Improved VMAT planning for head and neck tumors with an advanced optimization algorithm

OBJECTIVE In this study, the "Progressive Resolution Optimizer PRO3" (Varian Medical Systems) is compared to the previous version "PRO2" with respect to its potential to improve dose sparing to the organs at risk (OAR) and dose coverage of the PTV for head and neck cancer patients. MATERIALS AND METHODS For eight head and neck cancer patients, volumetric modulated arc therapy (VMAT) treatment plans were generated in this study. All cases have 2-3 phases and the total prescribed dose (PD) was 60-72Gy in the PTV. The study is mainly focused on the phase 1 plans, which all have an identical PD of 54Gy, and complex PTV structures with an overlap to the parotids. Optimization was performed based on planning objectives for the PTV according to ICRU83, and with minimal dose to spinal cord, and parotids outside PTV. In order to assess the quality of the optimization algorithms, an identical set of constraints was used for both, PRO2 and PRO3. The resulting treatment plans were investigated with respect to dose distribution based on the analysis of the dose volume histograms. RESULTS For the phase 1 plans (PD=54Gy) the near maximum dose D2% of the spinal cord, could be minimized to 22±5 Gy with PRO3, as compared to 32±12Gy with PRO2, averaged for all patients. The mean dose to the parotids was also lower in PRO3 plans compared to PRO2, but the differences were less pronounced. A PTV coverage of V95%=97±1% could be reached with PRO3, as compared to 86±5% with PRO2. In clinical routine, these PRO2 plans would require modifications to obtain better PTV coverage at the cost of higher OAR doses. CONCLUSION A comparison between PRO3 and PRO2 optimization algorithms was performed for eight head and neck cancer patients. In general, the quality of VMAT plans for head and neck patients are improved with PRO3 as compared to PRO2. The dose to OARs can be reduced significantly, especially for the spinal cord. These reductions are achieved with better PTV coverage as compared to PRO2. The improved spinal cord sparing offers new opportunities for all types of paraspinal tumors and for re-irradiation of recurrent tumors or second malignancies.

SOMS: SurrOgate MultiStart algorithm for use with nonlinear programming for global optimization

SOMS is a general surrogate-based multistart algorithm, which is used in combination with any local optimizer to find global optima for computationally expensive functions with multiple local minima. SOMS differs from previous multistart methods in that a surrogate approximation is used by the multistart algorithm to help reduce the number of function evaluations necessary to identify the most promising points from which to start each nonlinear programming local search. SOMS’s numerical results are compared with four well-known methods, namely, Multi-Level Single Linkage (MLSL), MATLAB’s MultiStart, MATLAB’s GlobalSearch, and GLOBAL. In addition, we propose a class of wavy test functions that mimic the wavy nature of objective functions arising in many black-box simulations. Extensive comparisons of algorithms on the wavy testfunctions and on earlier standard global-optimization test functions are done for a total of 19 different test problems. The numerical results indicate that SOMS performs favorably in comparison to alternative methods and does especially well on wavy functions when the number of function evaluations allowed is limited.

SOP: Parallel surrogate global optimization with Pareto center selection for computationally expensive single objective problems

This paper presents a parallel surrogate-based global optimization method for computationally expensive objective functions that is more effective for larger numbers of processors. To reach this goal, we integrated concepts from multi-objective optimization and tabu search into, single objective, surrogate optimization. Our proposed derivative-free algorithm, called SOP, uses non-dominated sorting of points for which the expensive function has been previously evaluated. The two objectives are the expensive function value of the point and the minimum distance of the point to previously evaluated points. Based on the results of non-dominated sorting, P points from the sorted fronts are selected as centers from which many candidate points are generated by random perturbations. Based on surrogate approximation, the best candidate point is subsequently selected for expensive evaluation for each of the P centers, with simultaneous computation on P processors. Centers that previously did not generate good solutions are tabu with a given tenure. We show almost sure convergence of this algorithm under some conditions. The performance of SOP is compared with two RBF based methods. The test results show that SOP is an efficient method that can reduce time required to find a good near optimal solution. In a number of cases the efficiency of SOP is so good that SOP with 8 processors found an accurate answer in less wall-clock time than the other algorithms did with 32 processors.

