Beltran, Chris

Abstract Introduction: To evaluate a proof-of-concept three-dimensional surface reconstruction technique using a hybrid LiDAR and RGB sensor system with an open-source, GPU-accelerated pipeline. The goal is to generate photorealistic digital twins of phantom surfaces for integration into radiotherapy collision avoidance workflows. Methods: A portable Intel RealSense sensor was used to acquire synchronized depth and color images. Sensor performance, including depth accuracy, fill rate, and planar root mean square error, was evaluated to determine optimal scan range. A reconstruction pipeline was implemented using the Open3D library with a voxel-based framework, signed distance function integration, ray casting, and color and depth-based simultaneous localization and mapping for pose tracking. Surface meshes were generated using the Marching Cubes algorithm. Validation involved scanning rectangular box phantoms and an anthropomorphic Rando phantom in a single circular motion. Reconstructed models were registered to CT-derived meshes using manual point picking and iterative closest point alignment. Accuracy was assessed using cloud-to-mesh distance metrics and compared to Poisson surface reconstruction. Results: Highest accuracy was observed within the 0.3 to 2.0 meter range. Volume differences for box models were within five millimeters. The Rando phantom showed a registration error of 1.8 millimeters and full overlap with the CT reference. Mean signed distance was minus 0.32 millimeters with a standard deviation of 3.85 millimeters. Conclusions: This technique enables accurate, realistic surface modeling using low-cost, open-source tools and supports future integration into radiotherapy digital twin systems.

Microdosimetry plays a critical role in particle therapy by quantifying energy deposition within microscopic domains to assess biological effects. This study evaluates the influence of different microdosimetric functions (MFs) and domain geometries (DGs) on relative biological effectiveness (RBE) predictions in carbon ion radiotherapy. Specifically, we compare the analytical microdosimetric function (AMF), calculated for spherical domains and implemented in PHITS, with the Kiefer–Chatterjee (KC) track structure model, which is conventionally applied to cylindrical geometries. To enable a direct comparison, we also introduce a novel implementation of the KC model for spherical domains. Using both models, specific energy distributions were calculated across a range of domain sizes and geometries. These distributions were input into the modified microdosimetric kinetic model (mMKM) to calculate RBE for the HSG cell line and compared against published in vitro data. The results show that both microdosimetric function and domain geometry significantly affect microdosimetric spectra and the resulting RBE, with deviations exceeding 10% when fixed mMKM parameters are used. Parameter optimization within the mMKM enables alignment across models. Our findings emphasize that microdosimetric function and domain geometry selection must be explicitly accounted for in microdosimetry-based RBE modeling, and that model parameters must be tuned accordingly to ensure consistent and biologically accurate predictions.

Abstract Background Projectile and target fragmentation are nuclear phenomena that can influence the computation of the linear energy transfer (LET) and the relative biological effectiveness (RBE) in external radiotherapy with accelerated ions. Correlations between these two quantities are routinely established during radiobiological experiments to interpret the results and to develop and calibrate RBE models. Purpose This study systematically evaluates the impact of secondary fragments and irradiation scenarios on the dose‐averaged LET, the RBE, and their correlation in the case of exposures to clinically relevant ion beams. Methods 57 600 microdosimetric lineal energy spectra and corresponding LET distributions were simulated with the Monte Carlo code PHITS across different scenarios, including track segment calculations, pristine, and spread‐out Bragg peaks of 1 H, 4 He, 12 C, 16 O, and 20 Ne ions within water phantoms. The LET distributions were analyzed to calculate the dose‐average LET, both including or excluding the contribution of secondary ions of an element different from the primary beam. Similarly, the lineal energy distributions were processed in conjunction with the Mayo Clinic Florida microdosimetric kinetic model to compute the RBE for two theoretical cell lines ( α / β = 2 and 10 Gy in the case of 6 MV x‐rays). Correlations between the RBE and dose‐averaged LET were established by analyzing the simulation results within water phantoms and then compared to the corresponding trends from the track segment calculations. Results Excluding secondary fragments had a pronounced impact on the dose‐averaged LET and the RBE, particularly in the entrance regions of proton beams and close to the distal edge of heavier ions. The correlations between the RBE and the dose‐averaged LET were not universal, but highly dependent on the irradiation scenario. For proton beams only, the dose‐averaged LET of hydrogen ions served as a practical first‐order descriptor of the RBE. Track segment simulations, commonly used for calibrating and benchmarking RBE models, provided a reasonable approximation for low‐energy beams but failed to fully capture the complexity of polyenergetic radiation fields. Conclusions Secondary fragments can substantially affect the dose‐averaged LET and the RBE, even in proton beams. The dose‐averaged LET, including or not the contributions from secondary fragments, was generally unable to adequately capture RBE variations across different scenarios. A more comprehensive approach, considering microdosimetric distributions, is necessary to accurately describe RBE variations in ion therapy.

