Robust optimization for intensity modulated radiation therapy treatment planning under uncertainty. Chu, M., Zinchenko, Y., Henderson, S. G., & Sharpe, M. B. Physics in Medicine and Biology, 50:5463–5477, 2005. ![Robust optimization for intensity modulated radiation therapy treatment planning under uncertainty [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper abstract bibtex The recent development of Intensity Modulated Radiation Therapy (IMRT) allows the dose distribution to be tailored to match the tumour's shape and position, avoiding damage to healthy tissue to a greater extent than previously possible. Traditional treatment plans assume the target structure remains in a fixed location throughout treatment. However, many studies have shown that because of organ motion, inconsistencies in patient positioning over the weeks of treatment, etc., the tumour location is not stationary. We present a probabilistic model for the IMRT inverse problem and show that it is identical to using robust optimization techniques, under certain assumptions. For a sample prostate case, our computational results show that this method is computationally feasible and promising - compared to traditional methods, our model has the potential to find treatment plans that are more adept at sparing healthy tissue while maintaining the prescribed dose to the target.
@article{chuetal05,
abstract = {The recent development of Intensity Modulated Radiation Therapy (IMRT) allows the dose distribution to be tailored to match the tumour's shape and position, avoiding damage to healthy tissue to a greater extent than previously possible. Traditional treatment plans assume the target structure remains in a fixed location throughout treatment. However, many studies have shown that because of organ motion, inconsistencies in patient positioning over the weeks of treatment, etc., the tumour location is not stationary. We present a probabilistic model for the IMRT inverse problem and show that it is identical to using robust optimization techniques, under certain assumptions. For a sample prostate case, our computational results show that this method is computationally feasible and promising - compared to traditional methods, our model has the potential to find treatment plans that are more adept at sparing healthy tissue while maintaining the prescribed dose to the target.},
author = {Millie Chu and Yuriy Zinchenko and Shane G. Henderson and Michael B. Sharpe},
date-added = {2016-01-10 16:07:54 +0000},
date-modified = {2016-01-10 16:07:54 +0000},
journal = {Physics in Medicine and Biology},
pages = {5463--5477},
title = {Robust optimization for intensity modulated radiation therapy treatment planning under uncertainty},
url_paper = {pubs/ChuIMRT.pdf},
volume = {50},
year = {2005}}
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