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...

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Bibliographic Details
Published in:Medical physics (Lancaster) Vol. 30; no. 10; pp. 2822 - 2831
Main Authors: Birkner, M., Yan, D., Alber, M., Liang, J., Nüsslin, F.
Format: Journal Article
Language:English
Published: United States American Association of Physicists in Medicine 01.10.2003
Subjects:
algorithm theory
Algorithms
Anatomy
cancer
Computed radiography
Computed tomography
computerised tomography
diagnostic radiography
Dosimetry/exposure assessment
feedback
Humans
Image Processing, Computer-Assisted
Male
Medical imaging
Medical radiation safety
Models, Statistical
Models, Theoretical
optimisation
Optimization
Phantoms, Imaging
Physicists
Process monitoring and control
Prostatic Neoplasms - radiotherapy
Radiation monitoring, control, and safety
radiation protection
radiation therapy
Radiotherapy - methods
Radiotherapy Dosage
Radiotherapy Planning, Computer-Assisted
Radiotherapy, Conformal
Tissues
Tomography, X-Ray Computed - methods
tumours
Verification
ISSN:0094-2405, 2473-4209
Online Access:Get full text
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Summary: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.
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ISSN:0094-2405
2473-4209
DOI:10.1118/1.1610751