Linear energy transfer (LET) verification was conducted using a silicon-on-insulator (SOI) microdosimeter during the commissioning of LET-optimized carbon-ion radiotherapy. This advanced treatment technique is expected to improve local control rates, especially in hypoxic tumors. Approach: An SOI microdosimeter with a cylindrical sensitive volume of 30 μm diameter and 5 μm thickness was used. Simple cubic plans and patient plans using the carbon-ion beams were created by treatment planning system, and the calculated LETd values were compared with the measured LETd values obtained by the SOI microdosimeter. Main results: Reasonable agreement between the measured and calculated LETd was seen in the plateau region of depth LETd profile, whereas the measured LETd were below the calculated LETd in the peak region, specifically where LETd exceeds 75 keV/μm. The discrepancy in the peak region may arise from the uncertainties in the calibration process of the SOI microdosimeter. Excluding the peak region, the average ratio and standard deviation between measured and calculated LETd values were 0.996 and 7%, respectively. Significance: This verification results in the initiation of clinical trials for LET-optimized carbon-ion radiotherapy at QST Hospital, National Institutes for Quantum Science and Technology.

To evaluate the feasibility of transspine focused ultrasound using simulation-based phase corrections from a CT-derived ray acoustics model.Bilateral transspine focusing was performed inhuman vertebrae with a spine-specific ultrasound array. Ray acoustics-derived phase correction was compared to geometric focusing and a hydrophone-corrected gold standard. Planar hydrophone scans were recorded in the spinal canal and three metrics were calculated: target pressure, coronal and sagittal focal shift, and coronal and sagittal Sørensen-Dice similarity to the free-field.analysis was performedto assess the impact of windows between vertebrae on focal shift.Hydrophone correction reduced mean sagittal plane shift from 1.74 ± 0.82 mm to 1.40 ± 0.82 mm and mean coronal plane shift from 1.07 ± 0.63 mm to 0.54 ± 0.49 mm. Ray acoustics correction reduced mean sagittal plane and coronal plane shift to 1.63 ± 0.83 mm and 0.83 ± 0.60 mm, respectively. Hydrophone correction increased mean sagittal similarity from 0.48 ± 0.22 to 0.68 ± 0.19 and mean coronal similarity from 0.48 ± 0.23 to 0.70 ± 0.19. Ray acoustics correction increased mean sagittal and coronal similarity to 0.53 ± 0.25 and 0.55 ± 0.26, respectively. Target pressure was relatively unchanged across beamforming methods.analysis found that, for some targets, unoccluded paths may have increased focal shift.. Gold standard phase correction significantly reduced coronal shift and significantly increased sagittal and coronal Sørensen-Dice similarity (< 0.05). Ray acoustics-derived phase correction reduced sagittal and coronal shift and increased sagittal and coronal similarity but did not achieve statistical significance. Across beamforming methods, mean focal shift was comparable to MRI resolution, suggesting that transspine focusing is possible with minimal correction in favourable targets. Future work will explore the mitigation of acoustic windows with anti-focus control points.

The method combining Monte Carlo (MC) simulation and mesh-type cell models provides a way to accurately assess the cellular dose induced by-emitters. Although this approach allows for a specific evaluation of various nuclides and cell type combinations, the associated time cost for obtaining results is relatively high. In this work, we propose a Microdosimetric assessment method for Internal exposure of-emitters based on Mesh-type Cell cluster models (abbreviated as MIMC-). This approach is applied to evaluate the dose in various types of cells (human bronchial epithelial cells, BEAS-2B; normal human liver cells, L-O2; and normal human small intestine epithelial cells, FHs74Int) exposed to-emitters. Furthermore, microdosimetric quantity based on the cell cluster model are employed to estimate the relative biological effectiveness (RBE) of-emitters. The results indicate that this method can accurately and rapidly predict cellular doses caused by different types of-emitters, significantly mitigating the efficiency challenges associated with directly employing MC to estimate the overall dose of the mesh-type cell cluster model. In comparison with results obtained from direct simulations of uniform administration of- sources using PHITS for validation, the cellular cluster overall-values obtained through MIMC-show discrepancies mostly below 5%, with the minimum deviation reaching 1.35%. Small sampling sizes within the cell nucleus led to larger average lineal energies. In comparison to C-14, the differences in cellular cluster average lineal energy for Cs-134, Cs-137, and I-131 are negligible, resulting in close numerical estimations of RBE based on lineal energy. The MIMC-can be extended to diverse cell types and-emitters. Additionally, the RBE assessment based on the cell cluster model offers valuable insights for predicting radiobiological damage resulting from internal exposure by-emitters. This method is expected to find applicability in various realistic scenarios, including radiation protection and radioligand therapy.

