Comparing gold nano-particle enhanced radiotherapy with protons, megavoltage photons and kilovoltage photons: a Monte Carlo simulation

Gold nanoparticles (GNPs) have shown potential to be used as a radiosensitizer for radiation therapy. Despite extensive research activity to study GNP radiosensitization using photon beams, only a few studies have been carried out using proton beams. In this work Monte Carlo simulations were used to assess the dose enhancement of GNPs for proton therapy. The enhancement effect was compared between a clinical proton spectrum, a clinical 6 MV photon spectrum, and a kilovoltage photon source similar to those used in many radiobiology lab settings. We showed that the mechanism by which GNPs can lead to dose enhancements in radiation therapy differs when comparing photon and proton radiation. The GNP dose enhancement using protons can be up to 14 and is independent of proton energy, while the dose enhancement is highly dependent on the photon energy used. For the same amount of energy absorbed in the GNP, interactions with protons, kVp photons and MV photons produce similar doses within several nanometers of the GNP surface, and differences are below 15% for the first 10 nm. However, secondary electrons produced by kilovoltage photons have the longest range in water as compared to protons and MV photons, e.g. they cause a dose enhancement 20 times higher than the one caused by protons 10 μm away from the GNP surface. We conclude that GNPs have the potential to enhance radiation therapy depending on the type of radiation source. Proton therapy can be enhanced significantly only if the GNPs are in close proximity to the biological target.

Outage performance of cognitive cooperative networks with relay selection over double-Rayleigh fading channels

This study considers a dual-hop cognitive inter-vehicular relay-assisted communication system where all
communication links are non-line of sight ones and their fading is modelled by the double Rayleigh fading distribution.
Road-side relays (or access points) implementing the decode-and-forward relaying protocol are employed and one of
them is selected according to a predetermined policy to enable communication between vehicles. The performance of
the considered cognitive cooperative system is investigated for Kth best partial and full relay selection (RS) as well as
for two distinct fading scenarios. In the first scenario, all channels are double Rayleigh distributed. In the second
scenario, only the secondary source to relay and relay to destination channels are considered to be subject to double
Rayleigh fading whereas, channels between the secondary transmitters and the primary user are modelled by the
Rayleigh distribution. Exact and approximate expressions for the outage probability performance for all considered RS
policies and fading scenarios are presented. In addition to the analytical results, complementary computer simulated
performance evaluation results have been obtained by means of Monte Carlo simulations. The perfect match between
these two sets of results has verified the accuracy of the proposed mathematical analysis.

Gold nanoparticle induced vasculature damage in radiotherapy: Comparing protons, megavoltage photons, and kilovoltage photons

Purpose: The purpose of this work is to investigate the radiosensitizing effect of gold nanoparticle (GNP) induced vasculature damage for proton, megavoltage (MV) photon, and kilovoltage (kV) photon irradiation. Methods: Monte Carlo simulations were carried out using tool for particle simulation (TOPAS) to obtain the spatial dose distribution in close proximity up to 20 µm from the GNPs. The spatial dose distribution from GNPs was used as an input to calculate the dose deposited to the blood vessels. GNP induced vasculature damage was evaluated for three particle sources (a clinical spread out Bragg peak proton beam, a 6 MV photon beam, and two kV photon beams). For each particle source, various depths in tissue, GNP sizes (2, 10, and 20 nm diameter), and vessel diameters (8, 14, and 20 µm) were investigated. Two GNP distributions in lumen were considered, either homogeneously distributed in the vessel or attached to the inner wall of the vessel. Doses of 30 Gy and 2 Gy were considered, representing typical in vivo enhancement studies and conventional clinical fractionation, respectively. Results: These simulations showed that for 20 Au-mg/g GNP blood concentration homogeneously distributed in the vessel, the additional dose at the inner vascular wall encircling the lumen was 43% of the prescribed dose at the depth of treatment for the 250 kVp photon source, 1% for the 6 MV photon source, and 0.1% for the proton beam. For kV photons, GNPs caused 15% more dose in the vascular wall for 150 kVp source than for 250 kVp. For 6 MV photons, GNPs caused 0.2% more dose in the vascular wall at 20 cm depth in water as compared to at depth of maximum dose (Dmax). For proton therapy, GNPs caused the same dose in the vascular wall for all depths across the spread out Bragg peak with 12.7 cm range and 7 cm modulation. For the same weight of GNPs in the vessel, 2 nm diameter GNPs caused three times more damage to the vessel than 20 nm diameter GNPs. When the GNPs were attached to the inner vascular wall, the damage to the inner vascular wall can be up to 207% of the prescribed dose for the 250 kVp photon source, 4% for the 6 MV photon source, and 2% for the proton beam. Even though the average dose increase from the proton beam and MV photon beam was not large, there were high dose spikes that elevate the local dose of the parts of the blood vessel to be higher than 15 Gy even for 2 Gy prescribed dose, especially when the GNPs can be actively targeted to the endothelial cells. Conclusions: GNPs can potentially be used to enhance radiation therapy by causing vasculature damage through high dose spikes caused by the addition of GNPs especially for hypofractionated treatment. If GNPs are designed to actively accumulate at the tumor vasculature walls, vasculature damage can be increased significantly. The largest enhancement is seen using kilovoltage photons due to the photoelectric effect. Although no significant average dose enhancement was observed for the whole vasculature structure for both MV photons and protons, they can cause high local dose escalation (>15 Gy) to areas of the blood vessel that can potentially contribute to the disruption of the functionality of the blood vessels in the tumor.

