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Calibration Method for ML Estimation of 3D Interaction Position in a Thick Gamma-Ray Detector

机译:厚伽马射线探测器中3D相互作用位置的ML估计的校准方法

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摘要

High-energy (> 100 keV) photon detectors are often made thick relative to their lateral resolution in order to improve their photon-detection efficiency. To avoid issues of parallax and increased signal variance that result from random interaction depth, we must determine the 3D interaction position in the imaging detector. With this goal in mind, we examine a method of calibrating response statistics of a thick-detector gamma camera to produce a maximum-likelihood estimate of 3D interaction position. We parameterize the mean detector response as a function of 3D position, and we estimate these parameters by maximizing their likelihood given prior knowledge of the pathlength distribution and a complete list of camera signals for an ensemble of gamma-ray interactions. Furthermore, we describe an iterative method for removing multiple-interaction events from our calibration data and for refining our calibration of the mean detector response to single interactions. We demonstrate this calibration method with simulated gamma-camera data. We then show that the resulting calibration is accurate and can be used to produce unbiased estimates of 3D interaction position.
机译:高能量(> 100 keV)光子探测器通常相对于其横向分辨率要厚一些,以提高其光子探测效率。为了避免由于随机交互深度导致的视差和信号方差增加的问题,我们必须确定成像探测器中的3D交互位置。出于这个目标,我们研究了一种校准厚探测器伽马相机响应统计的方法,以产生3D交互位置的最大似然估计。我们将平均检测器响应参数化为3D位置的函数,并在给定路径长度分布的先验知识和完整的伽马射线相互作用照相机信号列表的情况下,通过最大化其可能性来估计这些参数。此外,我们描述了一种迭代方法,用于从校准数据中删除多重交互事件,并完善我们对平均检测器对单个交互的响应的校准。我们用模拟的伽马相机数据演示了这种校准方法。然后,我们证明所产生的校准是准确的,并可用于产生3D交互位置的无偏估计。

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