Purpose This study aims to identify the optimal virtual monoenergetic image (VMI) energy levels for fast-kilovolt-switching dual-energy CT (FVS-DECT) to replace conventional single-energy CT (SECT) in proton therapy, and validate the accuracy of DECT-derived stopping power ratio (SPR) maps for dose calculation. Methods The Catphan503 phantom was scanned using FVS-DECT to reconstruct VMIs (40–140 keV). Image quality metrics were evaluated to identify the VMI energy level most comparable to 120 kVp SECT. The Cirs062 phantom was used to establish the optimal VMI pair for SPR estimation. Validation was performed using the Gammex467 phantom and a custom phantom containing porcine tissue samples. Reference SPR values for the porcine tissues were derived from their measured mass densities and elemental compositions obtained by chemical analysis. Six single-field intensity-modulated proton therapy (IMPT) plans were generated on the custom phantom to evaluate dose calculation accuracy. Results 70 keV VMIs provided CT numbers and image quality comparable to 120-kVp SECT. The combination of 70 keV and 120 keV VMIs enabled accurate SPR prediction. Mean absolute SPR errors were 0.8% and 0.7% for the Gammex467 and custom phantoms, respectively. Compared with the reference dose calculated on SPRREF maps, IMPT plans recalculated on DECT-derived SPR maps achieved mean gamma passing rates of 93.8% and 98.2% under 2 mm/2% and 3 mm/3% criteria, respectively. Except for the plan through the porcine lung, D1% and D99% changes were within 1%, and the mean ΔR80 was within 0.5 mm. Conclusion 70 keV VMIs can potentially replace SECT for contouring. DECT VMIs at 70 and 120 keV allow accurate SPR prediction and improve dose calculation accuracy compared with SECT-based methods.

Stereotactic arrhythmia radioablation (STAR) is an emerging treatment for refractory or recurrent arrhythmias. Compared with conventional stereotactic body radiotherapy (SBRT), STAR involves greater complexity in target delineation, motion management, and organs at risk (OARs) protection, yet it lacks established consensus clinical guidelines, and workflow-specific risk analyses remain limited. To develop a C-arm linear accelerator (LINAC)-based STAR workflow and perform a failure modes and effects analysis (FMEA) with fault tree analysis (FTA), supplemented by an exploratory segment-based anatomical proximity analysis. A multidisciplinary team constructed a process map for C-arm LINAC-based STAR and identified potential failure modes across the workflow. Risks were scored using occurrence, severity, and detectability to calculate risk priority numbers (RPNs), and FTA was used to analyze causal pathways. In a predefined exploratory imaging subgroup of eight patients with ventricular tachycardia and simulation-acquired coronary computed tomography angiography (CCTA), we divided the left ventricle according to the 17-segment model and measured the minimum distances from each segment to adjacent OARs as an anatomical surrogate of exposure likelihood. Seventy-nine failure modes were identified, of which 17 were classified as high risk. The highest-risk failure modes were concentrated in target delineation and motion-related steps, including inaccurate multimodal image registration (RPN 432), improper motion evaluation/management (RPN 336), and diagnostic error of arrhythmia substrate definition (RPN 320). In the exploratory imaging subgroup (n = 8), segmental spatial patterns were moderately consistent across patients, with most standard deviations of minimum distance below 2.0 cm, although variability remained for several segment-OAR relationships. Segments 4, 5, and 10 were each located within 2 cm of both the stomach and esophagus, indicating relatively higher anatomical proximity-related risk. In this FMEA of C-arm LINAC-based STAR, the principal high-risk workflow steps were concentrated in substrate definition, image registration, and motion management. The segment-based analysis provides an exploratory anatomical risk-mapping framework for OAR awareness. Further multicenter studies are needed to refine workflow risk prioritization and evaluate the clinical relevance of the segment-based anatomical findings. Not applicable.

