### Optimization and Systems Theory Seminar

Friday, April 20, 2001, 11.00-12.00, Room 3721, Lindstedtsvägen 25

**
Johan Löf **

RaySearch
Laboratories

Stockholm

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.
*