Optimization and Systems Theory Seminar
Friday, April 20, 2001, 11.00-12.00, Room 3721, Lindstedtsvägen 25

Johan Löf
RaySearch Laboratories
E-mail: johan.lof@raysearchlabs.com

A framework for computation of optimal radiation therapy plans

A general framework for optimization of radiation therapy has been developed. The framework has been implemented in a new object-oriented code called ORBIT, and some of the principal capabilities of this code are presented. It is shown that simultaneous optimization of beam directions and intensity modulation can considerably improve the treatment outcome, especially for few field techniques. The flexibility in formulating and solving clinically relevant optimization problems is also demonstrated. In this context a new optimization strategy, called P++, is introduced. This strategy makes it possible to minimize complications with only a marginal reduction in the probability for complication-free cure, P+.

ORBIT has been closely integrated with a clinical radiation treatment planning system, which thus combines ORBIT's advanced ability to optimize treatment plans with the clinical versatility and accuracy of forward dose calculation algorithms.

By viewing a fractionated treatment as a dynamical system, the time structure of radiation therapy optimization can be used to organize the calculations in a recursive manner. For this purpose, a new mathematical framework for calculating the probability of complication-free tumor control, and its expectation value, has been constructed. All the main clinical parameters and events that affect P+ are gathered into four sequences of data that describe the delivered energy-fluence distributions, patient geometry, radiobiological response parameters, and time-dose fractionation schedule. Because of the difficulty in measuring all aspects of the intra- and interfractional variations in the patient geometry, such as internal organ displacements and deformations, as well as inter-patient variations in radiation sensitivity, such uncertainties are accounted for by the method of stochastic optimization.

The dynamic optimization approach to radiotherapy planning allows for information feedback so that patient-specific data that are generated as the treatment proceeds (e.g., by in vivo dosimetry, portal imaging, radiotherapeutic computed tomography, PET- and MR-imaging) can be used to refine the beam configurations and beam shapes in the subsequent treatment sessions.

Calendar of seminars
Last update: April 17, 2001 by Anders Forsgren, anders.forsgren@math.kth.se.