相似性指数 (SSIM) 来评估图像质量.
使用子相似系数 (DSC),豪斯多夫距离 (HD) 和平均表面距离 (MSD) 对专家划分进行评估.

主要成果:

与CT相比,PxCGAN产生了具有增强软组织对比度的sMRI.
与Pix2Pix和CycleGAN相比,PxCGAN表现出优越的PSNR和SSIM (p < 0.01).与Px2Pix和CycleGAN相比,PxCGAN表现出优越的PSNR和SSIM (p < 0.01).与Px2Pix和CycleGAN相比,PxCGAN表现出优越的PSNR和SSIM (p < 0.01).与Px2Pix和CycleGAN相比,PxCGAN表现出优越的PSNR和SSIM.
通过sMRI进行前列腺细分,可以在真实MRI (rMRI) 的观察者间变化范围内获得准确性.

结论:

PxCGAN有效地从CT扫描中产生sMRI,改善前列腺成像软组织对比度.
使用sMRI进行前列腺细分的准确性与使用真实MRI实现的准确性相当,即使考虑到观察者之间的变化.
这种基于GAN的方法提供了一个可行的解决方案,用于精确的前列腺细分在MRI访问是有限的HDR臂疗法时.

关键词:

没有了,没有了,没有了.HDR 前列腺支臂疗法合成核磁共振成像 (MRI) 是一种合成核磁共振成像.前列腺的细分前列腺的细分.

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