Secrecy Capacity Analysis Over κ–μ Fading Channels: Theory and Applications

In this paper, we consider the transmission of confidential information over a κ-μ fading channel in the presence of an eavesdropper who also experiences κ-μ fading. In particular, we obtain novel analytical solutions for the probability of strictly positive secrecy capacity (SPSC) and a lower bound of secure outage probability (SOPL) for independent and non-identically distributed channel coefficients without parameter constraints. We also provide a closed-form expression for the probability of SPSC when the μ parameter is assumed to take positive integer values. Monte-Carlo simulations are performed to verify the derived results. The versatility of the κ-μ fading model means that the results presented in this paper can be used to determine the probability of SPSC and SOPL for a large number of other fading scenarios, such as Rayleigh, Rice (Nakagamin), Nakagami-m, One-Sided Gaussian, and mixtures of these common fading models. In addition, due to the duality of the analysis of secrecy capacity and co-channel interference (CCI), the results presented here will have immediate applicability in the analysis of outage probability in wireless systems affected by CCI and background noise (BN). To demonstrate the efficacy of the novel formulations proposed here, we use the derived equations to provide a useful insight into the probability of SPSC and SOPL for a range of emerging wireless applications, such as cellular device-to-device, peer-to-peer, vehicle-to-vehicle, and body centric communications using data obtained from real channel measurements.

A Bias Compensated Cross-Relation approach to Thermocouple Characterisation

The measurement of fast changing temperature fluctuations is a challenging problem due to the inherent limited bandwidth of temperature sensors. This results in a measured signal that is a lagged and attenuated version of the input. Compensation can be performed provided an accurate, parameterised sensor model is available. However, to account for the in influence of the measurement environment and changing conditions such as gas velocity, the model must be estimated in-situ. The cross-relation method of blind deconvolution is one approach for in-situ characterisation of sensors. However, a drawback with the method is that it becomes positively biased and unstable at high noise levels. In this paper, the cross-relation method is cast in the discrete-time domain and a bias compensation approach is developed. It is shown that the proposed compensation scheme is robust and yields unbiased estimates with lower estimation variance than the uncompensated version. All results are verified using Monte-Carlo simulations.

A Bias Compensated Cross-Relation approach to Thermocouple Characterisation

The measurement of fast changing temperature fluctuations is a challenging problem due to the inherent limited bandwidth of temperature sensors. This results in a measured signal that is a lagged and attenuated version of the input. Compensation can be performed provided an accurate, parameterised sensor model is available. However, to account for the influence of the measurement environment and changing conditions such as gas velocity, the model must be estimated in-situ. The cross-relation method of blind deconvolution is one approach for in-situ characterisation of sensors. However, a drawback with the method is that it becomes positively biased and unstable at high noise levels. In this paper, the cross-relation method is cast in the discrete-time domain and a bias compensation approach is developed. It is shown that the proposed compensation scheme is robust and yields unbiased estimates with lower estimation variance than the uncompensated version. All results are verified using Monte-Carlo simulations.