This paper describes the design and testing of a new form of convolutional neural network, a regenerative neural network (ReGeNN), for application to rotating scatter mask gamma imaging. The network was trained using detector responses for realistic source distributions simulated in MCNP v6.1.4. ReGeNN was shown to reconstruct the source images with excellent quality when trained under ideal conditions. When comparing to standard maximum-likelihood expectation–maximization algorithms, ReGeNN reduced the relative error from 145% to 33% and increased the precision from 27% to 85% averaged over the 24 distributed sources tested. The network also demonstrated robust learning capabilities after successfully training on noisy input data, with only relatively minor degradation to the source reconstruction quality. An analysis of variance study determined that the most significant factor affecting the reconstruction quality was the source’s shape, with ring-type source distributions having the worst performance. The interaction between the source’s size and direction was also discovered to have a small effect as larger sources located near the bottom of the system’s field-of-view contained more phantoms within the reconstruction. Reconstruction quality was lower for responses exceeding the training noise level and for source distributions not included in the training set, indicating the importance of robust training data. The results show a significant improvement over more conventional algorithms, suggesting that real-time gamma imaging with the rotating scatter mask may be not only plausible, but practical for the first time. ReGeNN may readily be adapted for similar time-encoded radiation imaging systems, but the neural network methods described also have significant application potential towards other imaging systems.

本文介绍了一种新形式的卷积神经网络，即再生神经网络（ReGeNN）的设计和测试，该技术将应用于旋转散射掩模伽马成像。使用检测器响应对网络进行了训练，以获取MCNP v6.1.4中模拟的真实源分布。在理想条件下训练时，ReGeNN被证明可以以优异的质量重建源图像。与标准的最大似然期望最大化算法相比，ReGeNN可以将24种分布式源的平均相对误差从145％降低到33％，并将精度从27％提高到85％。在成功训练了嘈杂的输入数据之后，该网络还展示了强大的学习能力，而对源重构质量的影响相对较小。方差分析研究确定，影响重建质量的最重要因素是震源的形状，环形震源分布的性能最差。还发现源的大小和方向之间的交互作用很小，因为位于系统视场底部附近的较大源在重建过程中包含了更多的幻像。对于超出训练噪声水平的响应以及训练集中未包括的源分布，重建质量较低。这表明强大的训练数据非常重要。结果表明，与更常规的算法相比，有了显着的改进，这表明使用旋转散射掩模进行实时伽马成像不仅是合理的，而且是首次实用。