Global Optimization with Sparse and Local Gaussian Process Models

We present a novel surrogate model-based global optimization framework allowing a large number of function evaluations. The method, called SpLEGO, is based on a multi-scale expected improvement (EI) framework relying on both sparse and local Gaussian process (GP) models. First, a bi-objective approach relying on a global sparse GP model is used to determine potential next sampling regions. Local GP models are then constructed within each selected region. The method subsequently employs the standard expected improvement criterion to deal with the exploration-exploitation trade-off within selected local models, leading to a decision on where to perform the next function evaluation(s). The potential of our approach is demonstrated using the so-called Sparse Pseudo-input GP as a global model. The algorithm is tested on four benchmark problems, whose number of starting points ranges from 102 to 104. Our results show that SpLEGO is effective and capable of solving problems with large number of starting points, and it even provides significant advantages when compared with state-of-the-art EI algorithms.

Verification of the clinical implementation of the respiratory gated beam delivery technique with synchrotron-based proton irradiation

The clinical advantage for protons over conventional high-energy x-rays stems from their unique depth-dose distribution, which delivers essentially no dose beyond the end of range. In order to achieve it, accurate localization of the tumor volume relative to the proton beam is necessary. For cases where the tumor moves with respiration, the resultant dose distribution is sensitive to such motion. One way to reduce uncertainty caused by respiratory motion is to use gated beam delivery. The main goal of this dissertation is to evaluate the respiratory gating technique in both passive scattering and scanning delivery mode. Our hypothesis for the study was that optimization of the parameters of synchrotron operation and respiratory gating can lead to greater efficiency and accuracy of respiratory gating for all modes of synchrotron-based proton treatment delivery. The hypothesis is tested in two specific aims. The specific aim #1 is to assess the efficiency of respiratory-gated proton beam delivery and optimize the synchrotron operations for the gated proton therapy. A simulation study was performed and introduced an efficient synchrotron operation pattern, called variable Tcyc. In addition, the simulation study estimated the efficiency in the respiratory gated scanning beam delivery mode as well. The specific aim #2 is to assess the accuracy of beam delivery in respiratory-gated proton therapy. The simulation study was extended to the passive scattering mode to estimate the quality of pulsed beam delivery to the residual motion for several synchrotron operation patterns with the gating technique. The results showed that variable Tcyc operation can offer good reproducible beam delivery to the residual motion at a certain phase of the motion. For respiratory gated scanning beam delivery, the impact of motion on the dose distributions by scanned beams was investigated by measurement. The results showed the threshold for motion for a variety of scan patterns and the proper number of paintings for normal and respiratory gated beam deliveries. The results of specific aims 1 and 2 provided supporting data for implementation of the respiratory gating beam delivery technique into both passive and scanning modes and the validation of the hypothesis.

Identifying and Targeting Molecular Deregulations in Uterine Leiomyosarcoma: A Role for mTOR Inhibition-Based Combination Therapies

Uterine leiomyosarcoma (ULMS) is an aggressive malignancy characterized by marked chemoresistance, frequent relapses, and poor outcome. Despite efforts to improve survival over the past several decades, only minimal advances have been made. Hence, there is an urgent and unmet need for better understanding of the molecular deregulations that underlay ULMS and development of more effective therapeutic strategies. This work identified several common deregulations in a large (n=208) tissue microarray of ULMS compared to GI smooth muscle, myometrium, and leiomyoma controls. Our results suggest that significant loss of smooth muscle and gynecological differentiation markers is common in ULMS, a finding that could help render improved ULMS diagnosis, especially for advanced disease. Similarly to reports in other malignancies, we found that several cancer-related proteins were differentially expressed; these could be useful together as biomarkers for ULMS. Notably, we identified significant upregulation and overexpression of the mTOR pathway in ULMS, examined the possible contribution of tyrosine kinase receptor deregulation promoting mTOR activation, and unraveled a role for pS6RP and p4EBP1 as molecular disease prognosticators. The significance of mTOR activation in ULMS and its potential as a therapeutic target were further investigated. Rapamycin abrogated ULMS cell growth and cell cycle progression in vitro but induced only sight growth delay in vivo. Given that effective mTOR therapies likely require combination mTOR blockade with inhibition of other targets, coupled with recent observations suggesting that Aurora A kinase (Aurk A) deregulations commonly occur in ULMS, the preclinical impact of dually targeting both pathways was evaluated. Combined therapy with rapamycin (an mTORC1 inhibitor) and MLN8237 (an investigational Aurk A inhibitor) profoundly and synergistically abrogated ULMS growth in vitro. Interestingly, the superior effects were noted only when MLN8237 was pre-administered. This novel therapeutic combination and scheduling regimen resulted in marked tumor growth inhibition in vivo. Together, these data support further exploration of dual mTOR and Aurk A blockade for the treatment of human ULMS.