BACKGROUND:Failure mode and effects analysis (FMEA) is a widely used proactive tool for risk assessment in radiotherapy. With the introduction of 3D-printed template-assisted intracavitary/interstitial brachytherapy (3DP-IC/IS), the increasing procedural complexity and newly introduced steps highlight the need for systematic risk management. However, the traditional FMEA approach has been criticized for its mathematical and logical limitations. PURPOSE:To develop an enhanced FMEA framework using a cloud model and data envelopment analysis (DEA), and to validate its performance through a comparative risk assessment against the traditional FMEA method in 3DP-IC/IS. METHODS:The analysis was performed on a dataset from 80 patients who underwent 3DP-IC/IS. The proposed framework incorporates a cloud model to manage the fuzziness and randomness of linguistic risk assessments and a modified DEA model for multi-criteria decision-making. Both the proposed and traditional FMEA methods were used to rank failure modes (FMs) in the 3DP-IC/IS workflow. Risk priority numbers and model-derived efficiency scores were calculated to identify high-priority FMs, and the ranking results from both methods were compared to evaluate performance. RESULTS:A total of 66 FMs were analyzed. The average rank shift between the traditional and introduced FMEA was 4.39 (max: 17, min: 0). Among the top 20 FMs, 18 were consistent between the two methods. "The introduced FMEA uniquely identified "Insufficient checking of needle labels" and "Excessive number of needles" as high risk." The proposed framework also increased risk-ranking discrimination, reducing the number of FMs with identical scores from 12 groups to a single group. The two highest risk FMs-"Unreasonable needle track design" and "Error in identifying non-coplanar needle number"-were consistently ranked first by both methods. Notably, the proposed framework reclassified four FMs as high risk, which demonstrated improved sensitivity to risks potentially underestimated by traditional FMEA. CONCLUSION:We developed a hybrid cloud model-DEA-FMEA framework that mitigates key limitations of traditional risk assessment. When applied to 3DP-IC/IS, this framework demonstrated both feasibility and an enhanced ability to identify and prioritize critical FMs. Our findings highlight its clinical value for improving quality assurance and patient safety, especially in complex, high risk radiotherapy procedures.

Artificial intelligence (AI) has been widely applied in healthcare; however, the potential risks it introduces still require attention. At present, safety risk analysis in healthcare largely relies on the traditional failure mode and effect analysis (FMEA), which has certain limitations in risk evaluation and prioritization. To address these gaps, we developed an enhanced FMEA (eFMEA) framework tailored to such workflows. Expert ratings for three risk criteria were converted into fuzzy values, and a consensus-reaching process (CRP) was employed to measure and improve consensus levels across failure modes(FMs). Expert weights were assigned using the Ordered Weighted Averaging (OWA) operator based on consensus contributions, while criteria weights were derived via the Entropy Weighting Method (EWM). Risk scores were computed using the Gained and Lost Dominance Score (GLDS). Among 86 FMs analyzed, 27 initially showed low consensus; only two remained low after consensus adjustment. Severity received the highest weight, aligning with clinical expectations. Comparative analysis with traditional FMEA and another multi-criteria decision-making model further demonstrated the robustness of the eFMEA method. And eFMEA resolved tied ranks and revealed distinct prioritization differences for several FMs. By integrating fuzzy theory, EWM, CRP, and GLDS, the proposed eFMEA framework overcomes key methodological shortcomings of traditional FMEA and offers a more robust and clinically applicable risk assessment tool.

Objectives.Stereotactic arrhythmia radioablation (STAR) offers a non-invasive treatment option for refractory tachycardia; however, precise dose delivery remains challenging due to the complexity of cardiorespiratory motion. This study evaluated the geometric and dosimetric performance of multiple deformable image registration (DIR) algorithms using ECG-gated four-dimensional CT (ECG-4DCT) in both virtual phantom and clinical datasets.Approach. ECG-4DCT data from the extended cardiac-torso phantom and 20 patients were analyzed across 10 cardiac phases using six DIR algorithms, with a seventh algorithm, TransMorph, additionally evaluated on the clinical datasets. Registration accuracy was assessed using the Dice similarity coefficient (DSC), Hausdorff distance (HD95), and average surface distance, while dosimetric accuracy was evaluated using dose-volume histogram metrics andγanalysis. 4D dynamic dose (4DDD) variability was quantified using the coefficient of variation (CV) and maximum pairwise absolute dose difference (MPADD).Main Results. Registration accuracy was lowest between the end-systolic and end-diastolic phases in both phantom and clinical datasets. In the phantom study, MIM achieved the highestγpassing rate (89.6% at 1%/1 mm) and the slightest deviation from the reference, with differences of -0.02 Gy inD95and -0.2% inV25. In the clinical datasets, patients without metallic implants exhibited reduced geometric accuracy and increased 4DDD variability (mean CVs of 0.01 forV25and 0.02 forD95; MPADDs up to 11.3% and 1.15 Gy). TransMorph achieved the highest geometric accuracy, with mean DSC values of 0.86 in patients without implants and 0.89 in those with implants; however, this improved geometry was accompanied by steeper deformation gradients and pronounced localized dose discrepancies.Significance. Current DIR algorithms remain limited in capturing complex cardiac motion for STAR. Geometric accuracy alone is insufficient to ensure physiologically plausible deformation, underscoring the need for cardiac-specific, physiology-constrained DIR frameworks to enable robust and clinically reliable 4DDD evaluation.

