Beam Orientation Optimization for Intensity Modulated Radiation Therapy using Adaptive l1 Minimization

Beam orientation optimization (BOO) is a key component in the process of IMRT treatment planning. It determines to what degree one can achieve a good treatment plan quality in the subsequent plan optimization process. In this paper, we have developed a BOO algorithm via adaptive l_1 minimization. Specifically, we introduce a sparsity energy function term into our model which contains weighting factors for each beam angle adaptively adjusted during the optimization process. Such an energy term favors small number of beam angles. By optimizing a total energy function containing a dosimetric term and the sparsity term, we are able to identify the unimportant beam angles and gradually remove them without largely sacrificing the dosimetric objective. In one typical prostate case, the convergence property of our algorithm, as well as the how the beam angles are selected during the optimization process, is demonstrated. Fluence map optimization (FMO) is then performed based on the optimized beam angles. The resulted plan quality is presented and found to be better than that obtained from unoptimized (equiangular) beam orientations. We have further systematically validated our algorithm in the contexts of 5-9 coplanar beams for 5 prostate cases and 1 head and neck case. For each case, the final FMO objective function value is used to compare the optimized beam orientations and the equiangular ones. It is found that, our BOO algorithm can lead to beam configurations which attain lower FMO objective function values than corresponding equiangular cases, indicating the effectiveness of our BOO algorithm.Comment: 19 pages, 2 tables, and 5 figure

Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future.

Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence-based treatment planning applications, such as deep learning-based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence-based treatment planning are discussed for future works

Improved Approximation Algorithms for Segment Minimization in Intensity Modulated Radiation Therapy

he segment minimization problem consists of finding the smallest set of integer matrices that sum to a given intensity matrix, such that each summand has only one non-zero value, and the non-zeroes in each row are consecutive. This has direct applications in intensity-modulated radiation therapy, an effective form of cancer treatment. We develop three approximation algorithms for matrices with arbitrarily many rows. Our first two algorithms improve the approximation factor from the previous best of $1+\log_2 h$ to (roughly) $3/2 \cdot (1+\log_3 h)$ and $11/6\cdot(1+\log_4{h})$, respectively, where $h$ is the largest entry in the intensity matrix. We illustrate the limitations of the specific approach used to obtain these two algorithms by proving a lower bound of $\frac{(2b-2)}{b}\cdot\log_b{h} + \frac{1}{b}$ on the approximation guarantee. Our third algorithm improves the approximation factor from $2 \cdot (\log D+1)$ to $24/13 \cdot (\log D+1)$, where $D$ is (roughly) the largest difference between consecutive elements of a row of the intensity matrix. Finally, experimentation with these algorithms shows that they perform well with respect to the optimum and outperform other approximation algorithms on 77% of the 122 test cases we consider, which include both real world and synthetic data.Comment: 18 page

A Dual-Beam Irradiation Facility for a Novel Hybrid Cancer Therapy

In this paper we present the main ideas and discuss both the feasibility and the conceptual design of a novel hybrid technique and equipment for an experimental cancer therapy based on the simultaneous and/or sequential application of two beams, namely a beam of neutrons and a CW (continuous wave) or intermittent sub-terahertz wave beam produced by a gyrotron for treatment of cancerous tumors. The main simulation tools for the development of the computer aided design (CAD) of the prospective experimental facility for clinical trials and study of such new medical technology are briefly reviewed. Some tasks for a further continuation of this feasibility analysis are formulated as well.Comment: 18 pages, 3 tables, 8 figures, 50 reference

Monte Carlo Modelling for Photon and Proton Therapy in Heterogenous Tissue and Prosthesis Material

Treatment outcomes in radiotherapy can be improved by reducing uncertainties in patient set-up, beam delivery and dose distribution. Clarification of arrangements can minimize the dose distributed to normal tissues, and facilitate dose escalation. However, heterogeneity can increase any ambiguities associated with dose distribution. The treatment planning system (TPS) cannot effectively calculate dose distribution in complex heterogeneous areas, which increases uncertainty. This research aims to study microscopic dose distribution in temporal bone, cochlea and pancreatic stents as applicable to modern radiotherapy treatments. To achieve this aim a multiscale approach will be used, as it provides essential information about differences in dose distribution between TPS/clinical CT and Monte Carlo (MC)/Micro CT for photons and protons. In the first part of this study, two DICOM series of pancreatic cancer patients were used with an inserted stent. A new model includes the atomic composition of the stent material, and new stent contouring was introduced to overcome a CT artefact. A PRIMO Monte Carlo model was tuned and compared with the TPS dose distribution and a one-beam volume-modulated arc therapy (VMAT) plan was created. A significant dose difference was observed when comparing the new model and TPS, suggesting increased uncertainty of the dose distribution in clinical practice. An open-access DICOM format of the data for the resected temporal bone and cochlea tissue was used with the FLUKA MC code to imitate potential high-dose scenarios associated with VMAT using the FLOOD option. Twenty-three photon and proton energy levels ranging from 0.055 to 5.5 MeV for photons and 37.59 to 124.83 MeV for protons were simulated separately to calculate dose distribution. Micro CT data shows three density levels in the temporal bone and cochlea. The photon distribution in the low energy range 0.055-0.09 MeV, the largest proportion of the dose (48.8%) was deposited within high-density bone, whereas above 0.125 MeV, the change on dose distribution started to occur where there was greater deposition in low-density tissue, reaching 53%. The dose distribution in the soft bone's intermediate density was 26.4% at 0.07 MeV and dropped to 19.7% at 2.5 MeV. There is a 29% percentage difference in dose distribution on the soft bone between the low and high energy. The dose distribution did not change significantly in proton between the low, intermediate and high-density areas. The dose distribution in 37.59 MeV shows 54.86% in low density, 19.75% in intermediate density and 25.39% in high density. A similar outcome was observed in high energy 124.83 MeV, a dose distribution was 54.21% in low density, 19.79% intermediate density and 26% in high density.An advanced model was created to connect the results to a clinical routine when treating brain tumours using the VMAT technique. Cases were selected from 280 data sets of patients diagnosis with gliomas. Eleven different scenarios were identified. The advanced model shows five cases with an enhanced mean dose. The TPS overestimated the mean dose in all cases. In some instances, A significant mean dose variance of 8.8% was noticed in two cases. Extra cases were selected with a distance between the target and cochlea less than 1 cm. The cases show a significant difference in the mean dose and normal tissue complication probability (NTCP) models. A model was created to connect the results with Gamma Knife treatment. Thirty-four cases of schwannoma were used, and four revealed a significant difference in the scattering dose to the cochlea. The maximum difference in mean dose achieved reached 8.3%.Uncertainty due to dose distribution can affect treatment outcomes. For example, hearing loss and tinnitus can be side effects of brain cancer radiotherapy treatment. It was found that increasing the dose led to a corresponding increased dose reaching the cochlea. Increasing the model accuracy using micro-CT data and MC computation helps to control the dose to the cochlea by controlling dose distribution. In addition, pancreatic cancer can help achieve higher dose escalation to provide better outcomes to patients. Using dose-to-medium calculation, manufactures data associated with stent materials, and models based on Micro CT of resected organs can reveal uncertainty in dose distribution in heterogeneous areas