Scout Particles Research Articles

Beyond the MAA-Y90 Paradigm: The Evolution of Radioembolization Dosimetry Approaches and Scout Particles.

Radioembolization is a well-established treatment for primary and metastatic liver cancer. There is increasing interest in personalized treatment planning supported by dosimetry, as it provides an opportunity to optimize dose delivery to tumor and minimize nontarget deposition, which demonstrably increases the efficacy and safety of this therapy. However, the optimal dosimetry procedure in the radioembolization setting is still evolving; existing data are limited as few trials have prospectively tailored dose based on personalized planning and predominantly semi-empirical methods are used for dose calculation. Since the pretreatment or "scout" procedure forms the basis of dosimetry calculations, an accurate and reliable technique is essential. 99m Tc-MAA SPECT constitutes the current accepted standard for pretreatment imaging; however, inconsistent patterns in published data raise the question whether this is the optimal agent. Alternative particles are now being introduced to the market, and early indications suggest use of an identical scout and treatment particle may be superior to the current standard. This review will undertake an evaluation of the increasingly refined dosimetric methods driving radioembolization practices, and a horizon scanning exercise identifying alternative scout particle solutions. Together these constitute a compelling vision for future treatment planning methods that prioritize individualized care.

Multi-Mean Scout Particle Swarm Optimization (MMSCPSO) based Reactive Power Optimization in Large-Scale Power Systems

The particle swarm optimization (PSO) is a population-based algorithm belonging into metaheuristic algorithms and it has been used since many decades for handling and solving various optimization problems. However, it suffers from premature convergence and it can easily be trapped into local optimum. Therefore, this study presents a new algorithm called multi-mean scout particle swarm optimization (MMSCPSO) which solves reactive power optimization problem in a practical power system. The main objective is to minimize the active power losses in transmission line while satisfying various constraints. Control variables to be adjusted are voltage at all generator buses, transformer tap position and shunt capacitor. The standard PSO has a better exploitation ability but it has a very poor exploration ability. Consequently, to maintain the balance between these two abilities during the search process by helping particles to escape from the local optimum trap, modifications were made where initial population was produced by tent and logistic maps and it was subdividing it into sub-swarms to ensure good distribution of particles within the search space. Beside this, the idle particles (particles unable to improve their personal best) were replaced by insertion of a scout phase inspired from the artificial bee colony in the standard PSO. This algorithm has been applied and tested on IEEE 118-bus system and it has shown a strong performance in terms of active power loss minimization and voltage profile improvement compared to the original PSO Algorithm, whereby the MMSCPSO algorithm reduced the active power losses at 18.681% then the PSO algorithm reduced the active power losses at 15.457%. Hence, the MMSCPSO could be a better solution for reactive power optimization in large-scale power systems.

Quantitative 166Ho-microspheres SPECT derived from a dual-isotope acquisition with 99mTc-colloid is clinically feasible

PurposeAccurate dosimetry is essential in radioembolization. To this purpose, an automatic protocol for healthy liver dosimetry based on dual isotope (DI) SPECT imaging, combining holmium-166 (166Ho)-microspheres, and technetium-99 m (99mTc)-colloid was developed: 166Ho-microspheres used as scout and therapeutic particles, and 99mTc-colloid to identify the healthy liver. DI SPECT allows for an automatic and accurate estimation of absorbed doses, introducing true personalized dosimetry. However, photon crosstalk between isotopes can compromise image quality. This study investigates the effect of 99mTc downscatter on 166Ho dosimetry, by comparing 166Ho-SPECT reconstructions of patient scans acquired before (166Ho-only) and after additional administration of 99mTc-colloid (166Ho-DI).MethodsThe 166Ho-only and 166Ho-DI scans were performed in short succession by injecting 99mTc-colloid on the scanner table. To compensate for 99mTc downscatter, its influence was accounted for in the DI image reconstruction using energy window-based scatter correction methods. The qualitative assessment was performed by independent blinded comparison by two nuclear medicine physicians assessing 65 pairs of SPECT/CT. Inter-observer agreement was tested by Cohen’s kappa coefficient. For the quantitative analysis, two volumes of interest within the liver, VOITUMOR, and VOIHEALTHY were manually delineated on the 166Ho-only reconstruction and transferred to the co-registered 166Ho-DI reconstruction. Absorbed dose within the resulting VOIs, and in the lungs (VOILUNGS), was calculated based on the administered therapeutic activity.ResultsThe qualitative assessment showed no distinct clinical preference for either 166Ho-only or 166Ho-DI SPECT (kappa = 0.093). Quantitative analysis indicated that the mean absorbed dose difference between 166Ho-DI and 166Ho-only was − 2.00 ± 2.84 Gy (median 27 Gy; p value < 0.00001), − 5.27 ± 8.99 Gy (median 116 Gy; p value = 0.00035), and 0.80 ± 1.08 Gy (median 3 Gy; p value < 0.00001) for VOIHEALTHY, VOITUMOR, and VOILUNGS, respectively. The corresponding Pearson’s correlation coefficient between 166Ho-only and 166Ho-DI for absorbed dose was 0.97, 0.99, and 0.82, respectively.ConclusionThe DI protocol enables automatic dosimetry with undiminished image quality and accuracy.Clinical trialsThe clinical study mentioned is registered with Clinicaltrials.gov (NCT02067988) on 20 February 2014.