This study aims to investigate the feasibility of utilizing generative adversarial networks (GANs) to synthesize high-fidelity CT images from lower-resolution MR images. The goal is to reduce patient exposure to ionizing radiation while maintaining treatment accuracy and accelerating MR image acquisition. The primary focus is to determine the extent to which low-resolution MR images can be utilized to generate high-quality CT images through a systematic study of spatial resolution-dependent MRI-to-CT image conversion. Approach. Paired MRI-CT images were acquired from healthy control and tumor models, generated by injecting MDA-MB-231 and 4T1 tumor cells into the mammary fat pad of nude and BALB/c mice to ensure model diversification. To explore various MRI resolutions, we downscaled the highest-resolution MR image into three lower resolutions. Using a customized U-Net model, we automated region of interest masking for both MRI and CT modalities with precise alignment, achieved through three-dimensional affine paired MRI-CT registrations. Then our customized models, Nested U-Net Generative Adversarial Network (NUGAN) and Attention U-Net Generative Adversarial Network (AUGAN), were employed to translate low-resolution MR images into high-resolution CT images, followed by evaluation with separate testing datasets. Main Results. Our approach successfully generated high-quality CT images (0.142 mm²) from both lower-resolution (0.282 mm²) and higher-resolution (0.142 mm²) MR images, with no statistically significant differences between them, effectively doubling the speed of MR image acquisition. Our customized GANs successfully preserved anatomical details, addressing the typical loss issue seen in other MRI-CT translation techniques across all resolutions of MR image inputs. Significance. This study demonstrates the potential of using low-resolution MR images to generate high-quality CT images, thereby reducing radiation exposure and expediting MRI acquisition while maintaining accuracy for radiotherapy. .

Among various deep-network-based sparse-view CT image reconstruction studies, the sinogram upscaling network has been predominantly employed to synthesize additional view information. However, the performance of the sinogram-based network is limited in terms of removing aliasing streak artifacts and recovering low-contrast small structures. In this study, we used a view-by-view back-projection (VVBP) tensor-domain network to overcome such limitations of the sinogram-based approaches.The proposed method offers advantages of addressing the aliasing artifacts directly in the 3D tensor domain over the 2D sinogram. In the tensor-domain network, the multi-planal anti-aliasing modules were used to remove artifacts within the coronal and sagittal tensor planes. In addition, the data-fidelity-based refinement module was also implemented to successively process output images of the tensor network to recover image sharpness and textures.The proposed method showed outperformance in terms of removing aliasing artifacts and recovering low-contrast details compared to other state-of-the-art sinogram-based networks. The performance was validated for both numerical and clinical projection data in a circular fan-beam CT configuration.We observed that view-by-view aliasing artifacts in sparse-view CT exhibit distinct patterns within the tensor planes, making them effectively removable in high-dimensional representations. Additionally, we demonstrated that the co-domain characteristics of tensor space processing offer higher generalization performance for aliasing artifact removal compared to conventional sinogram-domain processing.

