aapm-ct-challenge-main

上传者: 44603934 | 上传时间: 2022-12-07 12:27:32 | 文件大小: 574KB | 文件类型: RAR
竞赛任务是利用数据驱动的重建技术从有限视角的扇形光束测量中恢复乳房模型幻影图像。这项挑战的独特之处在于,参赛者获得了一组地面真实图像及其无噪音的子采样正弦图(以及相关的有限视角过滤反投影图像),但没有获得实际的正向模型。 团队名称:robust-and-stable 完成工作:首先在一个数据驱动的几何校准步骤中估计扇形光束的几何。在随后的两步程序中,设计一个迭代的端到端网络,能够计算出接近精确的解决方案。(损失低到无法想象) 方法结构: 第一步:数据驱动的几何图形识别 第二步:预训练的U-Net作为计算支柱网络 第三步:迭代方案 方法内容(摘原文翻译,其中小部分增加自己的理解和解释,公式截原文的图,水印很恶心): 第一步 我们重建工作的第一步是从提供的训练数据中学习未知的前向算子(FBP算子)。断层扇形光束测量的连续版本是基于计算线积分。 其中x0是未知图像,L(s, )表示扇形光束坐标中的一条线,即是扇形旋转角度,s是编码传感器的位置。在一个理想化的情况下,扇形光束模型由以下几何参数指定(见图)。 d_source - X射线源到原点的距离

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