Ion radiotherapy requires accurate relative biological effectiveness (RBE) calculations to account for the markedly different biological effects of ions compared to photons. Microdosimetric RBE models rely on descriptions of the energy deposition at the microscopic scale, either through radial dose distributions (RDDs) or microdosimetric probability density distributions. While RDD approaches focus on the theoretical description of the energy deposition around the ion track, microdosimetric distributions offer the advantage of being experimentally measurable, which is crucial for quality assurance programs. As the results of microdosimetric RBE models depend on whether RDD or microdosimetric distributions are used, the model parameters are not interchangeable between these approaches. This study presents and validates a method to reproduce the published reference biological and clinical dose calculations at NIRS-QST for only carbon ion radiotherapy by using the modified microdosimetric kinetic model (MKM) alongside microdosimetric distributions instead of the reference RDD approach. To achieve this, Monte Carlo simulations were performed to estimate the variation of the radiation quality within and outside the field of pristine and spread-out Bragg peaks. By appropriately optimizing the modified MKM parameters for microdosimetric distributions assessed within water spheres, we successfully reproduced the results of calculations using the reference NIRS-QST RDD, generally within 2%.

Abstract Background All Hitachi proton pencil beam scanning facilities currently use discrete spot scanning (DSS). Mayo Clinic Florida (MCF) is installing a Hitachi particle therapy system with advanced technologies, including fast scan speeds, high beam intensity, rapid beam off control (RBOC), a skip spot function, and proton pencil beam scanning using dose driven continuous scanning (DDCS). A potential concern of RBOC is the generation of a shoulder at the end of the normal spot delivery due to a flap spot (FS) with a flap dose (FD), which has been investigated for carbon synchrotron but not for proton delivery. While investigated, for instance, for Hitachi's installation at MCF, this methodology could be applicable for all future high intensity proton deliveries. Purpose No Hitachi proton facility currently uses the proposed RBOC. This study aimed to understand the dosimetric impact of proton FD at MCF by simulating the FS with a Hitachi proton machine in research mode, reflecting the higher proton intensities expected with RBOC at MCF. Method Experiments were conducted to simulate MCF RBOC at Kyoto Prefecture University of Medicine (KPUM) in research mode, reducing delay time (Td) from 1.5 ms to 0.1 ms. 5,000 contiguous spots were delivered on the central axis for proton energies of 70.2, 142.5, and 220.0 MeV; at normal, high dose rate (HDR), and ultra‐high dose rate (uHDR) intensities; and at vertical and horizontal gantry angles for different Td. Measurements were taken using a fast oscilloscope and the nozzle's spot position monitor (SPM) and dose monitor (DM). A model was developed to predict FD dependence on beam intensity and assess the dosimetric impact for prostate and brain treatment plans. Two simulation types were planned: a flap DSS plan with FS at every spot and a flap DDCS plan with FS only at the end of each layer. Result FD was observed for RBOC with Td = 0.1 ms, showing no gantry angle dependence. FD increased with higher delayed dose rate (DDR), that is, beam intensity. The planning study showed dose volume histogram deterioration with increased FD compared to the clinical plan, but it was only significant for uHDR intensities. Deterioration was marginal in flap DSS plans for the HDR intensities planned at MCF, and flap DDCS plans were even less sensitive than flap DSS plans. Conclusion MCF is installing proton DDCS with higher beam intensities, a skip spot function, and fast beam‐off control. The resulting FD had an insignificant impact on dose distribution for two patient plans with both DSS and DDCS at the anticipated MCF intensities. However, significant dependence was observed in the case of uHDR. A method to measure the position and dose of the FS during commissioning is described in addition to recommendations for regular QA and log‐based proton patient‐specific quality assurance.

