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使用全连接神经网络联合估计半导体单光子发射计算机断层成像传感器中的相互作用位置和能量沉积。

Joint estimation of interaction position and energy deposition in semiconductor SPECT imaging sensors using fully connected neural network.

机构信息

Department of Nuclear, Plasma, and Radiological Engineering, University of Illinois at Urbana-Champaign, Urbana, United States of America.

Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, United States of America.

出版信息

Phys Med Biol. 2023 Apr 19;68(9). doi: 10.1088/1361-6560/aca740.

Abstract

Pixelated semiconductor detectors such as CdTe and CZT sensors suffer spatial resolution and spectral performance degradation induced by charge-sharing effects. It is critical to enhance the detector property through recovering the energy-deposition and position estimation.In this work, we proposed a fully-connected-neural-network-based charge-sharing reconstruction algorithm to correct the charge-loss and estimate the sub-pixel position for every multi-pixel charge-sharing event.Evident energy resolution improvement can be observed by comparing the spectrum produced by a simple charge-sharing addition method and the proposed energy correction methods. We also demonstrate that sub-pixel resolution can be achieved in projections obtained with a small pinhole collimator and an innovative micro-ring collimator.These achievements are crucial for multiple-tracer SPECT imaging applications, and for other semiconductor detector-based imaging modalities.

摘要

像素化半导体探测器,如 CdTe 和 CZT 传感器,会受到电荷共享效应导致的空间分辨率和光谱性能下降的影响。通过恢复能量沉积和位置估计来提高探测器性能至关重要。在这项工作中,我们提出了一种基于全连接神经网络的电荷共享重建算法,以纠正电荷损失并估计每个多像素电荷共享事件的亚像素位置。通过比较简单的电荷共享加法方法和所提出的能量校正方法产生的光谱,可以观察到明显的能量分辨率提高。我们还证明了可以在使用小孔准直器和创新的微环准直器获得的投影中实现亚像素分辨率。这些成果对于多示踪 SPECT 成像应用以及其他基于半导体探测器的成像方式至关重要。

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