Deep learning in classical x-ray ghost imaging for dose reduction

Ghost imaging (GI) is an unconventional technique that combines information from two correlated patterned light fields to compute an image of the object of interest. A standard pixelated camera records the structure of one light field (that does not interact with the object), and a bucket detector (or single-pixel camera) measures the total intensity of the second light field that is transmitted or scattered by the object. GI can be performed with visible light as well as penetrating radiation such as x-rays, electrons, etc. Penetrating radiation is usually ionizing and damages biological specimens; therefore, minimizing the dose of this radiation in a medical or biological imaging context is important. GI has been proposed as a potential way to achieve this. With prior knowledge of the object of interest, such as sparsity in a specific basis (e.g., Fourier basis) or access to a large data set for neural network training, it is possible to reconstruct an image of the object with a limited number of measurements. However, low sampling does not inherently equate to low dose. Here we specifically explore the scenario where reduced sampling corresponds to low-dose conditions. In this simulation-based paper, we examine how deep learning (DL) techniques could reduce dose in classical x-ray GI. Since GI is based on illumination patterns, we start by exploring optimal sets of patterns that allow us to reconstruct the image with the fewest measurements, or lowest sampling rate, possible. We then propose a DL neural network that can directly reconstruct images from GI measurements even when the sampling rate is extremely low. We demonstrate that our deep learning-based GI (DLGI) approach has potential in image reconstruction, with results comparable to direct imaging (DI) at the same dose. However, given the same prior knowledge and detector quantum efficiency, it is very challenging for DLGI to outperform DI under low-dose conditions. We discuss how it may be achievable due to the higher sensitivity of bucket detectors over pixel detectors.