We propose to develop deep learning models that can predict Pareto optimal dose distributions by using any given set of beam angles, along with patient anatomy, as input to train the deep neural networks. We implement and compare two deep learning networks that predict with two different beam configuration modalities. We generated Pareto optimal plans for 70 patients with prostate cancer. We used fluence map optimization to generate 500 IMRT plans that sampled the Pareto surface for each patient, for a total of 35,000 plans. We studied and compared two different models, Model I and Model II. Model I directly uses beam angles as a second input to the network as a binary vector. Model II converts the beam angles into beam doses that are conformal to the PTV. Our deep learning models predicted voxel-level dose distributions that precisely matched the ground truth dose distributions. Quantitatively, Model I prediction error of 0.043 (confirmation), 0.043 (homogeneity), 0.327 (R50), 2.80% (D95), 3.90% (D98), 0.6% (D50), 1.10% (D2) was lower than that of Model II, which obtained 0.076 (confirmation), 0.058 (homogeneity), 0.626 (R50), 7.10% (D95), 6.50% (D98), 8.40% (D50), 6.30% (D2). Treatment planners who use our models will be able to use deep learning to control the tradeoffs between the PTV and OAR weights, as well as the beam number and configurations in real time. Our dose prediction methods provide a stepping stone to building automatic IMRT treatment planning.
Mayo Clinic（梅奥诊所）；University of Georgia（佐治亚大学）；Technische Universität Dresden（德累斯顿工业大学）；Helmholtz-Zentrum Dresden - Rossendorf（德累斯顿-罗森多夫亥姆霍兹中心）；University of Virginia（弗吉尼亚大学）
Purpose: We present a framework for robust automated treatment planning using machine learning, comprising scenario-specific dose prediction and robust dose mimicking. Methods: The scenario dose prediction pipeline is divided into the prediction of nominal dose from input image and the prediction of scenario dose from nominal dose, each using a deep learning model with U-net architecture. By using a specially developed dose-volume histogram-based loss function, the predicted scenario doses are ensured sufficient target coverage despite the possibility of the training data being non-robust. Del…
The secondary neutron fields at the deep tumor therapy terminal at HIRFL (Heavy Ion Research Facility in Lanzhou) were investigated. The distributions of neutron ambient dose equivalent were measured with a FHT762 Wendi-II neutron ambient dose equivalent meter as the 12C ions with energies of 165, 207, 270, and 350 MeV/u bombarded on thick tissue-like targets. The thickness of targets used in experiments is larger than the range of the carbon ions. The neutron spectra and dose equivalent is simulated by using FLUKA code, and the results agree well with the experimental data. The experiment res…
In this research, we presented the method of inhalation therapy with noble gas micro-doses and results of clinical studies in hospital of Russian Academy of Sciences (RAS). We have designed a device that allows dosed injection of noble gas with a volume of about 2-4 ml with peroxide vapors into the nasal cavity. After testing on the authors of the project and obtaining a positive impact of xenon and krypton, a study was conducted on a group of conditionally healthy volunteers in the amount of 20 people. We have noted a positive effect from 18 people. And also, in this article we briefly descri…
Therapeutic protons acting on O18-substituted thymidine increase cytotoxicity in radio-resistant human cancer cells. We consider here the physics behind the irradiation during proton beam therapy and diagnosis using O18-enriched thymine in DNA, with attention to the effect of the presence of thymine-18 on cancer cell death.