A PSO based approach: Scout particle swarm algorithm for continuous global optimization problems

AbstractIn the literature, most studies focus on designing new methods inspired by biological processes, however hybridization of methods and hybridization way should be examined carefully to generate more suitable optimization methods. In this study, we handle Particle Swarm Optimization (PSO) and an efficient operator of Artificial Bee Colony Optimization (ABC) to design an efficient technique for continuous function optimization. In PSO, velocity and position concepts guide particles to achieve convergence. At this point, variable and stable parameters are ineffective for regenerating awkward particles that cannot improve their personal best position (Pbest). Thus, the need for external intervention is inevitable once a useful particle becomes an awkward one. In ABC, the scout bee phase acts as external intervention by sustaining the resurgence of incapable individuals. With the addition of a scout bee phase to standard PSO, Scout Particle Swarm Optimization (ScPSO) is formed which eliminates the most important handicap of PSO. Consequently, a robust optimization algorithm is obtained.ScPSO is tested on constrained optimization problems and optimum parameter values are obtained for the general use of ScPSO. To evaluate the performance, ScPSO is compared with Genetic Algorithm (GA), with variants of the PSO and ABC methods, and with hybrid approaches based on PSO and ABC algorithms on numerical function optimization. As seen in the results, ScPSO results in better optimal solutions than other approaches. In addition, its convergence is superior to a basic optimization method, to the variants of PSO and ABC algorithms, and to the hybrid approaches on different numerical benchmark functions. According to the results, the Total Statistical Success (TSS) value of ScPSO ranks first (5) in comparison with PSO variants; the second best TSS (2) belongs to CLPSO and SP-PSO techniques. In a comparison with ABC variants, the best TSS value (6) is obtained by ScPSO, while TSS of BitABC is 2. In comparison with hybrid techniques, ScPSO obtains the best Total Average Rank (TAR) as 1.375, and TSS of ScPSO ranks first (6) again. The fitness values obtained by ScPSO are generally more satisfactory than the values obtained by other methods. Consequently, ScPSO achieve promising gains over other optimization methods; in parallel with this result, its usage can be extended to different working disciplines.Highlights PSO parameters are ineffective to regenerate the awkward particle that cannot improve its pbest. An external intervention is inevitable once a particle becomes an awkward one. ScPSO is obtained with the addition of scout bee phase into the PSO. So an evolutionary method eliminating the most important handicap of PSO is gained. ScPSO is compared with the variants and with hybrid versions of PSO and ABC methods. According to the experiments, ScPSO results in better optimal solutions. The fitness values of ScPSO are generally more satisfactory than the others. Consequently, ScPSO achieve promising gains over other optimization methods. In parallel with this, its usage can be extended to different working disciplines.

A Novel Rotation Forest Modality Based on Hybrid NNs: RF (ScPSO-NN)

Neural Network (NN), hybrid NN methods and Rotation Forest (RF) ensemble classifier are preferred in pattern analysis owing to their ability for finding efficient solutions on different problems. NN architecture usually includes backpropagation type algorithms in which error is exposed to fluctuations. Hybrid NN methods are generally designed to improve the classification performance of NN. Scout Particle Swarm Optimization (ScPSO) is one of these optimization algorithms including the effective parts of Particle Swarm Optimization (PSO) and Artificial Bee Colony Optimization (ABC). Moreover, RF algorithm usually indicates the same performance as in hybrid NN methods, although it is comprised of Decision Tree (DT) classifiers. At this point, our paper investigates whether RF using the hybrid NNs can outperform other ensemble classifiers in binary-medical pattern classification, or not. With this intention, PSO, ABC and ScPSO are placed in NN algorithms instead of back propagation, and hybrid methods (PSO-NN, ABC-NN and ScPSO-NN) are realized. As a result, RF (PSO-NN), RF (ABC-NN) and RF (ScPSO-NN) architectures are obtained. Classification Accuracy (CA), Area Under Curve (AUC), Sensitivity, Specificity, F-measure, Gmean and Precision metrics are used for a statistical performance comparison, and a test based on 2-fold cross validation method was realized on five medical datasets.

Discrete Particle Swarm Optimization with Scout Particles for Library Materials Acquisition

Materials acquisition is one of the critical challenges faced by academic libraries. This paper presents an integer programming model of the studied problem by considering how to select materials in order to maximize the average preference and the budget execution rate under some practical restrictions including departmental budget, limitation of the number of materials in each category and each language. To tackle the constrained problem, we propose a discrete particle swarm optimization (DPSO) with scout particles, where each particle, represented as a binary matrix, corresponds to a candidate solution to the problem. An initialization algorithm and a penalty function are designed to cope with the constraints, and the scout particles are employed to enhance the exploration within the solution space. To demonstrate the effectiveness and efficiency of the proposed DPSO, a series of computational experiments are designed and conducted. The results are statistically analyzed, and it is evinced that the proposed DPSO is an effective approach for the studied problem.

Swarm Intelligence and Its Applications

Swarm Intelligence and Its Applications