KiT-RT: An Extendable Framework for Radiative Transfer and Therapy

Jonas Kusch; Steffen Schotthöfer; Pia Stammer; Jannick Wolters; Tianbai Xiao

doi:10.1145/3630001

KiT-RT: An Extendable Framework for Radiative Transfer and Therapy

Jonas Kusch
, Steffen Schotthöfer
, Pia Stammer
, Jannick Wolters
, Tianbai Xiao

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

In this article, we present Kinetic Transport Solver for Radiation Therapy (KiT-RT), an open source C++-based framework for solving kinetic equations in therapy applications available at https://github.com/CSMMLab/KiT-RT. This software framework aims to provide a collection of classical deterministic solvers for unstructured meshes that allow for easy extendability. Therefore, KiT-RT is a convenient base to test new numerical methods in various applications and compare them against conventional solvers. The implementation includes spherical harmonics, minimal entropy, neural minimal entropy, and discrete ordinates methods. Solution characteristics and efficiency are presented through several test cases ranging from radiation transport to electron radiation therapy. Due to the variety of included numerical methods and easy extendability, the presented open source code is attractive for both developers, who want a basis to build their numerical solvers, and users or application engineers, who want to gain experimental insights without directly interfering with the codebase.

Original language	English
Article number	38
Journal	ACM Transactions on Mathematical Software
Volume	49
Issue number	4
DOIs	https://doi.org/10.1145/3630001
State	Published - Dec 15 2023

Funding

Jonas Kusch has been funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 491976834. Pia Stammer is supported by the Helmholtz Association under the joint research school HIDSS4Health – Helmholtz Information and Data Science School for Health. The work of Steffen Schotthöfer is funded by the Priority Programme “Theoretical Foundations of Deep Learning (SPP2298)” by the Deutsche Forschungsgemeinschaft. The work of Steffen Schotthöfer is sponsored by the Office of Advanced Scientific Computing Research, U.S. Department of Energy, and performed at the Oak Ridge National Laboratory, which is managed by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan) . Tianbai Xiao acknowledges the support by National Science Foundation of China (12302381) and the computing resources provided by Hefei Advanced Computing Center. Jonas Kusch has been funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 491976834. Pia Stammer is supported by the Helmholtz Association under the joint research school HIDSS4Health - Helmholtz Information and Data Science School for Health. The work of Steffen Schotthöfer is funded by the Priority Programme 'Theoretical Foundations of Deep Learning (SPP2298)' by the Deutsche Forschungsgemeinschaft. The work of Steffen Schotthöfer is sponsored by the Office of Advanced Scientific Computing Research, U.S. Department of Energy, and performed at the Oak Ridge National Laboratory, which is managed by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). Tianbai Xiao acknowledges the support by National Science Foundation of China (12302381) and the computing resources provided by Hefei Advanced Computing Center.

Keywords

finite volume methods
Kinetic theory
machine learning
radiation therapy
radiation transport

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10.1145/3630001

Cite this

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title = "KiT-RT: An Extendable Framework for Radiative Transfer and Therapy",

abstract = "In this article, we present Kinetic Transport Solver for Radiation Therapy (KiT-RT), an open source C++-based framework for solving kinetic equations in therapy applications available at https://github.com/CSMMLab/KiT-RT. This software framework aims to provide a collection of classical deterministic solvers for unstructured meshes that allow for easy extendability. Therefore, KiT-RT is a convenient base to test new numerical methods in various applications and compare them against conventional solvers. The implementation includes spherical harmonics, minimal entropy, neural minimal entropy, and discrete ordinates methods. Solution characteristics and efficiency are presented through several test cases ranging from radiation transport to electron radiation therapy. Due to the variety of included numerical methods and easy extendability, the presented open source code is attractive for both developers, who want a basis to build their numerical solvers, and users or application engineers, who want to gain experimental insights without directly interfering with the codebase.",

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AU - Schotthöfer, Steffen

AU - Stammer, Pia

AU - Wolters, Jannick

AU - Xiao, Tianbai

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KiT-RT: An Extendable Framework for Radiative Transfer and Therapy

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