Although perovskite X-ray detectors have revealed promising properties, their dark currents are usually hundreds of times larger than the practical requirements. Here, we report a detector architecture with a unique shunting electrode working as a blanking unit to suppress dark current, and it theoretically can be reduced to zero. We experimentally fabricate the dark-current-shunting X-ray detector, which exhibits a record-low dark current of 51.1 fA at 5 V mm−1, a detection limit of 7.84 nGyair s−1, and a sensitivity of 1.3 × 104 μC Gyair−1 cm−2. The signal-to-noise ratio of our polycrystalline perovskite-based detector is even outperforming many previously reported state-of-the-art single crystal-based X-ray detectors by serval orders of magnitude. Finally, the proof-of-concept X-ray imaging of a 64 × 64 pixels dark-current-shunting detector array is successfully demonstrated. This work provides a device strategy to fundamentally reduce dark current and enhance the signal-to-noise ratio of X-ray detectors and photodetectors in general.

Direct reconstruction of positron emission tomography (PET) data using deep neural networks is a growing field of research. Initial results are promising, but often the networks are complex, memory utilization inefficient, produce relatively small 2-D image slices (e.g., 128 × 128), and low count rate reconstructions are of varying quality. This article proposes FastPET, a novel direct reconstruction convolutional neural network that is architecturally simple, memory space efficient, works for nontrivial 3-D image volumes and is capable of processing a wide spectrum of PET data including low-dose and multitracer applications. FastPET uniquely operates on a histoimage (i.e., image-space) representation of the raw data enabling it to reconstruct 3-D image volumes 67× faster than ordered subsets expectation maximization (OSEM). We detail the FastPET method trained on whole-body and low-dose whole-body data sets and explore qualitative and quantitative aspects of reconstructed images from clinical and phantom studies. Additionally, we explore the application of FastPET on a neurology data set containing multiple different tracers. The results show that not only are the reconstructions very fast, but the images are high quality and have lower noise than iterative reconstructions.

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We developed a transport-equation-based deterministic algorithm for computing three-dimensional brachytherapy dose distributions.The deterministic algorithm has been based on the integral transport equation. The algorithm provided us with the capability of computing dose distributions for multiple isotropic point and/or volumetric sources in a homogenous/heterogeneous medium. The algorithm results have been benchmarked against the results from the literature and MCNP results for isotropic point sources and volumetric sources.

In a previous article, we presented development and verification of an integral transport equation-based deterministic algorithm for computing three-dimensional brachytherapy dose distributions. Recently, we have included fluorescence radiation physics and parallel computation to the standing algorithms so that we can compute dose distributions for a large set of seeds without resorting to the superposition methods. The introduction of parallel computing capability provided a means to compute the dose distribution for multiple seeds in a simultaneous manner. This provided a way to study strong heterogeneity and shadow effects induced by the presence of multiple seeds in an interstitial brachytherapy implant. This article presents the algorithm for computing fluorescence radiation, algorithm for parallel computing, and display results for an 81-seed implant that has a perfect and imperfect lattice. The dosimetry data for a single model 6711 seeds is presented for verification and heterogeneity factor computations using simultaneous and superposition techniques are presented.

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