Objective.To develop and validate UNetrDose, a Transformer-based deep learning model designed for fast and accurate photon beamlet dose prediction. The study aims to achieve Monte Carlo (MC)-level dosimetric accuracy using only CT-derived electron density images and beamlet coordinates as input, enabling the efficient reconstruction of complete 3D dose distributions for intensity-modulated radiation therapy (IMRT) plans.Approach.For each beamlet, a fixed-size 3D image patch was extracted along its propagation path, centered on the beamlet trajectory. The geometric information, defined as the beamlet's relative position within the beam field, was used alongside the image patch as model input. The ground-truth dose distributions were generated using MC simulations. The proposed UNetrDose model combined convolutional layers for local feature extraction with Transformer modules to capture long-range dependencies. A total of 90 fixed-beam IMRT plans (51 esophageal and 39 rectal cases) were used for model training, validation, and testing. Model performance was comprehensively assessed, evaluating spatial accuracy with 3D gamma pass rates and clinical acceptability through dose-volume histogram comparisons and other dosimetric parameters.Main results.UNetrDose demonstrated high fundamental accuracy at the individual beamlet level, where pass rates for the stringent(1 mm, 1%) criterion exceeded 96% for both esophageal and rectal cases. This translated to excellent clinical performance on full IMRT plans, where the model achieved mean(2 mm, 2%) pass rates ranging fromfor esophageal cases tofor rectal cases. The model was also highly efficient, with an average inference time of approximately 28 ms per beamlet.Significance.UNetrDose offers a promising alternative to traditional dose calculation engines by providing a balance between high dosimetric accuracy and fast computation. Its ability to predict dose distributions using only electron density images and beamlet positions simplifies the workflow, making it highly applicable for time-sensitive clinical scenarios.

Purpose To accurately predicting and classifying the magnitude of tumor movement in lung cancer patients, a machine learning model based on clinical information and CT-based radiomics feature with artificial neural network (ANN) was developed. Methods and materials CT images of lung tumor of 110 patients were enrolled, comprising 165 tumor sites. Manually extracted 18 clinical features, while 1218 radiomics features were calculated based on CT images. Superior-inferior (SI) direction tumor amplitude values were computed from in-house 4DCT. The three models for predicting tumor motion and classifying active or no motion management strategy based on ANN are as follows: i) C-model which based on clinical and image features; ii) R-model which based on radiomic features; iii) hybrid model that combines two previous models. were utilized for assessing the predictive accuracy of three models. For assessing classification performance, the area under the curve (AUC), specificity and sensitivity were calculated. Results In the test dataset, the C-model showed an MAE of 2.95 mm and an R2 value of 0.51, while the R-model demonstrated an MAE of 1.34 mm and an R2 value of 0.88. The hybrid model exhibited the best performance with an MAE of 1.23 mm and an R2 value of 0.91. Sensitivity and AUC were 0.79 and 0.95, 1.00 and 0.99, 1.00 and 1.00 for the C-model, R-model and hybrid model. Conclusion Utilizing machine learning techniques based on clinical features combined with CT image information enables accurate prediction of lung cancer tumor motion range as well as effective classification of motion management strategies.

Purpose This study aims to develop novel dose-transfer-based calibration methods (central field [CF] and daisy chain [DC]) to overcome the limitations of conventional wide-field (WF) calibration for IC PROFILER (TM) ionization chamber arrays in MR-linac environments, specifically resolving dose profile anomalies and enhancing measurement consistency with water tank reference data. Methods The central field (CF) and daisy chain (DC) calibration methods were evaluated with water tank measurements as a reference and compared to the wide-field (WF) method from Sun Nuclear Corporation (SNC). Profiles were measured at depths of 0.9, 2.9, and 4.9 cm for field sizes ranging from 10 x 10 cm(2) to 40 x 22 cm(2). Accuracy was evaluated using flattening filter-free (FFF) beam characterization and gamma passing rate (GPR) with a 1 %/1 mm criterion. Statistical analyses included the Kruskal-Wallis and Mann-Whitney U tests. Effect size of GPR calculated using Cohen's d. Results The CF and DC methods showed superior agreement with water tank profiles compared to the SNC method. In larger fields (40 x 22 cm(2) and 30 x 22 cm(2)), the CF method achieved mean GPRs above 95.9 % and median values over 99.0 %, outperforming the SNC method (mean and median GPR: 89.2 %; all p-values <0.05 and d-values >0.9). The DC method also performed well, achieving higher GPRs in the 40 x 22 cm(2) field and consistent accuracy across smaller fields. Additionally, beam characterization metrics-including peak position, symmetry, penumbra, unflatness, and slope-exhibited smaller deviations from water tank measurements when using CF and DC methods, as reflected in more concentrated difference distributions. Conclusion The SNC method is insufficient for MR-linac due to magnetic field-induced variations in detector response, leading to significant discrepancies in dose profiles. In contrast, the CF and DC methods demonstrate superior accuracy, improving the reliability of QA workflows. By minimizing the risk of beam miscalibration, these methods ensure the safety and effectiveness of MR-linac-based radiotherapy.