Mild cognitive impairment (MCI) is a precursor stage of dementia characterized by mild cognitive decline in one or more cognitive domains, without meeting the criteria for dementia. MCI is considered a prodromal form of Alzheimer's disease (AD). Early identification of MCI is crucial for both intervention and prevention of AD. To accurately identify MCI, a novel multimodal 3D imaging data integration graph convolutional network (GCN) model is designed in this paper.The proposed model utilizes 3D-VGGNet to extract three-dimensional features from multimodal imaging data (such as structural magnetic resonance imaging and fluorodeoxyglucose positron emission tomography), which are then fused into feature vectors as the node features of a population graph. Non-imaging features of participants are combined with the multimodal imaging data to construct a population sparse graph. Additionally, in order to optimize the connectivity of the graph, we employed the pairwise attribute estimation （PAE） method to compute the edge weights based on non-imaging data, thereby enhancing the effectiveness of the graph structure. Subsequently, a population-based GCN integrates the structural and functional features of different modal images into the features of each participant for MCI classification.Experiments on the AD Neuroimaging Initiative demonstrated accuracies of 98.57%, 96.03%, and 96.83% for the normal controls (NC)-early MCI (EMCI), NC-late MCI (LMCI), and EMCI-LMCI classification tasks, respectively. The AUC, specificity, sensitivity, and F1-score are also superior to state-of-the-art models, demonstrating the effectiveness of the proposed model. Furthermore, the proposed model is applied to the ABIDE dataset for autism diagnosis, achieving an accuracy of 91.43% and outperforming the state-of-the-art models, indicating excellent generalization capabilities of the proposed model.This study demonstratethe proposed model's ability to integrate multimodal imaging data and its excellent ability to recognize MCI. This will help achieve early warning for AD and intelligent diagnosis of other brain neurodegenerative diseases.

. Intensity modulated proton therapy (IMPT) is susceptible to uncertainties in patient setup and proton range. Robust optimization is employed in IMPT treatment planning to ensure sufficient coverage of the clinical target volume (CTV) in predefined scenarios, albeit at a price of increased planning times. We investigated a deep learning (DL) strategy for dose predictions in individual error scenarios in head and neck cancer IMPT treatment planning, enabling direct evaluation of plan robustness. The model is able to differentiate between scenarios by using embeddings of the scenario index.. To accommodate resolution disparities in planning CT-scans and accommodate the setup error scenarios, we introduced scenario-specific isocentric distance maps as inputs to the DL models. For 392 H&N cancer patients, high-quality 9-scenario ground truth (GT) robust plans were generated with wish-list driven fully automated multi-criteria optimization. The scenario index is converted to one-hot-vector that is used to derive the scenarios embeddings through the training of the DL model, aiding the model to predict a scenario specific dose distribution.. The model achieved within 1%-point of agreement with the GT the predictedV95%of the voxelwise minimum dose for CTV Low and CTV High for 96% and 75% respectively of the test patients. Considering all robustness scenarios, median differences were 0.035%-point for CTV HighV95%, 0.11%-point for CTV LowV95%, 0.29 GyE for parotidsDmean, 0.7 GyE for submandibular glandsDmeanand 0.9 GyE for oral cavityDmean. Prediction of full 3D dose distributions for all scenarios took around 14 s.. Predicting individual scenarios for robust proton therapy using DL dose prediction is feasible, enabling direct robustness evaluation of the predicted scenario doses.

Artificial intelligence (AI) based treatment planning tools are being implemented in clinic. However, human interactions with such AI tools are rarely analyzed. This study aims to comprehend human planner's interaction with the AI planning tool and incorporate the analysis to improve the existing AI tool.An in-house AI tool for whole breast radiation therapy planning was deployed in our institution since 2019, among which 522 patients were included in this study. The AI tool automatically generates fluence maps of the tangential beams to create an. Human planner makes fluence edits deemed necessary and after attending physician approval for treatment, it is recorded as. Manual modification value maps were collected, which is the difference between theand the. Subsequently, a human-AI interaction (HAI) model using full scale connected U-Net was trained to learn such interactions and perform plan enhancements. The trained HAI model automatically modifies theto generate AI-modified plans (), simulating human editing. Its performance was evaluated against originalandshowed statistically significant improvement in hotspot control over the, with an average of 25.2cc volume reduction in breast V105% (= 0.011) and 0.805% decrease in Dmax (< .001). It also maintained the same planning target volume (PTV) coverage as the, demonstrating the model has captured the clinic focus of improving PTV hot spots without degrading coverage.The proposed HAI model has demonstrated capability of further enhancing thevia modeling human-AI tool interactions. This study shows analysis of human interaction with the AI planning tool is a significant step to improve the AI tool.

