相关论文: Proton arc therapy plan optimization with energy l…
Proton radiotherapy promises accurate dose delivery to a tumor and minimal dose deposition to all other tissues. However, in practice the planned dose distribution may not conform to the actual one due to noisy data and different types of…
Particle physics simulations are the cornerstone of nuclear engineering applications. Among them radiotherapy (RT) is crucial for society, with 50% of cancer patients receiving radiation treatments. For the most precise targeting of tumors,…
With the rapid advancement of devices requiring intensive computation, such as Internet of Things (IoT) devices, smart sensors, and wearable technology, the computational demands on individual platforms with limited resources have…
Project and task scheduling under uncertainty remains a fundamental challenge in program and project management, where accurate estimation of task durations and dependencies is critical for delivering complex, multi project systems. The…
Online adaptive radiotherapy (ART) requires accurate and efficient auto-segmentation of target volumes and organs-at-risk (OARs) in mostly cone-beam computed tomography (CBCT) images. Propagating expert-drawn contours from the pre-treatment…
Many state-of-the-art Segment Routing (SR) Traffic Engineering (TE) algorithms rely on Linear Program (LP)-based optimization. However, the poor scalability of the latter and the resulting high computation times impose severe restrictions…
The physical and clinical benefits of charged particle therapy (CPT) are well recognised and recent developments have led to the rapid emergence of facilities, resulting in wider adoption worldwide. Nonetheless, the availability of CPT and…
Purpose: Historically, spot scanning proton therapy (SSPT) treatment planning utilizes dose volume constraints and linear-energy-transfer (LET) volume constraints separately to balance tumor control and organs-at-risk (OARs) protection. We…
Proton radiography combined with X-ray computed tomography (CT) has been proposed to obtain a patient-specific calibration curve and reduce range uncertainties in cancer treatment with charged particles. The main aim of this study was to…
Routing algorithms for public transport, particularly the widely used RAPTOR and its variants, often face performance bottlenecks during the transfer relaxation phase, especially on dense transfer graphs, when supporting unlimited…
In this paper, we propose a novel methodology for path planning and scheduling for multi-robot navigation that is based on optimal transport theory and model predictive control. We consider a setup where $N$ robots are tasked to navigate to…
Lattice radiotherapy (LATTICE) is a form of spatially fractionated radiation therapy (SFRT) designed to deliver high doses to tumor regions while sparing surrounding tissues. Traditional LATTICE uses rigid vertex patterns, limiting…
Timely delivery and optimal routing remain fundamental challenges in the modern logistics industry. Building on prior work that considers single-package delivery across networks using multiple types of collaborative agents with restricted…
Background and Purpose: Increasing the number of arcs in volumetric modulated arc therapy (VMAT) allows for better intensity modulation and may improve plan quality. However, this leads to longer delivery times, which may cause patient…
Purpose: VT is a life-threatening arrhythmia commonly treated with catheter ablation; however, some cases remain refractory to conventional treatment. STAR has emerged as a non-invasive option for such patients. While photon-based STAR has…
Laser-accelerated protons have a great potential for innovative experiments in radiation biology due to the sub-picosecond pulse duration and high dose rate achievable. However, the broad angular divergence makes them not optimal for…
Proton therapy exploits the finite range of charged particles in tissue to achieve dose distributions no photon based modality can replicate. Yet the modality reaches fewer than 1 percent of patients who might benefit a gap rooted in cost…
Purpose: Beam angle selection is critical in proton therapy treatment planning, yet automated approaches remain underexplored. This study presents and evaluates GAMBAS, a novel, fast machine learning model for automatic beam angle…
Automating the radiotherapy treatment planning process is a technically challenging problem. The majority of automated approaches have focused on customizing and inferring dose volume objectives to used in plan optimization. In this work we…
A central problem in the field of radiation therapy (RT) is how to optimally deliver dose to a patient in a way that fully accounts for anatomical position changes over time. As current RT is a static process, where beam intensities are…