Objective.To determine beam quality correction factors,, in single-layer scanned carbon and helium ion beams using water calorimetry, thereby reducing uncertainties in dosimetry for light ion therapy.Approach.Water calorimetry measurements were performed under harmonized conditions at two synchrotron-based ion beam therapy centers (MedAustron, Austria and HIT, Germany). Measurements were conducted in the plateau region of single-layer scanned beams. Carbon ion beams covered nominal energies from 213.4-402.8 MeV/u, while helium ion beams were measured at 196.3 and 198.8 MeV/u. Multiple cylindrical (IBA FC65-G, PTW 30013) and plane-parallel (IBA PPC05, PPC40; PTW 34001, 34045) ionization chamber types were investigated.Results.-factors were determined with standard uncertainties of approximately 1%, depending on chamber type and measurement condition. For all investigated chambers, inter-center agreement was observed within the combined standard uncertainties. Chamber-to-chamber variability was below 0.5% for chamber types for which two samples were available, except for the IBA PPC05, for which differences up to 1.3% were observed. For carbon ion beams, the measured-factors are consistent with previously published water calorimetry-based results. However, systematic deviations up to 3% persist between experimentally determined and Monte Carlo-derivedvalues. The measured-factors for helium ions show trends that are broadly similar to those observed in carbon ion beams across all chamber types.Significance.This work expands the experimental-database for light ions by providing new carbon ion data and the first experimental-factors for helium ion beams. These results provide essential experimental data for future refinement of dosimetry recommendations, and highlight persistent discrepancies between the experimental and Monte Carlo-based-factors that require further investigation.

Objective.To measure beam quality correction factors (kQ) in single-layer scanned proton beams using water calorimetry for three ionization chamber types commonly used in clinical proton dosimetry.Approach.Measurements were performed at two proton therapy centers using clinical proton beams with nominal energies of 150, 220 and 226 MeV, at a reference depth of 4 cm. ThekQ-factors were obtained by comparing absorbed dose-to-water determinations from a water calorimeter with ionization chamber readings under identical conditions. Three ionization chamber models were investigated: the IBA FC65-G (cylindrical), PPC05 and PPC40 (plane-parallel). Two independent calorimeter setups were used across three measurement campaigns.Main results.The measuredkQ-factors showed strong agreement with current TRS-398 Rev. 1 (2024) recommendations and literature data. For the FC65-G chamber, excellent alignment was observed with previous water calorimetry-based measurements. For the PPC40 chamber, both chambers yielded consistent results within 0.5% of TRS-398 Rev. 1. For the PPC05 chambers, a maximum deviation of 1.4% was observed relative to TRS-398 Rev. 1, and inter-chamber variability was within 0.5%. The use of two calorimeter setups yielded consistent results within 0.2%, supporting their validity.Significance.This study presents new experimentalkQ-factors for ionization chambers in single-layer scanned proton beams, contributing to the currently scarce experimental database, providing further validation of the TRS-398 Rev. 1 (2024) recommendations, and supplying benchmark data for future Monte Carlo-basedkQcalculations.

Objective To assess the performance of compact cylindrical and plane-parallel ionization chambers that are currently not included in international dosimetry protocols in high-energy photon beams, and evaluate their suitability as reference chambers. Approach Four types of cylindrical (IBA RAZORTM Nano, CC01, CC04, CC08) and two types of plane-parallel (PPC40, PPC05) chambers were investigated. Measurements were performed for five photon beams with TPR20,10 values ranging from 0.680 to 0.764. Polarity, recombination and beam quality correction factors (kQ) were determined for all chamber types across all beam qualities. The kQ-factors were obtained by direct calibration against a water calorimeter. Chamber stability was evaluated based on available long-term calibration coefficients. Results The experimentally determined kQ -factors exhibited a standard uncertainty (k = 1) between 1.1% and 1.5%, with the largest sources of uncertainty stemming from the heat transfer within the calorimeter vessel and the chamber calibration coefficients. Results were consistent within 0.2% for chambers of the same type. Comparison with literature data from experiments and Monte Carlo simulations demonstrated strong agreement for all chambers except the CC01, where discrepancies are likely due to the presence of a steel electrode. Polarity and recombination correction factors were small and stable across all investigated beam qualities. Cylindrical chambers showed good stability, with somewhat larger variations observed for plane-parallel chambers. Significance This work expands the database of available kQ-factors for ionization chambers not currently included in dosimetry protocols and provides experimental data on their behavior in high-energy photon beams, contributing to their evaluation for use in reference dosimetry.

