A model of the effects of heavy ion radiation on human tissue

In heavy ion radiotherapy and space travel humans are exposed to energetic heavy ions (C, Si, Fe and others). This type of irradiation often produces more severe biological effects per unit dose than more common X-rays. A new Monte Carlo model generates a physical space with the complex geometry of human tissue or a cell culture based model of tissue, which is affected by the passage of ionizing radiation. For irradiation, the model relies on a physical code for the ion track structure; for tissues, cellular maps are derived from two- or three-dimensional confocal microscopy images using image segmentation algorithm, which defines cells as pixilated volumes. The model is used to study tissue-specific statistics of direct ion hits and the remote ion action on cells. As an application of the technique, we considered the spatial pattern of apoptotic cells after heavy ion irradiation. The pattern of apoptosis is modeled as a stochastic process, which is defined by the action cross section taken from available experimental data. To characterize the degree of apoptosis, an autocorrelation function that describes the spatial correlation of apoptotic cells is introduced. The values of the autocorrelation function demonstrate the effect of the directionality of the radiation track on the spatial arrangements of inactivated cells in tissue. This effect is intrinsic only to high linear-energy-transfer radiation.     

Biparametric linear estimation for CFAR against Weibull clutter

The authors deal with constant false alarm rate (CFAR) procedures against nonstationary clutter, modeled as a Weibull distributed process whose scale parameter α and shape parameter β are both variable. It is shown that conventional CFAR procedures, which compensate only for α, degrade intolerably as β deviates from β=2, namely, as the Rayleigh distributional assumption is violated. A biparametric CFAR procedure is shown to be suited to such situations. The authors introduce a logarithmic transformation to reduce the Weibull probability density function (pdf) to a Gumbel pdf, i.e., to the location-scale type, and then exploit the best linear unbiased estimation (BLUE) of location-scale parameters to adjust the detection threshold. True CFAR is thus achieved when the clutter is locally homogeneous. Resilience against local inhomogeneities can also be conferred since BLUE lends itself to censoring. Through a performance analysis, the influence of various system and distributional parameters is elicited     

Biparametric CFAR procedures for lognormal clutter

The authors consider procedures for constant false alarm rate in lognormal clutter, accounting for variations of both the scale and a shape parameter of the clutter. Adaptivity to both parameters is obtained through biparametric estimation based on a sliding window surrounding the radar cell under test. Some procedures exploiting best linear unbiased estimation (BLUE) are presented and compared to a previous procedure called Log-t, which uses maximum likelihood estimation (MLE). The comparison is carried on for both a homogeneous clutter environment and for instances of inhomogeneous environment (clutter edges and spurious targets). In the latter instances, some advantages of BLUE procedures which stem from the opportunity of censoring are highlighted