PRaVDA: The first solid-state system for proton computed tomography.

Purpose: Proton CT is widely recognised as a beneficial alternative to conventional X-ray CT for treatment planning in proton beam radiotherapy. A novel proton CT imaging system, based entirely on solid-state detector technology, is presented. Compared to conventional scintillator-based calorimeters, positional sensitive detectors allow for multiple protons to be tracked per read out cycle, leading to a potential reduction in proton CT scan time.

Towards real-time VMAT verification using a prototype, high-speed CMOS active pixel sensor.

This work investigates the feasibility of using a prototype complementary metal oxide semiconductor active pixel sensor (CMOS APS) for real-time verification of volumetric modulated arc therapy (VMAT) treatment. The prototype CMOS APS used region of interest read out on the chip to allow fast imaging of up to 403.6 frames per second (f/s).

Imaging of moving fiducial markers during radiotherapy using a fast, efficient active pixel sensor based EPID.

Purpose: The purpose of this work was to investigate the use of an experimental complementary metal-oxide-semiconductor (CMOS) active pixel sensor (APS) for tracking of moving fiducial markers during radiotherapy.

Methods: The APS has an active area of 5.4 × 5.

Estimation of protein secondary structure from synchrotron radiation circular dichroism spectra.

A new approach to estimate the fraction of secondary structures fractions from synchrotron radiation circular dichroism (SRCD) spectra is presented. The protein SRCD spectra are first approximated using radial basis function networks (RBFN) and the resulting set is used to train a self-organising map (SOM). Thus the data are arranged in a two-dimensional map in such a way that most similar proteins are close to each other and vice versa.

Basis function models of the CMAC network.

An interpretation of the Cerebellar Model Articulation Controller (CMAC) network as a member of the General Memory Neural Network (GMNN) architecture is presented. The usefulness of this approach stems from the fact that, within the GMNN formalism, CMAC can be treated as a particular form of a basis function network, where the basis function is inherently dependent on the type of input quantization present in the network mapping. Furthermore, considering the relative regularity of input-space quantization performed by CMAC, we are able to derive an expected (or average) form of the basis function characteristic of this network.

Image denoising using self-organizing map-based nonlinear independent component analysis.

This paper proposes the use of self-organizing maps (SOMs) to the blind source separation (BSS) problem for nonlinearly mixed signals corrupted with multiplicative noise. After an overview of some signal denoising approaches, we introduce the generic independent component analysis (ICA) framework, followed by a survey of existing neural solutions on ICA and nonlinear ICA (NLICA). We then detail a BSS method based on SOMs and intended for image denoising applications.

N-tuple Regression Network.

N-tuple neural networks (NTNNs) have been successfully applied to both pattern recognition and function approximation tasks. Their main advantages include a single layer structure, capability of realizing highly non-linear mappings and simplicity of operation. In this work a modification of the basic network architecture is presented, which allows it to operate as a non-parametric kernel regression estimator.