scholarly journals Improved reconstruction methodology of clinical electron energy spectra based on Tikhonov regularization and generalized simulated annealing

2021 ◽  
Vol 19 (6) ◽  
pp. 622-632
Author(s):  
Jorge Homero Wilches Visbal ◽  
Patrícia Nicolucci
Keyword(s):  
Electron Beam ◽  
Simulated Annealing ◽  
Energy Spectrum ◽  
Dose Calculation ◽  
Optimization Method ◽  
Fredholm Equation ◽  
Depth Dose ◽  
Passing Rate ◽  
Accurate Dose ◽  
Depth Dose Curve

Electron beam radiotherapy is the most widespread treatment modality todeal with superficial cancers. In electron radiotherapy, the energy spectrum isimportant for electron beam modelling and accurate dose calculation. Since thepercentage depth-dose (PDD) is a function of the beam’s energy, the reconstruction of the spectrum from the depth-dose curve represents an inverse problem.Thus, the energy spectrum can be related to the depth-dose by means of anappropriate mathematical model as the Fredholm equation of the first kind.Since the Fredholm equation of the first kind is ill-posed, some regularizationmethod has to be used to achieve a useful solution. In this work the Tikhonovregularization function was solved by the generalized simulated annealing optimization method. The accuracy of the reconstruction was verified by thegamma index passing rate criterion applied to the simulated PDD curves forthe reconstructed spectra compared to experimental PDD curves. Results showa good coincidence between the experimental and simulated depth-dose curvesaccording to the gamma passing rate better than 95% for 1% dose difference(DD)/1 mm distance to agreement (DTA) criteria. Moreover, the results showimprovement from previous works not only in accuracy but also in calculationtime. In general, the proposed method can help in the accuracy of dosimetryprocedures, treatment planning and quality control in radiotherapy.

The depth-dose curve depends on the inhomogeneity of the material irradiated by electron beam

2019 ◽  
Vol 163 ◽  
pp. 22-25 ◽  
Author(s):  
Nguyen Anh Tuan ◽  
Chau Van Tao ◽  
Chary Rangacharyulu
Keyword(s):  
Electron Beam ◽  
Depth Dose ◽  
Dose Curve ◽  
Depth Dose Curve

Treatment planning aspects and Monte Carlo methods in proton therapy

2015 ◽  
Vol 30 (17) ◽  
pp. 1540022 ◽  
Author(s):  
Michael K. Fix ◽  
Peter Manser
Keyword(s):  
Monte Carlo ◽  
Dose Calculation ◽  
Proton Radiotherapy ◽  
Depth Dose ◽  
Accurate Dose ◽  
Calculation Algorithms ◽  
Calculated Dose ◽  
Dose Calculation Algorithms ◽  
Proton Track ◽  
Near Future

Over the last years, the interest in proton radiotherapy is rapidly increasing. Protons provide superior physical properties compared with conventional radiotherapy using photons. These properties result in depth dose curves with a large dose peak at the end of the proton track and the finite proton range allows sparing the distally located healthy tissue. These properties offer an increased flexibility in proton radiotherapy, but also increase the demand in accurate dose estimations. To carry out accurate dose calculations, first an accurate and detailed characterization of the physical proton beam exiting the treatment head is necessary for both currently available delivery techniques: scattered and scanned proton beams. Since Monte Carlo (MC) methods follow the particle track simulating the interactions from first principles, this technique is perfectly suited to accurately model the treatment head. Nevertheless, careful validation of these MC models is necessary. While for the dose estimation pencil beam algorithms provide the advantage of fast computations, they are limited in accuracy. In contrast, MC dose calculation algorithms overcome these limitations and due to recent improvements in efficiency, these algorithms are expected to improve the accuracy of the calculated dose distributions and to be introduced in clinical routine in the near future.

scholarly journals The simulation of a linear accelerator using Monte Carlo method with EGSnrc Program

2019 ◽  
Vol 2 (4) ◽  
pp. 103-111
Author(s):  
Tai Thanh Duong ◽  
Tuan Duc Hoang ◽  
Oanh Thi Luong ◽  
Loan Thi Hong Truong ◽  
Son Dong Nguyen
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Excellent Agreement ◽  
Linear Accelerator ◽  
Evaluation Method ◽  
Dose Calculation ◽  
Calculation Algorithm ◽  
Simulation Accuracy ◽  
Depth Dose ◽  
Passing Rate

The Monte Carlo method is considered to be the most accurate algorithm for dose calculation in radiotherapy. Linear accelerator accurate simulation is required as a prior condition for Monte Carlo dose calculation algorithm. In this study, the 6 MV photon beams from a Siemens Primus Linear Accelerator (LINAC) M5497 at the Dong Nai General Hospital was modelled by using EGSnrc. The BEAMnrc và DOSXYZnrc user code were used for simulation the head of LINAC and the dose distribution in water phantom. The percentage depth dose (PDD) and beam profiles (OCR) were calculated and then compared with the measured ones in order to evaluate the simulation accuracy. Excellent agreement was found between simulations and measurements with an average difference of 1.26 % for PDD, less than 2 % for OCR. In addition, the gamma evaluation method for simulation was also performed using an in-house Matlab code. The percentage gamma passing rate was 100% for PDD and 98.5 % for OCR with 3 % dose difference and 3 mm distance to agreement as acceptance criteria. The result showed that we had successfully simulated LINAC with excellent agreement.

scholarly journals Empirical method for modeling the percent depth dose curves of electron beam in radiation therapy

