Learning the relationship between patient geometry and beam intensity in breast intensity-modulated radiotherapy
Renzhi Lu
Radke, R.J.
Hong, L.
Chen-Shou Chui
Jianping Xiong
Yorke, E.
Jackson, A.
Electr., Comput., & Syst. Eng. Dept., Rensselaer Polytech. Inst., Troy, NY;
This paper appears in: Biomedical Engineering, IEEE Transactions on
Publication Date: May 2006
Volume: 53,
Issue: 5
On page(s): 908-920
ISSN: 0018-9294
INSPEC Accession Number: 8962037
Digital Object Identifier: 10.1109/TBME.2005.863987
Current Version Published: 2006-04-18
Abstract
Intensity modulated radiotherapy (IMRT) has become an effective tool for cancer treatment with radiation. However, even expert radiation planners still need to spend a substantial amount of time adjusting IMRT optimization parameters in order to get a clinically acceptable plan. We demonstrate that the relationship between patient geometry and radiation intensity distributions can be automatically inferred using a variety of machine learning techniques in the case of two-field breast IMRT. Our experiments show that given a small number of human-expert-generated clinically acceptable plans, the machine learning predictions produce equally acceptable plans in a matter of seconds. The machine learning approach has the potential for greater benefits in sites where the IMRT planning process is more challenging or tedious
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