Integration of dynamic table translations into dynamic trajectory radiotherapy and mixed photon-electron beam radiotherapy

Radiotherapy aims at delivering a lethal dose of radiation to tumor cells while sparing the surrounding healthy tissue and organs. Highly specialized devices, such as C-arm linear accelerators (linacs), have been developed for external beam radiotherapy, which deliver high-energy photon and electron beams. Over the last decades, several improvements in photon beam radiotherapy, such as introducing the photon multileaf collimator (pMLC), enabled intensity-modulated radiotherapy (IMRT), resulting in improved target conformality compared to 3D conformal techniques. Volumetric modulated arc therapy (VMAT) improves the delivery efficiency while maintaining the dosimetric plan quality of IMRT by using dynamic gantry rotation during beam on. Next to the dynamic gantry rotation, also the table and the collimator can rotate dynamically during beam on. This is used in a technique called dynamic trajectory radiotherapy (DTRT). Furthermore, the table can also translate dynamically in three directions, enabling non-isocentric DTRT. However, the potential of dynamic table translations for radiotherapy on a C-arm linac is unexplored. Thus, in this thesis, treatment techniques including dynamic table translations are developed, and potential use cases are shown. A treatment planning process (TPP) for non-isocentric DTRT is developed to create treatment plans with photon beams including dynamic gantry, collimator, and table rotation and dynamic table translation. The intensity modulation optimization of the TPP is based on a hybrid column generation and simulated annealing direct aperture optimization algorithm. The TPP is used to create non-isocentric DTRT plans and several potential use cases for non-isocentric DTRT are demonstrated: While maintaining treatment plan quality, the delivery efficiency is improved by using non-isocentric DTRT instead of multi-isocentric IMRT for craniospinal irradiation. Extending the source-to-target distance in DTRT plans reduces the risk of collision between the gantry and the patient or table and enables additional beam directions, which could be exploited to improve the dosimetric treatment plan quality compared to isocentric DTRT. Contrary to photon beam radiotherapy, electron treatments are still applied using patient-specific cut-outs placed in an applicator. By using the pMLC for electron beam collimation instead of the cut-outs, efficient electron beam treatments are possible. Further, the use of the pMLC facilitates mixed photon-electron beam radiotherapy (MBRT). An MBRT technique using pMLC-collimated electron arcs instead of electron beams with a static gantry angle is developed, resulting in improved delivery efficiency while maintaining the dosimetric plan quality of MBRT plans using electron beams with a static gantry angle. One of the challenges of DTRT on C-arm linacs is accurately predicting potential collisions between the gantry, the patient, and the table during treatment planning. Thus, a collision prediction tool is developed, which is able to predict possible collision interlocks. The tool was successfully validated against measurements. The created treatment plans for non-isocentric DTRT and MBRT were shown to be accurately deliverable on a C-arm linac. For several treatment plans, the dosimetric accuracy was successfully validated using film measurements. In conclusion, this thesis demonstrates the benefits of dynamic table translations in photon and electron beam radiotherapy. With the demonstrated benefits of improved dosimetric treatment plan quality, delivery efficiency, and collision risk, dynamic table translations further facilitate the use of MBRT and DTRT treatment techniques in clinics in the future

Comparing Optimization Methods for Radiation Therapy Patient Scheduling using Different Objectives

Radiation therapy (RT) is one of the most common technologies used to treat cancer. To better use resources in RT, optimization models can be used to automatically create patient schedules, a task that today is done manually in almost all clinics. This paper presents a comprehensive study of different optimization methods for modeling and solving the RT patient scheduling problem. The results can be used as decision support when implementing an automatic scheduling algorithm in practice. We introduce an Integer Linear Programming (IP) model, a column generation IP model (CG-IP), and a Constraint Programming model. Patients are scheduled on multiple machine types considering their priority for treatment, session duration and allowed machines, while taking expected future patient arrivals into account. Different cancer centers may have different scheduling objectives, and therefore each model is solved using multiple different objective functions, including minimizing waiting times, and maximizing the fulfillment of patients' preferences for treatment times. The test data is generated from historical data from Iridium Netwerk, a large cancer center in Belgium with 10 linear accelerators. The results demonstrate that the CG-IP model can solve all the different problem instances to a mean optimality gap of less than 1% within one hour. The proposed methodology provides a tool for automated scheduling of RT treatments and can be generally applied to RT centers.Comment: 20 pages, 4 figures, Submitted to Operations Research Foru

Delivery time reduction for mixed photon-electron radiotherapy by using photon MLC collimated electron arcs.

Electron arcs in mixed-beam radiotherapy (Arc-MBRT) consisting of intensity-modulated electron arcs with dynamic gantry rotation potentially reduce the delivery time compared to mixed-beam radiotherapy containing electron beams with static gantry angle (Static-MBRT). This study aims to develop and investigate a treatment planning process (TPP) for photon multileaf collimator (pMLC) based Arc-MBRT.

Approach: An existing TPP for Static-MBRT plans is extended to integrate electron arcs with a dynamic gantry rotation and intensity modulation using a sliding window technique. The TPP consists of a manual setup of electron arcs, and either static photon beams or photon arcs, shortening of the source-to-surface distance for the electron arcs, initial intensity modulation optimization, selection of a user-defined number of electron beam energies based on dose contribution to the target volume and finally, simultaneous photon and electron intensity modulation optimization followed by full Monte Carlo dose calculation. Arc-MBRT plans, Static-MBRT plans, and photon-only plans were created and compared for four breast cases. Dosimetric validation of two Arc-MBRT plans was performed using film measurements.

Main results: The generated Arc-MBRT plans are dosimetrically similar to the Static-MBRT plans while outperforming the photon-only plans. The mean heart dose is reduced by 32% on average in the MBRT plans compared to the photon-only plans. The estimated delivery times of the Arc-MBRT plans are similar to the photon-only plans but less than half the time of the Static-MBRT plans. Measured and calculated dose distributions agree with a gamma passing rate of over 98% (3% global, 2 mm) for both delivered Arc-MBRT plans. 

Significance: A TPP for Arc-MBRT is successfully developed and Arc-MBRT plans showed the potential to improve the dosimetric plan quality similar as Static-MBRT while maintaining short delivery times of photon-only treatments. This further facilitates integration of pMLC-based MBRT into clinical practice

A shortest path-based approach to the multileaf collimator sequencing problem

AbstractThe multileaf collimator sequencing problem is an important component in effective cancer treatment delivery. The problem can be formulated as finding a decomposition of an integer matrix into a weighted sequence of binary matrices whose rows satisfy a consecutive ones property. Minimising the cardinality of the decomposition is an important objective and has been shown to be strongly NP-hard, even for a matrix restricted to a single column or row. We show that in this latter case it can be solved efficiently as a shortest path problem, giving a simple proof that the one-row problem is fixed-parameter tractable in the maximum intensity. We develop new linear and constraint programming models exploiting this result. Our approaches significantly improve the best known for the problem, bringing real-world sized problem instances within reach of exact algorithms