Design of a low-dose, stationary, tomographic Molecular Breast Imaging system using 3D position sensitive CZT detectors

Molecular Breast Imaging (MBI) has been shown to have high sensitivity for lesion detection, particularly in patients with dense breasts where conventional mammography is limited. However, relatively high radiation dose and long imaging time are limiting factors. Most current MBI systems are based on planar imaging. Improved performance can be achieved using tomographic techniques, which normally involve detector motion. Our goal is to develop a low-dose stationary tomographic MBI system with similar or better performance in terms of lesion detection compared to planar MBI. The proposed system utilizes two opposing CZT detectors with high intrinsic resolution and depth of interaction (DOI) capability, combined with densely packed multi-pinhole collimators. This leads to improved efficiency and adequate angular sampling, but also to significant multiplexing (MX), which can result in artefacts. We have developed de-MX algorithms that take advantage of the DOI information. We have performed both analytic and Monte Carlo simulations to demonstrate the feasibility, optimize the design and investigate the expected performance of the proposed system. Lesion detectability was preserved with reduction of acquisition time (or radiation dose) by a factor of 2 compared to planar images at the lowest reported dose. The first prototype is under evaluation at Kromek

Towards improved cover glasses for photovoltaic devices

For the solar energy industry to increase its competitiveness there is a global drive to lower the cost of solar generated electricity. Photovoltaic (PV) module assembly is material-demanding and the cover glass constitutes a significant proportion of the cost. Currently, 3 mm thick glass is the predominant cover material for PV modules, accounting for 10-25% of the total cost. Here we review the state-of-the-art of cover glasses for PV modules and present our recent results for improvement of the glass. These improvements were demonstrated in terms of mechanical, chemical and optical properties by optimizing the glass composition, including addition of novel dopants, to produce cover glasses that can provide: (i) enhanced UV protection of polymeric PV module components, potentially increasing module service lifetimes; (ii) re-emission of a proportion of the absorbed UV photon energy as visible photons capable of being absorbed by the solar cells, thereby increasing PV module efficiencies; (iii) Successful laboratory-scale demonstration of proof-of-concept, with increases of 1-6% in Isc and 1-8% Ipm. Improvements in both chemical and crack resistance of the cover glass were also achieved through modest chemical reformulation, highlighting what may be achievable within existing manufacturing technology constraints

An FPGA Implementation of Convolutional Spiking Neural Networks for Radioisotope Identification

This paper details the FPGA implementation methodology for Convolutional Spiking Neural Networks (CSNN) and applies this methodology to low-power radioisotope identification using high-resolution data. Power consumption of 75 mW has been achieved on an FPGA implementation of a CSNN, with an inference accuracy of 90.62% on a synthetic dataset. The chip validation method is presented. Prototyping was accelerated by evaluating SNN parameters using SpiNNaker neuromorphic platform.Comment: 5 pages, 10 FIGURES, IEEE ISCAS 202