Use of proximal operator graph solver for radiation therapy inverse treatment planning

Conic formulation of fluence map optimization problems

The convexity of objectives and constraints in fluence map optimization (FMO) for radiation therapy has been extensively studied. Next to convexity, there is another important characteristic of optimization functions and problems, which has thus far not been considered in FMO literature: conic representation. Optimization problems that are conically representable using quadratic, exponential and power cones are solvable with advanced primal-dual interior-point algorithms. These algorithms guarantee an optimal solution in polynomial time and have good performance in practice. In this paper, we construct conic representations for most FMO objectives and constraints. This paper is the first that shows that FMO problems containing multiple biological evaluation criteria can be solved in polynomial time. For fractionation-corrected functions for which no exact conic reformulation is found, we provide an accurate approximation that is conically representable. We present numerical results on the TROTS data set, which demonstrate very stable numerical performance for solving FMO problems in conic form. With ongoing research in the optimization community, improvements in speed can be expected, which makes conic optimization a promising alternative for solving FMO problems.

The value of brachytherapy in the age of advanced external beam radiotherapy: a review of the literature in terms of dosimetry

Brachytherapy (BT) has long been used for successful treatment of various tumour entities, including prostate, breast and gynaecological cancer. However, particularly due to advances in modern external beam techniques such as intensity-modulated radiotherapy (IMRT), volume modulated arc therapy (VMAT) and stereotactic body radiotherapy (SBRT), there are concerns about its future. Based on a comprehensive literature review, this article aims to summarize the role of BT in cancer treatment and highlight its particular dosimetric advantages. The authors conclude that image-guided BT supported by inverse dose planning will successfully compete with high-tech EBRT in the future and continue to serve as a valuable modality for cancer treatment.

Intelligent IoT-based large-scale inverse planning system considering postmodulation factors

The model and algorithm of intensity-modulated radiotherapy (IMRT) are updated increasingly quickly, but the hardware upgrade of primary hospitals often lags behind. The new generation of intelligent precise radiotherapy platforms provides users with intelligent medical consortium services using big data, artificial intelligence and industrial Internet of Things technology. This technology can ensure that under the real-time guidance of a professional medical consortium, primary hospitals can realize rapid large-scale reverse planning design and can more accurately consider many factors of postprocessing. Although large-scale healthcare systems, such as volumetric-modulated arc therapy and other accurate radiotherapy technologies, have developed rapidly, the development of step-and-shoot-mode IMRT technology is still very important for developing countries. For software, in addition to the conformity of the dose distribution, the modulation speed, convenience and stability of the later dose delivery should also be considered in inverse planning. Therefore, this paper analyzes the main problems in conventional IMRT inverse planning, including the smoothing of the fluence map, the selection of the gantry angle and the dose leakage of tongue–groove effects. To address these issues, a novel Intelligent IoT-based large-scale inverse planning strategy with the key factors of the postmodulation is developed, and a detailed flow chart is also provided. The scheme consists of two steps. The first step is to obtain a relatively optimal combination of gantry angles by considering the dose distribution requirements and constraints and the modulation requirements and constraints. The second step is to optimize the intensity map, to smooth the map based on prior knowledge according to the determined angles, and to obtain the final modulation scheme according to the relevant objectives and constraints of the map decomposition (leaf sequencing). In an experiment, we calculate and validate the clinical head and neck case. Because of the special gantry angle selection, the angle combination is optimized from the initial equivalent distribution to adapt to the target area and protect the nontarget area. The value of the objective function varies greatly after the optimization, especially in the target area, and the target value decreases by approximately 10%. On this basis, we smooth the fluence map by a partial differential equation with prior knowledge and a minimization of the total number of monitor units. It is also shown from the objective function value that the target value is essentially unchanged for the target area, while for the nontarget area, the value decreases by 16%, which is very impressive.

Optimization based trajectory planning for real-time 6DoF robotic patient motion compensation systems

Purpose

Robotic stabilization of a therapeutic radiation beam with respect to a dynamically moving tumor target can be accomplished either by moving the radiation source, the patient, or both. As the treatment beam is on during this process, the primary goal is to minimize exposure of normal tissue to radiation as much as possible when moving the target back to the desired position. Due to the complex mechanical structure of 6 degree-of-freedom (6DoF) robots, it is not intuitive as to what 6 dimensional (6D) correction trajectory is optimal in achieving such a goal. With proportional-integrative-derivative (PID) and other controls, the potential exists that the controller may generate a trajectory that is highly curved, slow, or suboptimal in that it leads to unnecessary exposure of healthy tissue to radiation. This work investigates a novel feedback planning method that takes into account a robot’s mechanical joint structure, patient safety tolerances, and other system constraints, and performs real-time optimization to search the entire 6D trajectory space in each time cycle so it can respond with an optimal 6D correction trajectory.

