Ruifen Cao

Multi-objective optimization of inverse planning for accurate radiotherapy

The multi-objective optimization of inverse planning based on the Pareto solution set, according to the multi-objective character of inverse planning in accurate radiotherapy, was studied in this paper. Firstly, the clinical requirements of a treatment plan were transformed into a multi-objective optimization problem with multiple constraints. Then, the fast and elitist multi-objective Non-dominated Sorting Genetic Algorithm (NSGA- II) was introduced to optimize the problem. A clinical example was tested using this method. The results show that an obtained set of non-dominated solutions were uniformly distributed and the corresponding dose distribution of each solution not only approached the expected dose distribution, but also met the dose-volume constraints. It was indicated that the clinical requirements were better satisfied using the method and the planner could select the optimal treatment plan from the non-dominated solution set.

A Self-adaptive Evolutionary Algorithm for Multi-objective Optimization

Evolutionary algorithm has gained a worldwide popularity among multi-objective optimization. The paper proposes a self-adaptive evolutionary algorithm (called SEA) for multi-objective optimization. In the SEA, the probability of crossover and mutation, P cand P m, are varied depending on the fitness values of the solutions. Fitness assignment of SEA realizes the twin goals of maintaining diversity in the population and guiding the population to the true Pareto Front; fitness value of individual not only depends on improved density estimation but also depends on non-dominated rank. The density estimation can keep diversity in all instances including when scalars of all objectives are much different from each other. SEA is compared against the Non-dominated Sorting Genetic Algorithm (NSGA-II) on a set of test problems introduced by the MOEA community. Simulated results show that SEA is as effective as NSGA-II in most of test functions, but when scalar of objectives are much different from each other, SEA has better distribution of non-dominated solutions.

Improved Model on Minimizing Static Intensity Modulation Delivery Time

In Intensity Modulated Radiation Therapy static intensity modulation delivery, leaf sequencing is basically a process where the intensity map is broken down into subfields or segments that can be implemented by multileaf collimators. The purpose of this study was to investigate a new method to improve the Langer’s integer programming model. The method is to choose the horizontal or orthogonal leaf direction of intensity map according with Xia and Verhy’s alogrithm and invoke a new constraint to optimize multileaf collimator leaf travel distances. A comparative study of three different leaf sequencing methods, Xia and Verhey, Langer et al., and our improved model is presented. The numerical outputs reveal that model modifications can yield better results according with the three criteria: total number of monitor unit, number of segment and leaf travel distances. Keywordsmultileaf collimator; leaf sequencing; leaf travel distances

Deformable image registration of CT images for automatic contour propagation in radiation therapy

Radiotherapy treatment plan may be replanned due the changes of tumors and organs at risk (OARs) during the treatment. Deformable image registration (DIR) based Computed Tomography (CT) contour propagation in the routine clinical setting is expected to reduce time needed for necessary manual tumors and OARs delineations and increase the efficiency of replanning. In this study, a DIR method was developed for CT contour propagation. Prior structure delineations were incorporated into Demons DIR, which was represented by adding an intensity matching term of the delineated tissues pairs to the energy function of Demons. The performance of our DIR was evaluated with five clinical head-and-neck and five lung cancer cases. The experimental results verified the improved accuracy of the proposed registration method compared with conventional registration and Demons DIR.

SU‐E‐T‐21: A Grid Intensity‐Based Dose Algorithm to Realize MLC Irregular and Inhomogeneous Field Modeling for Monte Carlo Clinical Application

