1 Recent Progress in Inverse Treatment Planning L Xing*, C Cotrutz, A Pugachev, J Lian, S Crooks, J G Li, S Hunjun, D Yang, A Boyer Department of Radiation Oncology, Stanford University Stanford, CA 94305-5304, USA Intensity modulated radiation therapy (IMRT) is being developed into an important modality in radiotherapy. Institutions worldwide are attempting or planning to integrate this new technology into their clinics. Before the IMRT implementation, it is desirable to understand the overall inverse planning process and how the general concept of inverse planning is implemented. This will help in making better decisions regarding which system is best suitable to your clinical environment and thus facilitate the implementation process. In general, there are three integral parts in IMRT: inverse planning [1, 2], dynamic delivery [3], and quality assurance [4-8]. The purpose of this talk is to present an overview of the state-of-the-art inverse planning algorithms as well as our perspectives on several practical issues relevant to the subject. In particular, we will identify the problems in currently available systems and described the techniques that have been or are being developed to overcome the problems. Radiation treatment planning requires the calculation of a set of parameters for the delivery of a certain radiation dose to the patient. Ideally, radiation dose distribution should be designed to conform perfectly to the entire tumor volume while completely avoiding surrounding normal tissues. Although achievement of this goal is practically impossible, a computer optimization can potentially simplify the tedious planning procedure and yield the best possible plans[1] [2]. Computer optimization becomes necessary for IMRT treatment planning because of the vast search space. The implementation of the general concept of inverse planning differs from system to system. The degree of optimality of the final solution is generally determined by the form of objective function and the methods to search for the minimum (or maximum) of the defined objective functions. The role of objective function is to establish a link between the output dose distribution and the input beam parameters (beamlet weights or beam profiels). The objective function measures the goodness of a selected plan and its choice is crucial for therapeutic plan optimization. The objective function can be based solely on dose or it can use a radiobiological model. The former is concerned with the interaction between radiation and matter and calls for accurate dose distributions, with the biological aspect being implicitly given in the physician's prescription. The biological model argues that optimization should be based on the biological effects produced by the underlying dose distributions. The treatment objective is usually stated as the maximization of the tumor control probability (TCP) while maintaining the normal tissue complications probability (NTCP) to within acceptable levels. A TCP is related to a dose distribution by the dose response function, which is not sufficiently understood. At this point, the dose-based approach is still widely used in practical optimization whereas biological models are often used conceptually. This is evidenced by the fact that all commercial inverse planning systems use dose-based (with or without dose-volume constraints) objective function.