Methods

Computer simulations were created for two 6DoF robotic patient support systems: a Stewart-Gough platform for moving a patient’s head in frameless maskless stereotactic radiosurgery, and a linear accelerator treatment table for moving a patient in prostate cancer radiation therapy. Motion planning was formulated as an optimization problem and solved at real-time speeds using the L-BFGS algorithm. Three planning methods were investigated, moving the platform as fast as possible (platform-D), moving the target along a straight-line (target-S), and moving the target based on the fastest descent of position error (target-D). Both synthetic motion and prior recorded human motion were used as input data and output results were analyzed.

Results

For randomly generated 6D step-like and sinusoidal synthetic input motion, target-D planning demonstrated the smallest net trajectory error in all cases. On average, optimal planning was found to have a 45% smaller target trajectory error than platform-D control, and a 44% smaller target trajectory error than target-S planning. For patient head motion compensation, only target-D planning was able to maintain a ≤0.5mm and ≤0.5deg clinical tolerance objective for 100% of the treatment time. For prostate motion, both target-S planning and target-D planning outperformed platform-D control.

Conclusions

A general 6D target trajectory optimization framework for robotic patient motion compensation systems was investigated. The method was found to be flexible as it allows control over various performance requirements such as mechanical limits, velocities, acceleration, or other system control objectives.

Teaching treatment planning for protons with educational open-source software: experience with FoCa and matRad

Open‐source, MATLAB‐based treatment planning systems FoCa and matRAD were used in a pilot project for training prospective medical physicists and postgraduate physics students in treatment planning and beam modeling techniques for proton therapy. In the four exercises designed, students learnt how proton pencil beams are modeled and how dose is calculated in three‐dimensional voxelized geometries, how pencil beam scanning plans (PBS) are constructed, the rationale behind the choice of spot spacing in patient plans, and the dosimetric differences between photon IMRT and proton PBS plans. Sixty students of two courses participated in the pilot project, with over 90% of satisfactory rating from student surveys. The pilot experience will certainly be continued.

Fast dose optimization for rotating shield brachytherapy

PURPOSE

To provide a fast computational method, based on the proximal graph solver (POGS) - A convex optimization solver using the alternating direction method of multipliers (ADMM), for calculating an optimal treatment plan in rotating shield brachytherapy (RSBT). RSBT treatment planning has more degrees of freedom than conventional high-dose-rate brachytherapy due to the addition of emission direction, and this necessitates a fast optimization technique to enable clinical usage.

METHODS

The multi-helix RSBT (H-RSBT) delivery technique was investigated for five representative cervical cancer patients. Treatment plans were generated for all patients using the POGS method and the commercially available solver IBM ILOG CPLEX. The rectum, bladder, sigmoid colon, high-risk clinical target volume (HR-CTV), and HR-CTV boundary were the structures included in our optimization, which applied an asymmetric dose-volume optimization with smoothness control. Dose calculation resolution was 1 × 1 × 3 mm³ for all cases. The H-RSBT applicator had 6 helices, with 33.3 mm of translation along the applicator per helical rotation and 1.7 mm spacing between dwell positions, yielding 17.5° emission angle spacing per 5 mm along the applicator.

RESULTS

For each patient, HR-CTV D₉₀ , HR-CTV D₁₀₀ , rectum D_2cc , sigmoid D_2cc , and bladder D_2cc matched within 1% for CPLEX and POGS methods. Also, similar EQD2 values between CPLEX and POGS methods were obtained. POGS was around 18 times faster than CPLEX. For all patients, total optimization times were 32.1-65.4 s for CPLEX and 2.1-3.9 s for POGS.

CONCLUSIONS

POGS reduced treatment plan optimization time approximately 18 times for RSBT with similar HR-CTV D₉₀ , organ at risk (OAR) D_2cc values, and EQD2 values compared to CPLEX, which is significant progress toward clinical translation of RSBT.