Definition of dose rate for FLASH pencil-beam scanning proton therapy: A comparative study

Purpose: Highlight the distinctions, both in terms of concept and numerical values, of the various definitions that can be established for the dose rate in PBS proton therapy. Methods: In an in silico study, five definitions of the dose rate, namely the PBS dose rate, the percentile dose rate, the maximum percentile dose rate, the average dose rate, and the dose averaged dose rate (DADR) were analyzed first through theoretical comparison, and then applied to a head and neck case. To carry out this study, a treatment plan utilizing a single energy level and requiring the use of a patient-specific range modulator was employed. The dose rate values were compared both locally and by means of dose rate volume histograms (DRVHs). Results: The PBS dose rate, the percentile dose rate, and the maximum percentile dose are definitions that are specifically designed to take into account the time structure of the delivery of a PBS treatment plan. Although they may appear similar, our study shows that they can vary locally by up to 10%. On the other hand, the DADR values were approximately twice as high as those of the PBS, percentile and maximum percentile dose rates, since the DADR disregards the periods when a voxel does not receive any dose. Finally, the average dose rate can be defined in various ways, as discussed in this paper. The average dose rate is found to be lower by a factor of approximately 1/2 than the PBS, percentile and maximum percentile dose rates. Conclusions: We have shown that using different definitions for the dose rate in FLASH proton therapy can lead to variations in calculated values ranging from a few percent to a factor of two. Since the dose rate is a critical parameter in FLASH radiation therapy, it is essential to carefully consider the choice of definition. However, to make an informed decision, additional biological data and models are needed

Optimal interference nulling for large arrays of coupled antennas

This thesis describes different classes of algorithms used in large coupled antenna arrays to produce a desired antenna radiation pattern by finding the proper excitation currents to apply on each antenna. Two type of methods are investigated: methods based on matrix computations and methods based on convex optimization. Those methods are applied to the SKA telescope and we show that they are well suited for a real life application.Master [120] : ingénieur civil en mathématiques appliquées, Université catholique de Louvain, 201

Data-driven learning and optimization approaches for proton therapy

Proton therapy is a type of radiation therapy that uses a beam of protons to irradiate cancerous tissues. In principle, it offers a physical advantage over conventional radiotherapy due to the very localized dose deposition of protons in the body, which can be exploited to decrease the dose received by healthy tissues, leading to fewer complications. However, this unique characteristic comes at the cost of high vulnerability to uncertainties, requiring extremely precise machinery from beam production to treatment delivery. Moreover, accelerating protons requires a heavier infrastructure than conventional radiotherapy, which increases the cost of proton therapy over photon therapy. This thesis aims at improving the accessibility, cost-effectiveness, and treatment quality of proton therapy by using data-driven approaches wherever they can bring added value. In the first part of this work, with the aim of improving equipment maintenance, we develop predictive maintenance solutions based on machine learning to detect incoming failures and decrease the overall system downtime. In the second part of the thesis, we concentrate on reducing the time needed to install a new proton therapy system by developing an automatic procedure based on mathematical optimization to speed up the calibration of a proton therapy beamline. The third and final part of the thesis is devoted to improving the treatment quality of mobile tumors in proton therapy. To achieve this goal, we develop a method based on a library of treatment plans that uses real-time information about the patient's anatomy via the acquisition of images to guide the treatment delivery.(FSA - Sciences de l'ingénieur) -- UCL, 202

Transfer learning in Bayesian optimization for the calibration of a beam line in proton therapy

Bayesian optimization (BO) is a type of black-box method used to optimize a costly objective function for which we have no access to derivatives. In practice, it is frequent that a series of similar problems has to be solved, with the problem data changing moderately between instances. We investigate a transfer learning approach based on BO that reuses information from a previous configuration in order to speed up subsequent optimizations. Our approach involves learning the noise variance to apply to the function values of the previous configuration and adapting the exploration-exploitation trade-off of the acquisition function from the previous configuration. We apply those ideas to the calibration of a beam line in proton therapy where the goal is to find magnet currents to obtain a desired shape for the beam of protons, and for which the calibration has to be repeated for several configurations. We show that reusing information from a previous configuration allows a reduction in the number of iterations by more than 80%, and that using BO is superior to the conventional Nelder-Mead algorithm for black box optimization and transfer learning

