Download Image-Guided IMRT by John M. Mathis PDF
By John M. Mathis
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Extra info for Image-Guided IMRT
Going to the top of the diagram we follow the physical patient: the skin marks are aligned on the lasers, the lasers being aligned with the CT room coordinates. Then the patient is moved to the treatment room, where lasers are used to align the patient’s skin marks again. However, the patient’s bony anatomy may have moved with respect to the skin marks and the tumor may have moved with respect to the bony anatomy. Going to the bottom of the diagram, we follow the patient’s CT scan through the planning process, to beam setup, and beam delivery.
2 Aperture-based Optimization . . . . Contour-based Optimization . . . . Direct Aperture Optimization . . . . . . . 39 . . 40 . . . . 41 41 42 42 References . . . . . . . . . . . . . . 1 Introduction One of the two prerequisites for the clinical application of IMRT was the development of inverse planning strategies – simply because the available forward planning strategies could not be applied to the optimization of the enormous number of treatment parameters suddenly required for the efﬁcient delivery of intensity modulated treatment ﬁelds.
If the margins are not equal in all directions, the expansion should be done using an ellipsoid shape instead. Stroom et al. provided an alternative margin recipe based on coverage probability. 7 times the total SD of random errors to ensure that, on average, 99% of the target volume receives 95% of the prescribed dose or more. A fundamental problem of coverage probabilities is that they tend to undervalue narrow tumor extensions, which are smeared out to very low probability levels and will not be included in the margin.