Technologies for Delivery of Proton and Ion Beams for Radiotherapy

Recent developments for the delivery of proton and ion beam therapy have been significant, and a number of technological solutions now exist for the creation and utilisation of these particles for the treatment of cancer. In this paper we review the historical development of particle accelerators used for external beam radiotherapy and discuss the more recent progress towards more capable and cost-effective sources of particles.Comment: 53 pages, 13 figures. Submitted to International Journal of Modern Physics

Accelerating Radiation Dose Calculation with High Performance Computing and Machine Learning for Large-scale Radiotherapy Treatment Planning

Radiation therapy is powered by modern techniques in precise planning and executionof radiation delivery, which are being rapidly improved to maximize its benefit to cancerpatients. In the last decade, radiotherapy experienced the introduction of advanced methodsfor automatic beam orientation optimization, real-time tumor tracking, daily planadaptation, and many others, which improve the radiation delivery precision, planning easeand reproducibility, and treatment efficacy. However, such advanced paradigms necessitatethe calculation of orders of magnitude more causal dose deposition data, increasing the timerequirement of all pre-planning dose calculation. Principles of high-performance computingand machine learning were applied to address the insufficient speeds of widely-used dosecalculation algorithms to facilitate translation of these advanced treatment paradigms intoclinical practice.To accelerate CT-guided X-ray therapies, Collapsed-Cone Convolution-Superposition(CCCS), a state-of-the-art analytical dose calculation algorithm, was accelerated through itsnovel implementation on highly parallelized GPUs. This context-based GPU-CCCS approachtakes advantage of X-ray dose deposition compactness to parallelize calculation acrosshundreds of beamlets, reducing hardware-specific overheads, and enabling acceleration bytwo to three orders of magnitude compared to existing GPU-based beamlet-by-beamletapproaches. Near-linear increases in acceleration are achieved with a distributed, multi-GPUimplementation of context-based GPU-CCCS.Dose calculation for MR-guided treatment is complicated by electron return effects(EREs), exhibited by ionizing electrons in the strong magnetic field of the MRI scanner. EREsnecessitate the use of much slower Monte Carlo (MC) dose calculation, limiting the clinicalapplication of advanced treatment paradigms due to time restrictions. An automaticallydistributed framework for very-large-scale MC dose calculation was developed, grantinglinear scaling of dose calculation speed with the number of utilized computational cores. Itwas then harnessed to efficiently generate a large dataset of paired high- and low-noise MCdoses in a 1.5 tesla magnetic field, which were used to train a novel deep convolutionalneural network (CNN), DeepMC, to predict low-noise dose from faster high-noise MC-simulation. DeepMC enables 38-fold acceleration of MR-guided X-ray beamlet dosecalculation, while remaining synergistic with existing MC acceleration techniques to achievemultiplicative speed improvements.This work redefines the expectation of X-ray dose calculation speed, making it possibleto apply new highly-beneficial treatment paradigms to standard clinical practice for the firsttime

Fast Monte Carlo Dose Calculation in Proton Therapy

This article examines the critical role of fast Monte Carlo dose calculations in advancing proton therapy techniques, particularly in the context of increasing treatment customization and precision. As adaptive radiotherapy and other patient-specific approaches evolve, the need for accurate and precise dose calculations, essential for techniques like proton-based stereotactic radiosurgery, becomes more prominent. These calculations, however, are time-intensive, with the treatment planning/optimization process constrained by the achievable speed of dose computations. Thus, enhancing the speed of Monte Carlo methods is vital, as it not only facilitates the implementation of novel treatment modalities but also improves the optimality of treatment plans. Today, the state-of-the-art in Monte Carlo dose calculation speeds is 106 - 107 protons per second. This review highlights the latest advancements in fast Monte Carlo dose calculations that have led to such speeds, including emerging artificial intelligence-based techniques, and discusses their application in both current and emerging proton therapy strategies.Comment: 41 page

Fast Monte Carlo dose calculation in proton therapy.