Purpose: A grid intensity‐based dose algorithm to realize MLC irregular‐inhomogeneous field modeling is presented for Monte Carlo clinical application in ARTS (Accurate Radiotherapy System). Methods: Linac modeling actually is a multi‐parameter optimization process, and especially depends on the composition and structure detail provided by venders. Maybe a real accelerator use could be substituted by an irregular‐inhomogeneous photon surface source with a compensatory contaminative electron source. MLC intensity map is regarded as many grid gather. The source photon is randomly sampled until its original grid intensity more than zero. The source particle weight is just equal to the grid intensity. The transmission direction of the source particle is decided by SID and MLC position just like a point source irradiation. The measurement data of a set of regular fields are used to commission a XH600D 6MV Linac. The 10cm*10cm field PDD is used to affirm the photon energy spectrum. Their half OARs at iso‐center surface of multiple regular fields are projected to MLC underside and fitted by Boltzmann function. A set of fitting coefficients are deduced. Their OAR differences before 1.5 cm depth are used to deduce the contaminative electron source. The MLC irregular‐inhomogeneous field is divided into several semi‐regular sub‐fields, whose side intensities and sub‐field width are confirmed by the corresponding field coefficients. This algorithm is implemented by adapting the open Monte Carlo code DPM. Results: This algorithm has been benchmarked with experiment data for regular fields. Basically the difference is under 2%/2mm. The rotational irregular‐inhomogeneous multi‐fields also are modeled. Conclusion: A grid intensity‐based dose algorithm is presented, which need not know the composition and structure inside a real Linac. This algorithm can simulate the irregular‐inhomogeneous field formed by MLC, and is promising for Monte Carlo clinical application.

SU‐E‐T‐19: Monte Carlo Simulation of XHA600D 6MV Linear Accelerator

Purpose: A Monte Carlo model of XHA600D 6MV Linac is built, which provides the tool to validate and analyses the beam feature for ARTS (Accurate Radiotherapy System). Methods: Monte Carlo method is a useful tool to build the Linac model for an accurate TPS. This work uses BEAMnrc and DOSXYZnrc to model XHA600D Linac. The simulation is divided into three parts: the first part is the accelerator fixed part for different fields, which includes from the target to the mirror. This part PSF (phase space file) is very important to ensure the precision of the following simulation. Thus the largest PSF is simulated without variance reduced techniques except for the high electron cut‐off (1MeV) to ensure the efficiency of recorded particles. The second part is the beam adjustable part including JAW and MLC to form different fields. A set of PSFs is obtained to store the different field information. The third part is the phantom simulation irradiating by the 2th PSF using DOSXYZnrc. The energy of the incident mono‐energy electron is adjusted by matching the simulation to the measured PDD and OAR for 10cmx10cm field, as well as referring to other fields, such as 5cmx5cm and 20cmx20cm. Results: After many iterations of trial and error, the optimized 6 MV is ensured for XHA600D 6MV. A Gaussian beam profile (with σ = 1 mm, FHWH= 0.0 MeV) is used for the incident electron beam at the target surface. The difference is under 2%/3mm for in the field and in the penumbra. An irregular field forming by MLC also is simulated. Conclusion: This work builds a Monte Carlo model of XHA600D 6MV Linac to provide the tool for the dose validation and the beam feature analysis for ARTS.

Optimization of Multileaf Collimator Leaf Sequences Based on Multiple Algorithms

To investigate the efficacy of Galvin, Bortfeld and Siochi leaf sequencing algorithm, a leaf sequencing optimization program that compares the results of typical clinical cases is presented. The output of this program is the number of segment and the total number of monitor unit of each clinical case based on the aforementioned three algorithms. Numerical results of each case are analyzed and tested by the movement of multileaf collimator. It shows that this comparison program is effective in clinical.

Impacts of lung and tumor volumes on lung dosimetry for nonsmall cell lung cancer

Abstract The purpose of this study was to determine the impacts of lung and tumor volumes on normal lung dosimetry in three‐dimensional conformal radiotherapy (3DCRT), step‐and‐shoot intensity‐modulated radiotherapy (ssIMRT), and single full‐arc volumetric‐modulated arc therapy (VMAT) in treatment of nonsmall cell lung cancers (NSCLC). All plans were designed to deliver a total dose of 66 Gy in 33 fractions to PTV for the 32 NSCLC patients with various total (bilateral) lung volumes, planning target volumes (PTVs), and PTV locations. The ratio of the lung volume (total lung volume excluding the PTV volume) to the PTV volume (LTR) was evaluated to represent the impacts in three steps. (a) The least squares method was used to fit mean lung doses (MLDs) to PTVs or LTRs with power‐law function in the population cohort (N = 32). (b) The population cohort was divided into three groups by LTRs based on first step and then by PTVs, respectively. The MLDs were compared among the three techniques in each LTR group (LG) and each PTV group (PG). (c) The power‐law correlation was tested by using the adaptive radiation therapy (ART) planning data of individual patients in the individual cohort (N = 4). Different curves of power‐law function with high R2 values were observed between averaged LTRs and averaged MLDs for 3DCRT, ssIMRT, and VMAT, respectively. In the individual cohort, high R2 values of fitting curves were also observed in individual patients in ART, although the trend was highly patient‐specific. There was a more obvious correlation between LTR and MLD than that between PTV and MLD.

