Purpose: Although surgery remains the treatment of choice, patients with carcinoma of the extrahepatic bile duct who are not candidates for surgical resection(∼75%) are treated palliatively with radiation therapy and/or chemotherapy. Intraluminal LDR or HDR brachytherapy has the well‐documented advantage of delivering a large target dose, while significantly sparing surrounding healthy tissues. Typically, treatment planning is performed with respect to source‐guide catheter(s) inserted along the biliary canal under fluoroscopic guidance. The prescription hull refers to a volume at a distance of 0.5–1.0 cm from catheter(s). Because of the irregular shape of the source guides, manual contouring of the prescription hull is time consuming and subject to errors. We describe, and illustrate with actual treatment plans, an automatic algorithm to outline the planning volume. Methods and Materials: The contouring of a single catheter can be considered as the trajectory generated by a moving ball. The path of the ball forms a guide curve, which is given by the digitized‐coordinates of the source‐guide catheter. Given a radius r that defines the thickness of the PTV, our volume‐definition algorithm applies curve fitting via Ferguson spline interpolation, followed by circle generation and sphere generation. For multiple catheters, the algorithm is more complex, requiring curve fitting, warping, slicing, and linking to obtain the resulting tumor shape. Results: For the biliary intraluminal case, the algorithm takes only seconds to generate a 3D‐planning volume. Applying it to complex hypothetical cases reveals that it handles irregular tumor volumes and shapes well, and returns a 3D‐planning volume within 1 CPU‐minute. Conclusion: The automatic volume‐definition algorithm works reliably and quickly, and results in plans that are demonstratively superior to those obtained by manual planning. Furthermore, the planning volume is a necessary input to computer‐based plan optimization. The volume‐definition algorithm improves the treatment dosimetric conformity, a factor that may contribute to improved clinical outcome.
Purpose: We present a multiple‐objective treatment planning framework that involves a wide range of treatment planning variables and multiple clinical objectives and constraints. Our goal is to compare the quality of resulting treatment plans from a multiple‐objective approach versus plans from current clinical systems. The techniques is applied across two clinical sites (thus with different clinician preferences and outcome endpoints) to observe consistency of the proposed methods. Methods and Materials:We combine preemptive programming and weighted sum approaches within a large‐scale discrete optimization framework to manage the multiple clinical objectives. Four objectives are optimized explicitly: PTV homogeneity, conformity, OAR doses and OAR DVHs. Elements of uncertainty in constraints (e.g., best possible limiting dose bounds to OARs) are preemptively determined and adaptively incorporated into the treatment planning model. Results:Compared to clinical plans, for a collection of 10 head‐and‐neck cases from 2 clinics, PTV homogeneity improves by 5–14% when using the multiple objective approach, whereas conformity improves by 2–5%. The mean‐dose to organs‐at‐risks reduces uniformly by 8–42% for parotid, 6–30% for mandible, 10–28% for larynx, and 7–22% for oral cavity. In particular, all critical structures receive significantly less dose, as the multiple objective approach focuses the radiation onto the tumor volume. The plans provide over 95% PTV coverage, as requested by the clinicians. Conclusion:The multiple‐objective approach produces very high‐quality plans. Although the resulting solution is sensitive with respect to the order the objectives are optimized and prioritized, across the two clinical sties, the multiple objective schema works consistently well. Specifically, at both sites, the multiple objective approach results in plans with drastic dose reduction to critical structures, while simultaneously improving PTV coverage, conformity, and dose homogeneity when compared to plans obtained via commercial planning systems. Elements of uncertainty in constraints can be preemptively determined. Computationally it takes about an hour to generate each plan.
This paper presents a methodology and algorithms of optimizing and smoothing the tool orientation control for 5-axis sculptured surface machining. A searching method in the machining configuration space (C-space) is proposed to find the optimal tool orientation by considering the local gouging, rear gouging and global tool collision in machining. Based on the machined surface error analysis, a boundary search method is developed first to find a set of feasible tool orientations in the C-space to eliminate gouging and collision. By using the minimum cusp height as the objective function, we first determine the locally optimal tool orientation in the C-space to minimize the machined surface error. Considering the adjacent part geometry and the alternative feasible tool orientations in the C-space, tool orientations are then globally optimized and smoothed to minimize the dramatic change of tool orientation during machining. The developed method can be used to automate the planning and programming of tool path generation for high performance 5-axis sculptured surface machining. Computer implementation and examples are also provided in the paper.
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