In this work, we propose a Machine Learning model that generates an adjustable 3D dose distribution for external beam radiation therapy for head-and-neck cancer treatments. In contrast to existing Machine Learning methods that provide a single model, we create pairs of models for each organ-at-risk, namely lower-extreme and upper-extreme models. These model pairs for an organ-at-risk propose doses that give lower and higher doses to that organ-at-risk, while also encapsulating the dose trade-off to other organs-at-risk. By weighting and combining the model pairs for all organs-at-risk, we are …
A new strategy for radiation therapy dosimetry planning (RTDP) used to reduce dose estimation errors due to respiratory motion in breast treatment was illustrated and evaluated in this study. On CT data set acquired for breast treatment, six different RTDP tangential techniques were performed: (i) three-dimensional conformal radiotherapy (3DCRT) with physical wedge, (ii) 3DCRT with virtual wedge, (iii) motion management technique (MMT) with physical wedge, (iv) MMT with virtual wedge, (v) 3DCRT with field-in-field, and (vi) intensity-modulated radiation therapy (IMRT) with direct aperture opti…
To accurately verify the dose of intensity-modulated radiation therapy (IMRT), we have used a global optimization method to investigate a new dose-verification algorithm. In practical application of this quality assurance (QA) procedure, verification of the dose using calculated and measured dose distributions involves a subtle problem in the region of high dose gradient. Consideration of systematic errors shows that the large dose differences in high-dose-gradient regions are due to the unexpected shift of measuring devices. We have proposed an optimization algorithm to correct this error, an…
We present a method of directly optimizing on deviations in clinical goal values in radiation therapy treatment planning. Using a new mathematical framework in which metrics derived from the dose-volume histogram are regarded as functionals of an auxiliary random variable, we are able to obtain volume-at-dose and dose-at-volume as infinitely differentiable functions of the dose distribution with easily evaluable function values and gradients. Motivated by the connection to risk measures in finance, which is formalized in this framework, we also derive closed-form formulas for mean-tail-dose an…
Secondary radiation in a radiation therapy environment is considered a cause for post-treatment side effects including secondary carcinogenesis. In a proton-therapy setup, neutrons deliver a considerable extra dose to the patient. Various measurements and calculations of neutron dose suggest a wide range of values for this quantity. This paper provides a comparison between the previously published values and analyzes the Monte Carlo calculated neutron dose from the present work using the neutron cross-section data. The purpose is to provide a range for the convergence of several neutron-absorb…
Fast and accurate dose predictions are one of the bottlenecks in treatment planning for microbeam radiation therapy (MRT). In this paper, we propose a machine learning (ML) model based on a 3D U-Net. Our approach predicts separately the large doses of the narrow high intensity synchrotron microbeams and the lower valley doses between them. For this purpose, a concept of macro peak doses and macro valley doses is introduced, describing the respective doses not on a microscopic level but as macroscopic quantities in larger voxels. The ML model is trained to mimic full Monte Carlo (MC) data. Comp…
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
With the advancement of treatment modalities in radiation therapy for cancer patients, outcomes have improved, but at the cost of increased treatment plan complexity and planning time. The accurate prediction of dose distributions would alleviate this issue by guiding clinical plan optimization to save time and maintain high quality plans. We have modified a convolutional deep network model, U-net (originally designed for segmentation purposes), for predicting dose from patient image contours of the planning target volume (PTV) and organs at risk (OAR). We show that, as an example, we are able…
Purpose: The presence of respiratory motion during radiation treatment leads to degradation of the expected dose distribution, both for target coverage and healthy-tissue sparing, particularly for techniques like pencil-beam scanning proton therapy which have dynamic delivery systems. While tools exist to estimate this degraded four-dimensional (4D) dose, they typically have one or more deficiencies such as ... Methods: To quickly compute the 4D-dose, the three main tasks of the calculator were run on graphics processing units (GPUs). These tasks were: simulating the delivery of the plan using…