AIMS:To assess the robustness of 4D-optimised IMPT and PAT plans against interplay effects in non-small cell lung cancer (NSCLC) patients with respiratory motion over 10 mm, and to provide insights into the use of proton-based stereotactic body radiotherapy (SBRT) for lung cancer with significant tumour movement. MATERIALS AND METHODS:Fourteen patients with early-stage NSCLC and tumour motion >10 mm were selected. Three hypofraction regimens were generated using 4D robust optimisation with the IMPT and PAT techniques. The nominal plan qualities for both techniques were compared, and their robustness against setup and range uncertainties was evaluated. 4D dynamic dose and the 4D static dose were generated to calculate ΔIMR(%) for interplay effects. RESULTS:PAT plans demonstrated superior target metrics such as D95 and D2, and offered enhanced protection for organs at risk (OARs), particularly in lung metrics, across multiple fractionation schemes (p < 0.05). The robustness of target coverage against setup and range uncertainties was better in PAT plans than IMPT, with average pass rates of 97.8% and 95.4%, respectively (p < 0.01). The interplay effect significantly affected target metrics in single-fraction plans, decreasing with more fractions, while its effect on OAR metrics was minimal. Median values for single-fraction plans were: ΔID98GTV was -3% for IMPT and -0.7% for PAT (p < 0.01); ΔID95GTV was -2.4% for IMPT and -0.6% for PAT (p < 0.01); ΔID2GTV was 3.2% for IMPT and 0.9% for PAT (p < 0.05). The interplay effects resulted in median homogeneity index deviations of 9.1% and 2% for the IMPT and PAT plans, respectively (p < 0.01). Different starting phases affected IMPT more significantly than PAT. CONCLUSION:PAT demonstrated greater robustness to interplay effects than IMPT for hypofractionated treatments of early-stage NSCLC, particularly in single-fraction schemes. Additionally, PAT showed good resilience to variations in different starting phases.

Radiation therapy is regarded as the mainstay treatment for cancer in clinic. Kilovoltage cone-beam CT (CBCT) images have been acquired for most treatment sites as the clinical routine for image-guided radiation therapy (IGRT). However, repeated CBCT scanning brings extra irradiation dose to the patients and decreases clinical efficiency. Sparse CBCT scanning is a possible solution to the problems mentioned above but at the cost of inferior image quality. To decrease the extra dose while maintaining the CBCT quality, deep learning (DL) methods are widely adopted. In this study, planning CT was used as prior information, and the corresponding strictly structure-preserved CBCT was simulated based on the attenuation information from the planning CT. We developed a hyper-resolution ultra-sparse-view CBCT reconstruction model, known as the planning CT-based strictly-structure-preserved neural network (PSSP-NET), using a generative adversarial network (GAN). This model utilized clinical CBCT projections with extremely low sampling rates for the rapid reconstruction of high-quality CBCT images, and its clinical performance was evaluated in head-and-neck cancer patients. Our experiments demonstrated enhanced performance and improved reconstruction speed.

The integration of magnetic resonance imaging with linear accelerators (Linacs) enhances adaptive radiotherapy by providing real-time imaging for improved treatment precision. However, the long-term performance of MR-Linac systems, particularly in clinical settings, remains insufficiently studied. Traditional quality assurance (QA) methods, relying on binary pass/fail criteria, may overlook critical system variations. This study applies statistical process control (SPC) techniques to evaluate the long-term performance of a 1.5T MR-Linac, focusing on optimization in beam quality, MR-to-MV alignment, MR imaging, and geometric distortion. A dual-phase SPC framework was applied to 1 year of daily and weekly QA data from an Elekta Unity MR-Linac. Phase I established performance benchmarks, while Phase II monitored deviations online. Evaluated parameters included beam output, symmetry, MR-to-MV alignment, signal-to-noise ratio (SNR), spatial linearity, slice profile, and geometric distortion across spherical volumes (DSVs). Stability and variability were quantified using control charts and process performance indices (Ppk). Beam quality was stable overall (Ppk ≥ 1.33), though output dose and transverse symmetry showed increased variability in Phase II, with dose Ppk declining from 3.13 to 1.33. MR-to-MV alignment was consistent, but Phi rotational and Z translational offsets showed variability after system upgrades. Imaging metrics, including SNR and spatial linearity, achieved A + performance (Ppk ≥ 1.67) in Phase II, while vertical spatial resolution was lower (Ppk 1.04–1.10). Geometric distortion was well-controlled, though larger DSVs (≥ 500 mm) showed increased AP-axis distortion (2.44 mm) compared to RL (1.37 mm) and FH (0.93 mm). SPC techniques dynamically identified stable parameters and areas for improvement. Key recommendations include enhanced alignment protocols for beam quality and MR-to-MV offsets, as well as targeted strategies to address geometric distortion in larger volumes and along the AP axis.

