A system capable of imaging gamma rays in 3D in near real time has been developed. A flexible software framework has been developed using Python to acquire, analyze, and finally visualize data from multiple sensors, including novel gamma ray imaging detectors and a Microsoft Kinect.
Introduction and Motivation Gamma-rays are photons with energies typically thousands to millions of times greater than the energy of visible light photons. The vastly higher energies of gamma-rays means that they interact differently with matter, necessitating new sensors and imaging methods to localize gamma ray sources. Many sensors and imaging approaches have been developed to image gamma-rays in 2D, as in a conventional camera, with applications in astronomy, medical imaging, and nuclear security. We have developed a mobile gamma-ray imaging system that merges data from both visual and gamma-ray imaging sensors to generate a visualization of the 3D gamma-ray distribution in real-time. This creates 3D maps of the physical environment and correlates that with the objects emitting gamma-rays. We have used Python to develop a flexible software framework for acquiring data from the multiple sensors, analyzing and merging data streams, and finally visualizing the resulting 3D gamma-ray maps.
Methods The system consists of a cart that contains a state-of-the art gamma-ray imaging system, called a Compton Imager, coupled with an RGB-D imaging system, a Microsoft Kinect. The software package has three main tasks: gamma-ray acquisition and processing, visual data processing, and finally the merger of these two streams. The gamma-ray data processing pipeline involves many computationally intensive tasks, thus a threaded structure built with multiprocessing forms the basis of the gamma-ray imaging framework. Furthermore, many other Pythonic tools have been used to meet our real-time goal; including numexpr, cython, and even the Python/C API. Several GUI frontends, built with TraitsUI or PySide for example, are used to monitor and control how the acquired data is processed in real-time, while a suite of real-time diagnostics are displayed with matplotlib. The visual pipeline is based on an open-source implementation of RGBDSLAM (http://wiki.ros.org/rgbdslam), which is built on the Robot Operating System (ROS) framework. Finally, these two data streams are sent to a laptop computer via pyzmq, where the final merger and imaging (by solving a statistical inversion problem constrained by the visual data) is accomplished. The results are then displayed as they are produced by the imaging algorithm using mayavi.
Results Link to Video: [https://www.dropbox.com/s/1w5yrqwepjcbpt1/Moving%20Cart%203D%20scene.mov]
This system has been used to demonstrate real-time volumetric gamma ray imaging for the first time . The results from a typical run are shown in the above video. The red line indicates the movement of the system through the environment, while the blue arrows represent an aspect of the gamma-ray data. The 3D point-cloud provided by RGBDSLAM appear incrementally as the system traverses the environment. In the end, the location of a small gamma-ray emitting source is correctly identified with the hotspot in the image.