Cone beam CT (CBCT) has been widely used for patient setup in image guided radiation therapy (IGRT). Radiation dose from CBCT scans has become a clinical concern. The purposes of this study are 1) to commission a GPU-based Monte Carlo (MC) dose calculation package gCTD for Varian On-Board Imaging (OBI) system and test the calculation accuracy, and 2) to quantitatively evaluate CBCT dose from the OBI system in typical IGRT scan protocols. We first conducted dose measurements in a water phantom. X-ray source model parameters used in gCTD are obtained through a commissioning process. gCTD accurac…
Radiotherapy of pregnant cancer patients is not common, but when applied accurate assessment of the fetus dose is required, especially since the treatment planning systems are not able and do not allow accurate assessment of out-of-field doses. Proton therapy significantly reduces out-of-field doses, such as dose to the fetus, when compared to the photon radiotherapy techniques and as such could be promising for pregnant cancer patients. Within this study Monte Carlo calculations are performed on the three different computational phantoms representing pregnant women, all in second trimester of…
We demonstrate the application of mixture density networks (MDNs) in the context of automated radiation therapy treatment planning. It is shown that an MDN can produce good predictions of dose distributions as well as reflect uncertain decision making associated with inherently conflicting clinical tradeoffs, in contrast to deterministic methods previously investigated in literature. A two-component Gaussian MDN is trained on a set of treatment plans for postoperative prostate patients with varying extents to which rectum dose sparing was prioritized over target coverage. Examination on a test…
The neutron dose distributions on observation distances and on observation angles were measured using a Wendi-II neutron dose-meter at the deep tumor therapy terminal at HIRFL (Heavy Ion Research Facility in Lanzhou) as 12C ions with energies from 165 to 350 MeV/u bombarding on thick solid water targets with different thickness according to the ion energies. The experimental results were compared with those calculated by FLUKA code. It is found that the experimental data was in good agreement with the calculated results. The neutron energy spectra were also studied by using the FLUKA code. The…
Wuhan University（武汉大学）；University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）；University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）；Shenzhen Bay Laboratory（深圳湾实验室）
Dose verification based on proton-induced positron emitters is a promising quality assurance tool and may leverage the strength of artificial intelligence. To move a step closer towards practical application, the sensitivity analysis of two factors needs to be performed: biological washout and depth selection. selection. A bi-directional recurrent neural network (RNN) model was developed. The training dataset was generated based upon a CT image-based phantom (abdomen region) and multiple beam energies/pathways, using Monte-Carlo simulation (1 mm spatial resolution, no biological washout). For …
For the in vivo range verification in proton therapy, it has been tried to measure the spatial distribution of the prompt gammas generated by the proton-induced interactions with the close relationship with the proton dose distribution. However, the high energy of the prompt gammas and background gammas are still problematic in measuring the distribution. In this study, we suggested a new method determining the in vivo range by utilizing the time structure of the prompt gammas formed with the rotation of a range modulation wheel (RMW) in the passive scattering proton therapy. To validate the M…
Purpose: To verify dose delivery and quality assurance of volumetric modulated arc therapy (VMAT) for head and neck cancer. Method: The Imaging and Radiation Oncology Core Houston (IROC-H) head and neck phantom with thermo- luminescent dosimeters (TLDs) and films, were imaged with computed tomography scan and the reconstructed image was transferred to pinnacle treatment planning system (TPS). On TPS the planning target volume (PTV), secondary target volume (STV) and organ at risk (OAR) were delineated manually and a treatment plan was made. The dose constraints were determined for the concerne…
Cao et al (2018) published an article on inverse planning based on dose-averaged linear energy transfer (LET). Their claim that the problem can be cast as a linear optimization model relies on an incorrect application of the Charnes-Cooper transformation. In this comment we show that their linear model is simlar to one where dose-averaged LET is multiplied with dose, explaining why their model was nonetheless able to improve the LET distribution.
