Purpose-To report the characteristics of prostate motion as tracked by the stereoscopic X-ray images of the implanted fiducials during hypofractionated radiotherapy with CyberKnife.Methods and Materials-Twenty one patients with prostate cancer who were treated with CyberKnife between January 2005 and September 2007 were selected for this retrospective study. The CyberKnife uses a stereoscopic X-ray system to obtain the position of the prostate target through the monitoring of implanted gold fiducial markers. If there is a significant deviation, the treatment is paused while the patient is repositioned by moving the couch. The deviations calculated from Xray images acquired within the time interval between two consecutive couch motions constitute a data set.Results-A total of 427 data sets and 4439 time stamps of X-ray images were analyzed. The mean duration for each data set is 697 s. At 30 s, a motion larger than 2 mm exists in about 5% of data sets. The percentage is increased to 8%, 11%, and 14% at 60 s, 90 s, and 120 s, respectively. A similar trend exists for other values of prostate motion.Conclusions-With proper monitoring and intervention during treatment, the prostate shifts observed among the patients can be kept within the tracking range of the CyberKnife. On average a sampling rate of ~40 s between consecutive X-rays is acceptable to ensure sub-millimeter tracking. However, there is significant movement variation among patients and higher sampling rate may be necessary in some patients.

This paper describes the algorithm and examines the performance of an intensity-modulated radiation therapy (IMRT) beam-angle optimization (BAO) system. In this algorithm successive sets of beam angles are selected from a set of predefined directions using a fast simulated annealing (FSA) algorithm. An IMRT beam-profile optimization is performed on each generated set of beams. The IMRT optimization is accelerated by using a fast dose calculation method that utilizes a precomputed dose kernel. A compact kernel is constructed for each of the predefined beams prior to starting the FSA algorithm. The IMRT optimizations during the BAO are then performed using these kernels in a fast dose calculation engine. This technique allows the IMRT optimization to be performed more than two orders of magnitude faster than a similar optimization that uses a convolution dose calculation engine. Any type of optimization criterion present in the IMRT system can be used in this BAO system. An objective function based on clinically-relevant dose-volume (DV) criteria is used in this study. This facilitates the comparison between a BAO plan and the corresponding plan produced by a planner since the latter is usually optimized using a DV-based objective function. A simple prostate case and a complex head-and-neck (HN) case were used to evaluate the usefulness and performance of this BAO method. For the prostate case we compared the BAO results for three, five and seven coplanar beams with those of the same number of equispaced coplanar beams. For the HN case we compare the BAO results for seven and nine non-coplanar beams with that for nine equispaced coplanar beams. In each case the BAO algorithm was allowed to search up to 1000 different sets of beams. The BAO for the prostate cases were finished in about 1-2 h on a moderate 400 MHz workstation while that for the head-and-neck cases were completed in 13-17 h on a 750 MHz machine. No a priori beam-selection criteria have been used in achieving this performance. In both the prostate and the head-and-neck cases, BAO is shown to provide improvements in plan quality over that of the equispaced beams. The use of DV-based objective function also allows us to study the dependence of the improvement of plan quality offered by BAO on the DV criteria used in the optimization. We found that BAO is especially useful for cases that require strong DV criteria. The main advantages of this BAO system are its speed and its direct link to a clinical IMRT system.

Currently, most intensity-modulated radiation therapy systems use dose-volume (DV)-based objectives. Although acceptable plans can be generated using these objectives, much trial and error is necessary to plan complex cases with many structures because numerous parameters need to be adjusted. An objective function that makes use of a generalized equivalent uniform dose (gEUD) was developed recently that has the advantage of involving simple formulae and fewer parameters. In addition, not only does the gEUD-based optimization provide the same coverage of the target, it provides significantly better protection of critical structures. However, gEUD-based optimization may not be superior once dose distributions and dose-volume histograms (DVHs) are used to evaluate the plan. Moreover, it is difficult to fine-tune the DVH with gEUD-based optimization. In this paper, we propose a method for combining the gEUD-based and DV-based optimization approaches to overcome these limitations. In this method, the gEUD optimization is performed initially to search for a solution that meets or exceeds most of the treatment objectives. Depending on the requirements, DV-based optimization with a gradient technique is then used to fine-tune the DVHs. The DV constraints are specified according to the gEUD plan, and the initial intensities are obtained from the gEUD plan as well. We demonstrated this technique in two clinical cases: aprostate cancer and ahead and neck cancer case. Compared with the DV-optimized plan, the gEUD plan provided better protection of critical structures and the target coverage was similar. However, homogeneities were slightly poorer. The gEUD plan was then fine-tuned with DV constraints, and the resulting plan was superior to the other plans in terms of the dose distributions. The planning time was significantly reduced as well. This technique is an effective means of optimizing individualized treatment plans.