Diagnostics and monitoring of radiological scenes are critical to the field of nuclear safety and here, the localization of radioactive hotspots is mandatory and remains a critical challenge. In order to perform gamma-ray imaging, one main method relies on indirect imaging by means of coded mask aperture associated with a position sensitive gamma-ray detector and a dedicated deconvolution algorithm. However, the deconvolution problem is non-injective, which implies limitations of the reconstruction performance, especially for spatially extended radioactive sources with respect to the angular resolution. In this paper, we present and evaluate a new method based on a deep learning algorithm with a convolutional neural network to overcome this limitation, in comparison with a classical iterative algorithm. Our deep learning algorithm is trained on simulated data of extended sources that may imply an intrinsic regularization of the neural network. We test it on real data acquired with a gamma camera system based on Caliste, a CdTe detector for high-energy photons.

放射场景的诊断和监测对于核安全领域至关重要，在此，放射性热点的定位是强制性的，并且仍然是一个严峻的挑战。为了执行伽马射线成像，一种主要方法依赖于通过与位置敏感伽马射线探测器相关联的编码掩模孔径和专用去卷积算法的间接成像。然而，解卷积问题是非内射的，这意味着重建性能的局限性，特别是对于角分辨率方面的空间扩展放射源。在本文中，我们提出并评估了一种基于深度学习算法的新方法，该算法具有卷积与经典迭代算法相比，神经网络克服了这一限制。我们的深度学习算法是在扩展源的模拟数据上进行训练的，这些数据可能意味着神经网络的内在正则化。我们在使用基于 Caliste（一种用于高能光子的 CdTe 探测器）的伽马相机系统获取的真实数据上对其进行测试。