Adaptive radiotherapy (ART) workflows are increasingly adopted to achieve dose escalation and tissue sparing under dynamic anatomical conditions. However, recontouring and time constraints hinder the implementation of real-time ART workflows. Various auto-segmentation methods, including deformable image registration, atlas-based segmentation, and deep learning-based segmentation (DLS), have been developed to address these challenges. Despite the potential of DLS methods, clinical implementation remains difficult due to the need for large, high-quality datasets to ensure model generalizability. This study introduces an InterVision framework for segmentation. The InterVision framework can interpolate or create intermediate visuals between existing images to generate specific patient characteristics. The InterVision model is trained in two steps: (1) generating a general model using the dataset, and (2) tuning the general model using the dataset generated from the InterVision framework. The InterVision framework generates intermediate images between existing patient image slides using deformable vectors, effectively capturing unique patient characteristics. By creating a more comprehensive dataset that reflects these individual characteristics, the InterVision model demonstrates the ability to produce more accurate contours compared to general models. Models are evaluated using the volumetric dice similarity coefficient (VDSC) and the Hausdorff distance 95% (HD95%) for 18 structures in 20 test patients. As a result, the Dice score was 0.81 ± 0.05 for the general model, 0.82 ± 0.04 for the general fine-tuning model, and 0.85 ± 0.03 for the InterVision model. The Hausdorff distance was 3.06 ± 1.13 for the general model, 2.81 ± 0.77 for the general fine-tuning model, and 2.52 ± 0.50 for the InterVision model. The InterVision model showed the best performance compared to the general model. The InterVision framework presents a versatile approach adaptable to various tasks where prior information is accessible, such as in ART settings. This capability is particularly valuable for accurately predicting complex organs and targets that pose challenges for traditional deep learning algorithms.

Abstract The repair of DNA double-strand breaks is a crucial yet delicate process which is affected by a multitude of factors. In this study, our goal is to analyse the influence of the linear energy transfer (LET) on the DNA repair kinetics. By utilizing the database of repair of DNA and aggregating the results of 84 experiments, we conduct various model fits to evaluate and compare different hypothesis regarding the effect of LET on the rejoining of DNA ends. Despite the considerable research efforts dedicated to this topic over the past decades, our findings underscore the complexity of the relationship between LET and DNA repair kinetics. This study leverages big data analysis to capture overall trends that single experimental studies might miss, providing a valuable model for understanding how radiation quality impacts DNA damage and subsequent biological effects. Our results highlight the gaps in our current understanding, emphasizing the pressing need for further investigation into this phenomenon.

Proton therapy is increasingly widespread and requires an accelerator to provide the high energy protons. Most often, the accelerators used for proton therapy are cyclotrons and the maximum initial beam energy (MIBE) is about 230 MeV or more to be able to achieve a range of approximately 30 cm in water. We ask whether such a high energy is necessary for adequate dosimetry for pathologies to be treated with proton beams. Eight patients of different clinical sites (brain, prostate, and head and neck cancers) were selected to conduct this study. We analyzed the tumor dose coverage and homogeneity, as well as healthy tissue protection for MIBE values of 120, 160, 180, 200 and 230 MeV. For each patient, a proton plan was developed using the particular MIBE and then using multifield optimization (MFO). In this way, 34 plans in total were generated to fulfill the unique clinical goals. This study found that MIBE of 120 MeV for brain tumors; 160 MeV for head and neck cancer; and remarkably, for prostate cancer, only 160 MeV for one patient case and 180 MeV for the remainder satisfied the clinical goals (words: 187 < approx. 200 words or less)

