We will develop methodologies for integrating radiotherapy procedures for optimized planning for formal beam delivery and tomographic verification. The methods will be applicable to contemporary radiotherapy as well as delivery systems that are under development.
We aim to provide a significant improvement in the delivery of dose to the tumor while avoiding important normal structures. We will extend the applicability of the convolution/superposition method developed in the previous funding for simulation of the delivery and verification of radiotherapy. We will improve and test an optimization algorithm, based on the convolution/superposition method and image reconstruction, to account for divergent beams, dynamic delivery and radiobiological responses. This algorithm will predict the optimal beam intensities needed to produce tumorcidal dose distributions which conform to target volumes yet spare nearby normal tissues. The clinical relevancy of the improved dose distributions will be quantified using radiobiological outcome estimators and techniques employed in prospective trials. We will develop an algorithm to reconstruct the dose distribution in the patient based upon a representation of the patient and dose information from a transit megavoltage imaging system.
These aims are relevant to """"""""conventional"""""""" fixed-field conformal radiotherapy and to exciting future systems such as dynamic multileaf collimators, tomotherapy, and robotic radiotherapy. We will use a computer-controlled phantom translator/rotator and temporal modulation to deliver optimal non-uniform beams to test the optimization algorithms and to simulate the effects of errors due to inaccurate setup, and patient motion during treatment which can seriously impact all types of beam delivery. This will quantify methods designed to minimize delivery errors using improved treatment verification.
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