Purpose: We propose a general framework for quantifying predictive uncertainties of dose-related quantities and leveraging this information in a dose mimicking problem in the context of automated radiation therapy treatment planning. Methods: A three-step pipeline, comprising feature extraction, dose statistic prediction and dose mimicking, is employed. In particular, the features are produced by a convolutional variational autoencoder and used as inputs in a previously developed nonparametric Bayesian statistical method, estimating the multivariate predictive distribution of a collection of p…
We give a scenario-based treatment plan optimization formulation that is equivalent to planning with geometric margins if the scenario doses are calculated using the static dose cloud approximation. If the scenario doses are instead calculated more accurately, then our formulation provides a novel robust planning method that overcomes many of the difficulties associated with previous scenario-based robust planning methods. In particular, our method protects only against uncertainties that can occur in practice, it gives a sharp dose fall-off outside high dose regions, and it avoids underdosage…
We study the propagation of nucleons and nuclei in tissue-like media within a Monte Carlo Model for Heavy-ion Therapy (MCHIT) based on the GEANT4 toolkit (version 8.2). The model takes into account fragmentation of projectile nuclei and secondary interactions of produced nuclear fragments. Model predictions are validated with available experimental data obtained for water and PMMA phantoms irradiated by monoenergetic carbon-ion beams. The MCHIT model describes well (1) the depth-dose distributions in water and PMMA, (2) the doses measured for fragments of certain charge, (3) the distributions …
Conventional cancer therapies include surgery, radiation therapy, chemotherapy, and, more recently, immunotherapy. These modalities are often combined to improve the therapeutic index. The general concept of radiation therapy is to increase the therapeutic index by creating a physical dose differential between tumors and normal tissues through precision dose targeting, image guidance, and high radiation beams that deliver radiation dose with high conformality, e.g., protons and ions. However, treatment and cure are still limited by normal tissue radiation toxicity, with many patients experienc…
Charged Particle Therapy is a technique for cancer treatment that exploits hadron beams, mostly protons and carbons. A critical issue is the monitoring of the dose released by the beam to the tumor and to the surrounding tissues. We present the design of a new tracking device for monitoring on-line the dose in ion therapy through the detection of secondary charged particles produced by the beam interactions in the patient tissues. In fact, the charged particle emission shape can be correlated with the spatial dose release and the Bragg peak position. The detector uses the information provided …
Beam modelling of Hitachi PROBEAT proton therapy system for a GPU-based Fast Monte Carlo dose engine
University of Nebraska Omaha（内布拉斯加大学奥马哈分校）；Rice University（莱斯大学）；The University of Texas MD Anderson Cancer（德克萨斯大学安德森癌症中心）；Mevion Medical System（美维医疗系统）；Emory University（埃默里大学）
Two full rotating gantry with different nozzles (Multipurpose nozzle with MLC, Scanning Dedicated nozzle) with conventional cyclotron system is installed and under commissioning for various proton treatment options at Samsung Medical Center in Korea. The purpose of this study is to investigate neutron dose equivalent per therapeutic dose, H/D, to x-ray imaging equipment under various treatment conditions with monte carlo simulation. At first, we investigated H/D with the various modifications of the beam line devices (Scattering, Scanning, Multi-leaf collimator, Aperture, Compensator) at isoce…
In this study, two proton beam delivery designs, passive scattering proton therapy (PSPT) and pencil beam scanning (PBS), were quantitatively compared in terms of dosimetric indices. The GATE Monte Carlo code was used to simulate the proton beam system; and the developed simulation engines were benchmarked with respect to the experimental measurements. A water phantom was used to simulate system energy parameters using a set of depth-dose data in the energy range of 120-235 MeV. To compare the performance of PSPT against PBS, multiple dosimetric parameters including FWHM, peak position, range,…
A split feasibility formulation for the inverse problem of intensity-modulated radiation therapy (IMRT) treatment planning with dose-volume constraints (DVCs) included in the planning algorithm is presented. It involves a new type of sparsity constraint that enables the inclusion of a percentage-violation constraint in the model problem and its handling by continuous (as opposed to integer) methods. We propose an iterative algorithmic framework for solving such a problem by applying the feasibility-seeking CQ-algorithm of Byrne combined with the automatic relaxation method (ARM) that uses cycl…
In intensity-modulated radiation therapy, optimal intensity distributions of incoming beams are decomposed into linear combinations of leaf openings of a multileaf collimator (segments). In order to avoid inefficient dose delivery, the decomposition should satisfy a number of dosimetric constraints due to suboptimal dose characteristics of small segments. However, exact decomposition with dosimetric constraints is only in limited cases possible. The present work introduces new heuristic segmentation algorithms for the following optimization problem: Find a segmentation of an approximated matri…