Background:The long acquisition time of three-dimensional (3D) magnetic resonance imaging (MRI) makes it vulnerable to motion-induced artifacts such as blurring and ghosting, which may compromise target delineation and dose accuracy. Although motion management strategies such as four-dimensional MRI and cine MRI have been proposed, the specific influence of respiratory parameters-particularly amplitude and frequency-on the geometric and dosimetric precision of magnetic resonance-guided adaptive radiotherapy (RT) remains inadequately quantified. This study thus aimed to systematically evaluate how respiratory-induced linear translational motion affects delineation accuracy and dose distribution in MRI-based adaptive RT. Methods:An MR-compatible motion phantom was employed to replicate patient-specific respiratory-induced translational motion, with amplitude and frequency variations extracted from real patient waveforms being incorporated. Eight distinct respiratory patterns were generated, and MR images were acquired with standard spin-spin relaxation time-weighted (T2W) three-dimensional (3D) Cartesian sequences for each pattern. The internal target volume (ITV) delineated by clinicians based on 3D MR images was compared with the reference ITV (ITVref) generated with the digital phantom. Key delineation metrics [e.g., Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean surface distance (MSD)] and dosimetric parameters (e.g., dose received by 95% of the volume) were evaluated, and statistical analyses were performed to assess the correlations between respiratory motion characteristics and the observed variations. Results:Respiratory amplitude significantly affected delineation accuracy and dosimetric consistency. The DSC decreased linearly with increasing amplitude, from 0.96 at 2.50 mm to 0.83 at 12.50 mm, while the HD and MSD increased proportionally (2.62 to 6.32 mm and 0.08 to 0.64 mm, respectively). Dosimetric analysis showed a notable reduction in ITVref dose coverage at higher amplitudes, with the dose received by 95% of the volume decreasing by 481.55 cGy at 12.50 mm relative to the prescribed total dose of 4,500 cGy. In contrast, respiratory frequency had minimal impact, with changes remaining within clinically acceptable ranges. Conclusions:This study investigated the impact of respiratory-induced linear translational motion on ITV delineation and dosimetric accuracy in MR-guided RT. It was found that large respiratory amplitudes significantly compromised geometric and dosimetric precision, whereas frequency had minimal influence. The results emphasize the limitations of 3D Cartesian MRI due to motion-averaged artifacts and support the development of advanced imaging techniques for improving ITV delineation accuracy.

Cone-beam computed tomography (CBCT) is a critical imaging modality in various medical fields, yet its repeated use poses radiation risks to patients. Low-dose CBCT image reconstruction aims to mitigate these risks while preserving image quality, which is crucial for clinical diagnosis and treatment. This review paper provides an in-depth analysis of the latest research progress in low-dose CBCT image reconstruction. We explore analytical reconstruction algorithms, iterative reconstruction algorithms, and deep learning approaches, each with distinct characteristics and applications. The paper comprehensively reviews the methods used for dose reduction in CBCT, the evolution of reconstruction algorithms, and their performance evaluations. We also identify challenges and limitations in current techniques, discussing potential future directions for low-dose CBCT reconstruction. Through a systematic literature search and analysis, this review offers a valuable reference for researchers and clinicians alike, aiming to advance the field of CBCT and enhance patient care through reduced radiation exposure and improved imaging outcomes.

