Geant4-based simulations of charge collection in CMOS Active Pixel Sensors

Geant4 is an object-oriented toolkit for the simulation of the interaction of particles and radiation with matter. It provides a snapshot of the state of a simulated particle in time, as it travels through a specified geometry. One important area of application is the modelling of radiation detector systems. Here, we extend the abilities of such modelling to include charge transport and sharing in pixelated CMOS Active Pixel Sensors (APSs); though similar effects occur in other pixel detectors. The CMOS APSs discussed were developed in the framework of the PRaVDA consortium to assist the design of custom sensors to be used in an energy-range detector for proton Computed Tomography (pCT). The development of ad-hoc classes, providing a charge transport model for a CMOS APS and its integration into the standard Geant4 toolkit, is described. The proposed charge transport model includes, charge generation, diffusion, collection, and sharing across adjacent pixels, as well as the full electronic chain for a CMOS APS. The proposed model is validated against experimental data acquired with protons in an energy range relevant for pCT

CMOS Active Pixel Sensors as energy-range detectors for proton Computed Tomography

Since the first proof of concept in the early 70s, a number of technologies has been proposed to perform proton CT (pCT), as a means of mapping tissue stopping power for accurate treatment planning in proton therapy. Previous prototypes of energy-range detectors for pCT have been mainly based on the use of scintillator-based calorimeters, to measure proton residual energy after passing through the patient. However, such an approach is limited by the need for only a single proton passing through the energy-range detector in a read-out cycle. A novel approach to this problem could be the use of pixelated detectors, where the independent read-out of each pixel allows to measure simultaneously the residual energy of a number of protons in the same read-out cycle, facilitating a faster and more efficient pCT scan. This paper investigates the suitability of CMOS Active Pixel Sensors (APSs) to track indi- vidual protons as they go through a number of CMOS layers, forming an energy-range telescope. Measurements performed at the iThemba Laboratories will be presented and analysed in terms of correlation, to confirm capability of proton tracking for CMOS APSs

Proton tracking for medical imaging and dosimetry

For many years, silicon micro-strip detectors have been successfully used as tracking detectors for particle and nuclear physics experiments. A new application of this technology is to the field of particle therapy, where radiotherapy is carried out by use of charged particles such as protons or carbon ions. Such a treatment has been shown to have advantages over standard x-ray radiotherapy and as a result of this, many new centres offering particle therapy are currently under construction—including two in the U.K.. The characteristics of a new silicon micro-strip detector based system for this application will be presented. The array uses specifically designed large area sensors in several stations in an x-u-v co-ordinate configuration suitable for very fast proton tracking with minimal ambiguities. The sensors will form a tracker capable of giving information on the path of high energy protons entering and exiting a patient. This will allow proton computed tomography (pCT) to aid the accurate delivery of treatment dose with tuned beam profile and energy. The tracker will also be capable of proton counting and position measurement at the higher fluences and full range of energies used during treatment allowing monitoring of the beam profile and total dose. Results and initial characterisation of sensors will be presented along with details of the proposed readout electronics. Radiation tests and studies with different electronics at the Clatterbridge Cancer Centre and the higher energy proton therapy facility of iThemba LABS in South Africa will also be shown

A new silicon tracker for proton imaging and dosimetry

For many years, silicon micro-strip detectors have been successfully used as tracking detectors for particle and nuclear physics experiments. A new application of this technology is to the field of particle therapy where radiotherapy is carried out by use of charged particles such as protons or carbon ions. Such a treatment has been shown to have advantages over standard x-ray radiotherapy and as a result of this, many new centres offering particle therapy are currently under construction around the world today. The Proton Radiotherapy, Verification and Dosimetry Applications (PRaVDA) consortium are developing instrumentation for particle therapy based upon technology from high-energy physics. The characteristics of a new silicon micro-strip tracker for particle therapy will be presented. The array uses specifically designed, large area sensors with technology choices that follow closely those taken for the ATLAS experiment at the HL-LHC. These detectors will be arranged into four units each with three layers in an x–u–v configuration to be suitable for fast proton tracking with minimal ambiguities. The sensors will form a tracker capable of tracing the path of ~200 MeV protons entering and exiting a patient allowing a new mode of imaging known as proton computed tomography (pCT). This will aid the accurate delivery of treatment doses and in addition, the tracker will also be used to monitor the beam profile and total dose delivered during the high fluences used for treatment. We present here details of the design, construction and assembly of one of the four units that will make up the complete tracker along with its characterisation using radiation tests carried out using a 90 Sr source in the laboratory and a 60 MeV proton beam at the Clatterbridge Cancer Centre

Expected proton signal sizes in the PRaVDA Range Telescope for proton Computed Tomography

Proton radiotherapy has demonstrated benefits in the treatment of certain cancers. Accurate measurements of the proton stopping powers in body tissues are required in order to fully optimise the delivery of such treaments. The PRaVDA Consortium is developing a novel, fully solid state device to measure these stopping powers. The PRaVDA Range Telescope (RT), uses a stack of 24 CMOS Active Pixel Sensors (APS) to measure the residual proton energy after the patient. We present here the ability of the CMOS sensors to detect changes in the signal sizes as the proton traverses the RT, compare the results with theory, and discuss the implications of these results on the reconstruction of proton tracks

Rate constrained self-organzing neural maps and efficient psychovisual methods for low bit rate video coding

The video coding problem is essentially an operational distortion-rate issue where the underlying input pixel data, probability distributions and dimensions are discrete, unknown and not smooth. In the low bit rate case the high resolution assumptions for vector quantization are not strictly valid and the problem is exacerbated. However, by considering the rate-constrained operational points on sets of self-organizing neural maps (SOMs), provides a methodology for selecting locally optimal vector quantizers. The learning process of the standard SOM algorithm is modified to minimize the distortion subject to a constraint of entropy approximation. The applied training set is adapted to suit the proposed coding environment. Operating in the discrete wavelet transform (DWT) domain is well suited to the inclusion of a psychovisual model. The spatial frequency response, the multiresolution scene analysis and the central focusing aspects of the visual cortex are incorporated into the model. The resulting video coding algorithm is bit rate scalable from 10 k bits per second (bits/s) and provides subjectively acceptable video at a fixed frame rate or 10 frames per second (f.p.s.) with a QCIF pixel resolutio

Estimating Relevant Input Dimensions for Self-Organizing Algorithms

Hammer B, Villmann T. Estimating Relevant Input Dimensions for Self-Organizing Algorithms. In: Allinson NM, Yin H, Allinson L, Slack J, eds. Advances in Self-Organising Maps. London: Springer; 2001: 173-180

Interpolating self-organising map (iSOM)

A new learning algorithm is presented for enhancing the scale or structure of an already trained self-organising map (SOM) without the need to re-use the original training data. Alternative methods for the insertion of these additional interpolating neurons, while still preserving the learnt topology, are presented together with two illustrative examples of the algorithm in operatio