Comparison of the polynomial model against explicit measurements of noise components for different mammography systems.

Given the adverse impact of image noise on the perception of important clinical details in digital mammography, routine quality control measurements should include an evaluation of noise. The European Guidelines, for example, employ a second-order polynomial fit of pixel variance as a function of detector air kerma (DAK) to decompose noise into quantum, electronic and fixed pattern (FP) components and assess the DAK range where quantum noise dominates. This work examines the robustness of the polynomial method against an explicit noise decomposition method. The two methods were applied to variance and noise power spectrum (NPS) data from six digital mammography units. Twenty homogeneously exposed images were acquired with PMMA blocks for target DAKs ranging from 6.25 to 1600 µGy. Both methods were explored for the effects of data weighting and squared fit coefficients during the curve fitting, the influence of the additional filter material (2 mm Al versus 40 mm PMMA) and noise de-trending. Finally, spatial stationarity of noise was assessed.Data weighting improved noise model fitting over large DAK ranges, especially at low detector exposures. The polynomial and explicit decompositions generally agreed for quantum and electronic noise but FP noise fraction was consistently underestimated by the polynomial method. Noise decomposition as a function of position in the image showed limited noise stationarity, especially for FP noise; thus the position of the region of interest (ROI) used for noise decomposition may influence fractional noise composition. The ROI area and position used in the Guidelines offer an acceptable estimation of noise components. While there are limitations to the polynomial model, when used with care and with appropriate data weighting, the method offers a simple and robust means of examining the detector noise components as a function of detector exposure.

Genetic isolation of insular populations of the Maghrebian bat, Myotis punicus, in the Mediterranean Basin

We investigate the population genetic structure of the Maghrebian bat, Myotis punicus, between the mainland and islands to assess the island colonization pattern and current gene flow between nearby islands and within the mainland. Location North Africa and the Mediterranean islands of Corsica and Sardinia. Methods We sequenced part of the control region (HVII) of 79 bats across 11 colonies. The phylogeographical pattern was assessed by analysing molecular diversity indices, examining differentiation among populations and estimating divergence time. In addition, we genotyped 182 bats across 10 colonies at seven microsatellite loci. We used analysis of molecular variance and a Bayesian approach to infer nuclear population structure. Finally, we estimated sex-specific dispersal between Corsica and Sardinia. Results Mitochondrial analyses indicated that colonies between Corsica, Sardinia and North Africa are highly differentiated. Within islands there was no difference between colonies, while at the continental level Moroccan and Tunisian populations were highly differentiated. Analyses with seven microsatellite loci showed a similar pattern. The sole difference was the lack of nuclear differentiation between populations in North Africa, suggesting a male-biased dispersal over the continental area. The divergence time of Sardinian and Corsican populations was estimated to date back to the early and mid-Pleistocene. Main conclusions Island colonization by the Maghrebian bats seems to have occurred in a stepping-stone manner and certainly pre-dated human colonization. Currently, open water seems to prevent exchange of bats between the two islands, despite their ability to fly and the narrowness of the strait of Bonifacio. Corsican and Sardinian populations are thus currently isolated from any continental gene pool and must therefore be considered as different evolutionarily significant units (ESU).