Background:Electronic portal imaging device (EPID)-based in vivo dosimetry (IVD) has attracted considerable interest in recent years. Nevertheless, the limited detection area of conventional EPID systems has hindered their broad clinical adoption. Therefore, this study evaluated a novel large-area EPID (65 cm × 61 cm) integrated into the HalosTx linear accelerator (linac), focusing on stability, imaging quality, field coverage, and error sensitivity for radiotherapy quality assurance (QA). Methods:Basic physical characteristics, including short-term repeatability, dose-response linearity, the influence of gantry angle on dose, and dose-rate dependencies, were first tested. Megavolt (MV) imaging quality tests were then performed to evaluate scaling, spatial resolution, uniformity, contrast, and noise. Subsequently, field coverage and sensitivity were tested across various treatment plans. Additionally, patient setup errors for a breast cancer treatment plan were simulated using a female thorax phantom to explore the sensitivity of the system to setup errors. Results:The EPID demonstrated high stability in basic physical characteristics, with maximum deviations of 0.25% for short-term repeatability, 0.6% for dose-response linearity, 0.757% for gantry angle influence, and 0.372% for dose-rate dependency. The results of scaling, spatial resolution, uniformity, contrast, and noise were 0 mm, 0.31 lp/mm, 99.39%, 0.74, and 161.33, respectively. Benefitting from the 65 cm × 61 cm detector effective area, this EPID successfully monitored all planning beam fields across various treatment plans. Conversely, the Vital Beam and Versa HD systems could only monitor 60% of the beam fields for breast cancer. The sensitivity to machine-related errors was calculated to be 0.2 mm for jaw position, 0.2 mm for multileaf collimator (MLC) position, 0.1% for linac output, 0.5° for collimator rotation. The phantom test for breast cancer highlighted the unique angle-dependent gamma analysis of this EPID system, wherein each beam was subdivided for further analysis. This approach revealed notably high sensitivity to setup errors, particularly in the angular ranges of 56.1°-140° clockwise and 140°-78° counterclockwise. Conclusions:Our preliminary findings indicate that the new EPID system exhibits good stability, favorable imaging quality, broad field coverage, and high sensitivity, suggesting its potential to facilitate novel EPID-based applications in radiotherapy.

Background:Cardiac stereotactic body radiotherapy (CSBRT) is a promising new option for patients with refractory arrhythmias, but complex combined cardiopulmonary motion poses a challenge for precise CSBRT treatment. At present, the dosimetric effects of cardiopulmonary motion on the actual delivery of CSBRT are still unclear, which may deter its widespread clinical application. This study aimed to evaluate the dosimetric effects of complex cardiorespiratory motion during CSBRT and explore the dosimetric advantages of respiratory gating. Methods:A dynamic cardiac phantom was used to simulate different patterns of cardiac pulsation, respiratory motion, and cardiorespiratory motion. Radiochromic film was used to measure the dose, with radiation doses measured for both free-breathing and respiratory-gated CSBRT across various motion pattern groups. The dose measured with a static phantom served as the reference. Subsequently, the measured dose distributions were compared with that of the reference to evaluate the dose difference, gamma passing rate (GPR), isodose width (IDW), and penumbra width. Results:Increased cardiorespiratory motion in CSBRT led to a decreased GPR (3%/2 mm), reduced 90% and 80% IDWs, and a broadened penumbra. Dose-blurring and interplay effects were also observed. Under free-breathing delivery, the mean GPR at the 3%/2 mm criterion was 42.6% in the large‑motion group. Compared with free-breathing delivery, respiratory‑gated delivery increased the mean GPR for the 3%/2 mm criterion by 19.4%. Respiratory amplitude had the greatest dosimetric effect, while heart rate, respiratory cycle, and onset phase had minimal effects. Conclusions:Cardiorespiratory motion introduced dose uncertainties and interplay effects during CSBRT, leading to dose variations in the target and non-uniform dose distributions in the peripheral region surrounding the target, including cold and hot spots. The adoption of gating techniques substantially improved dose precision in CSBRT, effectively mitigating dose uncertainties associated with cardiorespiratory motion. Our findings highlight the importance of implementing respiratory motion management techniques to enhance the effectiveness and safety of CSBRT.

Cone-beam computed tomography (CBCT) is widely used in dentistry, surgery, radiotherapy and other medical fields. However, repeated CBCT scans expose patients to additional radiation doses, increasing the risk of secondary malignant tumors. Low-dose CBCT image reconstruction technology, which employs advanced algorithms to reduce radiation dose while enhancing image quality, has emerged as a focal point of recent research. This review systematically examined deep learning-based methods for low-dose CBCT reconstruction. It compared different network architectures in terms of noise reduction, artifact removal, detail preservation, and computational efficiency, covering three approaches: image-domain, projection-domain, and dual-domain techniques. The review also explored how emerging technologies like multimodal fusion and self-supervised learning could enhance these methods. By summarizing the strengths and weaknesses of current approaches, this work provides insights to optimize low-dose CBCT algorithms and support their clinical adoption.

