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Treatment planning for heavy-ion radiotherapy: calculation and optimization of biologically effective dose

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Krämer, M, Scholz, M·2000·Physics in Medicine and Biology

Abstract

We describe a novel approach to treatment planning for heavy-ion radiotherapy based on the local effect model (LEM) which allows us to calculate the biologically effective dose not only for the target region but also for the entire irradiation volume. LEM is ideally suited for use as an integral part of treatment planning code systems for active dose shaping devices like the GSI raster scan system. Thus it has been incorporated into our standard treatment planning system for ion therapy (TRiP). Single intensity modulated fields can be optimized with respect to a homogeneous biologically effective dose. The relative biological effectiveness (RBE) is calculated separately for each voxel of the patient CT. Our radiobiologically oriented code system has been used since 1995 for the planning of irradiation experiments with cell cultures and animals such as rats and minipigs. It has been in regular and successful use for patient treatment planning since 1997.

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Metadata

Authors
Krämer, M., & Scholz, M.
Venue
Physics in Medicine and Biology
Published
2000-10-23
Volume
45
Issue
11
Pages
3319-3330
ISSN
  • 0031-9155 (ISSN-L)
  • 1361-6560
OpenAlex
W2060936143
Cited by
453
References
0
FWCI
4.29
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Treatment planning for heavy-ion radiotherapy: calculation and optimization of biologically effective dose. https://app.cochat.ai/papers/10.1088/0031-9155/45/11/314
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Krämer, M., & Scholz, M. (2000). Treatment planning for heavy-ion radiotherapy: calculation and optimization of biologically effective dose. Physics in Medicine and Biology. https://doi.org/10.1088/0031-9155/45/11/314

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.