We compared the treatment planning performance of RapidArc (RA) vs. CyberKnife (CK) for spinal stereotactic body radiation therapy (SBRT). Ten patients with spinal lesions who had been treated with CK were re-planned with RA, which consisted of two complete arcs. Computed tomography (CT) and volumetric dose data of CK, generated using the Multiplan (Accuray) treatment planning system (TPS) and the Ray-Trace algorithm, were imported to Varian Eclipse TPS in Dicom format, and the data were compared with the RA plan using analytical anisotropic algorithm (AAA) dose calculation. The optimized dose…
UT Southwestern Medical Center（德克萨斯大学西南医学中心）；University of North Texas（北德克萨斯大学）；UCLouvain（鲁汶大学）；Erasmus University Medical Center（伊拉斯姆斯大学医学中心）
This work aims to study the generalizability of a pre-developed deep learning (DL) dose prediction model for volumetric modulated arc therapy (VMAT) for prostate cancer and to adapt the model to three different internal treatment planning styles and one external institution planning style. We built the source model with planning data from 108 patients previously treated with VMAT for prostate cancer. For the transfer learning, we selected patient cases planned with three different styles from the same institution and one style from a different institution to adapt the source model to four targ…
Cancer is a primary cause of morbidity and mortality worldwide. The radiotherapy plays a more and more important role in cancer treatment. In the radiotherapy, the dose distribution maps in patient need to be calculated and evaluated for the purpose of killing tumor and protecting healthy tissue. Monte Carlo (MC) radiation transport calculation is able to account for all aspects of radiological physics within 3D heterogeneous media such as the human body and generate the dose distribution maps accurately. However, an MC calculation for doses in radiotherapy usually takes a great mass of time t…
Diffusing Alpha-emitters Radiation Therapy (DaRT) is a new method for treating solid tumors using alpha particles. Unlike conventional radiotherapy, where the physical models for dose calculations are known and routinely used, for DaRT a new framework, called the Diffusion-Leakage (DL) model, had to be developed. In this work we provide a detailed description of a finite-element numerical scheme for solving the time-dependent DL model equations for cylindrical DaRT sources (``seeds'') of finite diameter and length in two dimensions. Using a fully-implicit scheme and adaptive time step, this ap…
Purpose: To assess the effects of brain movements induced by heartbeat on dose distributions in synchrotron micro- and mini-beam radiaton therapy and to develop a model to help guide decisions and planning for future clinical trials. Methods: The Monte Carlo code PENELOPE was used to simulate the irradiation of a human head phantom with a variety of micro- and mini-beam arrays, with beams narrower than 100mum and above 500mum, respectively, and with radiation fields of 1cmx2cm and 2cmx2cm. The dose in the phantom due to these beams was calculated by superposing the dose profiles obtained for a…
In radiation therapy, mathematical methods have been used for optimizing treatment planning for delivery of sufficient dose to the cancerous cells while keeping the dose to critical surrounding structures minimal. This optimization problem can be modeled using mixed integer programming (MIP) whose solution gives the optimal beam orientation as well as optimal beam intensity. The challenge, however, is the computation time for this large scale MIP. We propose and investigate two novel heuristic approaches to reduce the computation time considerably while attaining high-quality solutions. We int…
Objective: We propose a semiautomatic pipeline for radiation therapy treatment planning, combining ideas from machine learning-automated planning and multicriteria optimization (MCO). Approach: Using knowledge extracted from historically delivered plans, prediction models for spatial dose and dose statistics are trained and furthermore systematically modified to simulate changes in tradeoff priorities, creating a set of differently biased predictions. Based on the predictions, an MCO problem is subsequently constructed using previously developed dose mimicking functions, designed in such a way…
Carbon ions offer significant advantages for deep-seated local tumors therapy due to their physical and biological properties. Secondary particles, especially neutrons caused by heavy ion reactions should be carefully considered in treatment process and radiation protection. For radiation protection purposes, the FLUKA Code was used in order to evaluate the radiation field at deep tumor therapy room of HIRFL in this paper. The neutron energy spectra, neutron dose and energy deposition of carbon ion and neutron in tissue-like media was studied for bombardment of solid water target by 430MeV/u C…
Adaptive Radiation Therapy (ART) was developed based on Image-guided Radiation Therapy (IGRT) and it is the trend of photon radiation therapy. To get a better use of Cone Beam CT (CBCT) images for ART, the CBCT system model was established based on Monte Carlo program and validated against the measurement. The BEAMnrc program was adopted to the KV x-ray tube. Both IOURCE-13 and ISOURCE-24 were chosen to simulate the path of beam particles. The measured Percentage Depth Dose (PDD) and lateral dose profiles under 1cm water were compared with the dose calculated by DOSXYZnrc program. The calculat…
