Accelerating Radiation Dose Calculation with High Performance Computing and Machine Learning for Large-scale Radiotherapy Treatment Planning

Radiation therapy is powered by modern techniques in precise planning and executionof radiation delivery, which are being rapidly improved to maximize its benefit to cancerpatients. In the last decade, radiotherapy experienced the introduction of advanced methodsfor automatic beam orientation optimization, real-time tumor tracking, daily planadaptation, and many others, which improve the radiation delivery precision, planning easeand reproducibility, and treatment efficacy. However, such advanced paradigms necessitatethe calculation of orders of magnitude more causal dose deposition data, increasing the timerequirement of all pre-planning dose calculation. Principles of high-performance computingand machine learning were applied to address the insufficient speeds of widely-used dosecalculation algorithms to facilitate translation of these advanced treatment paradigms intoclinical practice.To accelerate CT-guided X-ray therapies, Collapsed-Cone Convolution-Superposition(CCCS), a state-of-the-art analytical dose calculation algorithm, was accelerated through itsnovel implementation on highly parallelized GPUs. This context-based GPU-CCCS approachtakes advantage of X-ray dose deposition compactness to parallelize calculation acrosshundreds of beamlets, reducing hardware-specific overheads, and enabling acceleration bytwo to three orders of magnitude compared to existing GPU-based beamlet-by-beamletapproaches. Near-linear increases in acceleration are achieved with a distributed, multi-GPUimplementation of context-based GPU-CCCS.Dose calculation for MR-guided treatment is complicated by electron return effects(EREs), exhibited by ionizing electrons in the strong magnetic field of the MRI scanner. EREsnecessitate the use of much slower Monte Carlo (MC) dose calculation, limiting the clinicalapplication of advanced treatment paradigms due to time restrictions. An automaticallydistributed framework for very-large-scale MC dose calculation was developed, grantinglinear scaling of dose calculation speed with the number of utilized computational cores. Itwas then harnessed to efficiently generate a large dataset of paired high- and low-noise MCdoses in a 1.5 tesla magnetic field, which were used to train a novel deep convolutionalneural network (CNN), DeepMC, to predict low-noise dose from faster high-noise MC-simulation. DeepMC enables 38-fold acceleration of MR-guided X-ray beamlet dosecalculation, while remaining synergistic with existing MC acceleration techniques to achievemultiplicative speed improvements.This work redefines the expectation of X-ray dose calculation speed, making it possibleto apply new highly-beneficial treatment paradigms to standard clinical practice for the firsttime

Optimization Problems in Radiation Therapy Treatment Planning.

Radiation therapy is one of the most common methods used to treat many types of cancer. External beam radiation therapy and the models associated with developing a treatment plan for a patient are studied. External beams of radiation are used to deliver a highly complex so-called dose distribution to a patient that is designed to kill the cancer cells while sparing healthy organs and normal tissue. Treatment planning models and optimization are used to determine the delivery machine instructions necessary to produce a desirable dose distribution. These instructions make up a treatment plan. This thesis studies four problems in radiation therapy treatment plan optimization. First, treatment planners generate a plan with a number of competing treatment plan criteria. The relationship between criteria is not known a priori. A methodology is developed for physicians and treatment planners to efficiently navigate a clinically relevant region of the Pareto frontier generated by trading off these different criteria in an informed way. Second, the machine instructions for intensity modulated radiation therapy, a common treatment modality, consist of the locations of the external beams and the non-uniform intensity profiles delivered from each of these locations. These decisions are traditionally made with separate, sequential models. These decisions are integrated into a single model and propose a heuristic solution methodology. Third, volumetric modulated arc therapy (VMAT), a treatment modality where the beam travels in a coplanar arc around the patient while continuously delivering radiation, is a popular topic among optimizers studying treatment planning due to the difficult nature of the problem and the lack of a universally accepted treatment planning method. While current solution methodologies assume a predetermined coplanar path around the patient, that assumption is relaxed and the generation of a non-coplanar path is integrated into a VMAT planning algorithm. Fourth, not all patient information is available when developing a treatment plan pre-treatment. Some information, like a patient's sensitivity to radiation, can be realized during treatment through physiological tests. Methodologies of pre-treatment planning considering adaptation to new information are studied.PhDIndustrial and Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113366/1/troylong_1.pd

Studies of the linear energy transfer and relative biological effectiveness in proton therapy of pediatric brain tumors