2021 ◽  
Vol 27 (4) ◽  
pp. 315-321
Author(s):  
Dong-Ji Chen ◽  
Yan-Shan Zhang ◽  
Yan-Cheng Ye ◽  
Jia-Ming Wu
Keyword(s):  
Radiation Therapy ◽  
Electron Beam ◽  
Electron Energy ◽  
Empirical Method ◽  
Stopping Power ◽  
Power Calculation ◽  
Sigmoid Function ◽  
Depth Dose ◽  
Depth Dose Curve ◽  
Tail Function

Abstract Introduction: This study presents an empirical method to model the electron beam percent depth dose curve (PDD) using the primary and tail functions in radiation therapy. The modeling parameters N and n can be used to derive the depth relative stopping power of the electron energy in radiation therapy. Methods and Materials: The electrons PDD curves were modeled with the primary-tail function in this study. The primary function included exponential function and main parameters of N, µ while the tail function was composed by a sigmoid function with the main parameter of n. The PDD for five electron energies were modeled by the primary and tail function by adjusting the parameters of N, µ and n. The R50 and Rp can be derived from the modeled straight line of 80% to 20% region of PDD. The same electron energy with different cone sizes was also modeled with the primary-tail function. The stopping power for different electron energies at different depths can also be derived from the parameters of N, µ and n. Percent ionization depth curve can then be derived from the percent depth dose by dividing its depth relevant stopping power for comparing with the original water phantom measurement. Results: The main parameters N, n increase, but µ decreases in primary-tail function when electron energy increased. The relationship of parameters n, N and LN(-µ) with electron energy are n = 31.667 E0 - 88, N = 0.9975 E0 - 2.8535, LN(-µ) = -0.1355 E0 - 6.0986, respectively. Stopping power of different electron energy can be derived from n and N with the equation: stopping power = (−0.042 ln N E 0 + 1.072)e(−n−E0·5·10−5+0.0381·d), where d is the depth in water. Percent depth dose was derived from the percent reading curve by multiplying the stopping power relevant to the depth in water at certain electron energy. Conclusion: The PDD of electrons at different energies and field sizes can be modeled with an empirical model to deal with the stopping power calculation. The primary-tail equation provides a uncomplicated solution than a pencil beam or other numerical algorism for investigators to research the behavior of electron beam in radiation therapy.

Effect of Air Space on Depth Dose in Electron Beam Therapy

Radiology ◽  
1978 ◽  
Vol 126 (1) ◽  
pp. 249-251 ◽  
Author(s):  
Faiz M. Khan ◽  
Wilfred Sewchand ◽  
Seymour H. Levitt
Keyword(s):  
Electron Beam ◽  
Electron Beam Therapy ◽  
Depth Dose ◽  
Air Space

Trajectory Optimization Applied to Space Rendezvous

2013 ◽  
Vol 756-759 ◽  
pp. 3466-3470
Author(s):  
Xu Min Song ◽  
Qi Lin
Keyword(s):  
Simulated Annealing ◽  
Optimization Model ◽  
Trajectory Optimization ◽  
Optimization Problem ◽  
Optimization Method ◽  
Nonlinear Constraints ◽  
Design Variables ◽  
Adaptive Simulated Annealing ◽  
Simulation Results

The trajcetory plan problem of spece reandezvous mission was studied in this paper using nolinear optimization method. The optimization model was built based on the Hills equations. And by analysis property of the design variables, a transform was put forward , which eliminated the equation and nonlinear constraints as well as decreaseing the problem dimensions. The optimization problem was solved using Adaptive Simulated Annealing (ASA) method, and the rendezvous trajectory was designed.The method was validated by simulation results.

An Improved Shape Annealing Method for Truss Topology Generation

1994 ◽  
Author(s):  
Giridhar Reddy ◽  
Jonathan Cagan
Keyword(s):  
Simulated Annealing ◽  
Optimization Method ◽  
Geometric Constraints ◽  
Truss Structures ◽  
Model Design ◽  
Topology Generation ◽  
Truss Topology ◽  
Gradient Based ◽  
Annealing Algorithm ◽  
Annealing Method

Abstract A method for the design of truss structures which encourages lateral exploration, pushes away from violated spaces, models design intentions, and produces solutions with a wide variety of characteristics is introduced. An improved shape annealing algorithm for truss topology generation and optimization, based on the techniques of shape grammars and simulated annealing, implements the method. The algorithm features a shape grammar to model design intentions, an ability to incorporate geometric constraints to avoid obstacles, and a shape optimization method using only simulated annealing with more consistent convergence characteristics; no traditional gradient-based techniques are employed. The improved algorithm is illustrated on various structural examples generating a variety of solutions based on a simple grammar.

scholarly journals An enhanced krill herd optimization technique used for classification problem

2021 ◽  
Vol 30 (2) ◽  
pp. 354-364
Author(s):  
Firas Al-Mashhadani ◽  
Ibrahim Al-Jadir ◽  
Qusay Alsaffar
Keyword(s):  
Machine Learning ◽  
Simulated Annealing ◽  
Optimization Technique ◽  
Optimization Method ◽  
Classification Problem ◽  
Test Results ◽  
Internal Evaluation ◽  
Evaluation Measures ◽  
Search Optimization ◽  
Krill Herd

In this paper, this method is intended to improve the optimization of the classification problem in machine learning. The EKH as a global search optimization method, it allocates the best representation of the solution (krill individual) whereas it uses the simulated annealing (SA) to modify the generated krill individuals (each individual represents a set of bits). The test results showed that the KH outperformed other methods using the external and internal evaluation measures.

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