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Incorporating organ movements in IMRT treatment planning for prostate cancer: Minimizing uncertainties in the inverse planning process
Author(s) -
Unkelbach Jan,
Oelfke Uwe
Publication year - 2005
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.1929167
Subject(s) - radiation treatment planning , prostate cancer , process (computing) , medical physics , prostate , medicine , medical imaging , cancer , computer science , radiology , radiation therapy , operating system
We investigate an off‐line strategy to incorporate inter fraction organ movements in IMRT treatment planning. Nowadays, imaging modalities located in the treatment room allow for several CT scans of a patient during the course of treatment. These multiple CT scans can be used to estimate a probability distribution of possible patient geometries. This probability distribution can subsequently be used to calculate the expectation value of the delivered dose distribution. In order to incorporate organ movements into the treatment planning process, it was suggested that inverse planning could be based on that probability distribution of patient geometries instead of a single snapshot. However, it was shown that a straightforward optimization of the expectation value of the dose may be insufficient since the expected dose distribution is related to several uncertainties: first, this probability distribution has to be estimated from only a few images. And second, the distribution is only sparsely sampled over the treatment course due to a finite number of fractions. In order to obtain a robust treatment plan these uncertainties should be considered and minimized in the inverse planning process. In the current paper, we calculate a 3D variance distribution in addition to the expectation value of the dose distribution which are simultaniously optimized. The variance is used as a surrogate to quantify the associated risks of a treatment plan. The feasibility of this approach is demonstrated for clinical data of prostate patients. Different scenarios of dose expectation values and corresponding variances are discussed.