Column Generation-Based Techniques for Intensity-Modulated Radiation Therapy (IMRT) and Volumetric Modulated Arc Therapy (VMAT) Treatment Planning

RÉSUMÉ: Les statistiques ont estimé à environ 14,1 millions le nombre de cas de cancer en 2018 dans le monde, et qui devrait passer à 24 millions d’ici 2035. La radiothérapie est l’une des premières méthodes de traitement du cancer, qu’environ 50% des patients reçoivent au cours de leur maladie. Cette méthode endommage le matériel génétique des cellules cancéreuses, détruisant ainsi leur capacité de reproduction. Cependant, les cellules normales sont également affectées par le rayonnement ; par conséquent, le traitement doit être effectué de manière à maximiser la dose de rayonnement aux tumeurs, tout en minimisant les effets néfastes des radiations sur les tissus sains. Les techniques d’optimisation sont utilisées afin de déterminer la dose et la position du rayonnement à administrer au corps du patient. Ce projet aborde la radiothérapie externe à travers la radiothérapie par modulation d’intensité (IMRT), ainsi qu’une nouvelle forme appelée modulation d’intensité volumétrique par thérapie par arcs (VMAT). En IMRT, un nombre fini de directions sont déterminées pour le rayonnement du faisceau, tandis qu’en VMAT l’accélérateur linéaire tourne autour du corps du patient alors que le faisceau est allumé. Cette technologie permet de modifier dynamiquement la forme du faisceau et le débit de dose pendant le traitement. Le problème de planification du traitement consiste à choisir une séquence de distribution des formes de faisceaux, à optimiser le dé bit de dose du faisceau et à déterminer la vitesse de rotation du portique, si nécessaire. Cette recherche tire profit de la méthode de génération de colonnes, en tant que méthode d’optimisation efficace en particulier pour les problèmes à grande échelle. Cette technique permet d’améliorer le temps de traitement et les objectifs cliniques non linéaires et non convexes, dans la planification de traitement en VMAT. Un nouveau modèle multi-objectif de génération de colonnes pour l’IMRT est également développé. Dans le premier essai, nous développons un nouvel algorithme de génération de colonnes qui optimise le compromis entre le temps et la qualité du traitement délivré pour la planification de traitement en VMAT. Pour ce faire, une génération simultanée de colonnes et de rangées est développée, afin de relier les colonnes, contenant la configuration des ouvertures de faisceaux, aux rangées du modèle, représentant la restriction de temps de traitement. De plus, nous proposons une technique de regroupement par grappe modifiée, afin d’agréger des éléments de volume similaires du corps du patient, et de réduire efficacement le nombre de contraintes dans le modèle. Les résultats de calcul montrent qu’il est possible d’obtenir un traitement de haute qualité sur quatre processeurs en parallèle. Dans le deuxième essai, nous développons une approche de planification automatique intégrant les critères de l’histogramme dose-volume (DVH). Les DVH sont la représentation de dose la plus courante pour l’évaluation de la qualité de traitement en technologie VMAT. Nous profitons de la procédure itérative de génération de colonnes pour ajuster les paramètres du modèle lors de la génération d’ouverture, et répondre aux critères DVH non linéaires, sans tenir compte des contraintes dures dans le modèle. Les résultats sur les cas cliniques montrent que notre méthodologie a été significativement améliorée, pour obtenir des plans cliniquement acceptables sans intervention humaine par rapport à une simple optimisation VMAT. De plus, la comparaison avec un système de planification de traitement commercial existant montre que la qualité des plans obtenus à partir de la méthode proposée, en particulier pour les tissus sains, est largement meilleure alors que le temps de calcul est moindre. Dans le troisième essai, nous abordons la planification de traitement en IMRT, qui est formulée comme un problème d’optimisation convexe à grande échelle, avec un espace de faisabilité simplex. Nous intégrons d’abord une nouvelle approche de solution basée sur la méthode Frank-Wolfe, appelée Blended Conditional Gradients, dans la génération de colonnes, pour améliorer les performances de calcul de la méthode. Nous proposons ensuite une technique de génération de colonnes multi-objectif, pour obtenir directement des ouvertures qui se rapprochent d’un ensemble efficace de plans de traitement non dominés. A cette fin, nous trouvons les limites inférieure et supérieure du front de Pareto, et générons une colonne avec un vecteur de poids des objectifs pré-assigné ou nouveau, réduisant la distance maximale de deux bornes. Nous prouvons que cet algorithme converge vers le front de Pareto. Les résultats de recherche d’un bon compromis de traitement entre la destruction des volumes cibles et la protection des structures saines dans un espace objectif bidimensionnel, montrent l’efficacité de l’algorithme dans l’approche du front de Pareto, avec des plans de traitement livrables en 3 minutes environ, et évitant un grand nombre de colonnes. Cette méthode s’applique également à d’autres classes de problèmes d’optimisation convexe, faisant appel à la fois à une génération de colonnes et à une optimisation multi-objectifs.