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ACTIVITY-DEPENDENT, SPATIALLY-VARYING REGULARIZATION PARAMETER DESIGN FOR REGULARIZED IMAGE RECONSTRUCTION

机译:依赖于活动性,空间变化的规整参数设计,用于规整图像重建

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 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 δ can control the shape of a potential function that is applied as a penalty in the regularization term of the objective function.
机译:提供了一种方法和装置,用于通过利用空间变化的正则项优化目标函数来从伽马射线发射数据迭代地重建图像。使用基于活动级别图在空间上变化的正则化项重建图像,以在目标函数中在空间上变化正则化项。例如,可以在活动度较低的地方施加更大的平滑度(或更少的边缘保留度)。活动级别图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度项缩放/加权正则化项的正则化参数β,并且正则化参数β可以取决于灵敏度参数。边缘保持参数δ可以控制作为目标函数的正则项中的惩罚而施加的势函数的形状。

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