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基于深度学习的合成计算机断层扫描用于低场大脑磁共振引导辐射疗法
Yuhao Yan1, Joshua P Kim2, Siamak P Nejad-Davarani3
1Department of Human Oncology, University of Wisconsin-Madison, Madison, Wisconsin; Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin.
一个新的条件生成对抗网络从低场MRI扫描中创建高质量的合成CT图像,用于脑辐射治疗. 这种方法确保了适应性治疗的准确剂量计算,改善了患者的护理.
科学领域:
- 医疗成像医学成像
- 辐射疗法 辐射疗法
- 人工智能的人工智能
背景情况:
- 导向磁共振 (MR) 辐射疗法允许实时调整治疗.
- 精确的合成计算机断层扫描 (synCT) 在MR导向放射治疗中对剂量计算至关重要.
- 低场核磁共振成像在产生高保真性synCT时存在挑战.
研究的目的:
- 开发一个条件生成对抗网络 (cGAN) 来从大脑中低场MRI成像生成高保真度的synCT.
- 为了能够准确计算在线自适应MR导向放射治疗的剂量.
主要方法:
- 一个cGAN模型,包括一个9块残余网络生成器和一个PatchGAN区分器,在12名质瘤患者的MR-CT对上进行了训练.
- 该模型使用了四倍交叉验证.
- 通过MAE,PSNR和SSIM来评估SynCT质量. 用剂量测量性能通过剂量-体积组图度量和3D马分析进行评估.
主要成果:
- cGAN产生了高质量的synCT,MAE为70.9 ± 10.4 HU,PSNR为28.4 ± 1.5 dB,SSIM为0.87 ± 0.02.
- 达成了优异的剂量测量协议,目标剂量和风险器官剂量指标的差异最小.
- 马分析的通过率在1%/1毫米时超过92%,显示出高精度.
结论:
- 开发的cGAN模型成功地从低场MRI成像中生成高保真度的大脑synCTs.
- 协同CT显示出优异的剂量计性能,适用于适应性放射治疗中可靠的剂量计算.
- 这种方法显着有望增强在线自适应性脑磁共振导向辐射疗法.
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