The visual exploration of high-dimensional (HD) data has gained popularity through the use of dimensionality reduction (DR) techniques such as t-SNE and UMAP. However, the interpretability of low-dimensional (LD) embeddings produced by these nonlinear methods remains a challenge. Conversely, linear methods such as PCA are natively interpretable but fall behind regarding DR quality. To circumvent this trade-off, post-hoc interpretability methods have been introduced, where simpler models are used a posteriori to explain LD positions in terms of HD features. While these approaches can provide explanations for nonlinear DR methods without compromising DR quality, their downside is that they rely on approximations of the original LD embeddings which can lead to misinterpretations. In this paper, we propose a novel solution to the trade-off between DR quality and interpretability: a natively interpretable version of t-SNE. The key idea is to express the coordinates of each LD point as individual linear combinations of HD features and use regularization to promote local coherence of the various linear combination weights across the embedding. Experimental results demonstrate the effectiveness of our method in preserving HD structures while providing LD embeddings that are interpretable by design.

Objective.As proton arc therapy (PAT) approaches clinical implementation, optimizing treatment plans for this innovative delivery modality remains challenging, especially in addressing arc delivery time. Existing algorithms for minimizing delivery time are either optimal but computationally demanding or fast but at the expense of sacrificing many degrees of freedom. In this study, we introduce a flexible method for pre-selecting energy layers (EL) in PAT treatment planning before the actual robust spot weight optimization.Approach.Our EL pre-selection method employs metaheuristics to minimize a bi-objective function, considering a dynamic delivery time proxy and tumor geometrical coverage penalized as a function of selected organs-at-risk crossing. It is capable of parallelizing multiple instances of the problem. We evaluate the method using three different treatment sites, providing a comprehensive dosimetric analysis benchmarked against dynamic proton arc plans generated with early energy layer selection and spot assignment (ELSA) and IMPT plans in RayStation TPS.Result.The algorithm efficiently generates Pareto-optimal EL pre-selections in approximately 5 min. Subsequent PAT treatment plans derived from these selections and optimized within the TPS, demonstrate high-quality target coverage, achieving a high conformity index, and effective sparing of organs at risk. These plans meet clinical goals while achieving a 20%-40% reduction in delivery time compared to ELSA plans.Significance.The proposed algorithm offers speed and efficiency, producing high-quality PAT plans by placing proton arc sectors to efficiently reduce delivery time while maintaining good target coverage and healthy tissues sparing.

Objective.Achieving FLASH dose rate with pencil beam scanning intensity modulated proton therapy is challenging. However, utilizing a single energy layer with a ridge filter (RF) can maintain dose rate and conformality. Yet, changes in patient anatomy over the treatment course can render the RF obsolete. Unfortunately, creating a new RF is time-consuming, thus, incompatible with online adaptation. To address this, we propose to re-optimize the spot weights while keeping the same initial RF.Approach.Data from six head and neck cancer patients with a repeated computed tomography (CT2) were used. FLASH treatment plans were generated with three methods on CT2: 'full-adaptation' (FA), optimized from scratch with a new RF; 'spot-adaptation only' (SAO), re-using initial RF but adjusting plan spot weights; and 'no adaptation' (NoA) where the dose from initial plans on initial CT (CT1) was recomputed on CT2. The prescribed dose per fraction was 9 Gy. Different beam angles were tested for each CT2(1 beam per fraction). The FA, SAO and NoA plans were then compared on CT2.Main results.Fractions with SAO showed a median decrease of 0.05 Gy forD98% and a median increase of 0.03 Gy forD2% of CTV when compared to their homologous FA plans on nominal case. Median conformity number decreased by 0.03. Median max dose to spinal cord increased by 0.09 Gy. The largest median increase in mean dose to organs was 0.03 Gy to the mandible. The largest observed median difference in organs receiving a minimal dose rate of 40 Gy s-1was 0.5% for the mandible. Up to 16 of the 20 evaluated SAO fractions were thus deemed clinically acceptable, with up to 8 NoA plans already acceptable before adaptation.Significance.Proposed SAO workflow showed that for most of our evaluated plans, daily reprinting of RF was not necessary.

