Adapting inverse planning to patient and organ geometrical variation: algorithm and implementation.
Image guided radiotherapy has the potential to improve both tumour control and normal tissue sparing by including temporal patient specific geometry information into the adaptive planning process. In this study we present a practical method of image guided adaptive inverse planning based on computed tomography (CT) and portal image feedback during the treatment course. The method is based on a general description of the radiotherapy optimization problem subject to dynamic geometrical variations of the patient/organs. We will demonstrate the feasibility of off-line image feedback into the inverse planning process with the example of three prostate cancer patients. CT and portal images acquired during the early course of the treatment are used to predict the geometrical variation distribution of a patient and to re-optimize the treatment plan accordingly. We will study the convergence of the optimization problem with respect to the number of image measurements and adaptive feedback loops.
Abt. Medizinische Physik, Radioonkologische Uniklinik, Universität Tübingen, D-72076 Tübingen, Germany.
M
M
Birkner
D
D
Yan
M
M
Alber
J
J
Liang
F
F
Nüsslin
D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D015233:Models, Statistical; D008962:Models, Theoretical; D019047:Phantoms, Imaging; D011471:Prostatic Neoplasms / Q000532:radiotherapy; D011878:Radiotherapy / Q000379:methods; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted; D020266:Radiotherapy, Conformal; D014057:Tomography, X-Ray Computed / Q000379:methods*