Introduction

In this project we will address the problem of radiotherapy treatment planning optimization, for Intensity Modulated Radiation Therapy (IMRT) and Volumetric Modulated Arc Therapy (VMAT),

(Image obtained using Software CERR)

• What is IMRT?

  • Radiation therapy consists in the treatment of cancerous tissues using radiation, having as goal the destruction of cancerous cells and the preservation of healthy tissues. The treatment plan is based on the patient CT images, where the radiation oncologist delineates the target volume and organs at risk. The medical physicist and/or the dosimetrist are then responsible for performing the plan aiming to deliver the prescribed dose to the tumor while minimizing the irradiation of the surrounding healthy organs and tissues. The achievement of optimal radiotherapy treatments is done through the optimization of all possible variables that can influence the dose distribution.With IMRT, the number of degrees of freedom to be optimized is so large that computation of mathematical algorithms is mandatory to achieve valuable solutions. The selection of appropriate radiation incidence directions may be decisive for the quality of the treatment.

• What is VMAT?

  • The radiation is continuously delivered during the rotation of the gantry, instead of being delivered just at a discrete set of predetermined angles.

• Why is this problem important?

  • In clinical practice, most of the time, treatment planning is still a manual, trial-and-error procedure. In this project we will work on computer-aided inverse planning applied to radiation therapy treatments, contributing to the automation of this process.
    

• What do we propose to do?

  • Improving treatment quality and reducing planning time by developing new automated global optimization strategies for IMRT (non-coplanar) and VMAT (coplanar and non-coplanar).

  • Incorporating multiple objectives in the optimization procedures.

  • Creating and comparing optimized plans for different treatment modalities considering quality assessment metrics and making use of multicriteria analysis methodologies.

• Who are we?

  • This is an interdisciplinary project, with researchers having a solid background on computer science, optimization, medical physics and clinical practice. From the computer science and optimization side, the team has expertise in creating algorithms in multidisciplinary optimization including derivative-free algorithms and evolutionary computation. From the medical physics side, the team counts with specialized researchers, with a sound experience in treatment planning both from the research and from the clinical practice points of view. The synergies that will be possible to exploit set the grounds for a future quality research and contribute to the innovative character of our proposed work.

Contributing to automated treatment planning will have social and economic impacts especially considering our ageing population and the expected increase of cancer incidence. Simultaneously reducing the workload associated with treatment planning, and increasing treatment quality, will contribute to providing excellent healthcare without unbearable cost increase.

You cal also look at our previous project here.