Virtual planning of multicatheter brachytherapy implants for accelerated partial breast irradiation

Over the last decade, increasing numbers of breast cancer patients are being treated using interstitial radioactive implants (brachytherapy). Multiple catheters are placed in the breast using a free-hand or template-guided approach. The configuration of the catheters and their relation to the tumor target volume are crucial to effective treatment. Catheter insertion requires a high level of experience to produce an implant of excellent quality. Construction of optimal catheter configuration prior to the procedure (virtual planning) would reduce the dependence of implant quality on the skill of the physician. Currently available commercial planning systems do not allow for virtual planning. We have developed software which inputs the target anatomy for a prospective patient and matches it to a previously-delivered catheter configuration from an institutional implant library. The archived catheter arrangement is then customized to fit the new target volume. The user can actually explore the implications of variations in catheter number and spatial arrangement. This may significantly improve the quality of implants that would otherwise be designed strictly based on a physician's prior experience. In an academic environment, this new resource could lead to better, faster results in the treatment of breast cancer.

Predicting respiratory motion for four-dimensional radiotherapy

Adapting radiation delivery to respiratory motion is made possible through corrective action based on real-time feedback of target position during respiration. The advantage of this approach lies with its ability to allow tighter margins around the target while simultaneously following its motion. A significant hurdle to the successful implementation of real-time target-tracking-based radiation delivery is the existence of a finite time delay between the acquisition of target position and the mechanical response of the system to the change in position. Target motion during the time delay leads to a resultant lag in the system's response to a change in tumor position. Predicting target position in advance is one approach to ensure accurate delivery. The aim of this manuscript is to estimate the predictive ability of sinusoidal and adaptive filter-based prediction algorithms on multiple sessions of patient respiratory patterns. Respiratory motion information was obtained from recordings of diaphragm motion for five patients over 60 sessions. A prediction algorithm that employed both prediction models-the sinusoidal model and the adaptive filter model-was developed to estimate prediction accuracy over all the sessions. For each session, prediction error was computed for several time instants (response time) in the future (0-1.8 seconds at 0.2-second intervals), based on position data collected over several signal-history lengths (1-7 seconds at 1-second intervals). Based on patient data included in this study, the following observations are made. Qualitative comparison of predicted and actual position indicated a progressive increase in prediction error with an increase in response time. A signal-history length of 5 seconds was found to be the optimal signal history length for prediction using the sinusoidal model for all breathing training modalities. In terms of overall error in predicting respiratory motion, the adaptive filter model performed better than the sinusoidal model. With the adaptive filter, average prediction errors of less than 0.2 cm (1sigma) are possible for response times less than 0.4 seconds. In comparing prediction error with system latency error (no prediction), the adaptive filter model exhibited lesser prediction errors as compared to the sinusoidal model, especially for longer response time values (>0.4 seconds). At smaller response time values (<0.4 seconds), improvements in prediction error reduction are required for both predictive models in order to maximize gains in position accuracy due to prediction. Respiratory motion patterns are inherently complex in nature. While linear prediction-based prediction models perform satisfactorily for shorter response times, their prediction accuracy significantly deteriorates for longer response times. Successful implementation of real-time target-tracking-based radiotherapy requires response times less than 0.4 seconds or improved prediction algorithms.

Intraoperative dynamic dosimetry for prostate implants

This paper describes analytic tools in support of a paradigm shift in brachytherapy treatment planning for prostate cancer--a shift from standard pre-planning to intraoperative planning using dosimetric feedback based on the actual deposited seed positions within the prostate. The method proposed is guided by several desiderata: (a) bringing both planning and evaluation in the operating room (i.e. make post-implant evaluation superfluous) therefore making rectifications--if necessary--still achievable; (b) making planning and implant evaluation consistent by using the same imaging system (ultrasound); and (c) using only equipment commonly found in a hospital operating room. The intraoperative dosimetric evaluation is based on the fusion between ultrasound images and 3D seed coordinates reconstructed from fluoroscopic projections. Automatic seed detection and registration of the fluoroscopic and ultrasound information, two of the three key ingredients needed for the intraoperative dynamic dosimetry optimization (IDDO), are explained in detail. The third one, the reconstruction of 3D coordinates from projections, was reported in a previous article. The algorithms were validated using a custom-designed phantom with non-radioactive (dummy) seeds. Also, fluoroscopic images were taken at the conclusion of an actual permanent prostate implant and compared with data on the same patient obtained from radiographic-based post-implant evaluation. To offset the effect of organ motion the comparison was performed in terms of the proximity function of the two seed distributions. The agreement between the intra- and post-operative seed distributions was excellent.

Operator-free, film-based 3D seed reconstruction in brachytherapy

In brachytherapy implants, the accuracy of dose calculation depends on the ability to localize radioactive sources correctly. If performed manually using planar images, this is a time-consuming and often error-prone process-primarily because each seed must be identified on (at least) two films. In principle, three films should allow automatic seed identification and position reconstruction; however, practical implementation of the numerous algorithms proposed so far appears to have only limited reliability. The motivation behind this work is to create a fast and reliable system for real-time implant evaluation using digital planar images obtained from radiotherapy simulators, or mobile x-ray/fluoroscopy systems. We have developed algorithms and code for 3D seed coordinate reconstruction. The input consists of projections of seed positions in each of three isocentric images taken at arbitrary angles. The method proposed here consists of a set of heuristic rules (in a sense, a learning algorithm) that attempts to minimize seed misclassifications. In the clinic, this means that the system must be impervious to errors resulting from patient motion as well as from finite tolerances accepted in equipment settings. The software program was tested with simulated data, a pelvic phantom and patient data. One hundred and twenty permanent prostate implants were examined (105 125I and 15 103Pd) with the number of seeds ranging from 35 to 138 (average 79). The mean distance between actual and reconstructed seed positions is in the range 0.03-0.11 cm. On a Pentium III computer at 600 MHz the reconstruction process takes 10-30 s. The total number of seeds is independently validated. The process is robust and able to account for errors introduced in the clinic.