To develop an empirical model to predict carbon ion (C-ion) relative biological effectiveness (RBE). Approach. We used published cell survival data comprising 360 cell line/energy combinations to characterize the linear energy transfer (LET) dependence of cell radiosensitivity parameters describing the dose required to achieve a given survival level, e.g. 5% (D), which are linearly correlated between photon and C-ion radiations. Based on the LET response of the metrics D5% and D37%, we constructed a model containing four free parameters that predicts cells' linear quadratic model (LQM) survival curve parameters for C-ions, αand β, from the reference LQM parameters for photons, αand β, for a given C-ion LET value. We fit our model's free parameters to the training dataset and assessed its accuracy via leave-one out cross-validation. We further compared our model to the local effect model (LEM) and the microdosimetric kinetic model (MKM) by comparing its predictions against published predictions made with those models for clinically relevant LET values in the range of 23-107 keV/μm. Main Results. Our model predicted C-ion RBE within ±7%-15% depending on cell line and dose which was comparable to LEM and MKM for the same conditions. Significance. Our model offers comparable accuracy to the LEM or MKM but requires fewer input parameters and is less computationally expensive and whose implementation is so simple we provide it coded into a spreadsheet. Thus, our model can serve as a pragmatic alternative to these mechanistic models in cases where cell-specific input parameters cannot be obtained, the models cannot be implemented, or for which their computational efficiency is paramount.

. Prompt gamma-ray timing is an emerging technology in the field of particle therapy treatment verification. This system measures the arrival times of gamma rays produced in the patient body and uses the cyclotron radio frequency signal as time reference for the beam micro-bunches. Its translation into clinical practice is currently hindered by observed instabilities in the phase relation between the cyclotron radio frequency and the measured arrival time of prompt gamma rays. To counteract this, two proton bunch monitors are presented, integrated into the prompt gamma-ray timing workflow and evaluated.. The two monitors are (a) a diamond detector placed at the beam energy degrader, and (b) a cyclotron monitor signal measuring the phase difference between dee current and voltage. First, the two proton bunch monitors as well as their mutual correlation were characterized. Then, a prompt gamma-ray timing measurement was performed aiming to quantify the present magnitude of the phase instabilities and to evaluate the ability of the proton bunch monitors to correct for these instabilities.. It was found that the two new monitors showed a very high correlation for intermediate proton energies after the first second of irradiation, and that they were able to reduce fluctuations in the detected phase of prompt gamma rays. Furthermore, the amplitude of the phase instabilities had intrinsically decreased from about 700 ps to below 100 ps due to cyclotron upgrades.. The uncertainty of the prompt gamma-ray timing method for proton treatment verification was reduced. For routine clinical application, challenges remain in accounting for detector load effects, temperature drifts and throughput limitations.

Advanced pinhole collimation geometries optimized for preclinical high-energyimaging facilitate applications such asandemitter imaging, simultaneous multi-isotope PET and PET/SPECT, and positron range-free PET. These geometries replace each pinhole with a group of clustered pinholes (CPs) featuring smaller individual pinhole opening angles (POAs), enabling sub-mm resolution imaging up to ∼1 MeV. Further narrowing POAs while retaining field-of-view (FOV) may enhance high-energy imaging but faces geometrical constraints. Here, we detail how the novel twisted CPs (TCPs) address this challenge.We compared TCP and CP collimator sensitivity at equal system resolution (SR) and SR at matched sensitivity by tuning pinhole diameters forF (511 keV) andZr (909 keV). Additionally, simulated Derenzo phantoms at low activity (LA: 12 MBq ml) and high activity (HA: 190 MBq ml) levels, along with uniformity images, were compared to assess image resolution and uniformity.At equal SR, TCP increased average central FOV sensitivity by 15.6% forF and 29.4% forZr compared to CP. Image resolution was comparable, except forZr at LA, where TCP resolved 0.80 mm diameter rods compared to 0.90 mm for CP. Image uniformity was equivalent forF, while forZr TCP granted a 10.4% improvement. For collimators with matched sensitivity, TCP improved SR by 6.6% forF and 17.7% forZr while also enhancing image resolution; forF, rods distinguished were 0.65 mm (CP) and 0.60 mm (TCP) for HA, and 0.70 mm (CP and TCP) for LA. ForZr, image resolutions were 0.75 mm (CP) and 0.65 mm (TCP) for HA, and 0.90 mm (CP) and 0.80 mm (TCP) for LA. Image uniformity with TCP decreased by 18.3% forF but improved by 20.1% forZr.This study suggests that the TCP design has potential to improve high-energyimaging.