----------ABSTRACT: The statistics have estimated about 18.1 million cancer cases in 2018 around the world, which is expected to increase to 24 million by 2035. Radiation therapy is one of the most important cancer treatment methods, which about 50% of patients receive during their illness. This method works by damaging the genetic material within cancerous cells and destroying their ability to reproduce. However, normal cells are also affected by radiation; therefore, the treatment should be performed in such a way that it maximizes the dose of radiation to tumors, while simultaneously minimizing the adverse effects of radiations to healthy tissues. The optimization techniques are useful to determine where and how much radiation should be delivered to patient’s body. In this project, we address the intensity-modulated radiation therapy (IMRT) as a widelyused external radiotherapy method and also a novel form called volumetric modulated arc therapy (VMAT). In IMRT, a finite number of directions are determined for the beam radiation, while in VMAT, the linear accelerator rotates around the patient’s body while the beam is on. These technologies give us the ability of changing the beam shape and the dose rate dynamically during the treatment. The treatment planning problem consists of selecting a delivery sequence of beam shapes, optimizing the dose rate of the beam, and determining the rotation speed of the gantry, if required. In this research, we take advantages of the column generation technique, as a leading optimization method specifically for large-scale problems, to improve the treatment time and non-linear non-convex clinical objectives in VMAT treatment planning, and also develop a new multi-objective column generation framework for IMRT. In the first essay, we develop a novel column generation algorithm optimizing the trade-off between delivery time and treatment quality for VMAT treatment planning. To this end, simultaneous column-and-row generation is developed to relate the configuration of beam apertures in columns to the treatment time restriction in the rows of the model. Moreover, we propose a modified clustering technique to aggregate similar volume elements of the patient’s body and efficiently reduce the number of constraints in the model. The computational results show that a high-quality treatment is achievable using a four-thread CPU. In the second essay, we develop an automatic planning approach integrating dose-volume histogram (DVH) criteria, the most common method of treatment evaluation in practice, for VMAT treatment planning. We take advantage of the iterative procedure of column generation to adjust the model parameters during aperture generation and meet nonlinear DVH criteria without considering hard constraints in the model. The results on clinical cases show that our methodology had significant improvement to obtain clinically acceptable plans without human intervention in comparison to simple VMAT optimization. In addition, the comparison to an existing commercial treatment planning system shows the quality of the obtained plans from the proposed method, especially for the healthy tissues, is significantly better while the computational time is less. In the third essay, we address the IMRT treatment planning, which is formulated as a large scale convex optimization problem with simplex feasibility space. We first integrate a novel Frank-Wolfe-based solution approach, so-called Blended Conditional Gradients, into the column generation to improve the computational performance for the method. We then propose a multi-objective column generation technique to directly obtain apertures that approximate an efficient non-dominated set of treatment plans. To this end, we find lower and upper bounds for the Pareto front and generate a column with a pre-assigned or new weight-vector of the objectives, reducing the maximum distance of two bounds. We prove this algorithm converges to the Pareto front. The results in a two-dimensional objective space to find the trade-off plans between the treat of target volumes and sparing the healthy structures show the efficiency of the algorithm to approximate the Pareto front with deliverable treatment plans in about 3 minutes, avoiding a large number of columns. This method is also applicable for other classes of convex optimization problems requiring both column generation and multi-objective optimization

