Proton radiography to improve proton radiotherapy: Simulation study at different proton beam energies

To improve the quality of cancer treatment with protons, a translation of X-ray Computed Tomography (CT) images into a map of the proton stopping powers needs to be more accurate. Proton stopping powers determined from CT images have systematic uncertainties in the calculated proton range in a patient of typically 3-4\% and even up to 10\% in region containing bone~\cite{USchneider1995,USchneider1996,WSchneider2000,GCirrone2007,HPaganetti2012,TPlautz2014,GLandry2013,JSchuemann2014}. As a consequence, part of a tumor may receive no dose, or a very high dose can be delivered in healthy ti\-ssues and organs at risks~(e.g. brain stem)~\cite{ACKnopf2013}. A transmission radiograph of high-energy protons measuring proton stopping powers directly will allow to reduce these uncertainties, and thus improve the quality of treatment. The best way to obtain a sufficiently accurate radiograph is by tracking individual protons traversing the phantom (patient)~\cite{GCirrone2007,TPlautz2014,VSipala2013}. In our simulations we have used an ideal position sensitive detectors measuring a single proton before and after a phantom, while the residual energy of a proton was detected by a BaF$_{2}$ crystal. To obtain transmission radiographs, diffe\-rent phantom materials have been irradiated with a 3x3~cm$^{2}$ scattered proton beam, with various beam energies. The simulations were done using the Geant4 simulation package~\cite{SAgostinelli2003}. In this study we focus on the simulations of the energy loss radiographs for various proton beam energies that are clinically available in proton radiotherapy.Comment: 6 pages, 6 figures, Presented at Jagiellonian Symposium on Fundamental and Applied Subatomic Physics, 7-12 June, 2015, Krak\'ow, Polan

An Algorithmic Framework for Labeling Network Maps

Drawing network maps automatically comprises two challenging steps, namely laying out the map and placing non-overlapping labels. In this paper we tackle the problem of labeling an already existing network map considering the application of metro maps. We present a flexible and versatile labeling model. Despite its simplicity, we prove that it is NP-complete to label a single line of the network. For a restricted variant of that model, we then introduce an efficient algorithm that optimally labels a single line with respect to a given weighting function. Based on that algorithm, we present a general and sophisticated workflow for multiple metro lines, which is experimentally evaluated on real-world metro maps.Comment: Full version of COCOON 2015 pape

AGOR status report

The operations of the superconducting cyclotron AGOR over the past years will be reviewed. Reliability issues encountered after nearly 25 years of operation and mitigation measures to warrant reliable operation for the coming decade will be discussed. The research performed with AGOR has significantly shifted from fundamental physics to radiation biology and medical radiation physics, both in collaboration with the Groningen Proton Therapy Center, and radiation hardness studies. The radiation biology research will be substantially expanded in the coming years with a new beam line for image guided preclinical research. For this research new dose delivery modalities including scanning, spatial fractionation and very high dose rates are developed. In addition, a new program has been started on the production of exotic nuclei, for which a new superconducting solenoid fragment separator will be developed. For the radiation hardness testing a cocktail beam at 30 MeV/amu with several ion species up to Xe has been developed and is now routinely delivered for experiments. A cocktail at 15 MeV/amu up to Bi is under development

Multi-Granular Trend Detection for Time-Series Analysis

Time series (such as stock prices) and ensembles (such as model runs for weather forecasts) are two important types of one-dimensional time-varying data. Such data is readily available in large quantities but visual analysis of the raw data quickly becomes infeasible, even for moderately sized data sets. Trend detection is an effective way to simplify time-varying data and to summarize salient information for visual display and interactive analysis. We propose a geometric model for trend-detection in one-dimensional time-varying data, inspired by topological grouping structures for moving objects in two- or higher-dimensional space. Our model gives provable guarantees on the trends detected and uses three natural parameters: granularity, support-size, and duration. These parameters can be changed on-demand. Our system also supports a variety of selection brushes and a time-sweep to facilitate refined searches and interactive visualization of (sub-)trends. We explore different visual styles and interactions through which trends, their persistence, and evolution can be explored

ifCNV: A novel isolation-forest-based package to detect copy-number variations from various targeted NGS datasets

Copy-number variations (CNVs) are an essential component of genetic variation distributed across large parts of the human genome. CNV detection from next-generation sequencing data and artificial intelligence algorithms have progressed in recent years. However, only a few tools have taken advantage of machine-learning algorithms for CNV detection, and none propose using artificial intelligence to automatically detect probable CNV-positive samples. The most developed approach is to use a reference or normal dataset to compare with the samples of interest, and it is well known that selecting appropriate normal samples represents a challenging task that dramatically influences the precision of results in all CNV-detecting tools. With careful consideration of these issues, we propose here ifCNV, a new software based on isolation forests that creates its own reference, available in R and python with customizable parameters. ifCNV combines artificial intelligence using two isolation forests and a comprehensive scoring method to faithfully detect CNVs among various samples. It was validated using targeted next-generation sequencing (NGS) datasets from diverse origins (capture and amplicon, germline and somatic), and it exhibits high sensitivity, specificity, and accuracy. ifCNV is a publicly available open-source software (https://github.com/SimCab-CHU/ifCNV) that allows the detection of CNVs in many clinical situations

Corrigendum: Short-lived positron emitters in beam-on PET imaging during proton therapy (2015 Phys. Med. Biol. 60 8923)

Because of strong indications of multiple counting by the multi-channel scaler (MCS) during most of the experiments described in Dendooven et al (2015 Phys. Med. Biol. 60 8923–47), the production of short-lived positron emitters in the stopping of 55 MeV protons in water, carbon, phosphorus and calcium was remeasured. The new results are reported here. With proper single counting of the MCS, the new production rates are 1.1 to 2.9 times smaller than reported in Dendooven et al (2015 Phys. Med. Biol. 60 8923–47). The omission of the conversion from MCS time bin to time unit in the previous data analysis was corrected, leading to an increase of the production rate by a factor of 2.5 or 10 for some nuclides. The most copiously produced short-lived nuclides and their production rates relative to the relevant long-lived nuclides are: 12N (T 1/2  =  11 ms) on carbon (5.3% of 11C), 29P (T 1/2  =  4.1 s) on phosphorus (23% of 30P) and 38mK (T 1/2  =  0.92 s) on calcium (173% of 38gK). The number of decays integrated from the start of an irradiation as a function of time during the irradiation of PMMA and 4 tissue materials has been determined. For (carbon-rich) adipose tissue, 12N dominates up to 70 s. On bone tissue, 38mK dominates the beam-on PET counts from 0.2–0.7 s until about 80–110 s. Considering nuclides created on phosphorus and calcium, the short-lived ones provide 8 times more decays than the long-lived ones during a 70 s irradiation. Bone tissue will thus be much better visible in beam-on PET compared to PET imaging after an irradiation. From the estimated number of 12N PET counts, we conclude that, for any tissue, except carbon-poor ones, 12N PET imaging potentially provides equal quality proton range information as prompt gamma imaging with an optimized knife-edge slit camera