Wataru Takase

Extension of the Particle Therapy Simulation Framework to Hospital Information Systems and Multi-grid Environments

The Particle Therapy Simulation Framework (PTSIM) is a Geant4-based Monte Carlo simulation framework for particle therapy. The PTSIM provides a common platform to model a beam delivery system and a treatment head with patient data obtained from CT images. The PTSIM had already supported three Japanese proton and ion therapy facilities and three more in other countries The PTSIM allows particle therapy clinicians or researchers to simulate their own facility or an envisioned facility without requiring Geant4 expertise or programming. While Monte Carlo simulation is believed to be the most reliable method of dose calculation in particle therapy, the calculation time is critical in performing the simulation with sufficient statistical accuracy for clinical applications. There-fore, adopting a high-performance computing environment, such as a grid, is essential. In order to provide users with better usability and computer resources, we have developed an extension of the PTSIM in order to coordinate with hospital information systems and multi-grid environments. The extension consists of the PTSIM Web Interface for configuring the PTSIM, the DICOM-RT interface for communicating with a hospital information system of a particle therapy facility, and a Universal Grid Interface for performing a simulation in a multi-grid environment. In the present paper, we describe these three key components in detail.

Common platform of Monte Carlo dose calculation on universal grid interface with Geant4 based particle therapy simulation framework

While Monte Carlo (MC) simulation is believed to be the most reliable method of dose calculation in particle therapy, the simulation time is critical in attaining sufficient statistical accuracy for clinical applications. Therefore, parallelization of simulations is essential. This paper describes a common platform of MC dose calculation in grid-distributed computing environments. The platform is flexible and effective for dose calculation in both clinical and research applications for particle therapy. The platform consists of the universal grid interface (UGI) and the Geant4-based particle therapy simulation framework (PTSIM). The UGI, written in Python, provides a command-line interface for job submission, file manipulation, and monitoring in multiple-grid middleware environments. The PTSIM is a single software application for modeling a treatment port with patient data obtained from CT images. The common platform was constructed in grid computing environments using the computing resources in five institutions. The platform utilized these resources through the NAREGI grid middleware under UGI to provide stable computing resources and a common environment for MC dose calculation in particle therapy.

Recent updates and plan in Geant4 based particle therapy system simulation framework

Particle therapy system simulation framework, PTSIM, is a simulation framework based on Geant4 Monte Carlo simulation. It has been developed in the project, “Development of simulation framework for advanced radiotherapy”, funded by the Japan Science and Technology Agency (JST) in the program of Core Research for Evolutional Research and Technology (CREST), from 2003 to 2010. The PTSIM has provided a common platform to model proton and ion therapy facilities, allowing users who are not Geant4 experts to accurately and efficiently run Geant4 simulations with the pre-build configurations. Efforts on further development of PTSIM are still under way to include more functionality and improve the performance. The PTSIM has been upgraded with the extensions in the DICOM-RT interface to coordinate with hospital information system, the Web interface with universal grid environment, etc. In this paper, we report on our activities about these updates and plan for extending the PTSIM functionality with new requirements from users.

Particle Therapy Simulation Framework on GRID Environments

Dose calculation for particle therapy has been performed on GRID environments using a Geant4 based particle therapy simulation framework (PTSIM). PTSIM provides a common platform to model a beam line and treatment head with patient data from CT. PTSIM has already provided three of Japanese proton and ion therapy facilities and three more in other countries. At particle therapy facilities, dose analyses in clinical applications are preformed by a treatment planning system (TPS). While TPS includes a simple but very fast pencil beam algorithm (PBA), integration with full Geant4 Monte Carlo (MC) calculation is desirable. However, the computation time is an issue for applying MC calculation to treatment planning. In order to improve the computation time, we examined the performance of dose calculation in PTSIM on two GRID environments, LCG and NAREGI, respectively. In this paper, we describe the performance of dose calculation in PTSIM on these GRID environments.