SU-E-T-47: A Monte Carlo Model of a Spot Scanning Proton Beam Based On a Synchrotron Proton Therapy Accelerator

Purpose: To build the model of a spot scanning proton beam for the dose calculation of a synchrotron proton therapy accelerator, which is capable of accelerating protons from 50 up to 221 MeV. Methods: The spot scanning beam nozzle is modeled using TOPAS code, a simulation tool based on Geant4.9.6. The model contained a beam pipe vacuum window, a beam profile monitor, a drift chamber, two plane-parallel ionization chambers, and a spot-position monitor consisted of a multiwire ionization chamber. A water phantom is located with its upstream surface at the isocenter plane. The initial proton beam energy and anglar deflection are modeled using a Gaussian distribution with FWHM (Full Widths at Half Maximum) deponding on its beam energy. The phase space file (PSF) on a virtual surface located at the center between the two magnets is recorded. PSF is used to analyze the pencil beam features and offset the pencil beam position. The source model parameters are verificated by fitting the simulated Result to the measurement. Results: The simulated percentage depth dose (PDD) and lateral profiles of scanning pencil beams of various incident proton energies are verificated to the measurement. Generally the distance to agreement (DTA) of Bragg peaks is less than 0.2cm. The FWHM of Gaussian anglar distribution was adjusted to fit the lateral profile difference between the simulation and the measurement to less than 2∼3cm. Conclusion: A Monte Carlo model of a spot scanning proton beam was bullt based on a synchrotron proton therapy accelerator. This scanning pencil beam model will be as a block to build the broad proton beam as a proton TPS dose verification tool.

SU‐E‐I‐07: Response Characteristics and Signal Conversion Modeling of KV Flat‐Panel Detector in Cone Beam CT System

Purpose: The flat-panel detector response characteristics are investigated to optimize the scanning parameter considering the image quality and less radiation dose. The signal conversion model is also established to predict the tumor shape and physical thickness changes. Methods: With the ELEKTA XVI system, the planar images of 10cm water phantom were obtained under different image acquisition conditions, including tube voltage, electric current, exposure time and frames. The averaged responses of square area in center were analyzed using Origin8.0. The response characteristics for each scanning parameter were depicted by different fitting types. The transmission measured for 10cm water was compared to Monte Carlo simulation. Using the quadratic calibration method, a series of variable-thickness water phantoms images were acquired to derive the signal conversion model. A 20cm wedge water phantom with 2cm step thickness was used to verify the model. At last, the stability and reproducibility of the model were explored during a four week period. Results: The gray values of image center all decreased with the increase of different image acquisition parameter presets. The fitting types adopted were linear fitting, quadratic polynomial fitting, Gauss fitting and logarithmic fitting with the fitting R-Square 0.992, 0.995, 0.997 and 0.996 respectively. For 10cm water phantom, the transmission measured showed better uniformity than Monte Carlo simulation. The wedge phantom experiment show that the radiological thickness changes prediction error was in the range of (-4mm, 5mm). The signal conversion model remained consistent over a period of four weeks. Conclusion: The flat-panel response decrease with the increase of different scanning parameters. The preferred scanning parameter combination was 100kV, 10mA, 10ms, 15frames. It is suggested that the signal conversion model could effectively be used for tumor shape change and radiological thickness prediction. Supported by National Natural Science Foundation of China (81101132, 11305203) and Natural Science Foundation of Anhui Province (11040606Q55, 1308085QH138)