To evaluate spatially fractionated radiation therapy (SFRT) for very-high-energy electrons (VHEEs) delivered with pencil beam scanning.

The study of microcirculation can reveal important information related to pathology. Focusing on alterations that are represented by an obstruction of blood flow in microcirculatory regions may provide an insight into vascular biomarkers. The current in silico study assesses the capability of CEUS and SRU flow-quantification to study occlusive actions in a microvascular bed, particularly the ability to characterise known and model induced flow behaviours. The aim is to in- vestigate theoretical limits with the use of CEUS and the upcoming particle-tracking in SRU in order to propose realistic biomarker targets relevant for clinical diagnosis. Results from CEUS flow parameters display limitations congruent with prior investigations. Conventional resolution limits lead to signals dominated by large vessels, making discrimination of microvasculature specific signals difficult. Additionally, some occlusions lead to weakened parametric correlation against flow rate in the remainder of the network. Loss of correlation is dependent on the degree to which flow is redis- tributed, with comparatively minor redistribution correlating in accordance with ground truth measurements for change in mean transit time, dM T T (CEUS, R = 0.85; GT, R = 0.82) and change in peak intensity, dIp (CEUS, R = 0.87; GT, R = 0.96). Major redistributions, however, result in a loss of correlation, demonstrating that TIC-related parame- ters have efficacy dependent on the location of occlusion. Conversely, results from SRU processing provides accurate depiction of the anatomy and dynamics present in the vascular bed, that extends to individual microvessels. Correspon- dence between model vessel structure displayed in SRU maps with the ground truth was > 91% for cases of minor and major flow redistributions. In conclusion, SRU appears to be a highly promising technology in the quantification of subtle flow phenomena due ischaemia induced vascular flow redistribution.

Spot size stability is crucial in pencil beam scanning (PBS) proton therapy, and variations in spot size can disrupt dose distributions. Recently, a novel proton beam delivery method known as DynamicARC PBS scanning has been introduced. The current study investigates the dosimetric impact of spot size errors in DynamicARC proton therapy for head and neck (HNC), prostate, and lung cancers. Approach: Robustly optimized DynamicARC proton therapy plans were created for HNC (n=4), prostate (n=4), and lung (n=4) cancer patients. Spot size errors of ±10%, ±15%, and ±20% were introduced, and their effects on target coverage (D95% and D99%), homogeneity index (HI), and organ-at-risk (OAR) doses were analyzed across different cancer sites. Main Results: HNC and lung cancer plans showed greater vulnerability to spot size errors, with reductions in target coverage of up to 4.8% under -20% spot size errors. Dose homogeneity was also more affected in these cases, with HI degrading by 0.12 in lung cancer. Prostate cancer demonstrated greater resilience to spot size variations, even under errors of ±20%. For spot size errors ±10%, the oral cavity, parotid glands, and constrictor muscles experienced Dmean deviations within ±1.2%, while deviations were limited to ±0.5% for D10% of the bladder and rectum and ±0.3% for V20Gy(RBE) of the lungs. The robustness analysis indicated that lung cancer plans were most susceptible to robustness reductions caused by spot size errors, while HNC plans demonstrated moderate sensitivity. Conversely, prostate cancer plans demonstrated high robustness, experiencing only minimal reductions in target coverage. Significance: While the ±10% spot size tolerance is appropriate in the majority of the cases, lung cancer plans may require more stringent criteria. As DynamicARC becomes clinically available, measuring spot size errors in practice will be essential to validate these findings and refine tolerance thresholds for clinical use.

•Objective:In this study, we explore positron emission tomography(PET)/magnetic resonance imaging (MRI) joint reconstruction within a deeplearning (DL) framework, introducing a novel synergistic method. •Approach:We propose a new approach based on a variational autoencoder (VAE)constraint combined with the alternating direction method of multipliers (ADMM)optimization technique. We compare several VAE architectures, including jointVAE, mixture of experts (MoE) and product of experts (PoE), to determine theoptimal latent representation for the two modalities. We trained then evaluatedthe architectures on a brain PET/MRI dataset. •Main results:We showed that our approach takes advantage of each modalitysharing information to each other, which results in improved peak signal-to-noiseratio (PSNR) and structural similarity (SSIM) as compared with traditionalreconstruction methods, particularly for short acquisition times. We find that theone particular architecture, MMJSD, is the most effective for our methodology. •Significance:The proposed method outperforms classical approaches especiallyin noisy and undersampled conditions by making use of the two modalities together to compensate for the missing information.

