Currently in HDR brachytherapy planning, a manual fine-tuning of an objective function is necessary to obtain case-specific valid plans. This study intends to facilitate this process by proposing a patient-specific inverse planning algorithm for HDR prostate brachytherapy: GPU-based multi-criteria optimization (gMCO).Two GPU-based optimization engines including simulated annealing (gSA) and a quasi-Newton optimizer (gL-BFGS) were implemented to compute multiple plans in parallel. After evaluating the equivalence and the computation performance of these two optimization engines, one preferred optimization engine was selected for the gMCO algorithm. Five hundred sixty-two previously treated prostate HDR cases were divided into validation set (100) and test set (462). In the validation set, the number of Pareto optimal plans to achieve the best plan quality was determined for the gMCO algorithm. In the test set, gMCO plans were compared with the physician-approved clinical plans.Our results indicated that the optimization process is equivalent between gL-BFGS and gSA, and that the computational performance of gL-BFGS is up to 67 times faster than gSA. Over 462 cases, the number of clinically valid plans was 428 (92.6%) for clinical plans and 461 (99.8%) for gMCO plans. The number of valid plans with target V 100 coverage greater than 95% was 288 (62.3%) for clinical plans and 414 (89.6%) for gMCO plans. The mean planning time was 9.4 s for the gMCO algorithm to generate 1000 Pareto optimal plans.In conclusion, gL-BFGS is able to compute thousands of SA equivalent treatment plans within a short time frame. Powered by gL-BFGS, an ultra-fast and robust multicriteria optimization algorithm was implemented for HDR prostate brachytherapy. Plan pools with various trade-offs can be created with this algorithm. A largescale comparison against physician approved clinical plans showed that treatment plan quality could be improved and planning time could be significantly reduced with the proposed gMCO algorithm.

To translate any robot into a clinical environment, it is critical that the robot can seamlessly integrate with all the technology of a modern clinic. MRBot, an MR-stealth brachytherapy delivery device, was used in a closed-bore 3T MRI and a clinical brachytherapy cone beam CT suite. Targets included ceramic dummy seeds, MR-Spectroscopy-sensitive metabolite, and a prostate phantom. Acquired DICOM images were exported to planning software to register the robot coordinates in the imager’s frame, contour and verify target locations, create dose plans, and export needle and seed positions to the robot. The coordination of each system element (imaging device, brachytherapy planning system, robot control, robot) was validated with a seed delivery accuracy of within 2 mm in both a phantom and soft tissue. An adaptive workflow was demonstrated by acquiring images after needle insertion and prior to seed deposition. This allows for adjustment if the needle is in the wrong position. Inverse planning (IPSA) was used to generate a seed placement plan and coordinates for ten needles and 29 seeds were transferred to the robot. After every two needles placed, an image was acquired. The placed seeds were identified and validated prior to placing the seeds in the next two needles. The ability to robotically deliver seeds to locations determined by IPSA and the ability of the system to incorporate novel needle patterns were demonstrated. Shown here is the ability to overcome this critical step. An adaptive brachytherapy workflow is demonstrated which integrates a clinical anatomy-based seed location optimization engine and a robotic brachytherapy device. Demonstration of this workflow is a key element of a successful translation to the clinic of the MRI stealth robotic delivery system, MRBot.

Accurate insertion of needles to targets in 3D anatomy is required for numerous medical procedures. To reduce patient trauma, a “fireworks” needle insertion approach can be used in which multiple needles are inserted from a single small region on the patient’s skin to multiple targets in the tissue. In this paper, we explore motion planning for “fireworks” needle insertion in 3D environments by developing an algorithm based on Rapidly-exploring Random Trees (RRTs). Given a set of targets, we propose an algorithm to quickly explore the configuration space by building a forest of RRTs and to find feasible plans for multiple steerable needles from a single entry region. We present two path selection algorithms with different optimality considerations to optimize the final plan among all feasible outputs. Finally, we demonstrate the performance of the proposed algorithm with a simulation based on a prostate cancer treatment environment.

Alternative catheter patterns can provide the physician with additional ways to treat patients previously considered unsuited for brachytherapy treatment (pubic arch interference) and facilitate robotic guidance of catheter insertion. In addition, alternative catheter patterns may decrease toxicity by avoidance of the critical structures near the penile bulb while still fulfilling the RTOG criteria.