A solution for secure use of Kibana and Elasticsearch in multi-user environment

Monitoring is indispensable to check status, activities, or resource usage of IT services. A combination of Kibana and Elasticsearch is used for monitoring in many places such as KEK, CC-IN2P3, CERN, and also non-HEP communities. Kibana provides a web interface for rich visualization, and Elasticsearch is a scalable distributed search engine. However, these tools do not support authentication and authorization features by default. In the case of single Kibana and Elasticsearch services shared among many users, any user who can access Kibana can retrieve others information from Elasticsearch. In multi-user environment, in order to protect own data from others or share part of data among a group, fine-grained access control is necessary. The CERN cloud service group had provided cloud utilization dashboard to each user by Elasticsearch and Kibana. They had deployed a homemade Elasticsearch plugin to restrict data access based on a user authenticated by the CERN Single Sign On system. It enabled each user to have a separated Kibana dashboard for cloud usage, and the user could not access to others one. Based on the solution, we propose an alternative one which enables user/group based Elasticsearch access control and Kibana objects separation. It is more flexible and can be applied to not only the cloud service but also the other various situations. We confirmed our solution works fine in CC-IN2P3. Moreover, a pre-production platform for CC-IN2P3 has been under construction. We will describe our solution for the secure use of Kibana and Elasticsearch including integration of Kerberos authentication, development of a Kibana plugin which allows Kibana objects to be separated based on user/group, and contribution to Search Guard which is an Elasticsearch plugin enabling user/group based access control. We will also describe the effect on performance from using Search Guard.

SCALA: A framework for graphical operations for iRODS

Statistical Charts And Log Analyzer (SCALA) is a framework for graphically displaying operational data for a distributed data management system. The framework has been applied to the integrated Rule Oriented Data Management System (iRODS), but can be applied to any data management system. The framework allows operational information such as disk usage, number of users etc to be displayed. In addition SCALA also allows remote debugging through the discovery and display of iRODS error messages from log files. This makes the detection and debugging of errors in a running iRODS system simpler. The system has also been extended to display iRODS rules and their execution. This SCALA system is currently being used in the KEK production iRODS system. This paper describes the SCALA framework and it’s application to the iRODS.

Managing Cloud Images

The cloud offers scientists a lower barrier to use of the system. But, scientists using cloud middleware are faced with the having to create, manage and deploy the virtual images on the cloud infrastructure. In this paper we describe an application that uses the IBM Image Construction and Composition Tool (ICCT) to capture and create images. We describe the integration with the iRODS data management system that provides management allowing the user to keep track of which images are currently used and to switch between instances.

Capturing User-Generated Metadata

A key component of the management of digital data is the collection of metadata. Metadata is defined as data about data. It encompasses the description of the data and is essential in using or reusing the data. Metadata is often collected at the time the data are produced, but little attention is paid to the metadata that is created through the use or attempted use of the data. In this paper we propose an approach to enhance data management through the collection and management of metadata produced during the use or reuse of the data. The paper also describes an example of the approach applied to the iRODS data management system.

Experience of a low-maintenance distributed data management system

In this paper we report on the setup, deployment and operation of a low-maintenance, policy-driven distributed data management system for scientific data based on the integrated Rule Oriented Data System (iRODS). The system is located at KEK, Tsukuba, Japan with a satellite system at QMUL, London, UK. The system has been running stably in production for more than two years with minimal management overhead. The management tool that was developed to support the production system is also described. In addition we describe a simple XOR-based approach to file backup that reduces the amount of storage space consumed. In situations of large data volumes this approach can be of great benefit.

Universal Grid User Interface (UGI) for Multiple Grids and Clouds

This paper describes a practical application of a Universal Grid User Interface (UGI) to control resources with different kinds of middleware. Today, international scientific collaboration requires shared hardware and software resources provided by various kinds of Grid and Cloud middleware. We have designed and implemented a UGI that can provide a seamless environment for end users to use such remote resources (Grid or Cloud resources) with their local resources. The UGI functions include of job handling, manipulating files, general file cataloging, and monitoring jobs. UGI includes the SAGA (Simple API for Grid Applications) architecture and external components that are not supported by SAGA. Our prototype UGI implementation provides a Python API, a command line interface, and a Web interface. We show an example of a UGI application with a Web-based user interface for Particle Therapy Simulation (PTSim).