This study aims to develop a deep learning-based synthetic 4DCT (s4DCT) generation method from 4DCBCT to enhance the accuracy of dose calculation and respiratory motion management in adaptive radiotherapy for lung tumors. A Unet-based attention mechanism integrated with CycleGAN, incorporating structure-consistency loss called UGGAN-GC, was developed to generate s4DCT images and was compared with several commonly used models. 4DCT and 4DCBCT images of 17 lung tumor patients were included and randomly divided into training set, validation set and test set. Elastix was used to deformably register 4DCT to 4DCBCT to generate the ground truth for training and evaluation of image-quality and dose calculation. Quantitative and qualitative methods were used to assess the quality of regions of interest (ROIs) and images of s4DCT. 4DCT was deformably registered to 4DCBCT and s4DCT using Elastix to evaluate the Dice similarity coefficient (DSC) of ROIs and gross tumor volume (GTV) motion. The average intensity projections (AIP) of the ground truth were used to design photon and proton therapy plans. Dose distributions were compared between s4DCT-AIP and ground truth-AIP using gamma analysis and dose-volume histograms. The experimental results showed that UGGAN-GC eliminated streak artifacts, generated the clearest anatomical structures, and achieved the best HU correction for soft tissues. The MAEs of 4DCBCT, Unet, Pix2pix, Cut, Fastcut, CycleGAN, UGGAN, and UGGAN-GC were 117.65, 71.87, 64.73, 62.92, 62.14, 63.01, 59.97, and 59.66 HU, respectively. The gamma passing rate (GPR) (2%/2 mm) of photon plans exceeded 99.8% for all models. The ranking of proton plan GPR (2%/2 mm) was: UGGAN-GC (97.7%), CycleGAN (95.4%), UGGAN (95.2%), Fastcut (93.1%), Pix2pix (90.8%), Unet (89.9%), and Cut (87.7%). The s4DCT generated by UGGAN-GC demonstrated excellent image quality, characterized by high HU accuracy, structural similarity, and edge detail fidelity, and had the potential to achieve accurate dose calculation and respiratory motion management for online photon and proton therapy plans.

Purpose: While the Radixact tomotherapy system with iDMS typically uses proprietary planning software, thirdparty systems like RayStation offer alternative planning solutions. This study details the comprehensive commissioning process for the Radixact beam model in the RayStation treatment planning system. Methods: The beam model commissioning process included 5 parts: model creation and validation, fine-tuning of parameters, creation of the treatment couch, adjustment of dose normalization, and verification of the model. In the step of parameters tuning, minimum leaf open and close time, and transverse profile were adjusted. Five plans were designed to create the structure set of treatment couch. The dose normalization was adjusted to calibrate the calculated absolute dose with the measurement of 5 plans. ArcCHECK was used to measure patient plans to verify the model. Results: Minimum leaf open and close time were both set to 0.06 s, the average gamma passing rate (GPR) of transverse curves compared with gold beam data improved from 71.23 % to 98.60 % after adjustment. The final couch structure contained two parts: up and down with the density of 0.950 and 1.150 g/cm3. The originally calculated absolute dose was about 2 % lower, therefore, the value of dose normalization was increased by 2 % to 1.4399e-9. The average 3 %/2 mm GPR of the final model was 97.87 %, 98.70 %, and 99.10 % for plans of 1 cm, 2.5 cm, and 5 cm jaw width, respectively. Conclusion: The results showed that adjustment of beam models, creation of treatment couch, and adjustment of dose normalization should be taken into special consideration. After adjustment, the Radixact model in Raystation could be used in the clinic.