Particle Arc Therapy (PAT) is considered a promising technique to improve conformity and reduce toxicities. Robustly optimized PAT plans were evaluated versus Intensity Modulated Proton Therapy (IMPT) for oesophageal cancer in 17 patients. Impact of motion, setup and range uncertainties on target coverage, plan quality and Organs At Risk (OAR) doses were assessed. PAT (two 80°-200°arcs) reduced OAR doses (spinal canal D 0 . 05 cm 3 : 5.12 Gy (12.8%), lungs and heart D mean : 0.39 Gy (8.8%) and 0.83 Gy (10.5%)) while maintaining robustness. Similar toxicities were observed, but delivery time was doubled for PAT, indicating that further development is needed.

This paper details the design and architecture of PARROT (Platform for ARtificial intelligence guided Radiation Oncology Treatment), a free and open-source, Web-based platform that aims to improve therapeutic decision-making through advanced artificial intelligence (AI) techniques. The primary goals of PARROT are to facilitate the visualization of the outputs of AI models, to provide tools for annotating and modifying AI segmentation results, and to assist in comparing treatment plans and selecting the most suitable option. PARROT addresses common challenges in AI research, such as simplifying the execution and management of AI experiments and facilitating the comparison of results between published and institution-trained models. It enables users to interact with AI models for radiation oncology in real time, allowing them to understand and refine model outputs by visually inspecting and correcting biases. PARROT provides an end-to-end workflow with an easy-to-use graphical interface that promotes the implementation of AI models in clinical settings.

Background and Purpose: Proton arc therapy (PAT) is an emerging modality delivering continuously rotating proton beams. Current PAT planning approaches are time-consuming, making them unsuitable for online adaptation. This study proposes an accelerated workflow for adapting PAT plans. Materials and Methods: The proposed workflow transfers spots from initial computed tomography (CT) to the CT of the day, updates energy layers considering the initial pattern, and re-optimizes selected transferred spots based on their initial weights and impact on the objective function.A retrospective study was conducted on five head and neck patients who underwent plan adaptation on a repeated CT. PAT plans were generated with two different methods on the repeated CT: reference, created de novo, and smart-adapted, generated with the proposed adaptive workflow. Robust optimization was performed for all plans. Results: Smart-adapted plans achieved similar mean dose to organs at risk as the reference: the largest median increase of mean dose was 1.9 Gy to the mandible; the median of maximum dose to spinal cord was 0.5 Gy lower for the smart-adapted plans. The median target coverage, i.e. D98, to primary tumor and nodes of smart-adapted plans decreased by 0.2 and 0.4 Gy for the nominal case, and 0.4 and 0.6 Gy for the worst-case scenario; all smart-adapted plans met clinical objectives. The smart-adaptation method reduced average planning time from 19184 s to 5626 s, a 3.4-fold improvement. Conclusions: Smart-adapted plans achieve similar plan quality to the reference method, while significantly reducing plan generation time for new patient anatomy.

Large collections of high-dimensional data have become nearly ubiquitous across many academic fields and application domains, ranging from biology to the humanities. Since working directly with high-dimensional data poses challenges, the demand for algorithms that create low-dimensional representations, or embeddings, for data visualization, exploration, and analysis is now greater than ever. In recent years, numerous embedding algorithms have been developed, and their usage has become widespread in research and industry. This surge of interest has resulted in a large and fragmented research field that faces technical challenges alongside fundamental debates, and it has left practitioners without clear guidance on how to effectively employ existing methods. Aiming to increase coherence and facilitate future work, in this review we provide a detailed and critical overview of recent developments, derive a list of best practices for creating and using low-dimensional embeddings, evaluate popular approaches on a variety of datasets, and discuss the remaining challenges and open problems in the field.

