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Activity-dependent, spatially-varying regularization parameter design for regularized image reconstruction

机译:用于正则图像重建的活动依赖性,空间不同正则化参数设计

摘要

A method and apparatus is provided to iteratively reconstruct an image from gamma-ray emission data by optimizing an objective function with a spatially-varying regularization term. The image is reconstructed using a regularization term that varies spatially based on an activity-level map to spatially vary the regularization term in the objective function. For example, more smoothing (or less edge-preserving) can be imposed where the activity is lower. The activity-level map can be used to calculate a spatially-varying smoothing parameter and/or spatially-varying edge-preserving parameter. The smoothing parameter can be a regularization parameter β that scales/weights the regularization term relative to a data fidelity term of the objective function, and the regularization parameter β can depend on a sensitivity parameter. The edge-preserving parameter β can control the shape of a potential function that is applied as a penalty in the regularization term of the objective function.
机译:提供一种方法和装置,以通过利用空间不同的正则化术语优化目标函数来迭代地重建来自伽马射线发射数据的图像。使用正则化术语重建图像,该正则化术语基于活动级映射在空间上变化,以在客观函数中空间地改变正则化术语。例如,可以在活动较低的情况下施加更多平滑(或更少的边缘保留)。活动级映射可用于计算空间变化的平滑参数和/或空间变化的边缘保留参数。平滑参数可以是相对于目标函数的数据保真术语的正则化术语的正则化参数β,并且正则化参数β可以取决于灵敏度参数。边缘保留参数β可以控制应用于目标函数的正则化术语中作为惩罚的潜在功能的形状。

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