Frederic Brochu

Towards Quantitative Evaluation of Tissue Absorption Coefficients Using Light Fluence Correction in Optoacoustic Tomography

Optoacoustic tomography is a fast developing imaging modality, combining the high contrast available from optical excitation of tissue with the high resolution and penetration depth of ultrasound detection. Light is subject to both absorption and scattering when traveling through tissue; adequate knowledge of tissue optical properties and hence the spatial fluence distribution is required to create an optoacoustic image that is directly proportional to chromophore concentrations at all depths. Using data from a commercial multispectral optoacoustic tomography (MSOT) system, we implemented an iterative optimization for fluence correction based on a finite-element implementation of the delta-Eddington approximation to the Radiative Transfer Equation (RTE). We demonstrate a linear relationship between the image intensity and absorption coefficients across multiple wavelengths and depths in phantoms. We also demonstrate improved feature visibility and spectral recovery at depth in phantoms and with in vivo measurements, suggesting our approach could in the future enable quantitative extraction of tissue absorption coefficients in biological tissue.

Distributed analysis in ATLAS using GANGA

Distributed data analysis using Grid resources is one of the fundamental applications in high energy physics to be addressed and realized before the start of LHC data taking. The needs to manage the resources are very high. In every experiment up to a thousand physicists will be submitting analysis jobs to the Grid. Appropriate user interfaces and helper applications have to be made available to assure that all users can use the Grid without expertise in Grid technology. These tools enlarge the number of Grid users from a few production administrators to potentially all participating physicists. The GANGA job management system (http://cern.ch/ganga), developed as a common project between the ATLAS and LHCb experiments, provides and integrates these kind of tools. GANGA provides a simple and consistent way of preparing, organizing and executing analysis tasks within the experiment analysis framework, implemented through a plug-in system. It allows trivial switching between running test jobs on a local batch system and running large-scale analyzes on the Grid, hiding Grid technicalities. We will be reporting on the plug-ins and our experiences of distributed data analysis using GANGA within the ATLAS experiment. Support for all Grids presently used by ATLAS, namely the LCG/EGEE, NDGF/NorduGrid, and OSG/PanDA is provided. The integration and interaction with the ATLAS data management system DQ2 into GANGA is a key functionality. An intelligent job brokering is set up by using the job splitting mechanism together with data-set and file location knowledge. The brokering is aided by an automated system that regularly processes test analysis jobs at all ATLAS DQ2 supported sites. Large numbers of analysis jobs can be sent to the locations of data following the ATLAS computing model. GANGA supports amongst other things tasks of user analysis with reconstructed data and small scale production of Monte Carlo data.

Geant4 simulation of the Elekta XVI kV CBCT unit for accurate description of potential late toxicity effects of image-guided radiotherapy.

This paper describes the modelisation of the Elekta XVI Cone Beam Computed Tomography (CBCT) machine components with Geant4 and its validation against calibration data taken for two commonly used machine setups. Preliminary dose maps of simulated CBCTs coming from this modelisation work are presented. This study is the first step of a research project, GHOST, aiming to improve the understanding of late toxicity risk in external beam radiotherapy patients by simulating dose depositions integrated from different sources (imaging, treatment beam) over the entire treatment plan. The second cancer risk will then be derived from different models relating irradiation dose and second cancer risk.

Applying physical science techniques and CERN technology to an unsolved problem in radiation treatment for cancer: the multidisciplinary ‘VoxTox’ research programme

The VoxTox research programme has applied expertise from the physical sciences to the problem of radiotherapy toxicity, bringing together expertise from engineering, mathematics, high energy physics (including the Large Hadron Collider), medical physics and radiation oncology. In our initial cohort of 109 men treated with curative radiotherapy for prostate cancer, daily image guidance computed tomography (CT) scans have been used to calculate delivered dose to the rectum, as distinct from planned dose, using an automated approach. Clinical toxicity data have been collected, allowing us to address the hypothesis that delivered dose provides a better predictor of toxicity than planned dose.

Reinforcing user data analysis with Ganga in the LHC era: scalability, monitoring and user-support

Ganga is a grid job submission and management system widely used in the ATLAS and LHCb experiments and several other communities in the context of the EGEE project. The particle physics communities have entered the LHC operation era which brings new challenges for user data analysis: a strong growth in the number of users and jobs is already noticeable. Current work in the Ganga project is focusing on dealing with these challenges. In recent Ganga releases the support for the pilot job based grid systems Panda and Dirac of the ATLAS and LHCb experiment respectively have been strengthened. A more scalable job repository architecture, which allows efficient storage of many thousands of jobs in XML or several database formats, was recently introduced. A better integration with monitoring systems, including the Dashboard and job execution monitor systems is underway. These will provide comprehensive and easy job monitoring. A simple to use error reporting tool integrated at the Ganga command-line will help to improve user support and debugging user problems. Ganga is a mature, stable and widely-used tool with long-term support from the HEP community. We report on how it is being constantly improved following the user needs for faster and easier distributed data analysis on the grid.

User analysis of LHCb data with Ganga

GANGA (http://cern.ch/ganga) is a job-management tool that offers a simple, efficient and consistent user analysis tool in a variety of heterogeneous environments: from local clusters to global Grid systems. Experiment specific plug-ins allow GANGA to be customised for each experiment. For LHCb users GANGA is the officially supported and advertised tool for job submission to the Grid. The LHCb specific plug-ins allow support for end-to-end analysis helping the user to perform his complete analysis with the help of GANGA. This starts with the support for data selection, where a user can select data sets from the LHCb Bookkeeping system. Next comes the set up for large analysis jobs: with tailored plug-ins for the LHCb core software, jobs can be managed by the splitting of these analysis jobs with the subsequent merging of the resulting files. Furthermore, GANGA offers support for Toy Monte-Carlos to help the user tune their analysis. In addition to describing the GANGA architecture, typical usage patterns within LHCb and experience with the updated LHCb DIRAC workload management system are presented.