The Effects of Extending the Spectral Information Acquired by a Photon-counting Detector for Spectral CT

Photon-counting x-ray detectors with pulse-height analysis provide spectral information that may improve material decomposition and contrast-to-noise ratio (CNR) in CT images. The number of energy measurements that can be acquired simultaneously on a detector pixel is equal to the number of comparator channels. Some spectral CT designs have a limited number of comparator channels, due to the complexity of readout electronics. The spectral information could be extended by changing the comparator threshold levels over time, sub pixels, or view angle. However, acquiring more energy measurements than comparator channels increases the noise and/or dose, due to differences in noise correlations across energy measurements and decreased dose utilisation. This study experimentally quantified the effects of acquiring more energy measurements than comparator channels using a bench-top spectral CT system. An analytical and simulation study modeling an ideal detector investigated whether there was a net benefit for material decomposition or optimal energy weighting when acquiring more energy measurements than comparator channels. Experimental results demonstrated that in a two-threshold acquisition, acquiring the high-energy measurement independently from the low-energy measurement increased noise standard deviation in material-decomposition basis images by factors of 1.5–1.7 due to changes in covariance between energy measurements. CNR in energy-weighted images decreased by factors of 0.92–0.71. Noise standard deviation increased by an additional factor of due to reduced dose utilisation. The results demonstrated no benefit for two-material decomposition noise or energy-weighted CNR when acquiring more energy measurements than comparator channels. Understanding the noise penalty of acquiring more energy measurements than comparator channels is important for designing spectral detectors and for designing experiments and interpreting data from prototype systems with a limited number of comparator channels

COTS simulation package (CSP) interoperability - A solution to synchronous entity passing

In this paper we examine Commercial-Off-The- Shelf (COTS) Simulation Package (CSP) interoperability for one type of distributed simulation problem, namely synchronous entity passing. Synchronous entity passing is also referred to as the bounded buffer interoperability reference model. It deals with the case where for entities passed between models the receiving queue is bounded or the receiving workstation has limited capacity. This means the sending model must check the status of the receiving model before it can send entities. Correspondingly, the receiving model should update the status information dynamically when it changes. Similar to the work done on asynchronous entity passing, the High Level Architecture is chosen as the underlying standard to support reuse and interoperability. To simplify the integration of the CSP and the HLA, a middleware layer called DSManager is provided. Some new problems generated for synchronous entity passing are discussed and solutions are proposed together with a description of their implementation. Two sets of experiments are conducted to evaluate the solutions using a CSP Emulator (CSPE) which supports both standalone and distributed simulation

Simulated Clinical Trias: some design issues

Simulation is widely used to investigate real-world systems in a large number of fields, including clinical trials for drug development, since real trials are costly, frequently fail and may lead to serious side effects. This paper is a survey of the statistical issues arising in these simulated trials. We illustrate the broad applicability of this investigation tool by means of examples selected from the literature. We discuss the aims and the peculiarities of the simulation models used in this context, including a brief mention of the use of metamodels. Of special interest is the topic of the design of the virtual experiments, stressing similarities and differences with the design of real life trials. Since it is important for a computerized model to possess a satisfactory range of accuracy consistent with its intended application, real data provided by physical experiments are used to confirm the simulator : we illustrate validating techniques through a number of examples. We end the paper with some challenging questions on the scientificity, ethics and effectiveness of simulation in the clinical research, and the interesting research problem of how to integrate simulated and physical experiments in a clinical context.Simulation models; pharmacokinetics; pharmacodynamics; model validation; experimental design, ethics. Modelli di simulazione; farmacocinetica; farmacodinamica; validazione; disegno degli esperimenti; etica.

End-to-End Navigation in Unknown Environments using Neural Networks

We investigate how a neural network can learn perception actions loops for navigation in unknown environments. Specifically, we consider how to learn to navigate in environments populated with cul-de-sacs that represent convex local minima that the robot could fall into instead of finding a set of feasible actions that take it to the goal. Traditional methods rely on maintaining a global map to solve the problem of over coming a long cul-de-sac. However, due to errors induced from local and global drift, it is highly challenging to maintain such a map for long periods of time. One way to mitigate this problem is by using learning techniques that do not rely on hand engineered map representations and instead output appropriate control policies directly from their sensory input. We first demonstrate that such a problem cannot be solved directly by deep reinforcement learning due to the sparse reward structure of the environment. Further, we demonstrate that deep supervised learning also cannot be used directly to solve this problem. We then investigate network models that offer a combination of reinforcement learning and supervised learning and highlight the significance of adding fully differentiable memory units to such networks. We evaluate our networks on their ability to generalize to new environments and show that adding memory to such networks offers huge jumps in performanceComment: Workshop on Learning Perception and Control for Autonomous Flight: Safety, Memory and Efficiency, Robotics Science and Systems 201