Unsupervised Minimum Redundancy Maximum Relevance Feature Selection for Predictive Maintenance : Application to a Rotating Machine

Identifying and selecting optimal prognostic health indicators in the context of predictive maintenance is essential to obtain a good model and make accurate predictions. Several metrics have been proposed in the past decade to quantify the relevance of those prognostic parameters. Other works have used the well-known minimum redundancy maximum relevance (mRMR) algorithm to select features that are both relevant and non-redundant. However, the relevance criterion is based on labelled machine malfunctions which are not always available in real life scenarios. In this paper, we develop a prognostic mRMR feature selection, an adaptation of the conventional mRMR algorithm, to a situation where class labels are a priori unknown, which we call unsupervised feature selection. In addition, this paper proposes new metrics for computing the relevance and compares different methods to estimate redundancy between features. We show that using unsupervised feature selection as well as adapting relevance metrics with the dynamic time warping algorithm help increase the effectiveness of the selection of health indicators for a rotating machine case study

Real‐time image‐guided treatment of mobile tumors in proton therapy by a library of treatment plans: a simulation study

Purpose To improve target coverage and reduce the dose in the surrounding organs-at-risks (OARs), we developed an image-guided treatment method based on a precomputed library of treatment plans controlled and delivered in real-time. Methods A library of treatment plans is constructed by optimizing a plan for each breathing phase of a four dimensional computed tomography (4DCT). Treatments are delivered by simulation on a continuous sequence of synthetic computed tomographies (CTs) generated from real magnetic resonance imaging (MRI) sequences. During treatment, the plans for which the tumor are at a close distance to the current tumor position are selected to deliver their spots. The study is conducted on five liver cases. Results We tested our approach under imperfect knowledge of the tumor positions with a 2 mm distance error. On average, compared to a 4D robustly optimized treatment plan, our approach led to a dose homogeneity increase of 5% (defined as ) in the target and a mean liver dose decrease of 23%. The treatment time was roughly increased by a factor of 2 but remained below 4 min on average. Conclusions Our image-guided treatment framework outperforms state-of-the-art 4D-robust plans for all patients in this study on both target coverage and OARs sparing, with an acceptable increase in treatment time under the current accuracy of the tumor tracking technology

Direct Deterministic Nulling Techniques for Large Random Arrays Including Mutual Coupling

Effective approaches for interference nulling in large random planar arrays are presented. Starting from the,well-known subtraction technique, it is shown that the suppression is only effective when the mutual coupling is taken into account. Moreover, for practical antennas with significant cross-polarization levels, additional efforts are required to null interference in both co- and cross-patterns. Alternative techniques are proposed to efficiently generate null patterns that are able to suppress interferers from the output of the beamformer of large arrays. In particular, a technique exploiting the polarization diversity of modern multiantenna systems is proposed to effectively suppress a large number of interferers while offering high sensitivity. Examples related to SKA1-LOW stations are presented to illustrate the performance of these techniques. Throughout these examples, the embedded element patterns obtained using fast full-wave simulation techniques are exploited to fully account for the effects of mutual coupling

Predictive maintenance of a rotating condenser inside a synchrocyclotron

This paper investigates data-driven methods to predict failures of a rotating condenser (RotCo) inside a synchrocyclotron for a proton therapy treatment system [12]. Downtime caused by a failure of the system can lead to significant delays in the treatment of the patients, which is why having a reliable predictive maintenance system is essential. The condenser rotates at high speed and rolling bearing elements are responsible for maintaining low friction between the moving components. The aim is to predict failures of the bearing box which contains the shaft and the bearing elements. Several sensors within the cyclotron are constantly measuring multiple relevant signals but, notably, vibration data is not available. We leverage those time-series data to predict a few days in advance whether a failure is likely to happen. To do this, we propose a two-level approach that relies on combining the output of a classifier with an aggregator based on a custom business metric specifically designed for this problem