A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search

The job-shop scheduling problem is one of the most arduous combinatorial optimization problems. Flexible job-shop problem is an extension of the job-shop problem that allows an operation to be processed by any machine from a given set along different routes. This paper present a new approach based on a hybridization of the particle swarm and local search algorithm to solve the multi-objective flexible job-shop scheduling problem. The particle swarm optimization is a highly efficient and a new evolutionary computation technique inspired by birds' flight and communication behaviors. The multi-objective particle swarm algorithm is applied to the flexible job-shop scheduling problem based on priority. Also the presented approach will be evaluated for their efficiency against the results reported for similar algorithms (weighted summation of objectives and Pareto approaches). The results indicate that the proposed algorithm satisfactorily captures the multi-objective flexible job-shop problem and competes well with similar approaches.Flexible job-shop scheduling Multi-objective optimization Particle swarm optimization Local search

Harvesting of energy from Human Walking with a piezoelectric transducer to supply a medical instrument

Abstract Introduction: Nowadays, the advanced technology of designing electronic circuits in very small size and with very low power consumption has led to development of wearable and implantable medical devices. However, the electronic circuits need power supply that is usually provided by relatively large and heavy batteries. The discharged batteries have to replaced or recharged for long time operation of electronic circuits. A new promising approach to overcome these limitations is harvesting the required power from the human body itself. Materials and Methods: In this paper, a harvesting system which implements a high performance piezoelectric transducer in the shoe was developed and evaluated for supplying the power required by a basic electronic circuit as a model of a wearable medical device. Results: The developed system was able to harvest 0.8mw of steady power with the use of only one piezoelectric transducer. The power was used to supply a basic micro-controller based electronic system steadily without the need for any batteries. Conclusion: The results demonstrate that low power monitoring or rehabilitation instruments may be supplied without batteries by harvesting the available energy in the walking process. Keywords: Energy harvesting, Human walking, Piezoelectric Transducer, Wearable Medical Device

Time Variant Fuzzy Time Series Approach for Forecasting Using Particle Swarm Optimization

Fuzzy time series have been developed during the last decade to improve the forecast accuracy. Many algorithms have been applied in this approach of forecasting such as high order time invariant fuzzy time series. In this paper, we present a hybrid algorithm to deal with the forecasting problem based on time variant fuzzy time series and particle swarm optimization algorithm, as a highly efficient and a new evolutionary computation technique inspired by birds’ flight and communication behaviors. The proposed algorithm determines the length of each interval in the universe of discourse and degree of membership values, simultaneously. Two numerical data sets are selected to illustrate the proposed method and compare the forecasting accuracy with four fuzzy time series methods. The results indicate that the proposed algorithm satisfactorily competes well with similar approaches

Computational Insights into The Neuroprotective Action of Riluzole on 3-Acetylpyridine-Induced Ataxia in Rats

Objective: Intra-peritoneal administration of riluzole has been shown to preserve the membrane properties and firing characteristics of Purkinje neurons in a rat model of cerebellar ataxia induced by 3-acetylpyridine (3-AP). However, the exact mechanism(s) by which riluzole restores the normal electrophysiological properties of Purkinje neurons is not completely understood. Changes in the conductance of several ion channels, including the BK channels, have been proposed as a neuro protective target of riluzole. In this study, the possible cellular effects of riluzole on Purkinje cells from 3-AP-induced ataxic rats that could be responsible for its neuro protective action have been investigated by computer simulations.Materials and Methods: This is a computational stimulation study. The simulation environment enabled a change in the properties of the specific ion channels as the possible mechanism of action of riluzole. This allowed us to study the resulted changes in the firing activity of Purkinje cells without concerns about its other effects and interfering parameters in the experiments. Simulations were performed in the NEURON environment (Version 7.1) in a time step of 25 μs; analyses were conducted using MATLAB r2010a (The Mathworks). Data were given as mean ± SEM. Statistical analyses were performed by the student’s t test, and differences were considered significant if p<0.05.Results: The computational findings demonstrated that modulation of an individual ion channel current, as suggested by previous experimental studies, should not be considered as the only possible target for the neuro protective effects of riluzole to restore the normal firing activity of Purkinje cells from ataxic rats.Conclusion: Changes in the conductance of several potassium channels, including voltage-gated potassium (Kv1, Kv4) and big Ca2+-activated K+ (BK) channels may be responsible for the neuro protective effect of riluzole against 3-AP induced alterations in the firing properties of Purkinje cells in a rat model of ataxia

Design and implementation of a portable impedance cardiography system for noninvasive stroke volume monitoring

Measurement of the stroke volume (SV) and its changes over time can be very helpful for diagnosis of dysfunctions in the blood circulatory system and monitoring their treatments. Impedance cardiography (ICG) is a simple method of measuring the SV based on changes in the instantaneous mean impedance of the thorax. This method has received much attention in the last two decades because it is noninvasive, easy to be used, and applicable for continuous monitoring of SV as well as other hemodynamic parameters. The aim of this study was to develop a low-cost portable ICG system with high accuracy for monitoring SV. The proposed wireless system uses a tetrapolar configuration to measure the impedance of the thorax at 50 kHz. The system consists of carefully designed precise voltage-controlled current source, biopotential recorder, and demodulator. The measured impedance was analyzed on a computer to determine SV. After evaluating the system's electronic performance, its accuracy was assessed by comparing its measurements with the values obtained from Doppler echocardiography (DE) on 5 participants. The implemented ICG system can noninvasively provide a continuous measure of SV. The signal to noise ratio of the system was measured above 50 dB. The experiments revealed that a strong correlation (r = 0.89) exists between the measurements by the developed system and DE (P < 0.05). ICG as the sixth vital sign can be measured simply and reliably by the developed system, but more detailed validation studies should be conducted to evaluate the system performance. There is a good promise to upgrade the system to a commercial version domestically for clinical use in the future