AbstractBackgroundAdaptive radiotherapy (ART) workflows have been increasingly adopted to achieve dose escalation and tissue sparing under shifting anatomic conditions, but the necessity of recontouring and the associated time burden hinders a real‐time or online ART workflow. In response to this challenge, approaches to auto‐segmentation involving deformable image registration, atlas‐based segmentation, and deep learning‐based segmentation (DLS) have been developed. Despite the particular promise shown by DLS methods, implementing these approaches in a clinical setting remains a challenge, namely due to the difficulty of curating a data set of sufficient size and quality so as to achieve generalizability in a trained model.PurposeTo address this challenge, we have developed an intentional deep overfit learning (IDOL) framework tailored to the auto‐segmentation task. However, certain limitations were identified, particularly the insufficiency of the personalized dataset to effectively overfit the model. In this study, we introduce a personalized hyperspace learning (PHL)‐IDOL segmentation framework capable of generating datasets that induce the model to overfit specific patient characteristics for medical image segmentation.MethodsThe PHL‐IDOL model is trained in two stages. In the first, a conventional, general model is trained with a diverse set of patient data (n = 100 patients) consisting of CT images and clinical contours. Following this, the general model is tuned with a data set consisting of two components: (a) selection of a subset of the patient data (m < n) using the similarity metrics (mean square error (MSE), peak signal‐to‐noise ratio (PSNR), structural similarity index (SSIM), and the universal quality image index (UQI) values); (b) adjust the CT and the clinical contours using a deformed vector generated from the reference patient and the selected patients using (a). After training, the general model, the continual model, the conventional IDOL model, and the proposed PHL‐IDOL model were evaluated using the volumetric dice similarity coefficient (VDSC) and the Hausdorff distance 95% (HD95%) computed for 18 structures in 20 test patients.ResultsImplementing the PHL‐IDOL framework resulted in improved segmentation performance for each patient. The Dice scores increased from 0.810.05 with the general model, 0.83 for the continual model, 0.83 for the conventional IDOL model to an average of 0.87 with the PHL‐IDOL model. Similarly, the Hausdorff distance decreased from 3.06 with the general model, 2.84 for the continual model, 2.79 for the conventional IDOL model and 2.36 for the PHL‐IDOL model. All the standard deviations were decreased by nearly half of the values comparing the general model and the PHL‐IDOL model.ConclusionThe PHL‐IDOL framework applied to the auto‐segmentation task achieves improved performance compared to the general DLS approach, demonstrating the promise of leveraging patient‐specific prior information in a task central to online ART workflows.

AbstractBackgroundPhenomenological relative biological effectiveness (RBE) models for proton therapy, based on the dose‐averaged linear energy transfer (LET), have been developed to address the apparent RBE increase towards the end of the proton range. The results of these phenomenological models substantially differ due to varying empirical assumptions and fitting functions. In contrast, more theory‐based approaches are used in carbon ion radiotherapy, such as the microdosimetric kinetic model (MKM). However, implementing microdosimetry‐based models in LET‐based proton therapy treatment planning systems poses challenges.PurposeThis work presents a LET‐based version of the MKM that is practical for clinical use in proton radiotherapy.MethodsAt first, we derived an approximation of the Mayo Clinic Florida (MCF) MKM for relatively‐sparsely ionizing radiation such as protons. The mathematical formalism of the proposed model is equivalent to the original MKM, but it maintains some key features of the MCF MKM, such as the determination of model parameters from measurable cell characteristics. Subsequently, we carried out Monte Carlo calculations with PHITS in different simulated scenarios to establish a heuristic correlation between microdosimetric quantities and the dose averaged LET of protons.ResultsA simple allometric function was found able to describe the relationship between the dose‐averaged LET of protons and the dose‐mean lineal energy, which includes the contributions of secondary particles. The LET‐based MKM was used to model the in vitro clonogenic survival RBE of five human and rodent cell lines (A549, AG01522, CHO, T98G, and U87) exposed to pristine and spread‐out Bragg peak (SOBP) proton beams. The results of the LET‐based MKM agree well with the biological data in a comparable or better way with respect to the other models included in the study. A sensitivity analysis on the model results was also performed.ConclusionsThe LET‐based MKM integrates the predictive theoretical framework of the MCF MKM with a straightforward mathematical description of the RBE based on the dose‐averaged LET, a physical quantity readily available in modern treatment planning systems for proton therapy.