This article examines the critical role of fast Monte Carlo dose calculations in advancing proton therapy techniques, particularly in the context of increasing treatment customization and precision. As adaptive radiotherapy and other patient-specific approaches evolve, the need for accurate and precise dose calculations, essential for techniques like proton-based stereotactic radiosurgery, becomes more prominent. These calculations, however, are time-intensive, with the treatment planning/optimization process constrained by the achievable speed of dose computations. Thus, enhancing the speed of Monte Carlo methods is vital, as it not only facilitates the implementation of novel treatment modalities but also leads to more optimal treatment plans. Today, the state-of-the-art in Monte Carlo dose calculation speeds is 106 - 107protons per second. This review highlights the latest advancements in fast Monte Carlo dose calculations that have led to such speeds, including emerging artificial intelligence-based techniques, and discusses their application in both current and emerging proton therapy strategies

Detector Simulation Challenges for Future Accelerator Experiments

Detector simulation is a key component for studies on prospective future high-energy colliders, the design, optimization, testing and operation of particle physics experiments, and the analysis of the data collected to perform physics measurements. This review starts from the current state of the art technology applied to detector simulation in high-energy physics and elaborates on the evolution of software tools developed to address the challenges posed by future accelerator programs beyond the HL-LHC era, into the 2030–2050 period. New accelerator, detector, and computing technologies set the stage for an exercise in how detector simulation will serve the needs of the high-energy physics programs of the mid 21st century, and its potential impact on other research domains

Development of a VHEE accelerator in Sapienza for the treatment of deep seated tumors: planning and radioprotection challenges of a FLASH compact machine

Cancer treatment continues to be a major challenge in modern medicine, necessitating innovative therapies that enhance efficacy while minimizing side effects. Radiotherapy has evolved from conventional X-rays to advanced particle therapies, yet limitations persist that impact both therapeutic outcomes and patient quality of life. Very High Energy Electrons (VHEE) have emerged as a promising alternative for deep-seated tumor treatment, but technical challenges have historically hindered their clinical adoption. Recent advancements in accelerator technology, particularly compact high-gradient LINACs operating in the C-band and X-band, along with the discovery of the FLASH effect—where ultra-high dose rate irradiation significantly reduces healthy tissue toxicity—have renewed interest in VHEE therapy. The SAFEST project (SApienza Flash Electron Source for radio-Therapy) at Sapienza University of Rome aims to develop a hospital-compatible compact LINAC capable of generating VHEE beams at FLASH intensities, paving the way for clinical trials. This Ph.D. research focuses on the development of a dedicated Treatment Planning System (TPS) for VHEE therapy, with a particular emphasis on optimizing treatments in FLASH mode. The research encompasses a comprehensive analysis of VHEE radiotherapy, including dosimetric evaluations, beam dynamics, and treatment optimization strategies for different tumor types, such as intracranial lesions and pancreatic cancer. Additionally, the study addresses key radioprotection challenges, proposing shielding solutions to ensure the safe clinical implementation of VHEE LINACs. By leveraging cutting-edge computational tools and optimization algorithms, this work contributes to the establishment of VHEE as a viable clinical alternative to conventional radiotherapy and particle therapy. The results suggest that, under certain conditions, VHEE could enable dose escalation at the target volume while preserving healthy tissue integrity, reinforcing its potential as a transformative approach in cancer treatment

Status of the hadrontherapy projects in Europe

International audienceRobert R. Wilson, an accelerator pioneer working with E.O. Lawrence at Berkeley, made the first proposal to treat cancer using proton beams more than 60 year ago (1946). In a first period, many accelerators built for nuclear and particle physics research have been progressively used to treat patients all over the word. It was the time of the hadrontherapy “first generation”. As it has been the case for the evolution of the synchrotron radiation sources, starting with Loma Linda in 1990, a “second generation” of dedicated facilities has then been built to treat cancer without the constraints imposed to “parasitic users”. The total number of patients treated with protons was about 10.000 in 1993 and 50.000 in 2006, the progression looks to be exponential. Hadrontherapy is today part of the medical business landscape, several companies sell turnkey protontherapy centres and the fight is started for carbon centres. The status of some hadrontherapy projects in Europe (existing facilities and future projects) as well as the industrial aspects will be presented in this framework