Cone beam computed tomography (CBCT) faces significant challenges in clinical translation, including radiation dose accumulation, metal artifact interference, and insufficient quantitative accuracy. This review comprehensively analyzes recent innovations addressing these limitations through synergistic hardware integration and computational imaging optimization. Hardware advancements demonstrate paradigm-shifting progress: (1) carbon nanotube (CNT) field-emission X-ray Bources enable multi-source array configurations with rapid pulsed operation, reducing radiation exposure by 30%-50% while extending axial coverage. Clinical validations confirm 60% improvement in Hounsfield unit (HU) accuracy and 30%-50% enhancement in soft-tissue contrast-to-noise ratio compared to conventional CBCT (2) Photon-counting detectors (PCD) eliminate scintillator conversion stages, directly translating X-ray photons into electronic signals to maintain high contrast at ultra-low doses. Experimental studies verify reduced electronic noise and improved Swank noise Characteristics. (3) Dual-layer flat-panel detectors (DL-FPD) acquire simultaneous low and high-energy projections through spectral separation (200/550 mu m CsI scintillators 1 mm Cu filter), achieving iodine quantification errors <5% for dual-energy CBCT (DE-CBCT). Integration with fast-kV switching further elevates contrast-to-noise ratio (CNR) by $0.5%. (4) Two-dimensional antiscatter grids (2D-ASG) combined with grid-based scatter sampling (GSS) algorithms reduce water phantom CT number nonuniformity from 20 HU to 10 HU (50% improvement) and decrease metal artifact-Induced CT value deviations in dental implants from 510 HU to 146 HU. Algorithmic innovations leverage artificial intelligence for performance breakthroughs: (1) dual-domain reconstruction Frameworks synergize projection and image-domain features using convolutional neural networks (CNNs) and generative adversarial networks (GANs), restoring anatomical details from sparse-view acquisitions (< 50% projections) while maintaining structural fidelity. (2) Diffusion models (DM) resolve ill-posed inverse problems through probabilistic generation, enabling high-fidelity synthetic CT (SCT) conversion with clinically acceptable HU errors for radiotherapy dose calculation. Energy-guided DM architectures further separate domain-invariant features from CBCT-specific noise. (3) Metal artifact reduction integrates DE-CBCT material decomposition (70-150 keV virtual monoenergetic imaging) with attention-guided dual-encoder networks, suppressing streak artifacts by 71% at metal-tissue interfaces. (4) Quantitative accuracy optimization via contrastive learning frameworks improves bone mineral density measurement Concordance with standard CT (R-2> 0.95) and reduces HU prediction errors by 30% in soft-tissue segmentation Clinical translation is accelerated by these synergies: CNT-based DE-CBCT eliminates metal artifacts in dental restorative assessments, while DL-FPD enables real-time intraoperative guidance for spinal robotics. Future trajectories include portable multi-source CBCT systems, federated learning for cross-institutional model generalization, and augmented reality integration for dynamic dose modulation. These developments establish CBCT as a versatile platform for precision oncology, minimally invasive surgery, and image-guided interventions, ultimately achieving the tripartite goal of "lower radiation, higher quantitative accuracy, and broader clinical applicability".

Purpose/Objective(s) To evaluate the influence of cardiorespiratory motion on the quality of Cardiac Stereotactic Body Radiation Therapy (CSBRT) plans in photon and proton therapies. This study aimed to quantify the dose uncertainties induced by cardiorespiratory motion and to assess the efficacy of the motion-encompassing method. Materials/Methods This retrospective study analyzed 12 patients with refractory arrhythmia who underwent CSBRT. A 25-Gy prescription dose was delivered to 95% of the Internal target volume(ITV) in a single fraction while ignoring setup errors to render cardiorespiratory motion effects fully visible. Patient-specific cardiac and respiratory motion characteristics were assessed through four-dimensional cardiac computed tomography (4DcCT) and 4DCT, respectively. The study utilized 4D dose reconstruction techniques with in-house Python scripts to evaluate the dose uncertainties caused by cardiorespiratory motion. All plans were normalized to cover 95% of the ITV. For the 4D dose, V25 above 95% and D95 exceeding 25 Gy were considered to meet the prescription. Results The study found considerable variability in cardiorespiratory motion characteristics, with respiratory motion generally showing greater displacement than cardiac motion. Specifically, cardiac pulsation led to maximum displacement (DMX) values between 0.2 and 0.8 cm, whereas respiratory motion resulted in DMX values ranging from 0.3 to 2.0 cm. This difference highlights the varying impacts of heartbeats and breathing patterns on the movement of the target and surrounding cardiac structures during treatment. In photon therapy, the average V25 and D95 values were 94.3% and 24.94 Gy in 4DcCT (P>0.05), and 93.5% and 24.66 Gy in 4DCT, respectively (P>0.05). Cardiac pulsation caused a slight decrease in V25 by an average of 1% and up to 4%. Conversely, respiratory motion had a more significant effect on V25, reducing it by an average of 2% and up to 11%.6 patients in the 4DCT and 8 in the 4DcCT failed to meet the prescribed requirements. The variations in homogeneity index (HI) and gradient index (GI) due to cardiorespiratory motions did not exhibit a significant difference (P>0.05), with respiratory motion causing more notable deviations. For proton therapy, the average V25 and D95 values were 94.4% and 24.99 Gy in 4DcCT (P>0.05) and 89.2% and 24.69 Gy in 4DCT (P<0.05), respectively. 11 patients in the 4DCT and 6 in the 4DcCT failed to meet the prescribed requirements. The dose uncertainties were significant for HI and GI in both 4DCT and 4DcCT (P<0.05). Conclusion Cardiorespiratory motion marginally affects photon therapy plans but has a significant impact on proton therapy. While motion-encompassing techniques can somewhat mitigate dose uncertainties in photon therapy, they are less effective in proton therapy, where significant dose deviations remain in some patients. This underscores the critical importance of 4D dose evaluations in ensuring the precision and safety of CSBRT, especially for proton therapy.