一个隐含的注册框架,将科尔莫戈罗夫-阿诺德网络与图像引导辐射治疗的速度规范化集成在一起
Pulin Sun1,2, Chulong Zhang1,2, Zhenyu Yang1,2
1Medical Physics Graduate Program, Duke Kunshan University, Kunshan 215316, China.
这项研究引入了一种使用科尔莫戈罗夫-阿诺德网络 (KANs) 的新型注册方法,用于在图像引导放射治疗 (IGRT) 中更有效,更准确的可变形图像注册. 基于KAN的方法提高了计算速度并保持了转换质量.
科学领域:
- 医疗成像医学成像
- 计算解剖学的计算解剖学
- 放射治疗 物理 物理
背景情况:
- 可变形图像的注册对于图像导向放射治疗 (IGRT) 至关重要,但它面临着计算成本和数据依赖性的挑战.
- 目前的方法,如代算法和监督深度学习有局限性.
- 隐式神经表示 (INR) 具有潜力,但多层感知器 (MLP) 可能会与复杂的变形作斗争.
研究的目的:
- 开发一种新的基于INR的框架,用于使用Kolmogorov-Arnold网络 (KANs) 进行可变形图像注册.
- 为了将变形模型作为一个连续的,时间变化的速度场,由KANs.参数化.
- 为医疗图像注册建立一个新的范式,超越基于MLP的INR.
主要方法:
- 一个基于INR的新型注册框架,利用使用Jacobi多项式构建的KAN来参数化速度场.
- 通过KAN.有效估计速度场的低维主要组成部分.
- 通过反向主要组件分析和用于变形推导的时间整合来重建速度场.
- 速度调节以确保流和拓保护的转换.
主要成果:
- 与直接速度场建模相比,基于KAN的方法实现了大约70%的计算效率提高.
- 与传统的代方法相比,在注册准确度方面表现出高达6%的改善.
- 在盆腔CT-CBCT数据集上,与基于MLP的INR基线相比,登记准确度提高了~3%.
结论:
- 拟议的基于KAN的INR框架为IGRT中的可变形图像注册提供了一个高效和可通用的替代方案.
- 这种KAN集成代表了医疗图像注册的新方法.
- 该方法确保了流和拓保护的转换,提高了注册的准确性和效率.
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