[基于扩散生成对抗网络的磁共振成像和计算机断层扫描图像的交叉模式翻译]
Hong Shao1, Yixuan Jing1, Wencheng Cui1
1School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, P. R. China.
概括
本研究引入了扩散生成对抗网络,用于医疗图像的跨模态翻译,增强伪计算机断层扫描 (CT) 图像生成中的现实性和解剖学准确性,用于放射治疗规划.
科学领域:
- 医学成像医学成像
- 人工智能的人工智能是人工智能.
- 放射治疗规划 放射治疗规划
背景情况:
- 医学图像交叉模式翻译在解剖学保存,图像现实性和高频信息丢失方面面临挑战.
- 精确的计算机断层扫描 (CT) 图像生成对于有效的放射治疗计划至关重要.
研究的目的:
- 开发一种使用扩散生成对抗网络的新型医疗图像交叉模式翻译方法.
- 为了提高由MRI数据生成的伪CT图像的现实性和解剖学准确性.
- 为了增强用于放射治疗规划的CT图像的效用.
主要方法:
- 使用无监督翻译模块将磁共振成像 (MRI) 转换为伪计算机断层扫描 (CT) 图像.
- 使用非线性频率分解模块提取高频CT图像信息.
- 集成了一个扩散生成对抗网络,指导使用高频CT图像进行反向过程,以最终生成CT图像.
主要成果:
- 产生的大脑CT图像的Fréchet发射距离 (FID) 为33.1597,SSIM为89.84%,PSNR为35.5965dB,MSE为17.8739.
- 生成的盆腔CT图像实现了FID为33.9516,SSIM为91.30%,PSNR为34.8707dB,MSE为17.4658. 这些图像都实现了FID为33.9516,SSIM为91.30%,PSNR为34.8707.
- 该模型成功生成了高度现实的CT图像,并保持了解剖学准确度.
结论:
- 拟议的扩散生成对抗网络方法有效地解决了医疗图像跨模态翻译的局限性.
- 生成的伪CT图像显示出高准确度和解剖学准确度,适合用于放射治疗计划.
- 这种方法通过从MRI数据提供可靠的CT图像合成来提高诊断效率.
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