To provide a novel and personalized method (and Irradiation Personalized) using patient-specific circulating blood flows and individualized time-dependent irradiation distributions, to quantify the dose delivered to blood in large vessels during proton or photon external beam radiotherapy.Patient-specific data were obtained from ten cancer patients undergoing radiotherapy, including the blood velocity field in large vessels and the temporal irradiation scheme using photons or protons. The large vessels and the corresponding blood flow velocities are obtained from phase-contrast MRI sequences. The blood dose is obtained discretizing the fluid into individual blood particles (BPs). A Lagrangian approach was applied to simulate the BPs trajectories along the vascular velocity field flowlines. Beam delivery dynamics was obtained from beam delivery machine measurements. The whole IS is split into a sequence of successive IEs, each one with its constant dose rate, as well as its corresponding initial and final time. Calculating the dose rate and knowing the spatiotemporal distribution of BPs, the dose is computed by accumulating the energy received by each BP as the time-dependent irradiation beams take place during the treatment.Blood dose volume histograms from proton therapy and photon radiotherapy patients were assessed. The irradiation times distribution is obtained for BPs in both modalities. Two dosimetric parameters are presented: (i), representing the minimum dose received by the 3% of BPs receiving the highest doses, and (ii), denoting the blood volume percentage that has received at least 0.5 Gy.A novel methodology is proposed for quantifying the circulating blood dose along large vessels. This methodology involves the use of patient-specific vasculature, blood flow velocity field, and dose delivery dynamics recovered from the irradiation machine. Relevant parameters that affect the dose received, as the distance between large vessels and CTV, are identified.

. To compare in reproducible and equalized conditions the performance of two independent proton range verification systems based on prompt gamma-ray detectors from two different proton therapy centers.. An anthropomorphic head phantom with calibrated stopping power, serving as ground truth, was irradiated with comparable treatment plans, spot positions and energies in both facilities. Clinical beam current, tumor contour and dose were used. The absolute range measurement was compared to the expected value according to the ground truth. The statistical precision was assessed by repeating each measurement ten times. Sensitivity to relative range shifts was evaluated by introducing 2 mm and 5 mm plastic slabs on half of the field.. The resulting absolute range accuracy was within 2.4 mm in all cases. Relative range shifts were detected with deviations lower than 14%.. The performance of both systems was deemed worthy of clinical application for the detection of range deviations. This study represents the first comparison of independent prompt gamma-ray-based proton range verification systems under equalized conditions with realistic treatment fields and beam currents.

: To present a long short-term memory (LSTM)-based prompt gamma (PG) emission prediction method for proton therapy.: Computed tomography (CT) scans of 33 patients with a prostate tumor were included in the dataset. A set of 10histories proton pencil beam (PB)s was generated for Monte Carlo (MC) dose and PG simulation. For training (20 patients) and validation (3 patients), over 6000 PBs at 150, 175 and 200 MeV were simulated. 3D relative stopping power (RSP), PG and dose cuboids that included the PB were extracted. Three models were trained, validated and tested based on an LSTM-based network: (1) input RSP and output PG, (2) input RSP with dose and output PG (single-energy), and (3) input RSP/dose and output PG (multi-energy). 540 PBs at each of the four energy levels (150, 175, 200, and 125-210 MeV) were simulated across 10 patients to test the three models. The gamma passing rate (2%/2 mm) and PG range shift were evaluated and compared among the three models.: The model with input RSP/dose and output PG (multi-energy) showed the best performance in terms of gamma passing rate and range shift metrics. Its mean gamma passing rate of testing PBs of 125-210 MeV was 98.5% and the worst case was 92.8%. Its mean absolute range shift between predicted and MC PGs was 0.15 mm, where the maximum shift was 1.1 mm. The prediction time of our models was within 130 ms per PB.: We developed a sub-second LSTM-based PG emission prediction method. Its accuracy in prostate patients has been confirmed across an extensive range of proton energies.