Proton therapy offers a reduction in dose to normal tissue compared to conventional photon-based radiotherapy. This is of particular benefit for pediatric patients as the majority are expected to become long-term survivors. Children are therefore often referred to proton therapy in order to reduce the risk of radiation induced side effects. Protons are also slightly more biologically effective compared to photons, quantified by the clinically applied relative biological effectiveness (RBE) of 1.1. However, both experimental and clinical data points to a variable RBE, which depends on tissue type, dose level, biological endpoint, and the linear energy transfer (LET). Multiple variable RBE models have therefore been developed with the aim of quantifying the RBE variation. Brain tumor patients are often at high risk of radiation damage to the brainstem - a vital organ where injury can lead to devastating side effects. Minimizing doses to the brainstem has therefore a high priority during treatment planning. However, the brainstem may also be adversely affected by the LET and variable RBE, factors that are not explicitly accounted for in routine proton therapy. In this PhD project, for both double scattering and intensity modulated proton therapy (IMPT), the LET and variable RBE in the brainstem for pediatric brain tumor patients were studied using the FLUKA Monte Carlo (MC) code. In the first part of this project, the LET and RBE in the brainstem were studied for different tumor locations relative to the brainstem. Furthermore, techniques for reducing the LET in critical organs by changing the treatment field setup were explored (Paper I). Mean LET values in the brainstem more than doubled depending on the tumor location (3.2-6.6 keV/μm), however, the location with the highest brainstem LET values also had the lowest variable RBE-weighted mean dose in the brainstem (1.8-54.0 Gy(RBE)). Changing treatment field angles reduced the mean LET in the brainstem by 32%, however, with slightly increased brainstem dose. The results demonstrate that the LET and variable RBE-weighted dose are strongly influenced by tumor location and field configuration, and that both LET and variable RBE-weighted dose must be carefully considered when altering treatment plans. In the second part, multiple variable RBE models in treatment for pediatric brain tumors were investigated. The spatial agreement of isodose volumes from the models relative to the RBE of 1.1 were compared, focusing on the full brainstem and brainstem substructures (Paper II). Application of different model specific parameters were also explored. The RBE-weighted dose calculated from RBE models was highly dependent on the applied parameters, and also differed across models. Furthermore, the spatial agreement between different models decreased rapidly for higher doses, illustrating that the RBE effect is most critical at high doses and low volumes, where dose constraints commonly are applied. Hence, using RBE models in clinical settings requires model specific dose constraints. The majority of follow-up data from proton therapy come from patients treated with double scattering (DS) proton therapy. Therefore, a DS nozzle was implemented in the FLUKA MC code in order to obtain LET and variable RBE for previously treated patients (Paper III). After calibration, excellent agreement between measurements and MC simulations was achieved with range differences of spread-out Bragg peaks generally below 1 mm and lateral penumbra differences less than 1 mm. Recalculation of dose distributions in FLUKA were compared to original patient doses from the treatment planning system, with dose differences below 2%. LET and variable RBE were furthermore obtained for these patients. In the final part of this project, the DS nozzle implementation was used to recalculate 36 pediatric brain tumor patients in a retrospective case-control study where nine patients had experienced symptomatic brainstem toxicity. Differences in LET and variable RBE-weighted dose between cases and controls were examined for the full brainstem as well as multiple brainstem substructures. Median and maximum LET were on average higher for cases vs. controls for all substructures, with the highest difference in median LET of 15% in one of the substructures. Average differences between cases and controls increased for variable RBE-weighted doses compared to a fixed RBE of 1.1. While there was large interpatient variability for both LET and variable RBE-weighted doses, the average higher LET to the brainstem could be a contributor to brainstem toxicity. The results warrant individual assessment of LET/RBE for patients at risk of brainstem toxicity. Overall, this thesis has shown that elevated LET and increased RBE may occur in the brainstem for pediatric patients with brain tumors which could further contribute to brainstem toxicity. Clinical implementation of LET and variable RBE-weighted dose calculation is therefore well justified.Doktorgradsavhandlin

A Tutorial on Radiation Oncology and Optimization

Designing radiotherapy treatments is a complicated and important task that affects patient care, and modern delivery systems enable a physician more flexibility than can be considered. Consequently, treatment design is increasingly automated by techniques of optimization, and many of the advances in the design process are accomplished by a collaboration among medical physicists, radiation oncologists, and experts in optimization. This tutorial is meant to aid those with a background in optimization in learning about treatment design. Besides discussing several optimization models, we include a clinical perspective so that readers understand the clinical issues that are often ignored in the optimization literature. Moreover, we discuss many new challenges so that new researchers can quickly begin to work on meaningful problems

A Monte Carlo based phase space model for quality assurance of intensity modulated radiotherapy incorporating leaf specific characteristics

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134797/1/mp3409.pd