BACKGROUND:Proton arc therapy (PAT) has emerged as a promising approach for improving dose distribution, but also enabling simpler and faster treatment delivery in comparison to conventional proton treatments. However, the delivery speed achievable in proton arc relies on dedicated algorithms, which currently do not generate plans with a clear speed-up and sometimes even result in increased delivery time. PURPOSE:This study aims to address the challenge of minimizing delivery time through a hybrid method combining a fast geometry-based energy layer (EL) pre-selection with a dose-based EL filtering, and comparing its performance to a baseline approach without filtering. METHODS:Three methods of EL filtering were developed: unrestricted, switch-up (SU), and switch-up gap (SU gap) filtering. The unrestricted method filters the lowest weighted EL while the SU gap filtering removes the EL around a new SU to minimize the gantry rotation braking. The SU filtering removes the lowest weighted group of EL that includes a SU. These filters were combined with the RayStation dynamic proton arc optimization framework energy layer selection and spot assignment (ELSA). Four bilateral oropharyngeal and four lung cancer patients' data were used for evaluation. Objective function values, target coverage robustness, organ-at-risk doses and normal tissue complication probability evaluations, as well as comparisons to intensity-modulated proton therapy (IMPT) plans, were used to assess plan quality. RESULTS:The SU gap filtering algorithm performed best in five out of the eight cases, maintaining plan quality within tolerance while reducing beam delivery time, in particular for the oropharyngeal cohort. It achieved up to approximately 22% and 15% reduction in delivery time for oropharyngeal and lung treatment sites, respectively. The unrestricted filtering algorithm followed closely. In contrast, the SU filtering showed limited improvement, suppressing one or two SU without substantial delivery time shortening. Robust target coverage was kept within 1% of variation compared to the PAT baseline plan while organs-at-risk doses slightly decreased or kept about the same for all patients. CONCLUSIONS:This study provides insights to accelerate PAT delivery without compromising plan quality. These advancements could enhance treatment efficiency and patient throughput.

•Corrections of auto-segmented volumes are not all dosimetrically relevant.•Relevance of corrections for auto-segmented volumes can be predicted.•Slice-wise corrections with relevance indicator can preserve the plan quality.•Slice-wise corrections with such indicator can decrease the number of corrections.

Self-organizing maps (SOMs) have many advantages as a tool for exploratory data analysis. Combining vector quantization and topological relationships that are defined in a low-dimensional space, they can run on big data sets and are mostly immune to the curse of dimensionality in the data space. SOMs are used mainly for dimensionality reduction and marginally for clustering; however, SOMs also suffer from some shortcomings. Vector quantization makes them unable to embed all data points, only prototypes or centroid are mapped. Being defined as a regular grid in the low-dimensional space, dimensionality reduction and clustering with SOMs are indirect, as compared to methods of direct embedding like multi-dimensional scaling. Since 2008, t-SNE (t-distributed stochastic neighbor embedding) has raised growing interest, first in the machine learning community and now outside of it, with many applications in cell biology, for instance. Primarily used as a 2D embedding and visualization technique, t-SNE is more and more used as a clustering technique, which is capable of identifying meaningful clusters that classical clustering tools struggle to see. Quite counter-intuitively, t-SNE often better separates clusters in low-dimensional embeddings than clustering tools would do in the high-dimensional data space. To understand this paradox, several mechanisms of t-SNE can be framed as a distance transformation with the possibility (i) to denoise distances in high-dimensional spaces and (ii) to impose a strong inductive bias on them, which magnifies inter-cluster gaps. Despite these strengths, t-SNE is not free of drawbacks, which we quickly review to sketch perspectives of future developments.