Statistical approach for radioactivity detection: A brief review.

Radioactivity detection is a major research and development priority for many practical applications. Amongst the various technical challenges in this field is the need to carry out accurate low-level radioactivity measurements in the presence of a large fluctuations in the natural radiation background, while reducing the false alarm rates. The task becomes even more harder with high detection limits under low signal-to-background ratios.

Reconstruction, analysis and interpretation of posterior probability distributions of PET images, using the posterior bootstrap.

The uncertainty of reconstructed PET images remains difficult to assess and to interpret for the use in diagnostic and quantification tasks. Here we provide (1) an easy-to-use methodology for uncertainty assessment for almost any Bayesian model in PET reconstruction from single datasets and (2) a detailed analysis and interpretation of produced posterior image distributions. We apply a recent posterior bootstrap framework to the PET image reconstruction inverse problem and obtain simple parallelizable algorithms based on random weights and on existing maximum(MAP) (posterior maximum) optimization-based algorithms.

Validation of histogram-based virtual source models for different IGRT kV-imaging systems.

Purpose: Image-guided radiotherapy (IGRT) improves tumor control but its intensive use may entrain late side effects caused by the additional imaging doses. There is a need to better quantify the additional imaging doses, so they can be integrated in the therapeutic workflow. Currently, no dedicated software enables to compute patient-specific imaging doses on a wide range of systems and protocols.

PET Reconstruction of the Posterior Image Probability, Including Multimodal Images.

In PET image reconstruction, it would be useful to obtain the entire posterior probability distribution of the image, because it allows for both estimating image intensity and assessing the uncertainty of the estimation, thus leading to more reliable interpretation. We propose a new entirely probabilistic model: the prior is a distribution over possible smooth regions (distance-driven Chinese restaurant process), and the posterior distribution is estimated using a Gibbs Markov chain Monte Carlo sampler. Data from other modalities (here one or several MR images) are introduced into the model as additional observed data, providing side information about likely smooth regions in the image.

Development and implementation in the Monte Carlo code PENELOPE of a new virtual source model for radiotherapy photon beams and portal image calculation.

This work aims at developing a generic virtual source model (VSM) preserving all existing correlations between variables stored in a Monte Carlo pre-computed phase space (PS) file, for dose calculation and high-resolution portal image prediction. The reference PS file was calculated using the PENELOPE code, after the flattening filter (FF) of an Elekta Synergy 6 MV photon beam. Each particle was represented in a mobile coordinate system by its radial position (r s ) in the PS plane, its energy (E), and its polar and azimuthal angles (φ d and θ d ), describing the particle deviation compared to its initial direction after bremsstrahlung, and the deviation orientation.