MAXIMUM-LIKELIHOOD AS A COMMON COMPUTATIONAL FRAMEWORK IN TOMOTHERAPY

Tomotherapy is a dose delivery technique using helical or axial intens ity modulated beams. One of the strengths of the tomotherapy concept i s that it can incorporate a number of processes into a single piece of equipment. These processes include treatment optimization planning, d ose reconstruction and kilovoltage/megavoltage image reconstruction. A common computational technique that could be used for all of these pr ocesses would be very appealing. The maximum likelihood estimator, ori ginally developed for emission tomography, can serve as a useful tool in imaging and radiotherapy. We believe that this approach can play an important role in the processes of optimization planning, dose recons truction and kilovoltage and/or megavoltage image reconstruction. Thes e processes involve computations that require comparable physical meth ods. They are also based on equivalent assumptions, and they have simi lar mathematical solutions. As a result, the maximum likelihood approa ch is able to provide a common framework for all three of these comput ational problems. We will demonstrate how maximum likelihood methods c an be applied to optimization planning, dose reconstruction and megavo ltage image reconstruction in tomotherapy. Results for planning optimi zation, dose reconstruction and megavoltage image reconstruction will be presented. Strengths and weaknesses of the methodology are analysed . Future directions for this work are also suggested.