Design and Optimization of Four-dimensional Cone-beam Computed Tomography in Image-guided Radiation Therapy

The influence of respiratory motion on patient anatomy poses a challenge to accurate radiation therapy, especially in lung cancer treatment. Modern radiation therapy planning uses models of tumor respiratory motion to account for target motion in targeting. The tumor motion model can be verified on a per-treatment session basis with four-dimensional cone-beam computed tomography (4D-CBCT), which acquires an image set of the dynamic target throughout the respiratory cycle during the therapy session. 4D-CBCT is undersampled if the scan time is too short. However, short scan time is desirable in clinical practice to reduce patient setup time. This dissertation presents the design and optimization of 4D-CBCT to reduce the impact of undersampling artifacts with short scan times. This work measures the impact of undersampling artifacts on the accuracy of target motion measurement under different sampling conditions and for various object sizes and motions. The results provide a minimum scan time such that the target tracking error is less than a specified tolerance. This work also presents new image reconstruction algorithms for reducing undersampling artifacts in undersampled datasets by taking advantage of the assumption that the relevant motion of interest is contained within a volume-of-interest (VOI). It is shown that the VOI-based reconstruction provides more accurate image intensity than standard reconstruction. The VOI-based reconstruction produced 43% fewer least-squares error inside the VOI and 84% fewer error throughout the image in a study designed to simulate target motion. The VOI-based reconstruction approach can reduce acquisition time and improve image quality in 4D-CBCT.

Trade Coefficients and the Role of Elasticity in a Spatial CGE Model Based on the Armington Assumption

The Armington Assumption in the context of multi-regional CGE models is commonly interpreted as follows: Same commodities with different origins are imperfect substitutes for each other. In this paper, a static spatial CGE model that is compatible with this assumption and explicitly considers the transport sector and regional price differentials is formulated. Trade coefficients, which are derived endogenously from the optimization behaviors of firms and households, are shown to take the form of a potential function. To investigate how the elasticity of substitutions affects equilibrium solutions, a simpler version of the model that incorporates three regions and two sectors (besides the transport sector) is introduced. Results indicate: (1) if commodities produced in different regions are perfect substitutes, regional economies will be either autarkic or completely symmetric and (2) if they are imperfect substitutes, the impact of elasticity on the price equilibrium system as well as trade coefficients will be nonlinear and sometimes very sensitive.

Efficient Model-based 3D Tracking by Using Direct Image Registration

This thesis deals with the problem of efficiently tracking 3D objects in sequences of images. We tackle the efficient 3D tracking problem by using direct image registration. This problem is posed as an iterative optimization procedure that minimizes a brightness error norm. We review the most popular iterative methods for image registration in the literature, turning our attention to those algorithms that use efficient optimization techniques. Two forms of efficient registration algorithms are investigated. The first type comprises the additive registration algorithms: these algorithms incrementally compute the motion parameters by linearly approximating the brightness error function. We centre our attention on Hager and Belhumeur’s factorization-based algorithm for image registration. We propose a fundamental requirement that factorization-based algorithms must satisfy to guarantee good convergence, and introduce a systematic procedure that automatically computes the factorization. Finally, we also bring out two warp functions to register rigid and nonrigid 3D targets that satisfy the requirement. The second type comprises the compositional registration algorithms, where the brightness function error is written by using function composition. We study the current approaches to compositional image alignment, and we emphasize the importance of the Inverse Compositional method, which is known to be the most efficient image registration algorithm. We introduce a new algorithm, the Efficient Forward Compositional image registration: this algorithm avoids the necessity of inverting the warping function, and provides a new interpretation of the working mechanisms of the inverse compositional alignment. By using this information, we propose two fundamental requirements that guarantee the convergence of compositional image registration methods. Finally, we support our claims by using extensive experimental testing with synthetic and real-world data. We propose a distinction between image registration and tracking when using efficient algorithms. We show that, depending whether the fundamental requirements are hold, some efficient algorithms are eligible for image registration but not for tracking.

