An iterative filtered backprojection inverse treatment planning algorithm for tomotherapy

Abstract

An inverse treatment planning algorithm for tomotherapy is described. The algorithm iteratively computes a set of nonnegative beam intensity profiles that minimizes the least-square residual dose defined in the target and selected normal tissue regions of interest. At each iteration the residual dose distribution is transformed into a set of residual beam profiles using an inversion method derived from filtered backprojection image reconstruction theory. These "residual" profiles are used to correct the current beam profile estimates resulting in new profile estimates. Adaptive filtering is incorporated into the inversion model so that the gross structure of the dose distribution is optimized during initial iterations of the algorithm, and the fine structure corresponding to edges is obtained at later iterations. A three dimensional, kernel based, convolutions/superposition dose model is used to compute dose during each iteration. Two clinically relevant treatment planning examples are presented illustrating the use of the algorithm for planning conformal radiotherapy of the breast and the prostate. Solutions are generally achieved in 10-20 iterations requiring about 20 h of CPU time using a midrange workstation. The majority of the calculation time is spent on the three-dimensional dose calculation. The inverse treatment planning algorithm is a useful research tool for exploring the potential of tomotherapy for conformal radiotherapy. Further work is needed to (a) achieve clinically acceptable computation times; (b) verify the algorithm using multileaf collimator technology; and (c) extend the method to biological objectives.