# Dose optimization

### Anatomy based inverse optimization

In case of optimization for high dose rate brachytherapy there are many techniques that are used. First of all there are phenomenological optimisation methods such as geometrical optimisation or graphical optimisation. But there are also anatomy based optimisation techniques. Such techniques take into account the anatomy of the region and have to consider many objectives, which are in conflict such as coverage of the planning target volume (PTV) with a specified dose and protection of surrounding tissue and organs at risk (OAR).

Those objectives can be either the variance of the dose in the volume or in the surface of the organs and/or PTV. Also DVH based objectives can be used in optimization procedure. The objectives can either be combined into a single objective function f formed by a weighted sum of the individual objective functions, or use Multi Objective based Optimization and so produce more solutions.

In the first case the optimal value for the i-th objective found by an optimization algorithm depends on the weights (importance factors) used and may not be the best possible result.

### Multi-objective optimization methods

It is still not clear that the problem is not a single objective problem but a multiobjective problem with contradictory and competing objectives that requires the determination of at least a portion of the Pareto front. Today when there are no exact established quality criteria the planner cannot guide the system exactly to a single solution on the Pareto front.

It is true that speed is an important factor. Using only a very small set of importance factors, 1-2 usually, the planner is left without information of what actually is possible. The true multiobjective optimisation is free of importance factors. Even if a set of importance factors which somehow gives reasonable results can be found for some fixed topology it requires time to find these and a significant time has to be spent. A multiobjective algorithm does not require such training.

Sometimes although it seems strange in principle it is easier to calculate all possible solutions at once than only one particular. This is true for true multiobjective optimisation algorithms. In a single objective optimisation there is only a single decision, accept the solution or not. If not a new solution must be calculated or a manual manipulation of dwell times or dose rescaling remains which takes a lot of time, usually much more than the automatic optimisation.

A true multiobjective method requires a set of objectives functions that are more intuitive for the planner than variances of dose distributions. The most natural way from a dosimetric based approach seems to be the use of dose-volume histogram based objectives.

It is true that the new optimisation methods require a slightly more complex cooperation with planners and still the decision making process of these multiobjective methods can be improved with some additional tools. A dose optimisation procedure which does not require any human decision making process is difficult if not impossible to be realized.

Pi Medical is actively working in the research area of dose optimization for many years and the results of our research on this topic are published in scientific journals and implemented in commercial products.

Our efforts are currently focused on the development of tools which are user friendly, bringing the power of sophisticated algorithms to the average user and ensuring the quality of the results even for user with limited experience in treatment planning.

One of the main problems of the current dose optimization technology, as is implemented in the available Brachytherapy Treatment Planning Systems, is the lack of a fast, easy to understand, user friendly dose optimization environment affecting directly and interactively the plan and its quality.

For example, there is no direct physical meaning for the importance factors (IF) used to transform a multi-objective optimization problem to a single-objective one [1]. For given quality measures of the plan (e.g. the volume percentage V% at 100% dose for PTV) the user does not have a systematic way to select the importance factors values but a trial-error procedure. Due to the huge number of possible importance factors combinations and the unique geometry of each plan there are practical limitations to the quality standards that can be achieved following this empirical way.

As an answer to this need, Pi Medical developed the Adaptive DVH Shaper, a state-of-the-art tool, which gives to the user the power to “shape” the DVH in order to achieve the desired quality of plan.

The “Adaptive DVH Shaper” is an interactive graphical tool which helps the user to change - in real time - the shape of the DVH of a treatment plan, simply by moving the DVH control points using his mouse.

Firstly, the user loads the plan to be optimized and set up the optimization settings by selecting the objectives, assigning importance factors and defining the dose limits for them.

The algorithm uses a pre-process step where solution which are of clinical interest are stored. This pre-process step takes about 1minute for a typical case.

After the end of the pre-process, the user has the range of movement for the control points.  Now, the user can simply drag & drop the control points in the horizontal or vertical direction to achieve the desired characteristics for the optimized solution.

The system translates the new position of the control point and gives back an optimized plan with the DVH curve passing from the new position of the control point.

This is an advanced version of the DVH Shaper tool which is already available in the market.

#### References

1. M. Lahanas, D. Baltas, N. Zamboglou, "Anatomy-based three-dimensional dose optimization in brachytherapy using multiobjective genetic algorithms", Med. Phys. 26 (9), September 1999.
2. S. Giannouli, D. Baltas, N. Milickovic, M. Lahanas, C. Kolotas, N. Zamboglou, N. Uzunoglu, "Autoactivation of source dwell positions for HDR brachytherapy treatment planning", Med. Phys. 27 (11) , November 2000.
3. N. Milickovic, S. Giannouli, D. Baltas, M. Lahanas, C. Kolotas, N. Zamboglou, N. Uzunoglu, "Catheter autoreconstruction in computed tomography based brachytherapy treatment planning", Med. Phys. 27 (5), May 2000.
4. M. Lahanas, D. Baltas, S. Giannouli, N. Milickovic, and N. Zamboglou, "Generation of uniformly distributed dose points for anatomy-based three-dimensional dose optimization methods in brachytherapy", Med. Phys. 27 (5), May 2000.
5. K. Karouzakis, M. Lahanas, N. Milickovic, S. Giannouli, D. Baltas and N. Zamboglou, "Brachytherapy dose-volume histogram computations using optimized stratified sampling methods", Med. Phys. 29 (3), March 2002.
6. M. Lahanas, D. Baltas and N. Zamboglou, "A hybrid evolutionary algorithm for multi-objective anatomy-based dose optimization in high-dose-rate brachytherapy", Phys. Med.Biol. 48 (2003) 399-415.
7. M. Lahanas,D. Baltas and S. Giannouli, "Global convergence analysis of fast multiobjective gradient-based dose optimization algorithms for high-dose-rate brachytherapy", Phys. Med.Biol. 48 (2003) 599-617.