Laser-fired contact optimization in c-Si solar cells

In this work we study the optimization of laser-fired contact (LFC) processing parameters, namely laser power and number of pulses, based on the electrical resistance measurement of an aluminum single LFC point. LFC process has been made through four passivation layers that are typically used in c-Si and mc-Si solar cell fabrication: thermally grown silicon oxide (SiO2), deposited phosphorus-doped amorphous silicon carbide (a-SiCx/H(n)), aluminum oxide (Al2O3) and silicon nitride (SiNx/H) films. Values for the LFC resistance normalized by the laser spot area in the range of 0.65–3 mΩ cm2 have been obtained

A Methodological model to assist the optimization and risk management of mining investment decisions

Identifying, quantifying, and minimizing technical risks associated with investment decisions is a key challenge for mineral industry decision makers and investors. However, risk analysis in most bankable mine feasibility studies are based on the stochastic modelling of project “Net Present Value” (NPV)which, in most cases, fails to provide decision makers with a truly comprehensive analysis of risks associated with technical and management uncertainty and, as a result, are of little use for risk management and project optimization. This paper presents a value-chain risk management approach where project risk is evaluated for each step of the project lifecycle, from exploration to mine closure, and risk management is performed as a part of a stepwise value-added optimization process.

Self-organizing Routing Algorithm fo Wireless Sensors Networks (WSN) using Ant Colony Optimization (ACO) with Tinyos.

This paper describes the basic tools to work with wireless sensors. TinyOShas a componentbased architecture which enables rapid innovation and implementation while minimizing code size as required by the severe memory constraints inherent in sensor networks. TinyOS's component library includes network protocols, distributed services, sensor drivers, and data acquisition tools ? all of which can be used asia or be further refined for a custom application. TinyOS was originally developed as a research project at the University of California Berkeley, but has since grown to have an international community of developers and users. Some algorithms concerning packet routing are shown. Incar entertainment systems can be based on wireless sensors in order to obtain information from Internet, but routing protocols must be implemented in order to avoid bottleneck problems. Ant Colony algorithms are really useful in such cases, therefore they can be embedded into the sensors to perform such routing task.

Synthesis and characterization of GaAsSb-capped InAs-based QDIPs

Quantum dot infrared photodetectors (QDIPs) are very attractive for infrared imaging applications due to its promising features such as high temperature operation, normal incidence response and low dark current [1]. However, the key issue is to obtain a high quality active region which requires a structural optimization of the nanostructures. With using GaAsSb capping layer, the optical properties, such as the PL intensity and its full width at half maximum (FWHM), of InAs QDs have been improved in the range between 1.15 and 1.5 m, because of the reduction of the compressive strain in QDs and the increment of QD height [2]. In this work, we have demonstrated strong and narrow intraband photoresponse spectra from GaAsSb-capped InAs-based QDIPs

Mid-infrared photodetectors based on GaAsSb-capped InAs quantum dots

Quantum dot infrared photodetectors (QDIPs) are very attractive for many applications such as infrared imaging, remote sensing and gas sensing, thanks to its promising features such as high temperature operation, normal incidence response and low dark current [1]. However, the key issue is to obtain a high-quality active region which requires an optimization of the nanostructure. By using GaAsSb capping layer, InAs QDs have improved their optical emission in the range between 1.15 and 1.3 m (at Sb composition of 14 %), due to a reduction of a compressive strain in QD and an increment of a QD height [2]. In this work, we have demonstrated strong and narrow intraband photoresponses at ~ 5 m from GaAsSb-capped InAs/GaAs QDIPs under normal light-incidence.

Simulation of a mechanical thrombectomy device based in the use of self-expandable stents for the blood clots extraction

Recently, we have presented some studies concerning the analysis, design and optimization of one experimental device developed in the UK - GPTAD - which has been designed to remove blood clots without the need to make contact with the clot itself, thereby potentially reducing the risk of problems such as downstream embolisation. Based on the idea of a modification of the previous device, in this work, we present a model based in the use of stents like the SolitaireTM FR, which is in contact with the clot itself. In the case of such devices, the stent is self-expandable and the extraction of the blood clot is faciliatated by the stent, which must be inside the clot. Such stents are generally inserted in position by using the guidewire inserted into the catheter. This type of modeling could potentially be useful in showing how the blood clot is moved by the various different forces involved. The modelling has been undertaken by analyzing the resistances, compliances and inertances effects. We model an artery and blood clot for range of forces for the guidewire. In each case we determine the interaction between blood clot, stent and artery.