. Current reference dosimetry methods for spatially fractionated radiation therapy (SFRT) assume a negligible beam quality change, perturbation, or volume-averaging correction factor. Therefore, the aim of this work was to investigate the impact of the grid collimators on the dosimetric characteristics of a 6 MV photon beam. A detector-specific correction factor,kQgrid,  Qmsr fgrid,fmsr, was proposed. Several dosimeters were evaluated for their ability to measure both reference dose and grid output factors (GOFs).. A Monte Carlo model of a grid collimator was created to study the change in the depth dose characteristics with the grid collimator. The impact of the collimator on the percent depth dose (PDD), electron contamination, and average photon energy was investigated. ThekQgrid,  Qmsr fgrid,fmsrcorrection factors were calculated for two reference-class micro ion chambers. The reference dose and GOFs were measured with a grid collimator using six ion chambers, two silicon diodes, and a diamond detector.The PDD in the presence of the grid was observed to be steeper compared to the open field. The average photon energy increased from 1.33 MeV to 1.74 MeV with the presence of the grid collimator. The dose contribution by scattered photons was significantly higher at deeper regions for the open field compared to the grid field. ThekQgrid,  Qmsr fgrid,fmsrcorrection was calculated to be <0.5%. The reference dose for all detectors, except for the CC13 and CC04 chambers, was within 1% of each other. The CC13 under-responded up to 3.2% due to volume-averaging effects. The GOFs calculated for all detectors, except Razor and A16, were within 1% of each other.. The phantom scatter dictates the change in the PDD with the presence of the grid. The micro ion chambers exhibit negligiblekQgrid,  Qmsr fgrid,fmsrcorrection. All detectors, except the CC13 ion chamber, were found to be suitable for SFRT reference dosimetry.

. Bragg peak measurements play a key role in the beam quality assurance in proton therapy. Used as base data for the treatment planning softwares, the accuracy of the data is crucial when defining the range of the protons in the patient.. In this paper a protocol to reconstruct a Pristine Bragg Peak exploring the direct correlation between the particle flux and the dose deposited by particles is presented. Proton flux measurements at the HollandPTC and FLUKA Monte Carlo simulations are used for this purpose. This new protocol is applicable to plastic scintillator detectors developed for Quality Assurance applications. In order to obtain the Bragg curve using a plastic fiber detector, a PMMA phantom with a decoupled and moveable stepper was designed. The step phantom allows to change the depth of material in front of the fiber detector during irradiations. The Pristine Bragg Peak reconstruction protocol uses the measured flux of particles at each position and multiplies it by the average dose obtained from the Monte Carlo simulation at each position.. The results show that with this protocol it is possible to reconstruct the Bragg Peak with an accuracy of about 470m, which is in accordance with the tolerances set by the AAPM.. It has the advantage to be able to overcome the quenching problem of scintillators in the high ionization density region of the Bragg peak.

. Low-dose computed tomography (LDCT) is an imaging technique that can effectively help patients reduce radiation dose, which has attracted increasing interest from researchers in the field of medical imaging. Nevertheless, LDCT imaging is often affected by a large amount of noise, making it difficult to clearly display subtle abnormalities or lesions. Therefore, this paper proposes a multiple complementary priors CT image reconstruction method by simultaneously considering both the internal prior and external image information of CT images, thereby enhancing the reconstruction quality of CT images.. Specifically, we propose a CT image reconstruction method based on weighted nonconvex low-rank regularized group sparse and deep image priors under hybrid plug-and-play framework by utilizing the weighted nonconvex low rankness and group sparsity of dictionary domain coefficients of each group of similar patches, and a convolutional neural network denoiser. To make the proposed reconstruction problem easier to tackle, we utilize the alternate direction method of multipliers for optimization.. To verify the performance of the proposed method, we conduct detailed simulation experiments on the images of the abdominal, pelvic, and thoracic at projection views of 45, 65, and 85, and at noise levels of1×105and1×106, respectively. A large number of qualitative and quantitative experimental results indicate that the proposed method has achieved better results in texture preservation and noise suppression compared to several existing iterative reconstruction methods.. The proposed method fully considers the internal nonlocal low rankness and sparsity, as well as the external local information of CT images, providing a more effective solution for CT image reconstruction. Consequently, this method enables doctors to diagnose and treat diseases more accurately by reconstructing high-quality CT images.