Modelling responses to spatially fractionated radiation fields using preclinical image-guided radiotherapy

OBJECTIVES: Radiotherapy is planned to achieve the optimal physical dose distribution to the target tumour volume whilst minimising dose to the surrounding normal tissue. Recent in vitro experimental evidence has demonstrated an important role for intercellular communication in radiobiological responses following non-uniform exposures. This study aimed to model the impact of these effects in the context of techniques involving highly modulated radiation fields or spatially fractionated treatments such as GRID therapy.

METHODS: Using the small animal radiotherapy research platform (SARRP) as a key enabling technology to deliver precision imaged-guided radiotherapy, it is possible to achieve spatially modulated dose distributions that model typical clinical scenarios. In this work, we planned uniform and spatially fractionated dose distributions using multiple isocentres with beam sizes of 0.5 - 5 mm to obtain 50% volume coverage in a subcutaneous murine tumour model, and applied a model of cellular response that incorporates intercellular communication to assess the potential impact of signalling effects with different ranges.

RESULTS: Models of GRID treatment plans which incorporate intercellular signalling showed increased cell killing within the low dose region. This results in an increase in the Equivalent Uniform Dose (EUD) for GRID exposures compared to standard models, with some GRID exposures being predicted to be more effective than uniform delivery of the same physical dose.

CONCLUSIONS: This study demonstrates the potential impact of radiation induced signalling on tumour cell response for spatially fractionated therapies and identifies key experiments to validate this model and quantify these effects in vivo.

ADVANCES IN KNOWLEDGE: This study highlights the unique opportunities now possible using advanced preclinical techniques to develop a foundation for biophysical optimisation in radiotherapy treatment planning.

Identification of Microturbine Model for Long-term Dynamic Analysis of Distribution Networks

As one of the most successfully commercialized distributed energy resources, the long-term effects of microturbines (MTs) on the distribution network has not been fully investigated due to the complex thermo-fluid-mechanical energy conversion processes. This is further complicated by the fact that the parameter and internal data of MTs are not always available to the electric utility, due to different ownerships and confidentiality concerns. To address this issue, a general modeling approach for MTs is proposed in this paper, which allows for the long-term simulation of the distribution network with multiple MTs. First, the feasibility of deriving a simplified MT model for long-term dynamic analysis of the distribution network is discussed, based on the physical understanding of dynamic processes that occurred within MTs. Then a three-stage identification method is developed in order to obtain a piecewise MT model and predict electro-mechanical system behaviors with saturation. Next, assisted with the electric power flow calculation tool, a fast simulation methodology is proposed to evaluate the long-term impact of multiple MTs on the distribution network. Finally, the model is verified by using Capstone C30 microturbine experiments, and further applied to the dynamic simulation of a modified IEEE 37-node test feeder with promising results.

On the statistics of transverse permeability of randomly distributed fibers

An RVE–based stochastic numerical model is used to calculate the permeability of randomly generated porous media at different values of the fiber volume fraction for the case of transverse flow in a unidirectional ply. Analysis of the numerical results shows that the permeability is not normally distributed. With the aim of proposing a new understanding on this particular topic, permeability data are fitted using both a mixture model and a unimodal distribution. Our findings suggest that permeability can be fitted well using a mixture model based on the lognormal and power law distributions. In case of a unimodal distribution, it is found, using the maximum-likelihood estimation method (MLE), that the generalized extreme value (GEV) distribution represents the best fit. Finally, an expression of the permeability as a function of the fiber volume fraction based on the GEV distribution is discussed in light of the previous results.