Background: Intraoperative radiotherapy (IORT) is a technique in which a single high radiation dose (10–20 Gy) is delivered directly to the tumor site during surgery. As this procedure is performed in the operating room, effective protection of the surrounding healthy tissues is of paramount importance. This protection is commonly achieved using shielding disks placed immediately adjacent to the tumor bed, where the material composition of these disks plays a crucial role in determining their shielding effectiveness. Aims and Objectives: The present study aimed to identify the optimal material combination and thickness for double-layer protective disks in order to maximize healthy tissue protection. Materials and Methods: Initially, the LIAC accelerator head, along with its applicator and a water phantom, was modeled using the MCNPX Monte Carlo code. The accuracy of the simulation was validated by comparing the percentage depth dose (PDD) obtained from Monte Carlo simulations with experimental dosimetry data. Optimization was performed by evaluating the transmission factor (TF), backscatter factor (BSF), and absorbed dose. Several new disk configurations—comprising PMMA + lead, PTFE + bismuth, steel + titanium, steel + copper, aluminum + copper, and aluminum + titanium—each with thicknesses of 6 mm and 8 mm, were simulated and compared with a reference disk. Results and Conclusion: Among all evaluated configurations, the PMMA + lead disk demonstrated the highest attenuation (65.8% at 8 mm thickness), along with the lowest BSF (7.1%) and TF (62.8%), making it the most effective option for protecting healthy tissues.
Background:Deep inspiration breath-hold (DIBH) can reduce cardiac radiation exposure in left-sided breast cancer, but resource limitations necessitate appropriate patient selection. Purpose:To develop and evaluate a machine learning-based tool for predicting heart mean dose and identifying patients who would benefit from DIBH using simple anatomical predictors in left-sided breast cancer. Materials and Methods:A retrospective study analyzed 120 patients' treatment plans on free-breathing (FB) scans from left-sided postmastectomy breast cancer patients treated with volumetric modulated arc therapy (VMAT). All plans were generated using three techniques: VMAT 2-field plan (VMAT-2P), VMAT 4-field plan (VMAT-4P), and VMAT 5-field plan (VMAT-5P). Two anatomical predictors, maximum heart distance (MHD) and heart-to-PTV distance (HPD), were measured. Elastic Net regression was used for continuous dose prediction, whereas logistic regression was applied for binary classification of DIBH necessity, using a 5 Gy heart mean dose threshold. An independent cohort (n = 25) with paired FB-DIBH scans validated predictions. Results:In the validation cohort (n = 25), DIBH reduced mean heart dose by 34% (1.72-1.86 Gy, P < 0.001 for both techniques) and decreased high-risk patients (>5 Gy) by 69%-80%. Strong correlations were observed between FB predictions and DIBH-achieved doses for anatomical parameters and VMAT-2P (r = 0.667-0.720, P < 0.001), with moderate correlation for VMAT-4P (r = 0.545, P = 0.005). In the independent test cohort from the model development dataset (n = 24), Elastic Net achieved mean absolute errors of 0.81-1.02 Gy. Logistic regression demonstrated 87.5% accuracy with 83%-92% sensitivity and 83%-92% specificity for VMAT-2P and VMAT-4P (area under the curve [AUC]: 0.85-0.94). The VMAT-5P technique showed reduced classification performance (58.3% accuracy, AUC: 0.83). Conclusions:Machine learning software demonstrated accurate prediction of mean heart dose during pre-planning for left-sided breast cancer, enabling informed DIBH selection for cardiac sparing based on simple anatomical metrics from FB computed tomography (CT) scans.
Introduction: The liver is highly susceptible to internal motion during stereotactic body radiotherapy, requiring effective motion management. This study used multiphase computed tomography (MPCT) to assess residual liver movement under deep inspiration breath hold (DIBH), abdominal compression (AC) and free breathing (FB), comparing patient-specific internal target volume (PSITV) and generalized internal target volume (GITV). Materials and Methods: A retrospective analysis of 57 patients with multiple hepatic targets was conducted, where motion management techniques included DIBH (n = 18), AC (n = 24), and FB (n = 15), with each patient undergoing five CT phases (noncontrast, arterial, portal, venous, and delayed). Maximum liver displacements for each patient in the craniocaudal (CC), mediolateral (ML), and anteroposterior (AP) directions were measured to generate PSITVs. The mean displacement across all patients for each technique was taken as the GITV. Results: Maximum motion occurred in the CC direction, highest in FB (6.73 ± 0.83 mm), followed by AC (5.91 ± 0.50 mm) and DIBH (5.36 ± 0.46 mm). ML and AP motions were similar across techniques: DIBH (2.92 ± 0.33 mm, 3.08 ± 0.38 mm), AC (3.02 ± 0.26 mm, 3.66 ± 0.51 mm), and FB (3.00 ± 0.31 mm, 3.37 ± 0.53 mm). Differences were not statistically significant (P > 0.3). PSITV was 3.3% larger than GITV in AC, while GITV exceeded PSITV by 2.5% in DIBH. Conclusion: MPCT identified residual hepatic motion across motion management techniques, highlighting PSITV’s role in improving dosimetric precision and reducing radiation exposure to adjacent organs and normal liver.
Background: Adaptive radiotherapy for non-small cell lung cancer (NSCLC) requires accurate image registration to account for anatomical changes during treatment. Artificial intelligence (AI)-based approaches have shown potential in improving automated landmark detection and deformable image registration. Aims and Objectives: This study aimed to investigate the application of AI for automated anatomic landmark detection and image deformation in NSCLC cases to support adaptive radiotherapy planning. Materials and Methods: A multimodal image registration approach combining cone-beam computed tomography (CBCT) and computed tomography (CT) images was implemented. The workflow consisted of multistage registration, beginning with rigid registration followed by deep-learning-based deformable registration using the VoxelMorph framework. A total of 1040 axial CBCT and CT images were used for training, validation, and testing of the landmark detection model based on the You Only Look Once (YOLO) algorithm. Various YOLO models were compared for spine landmark detection. Model performance was evaluated using Intersection over Union (IoU), Mean Average Precision (mAP), Dice Similarity Coefficient (DSC), and Target Registration Error (TRE). Results: Among the evaluated models, YOLOv3 achieved the highest accuracy for spine landmark detection, with an IoU of 0.818 and an mAP of 0.66. For organ-at-risk segmentation in deformable registration, YOLOv9 outperformed YOLOv8. Rigid registration demonstrated an average DSC of 0.88 ± 0.04 and a TRE of 1.7 ± 1.0 mm, within the tolerance range recommended by AAPM Task Group 132. Deformable registration using VoxelMorph achieved acceptable micro-DSC values; however, macro-DSC results indicated the need for further refinement. Conclusion: AI-based methods demonstrate promising performance for automated landmark detection and deformable image registration in adaptive radiotherapy for NSCLC. Nevertheless, further optimization is required to improve accuracy and reduce variability in registered images before routine clinical implementation.
Introduction: Structure duplication and margin-based cropping are routine but time-consuming steps in inverse treatment planning using the Eclipse treatment planning system (TPS). Manual execution is prone to errors such as accidental overwriting and inconsistent margins. This technical note describes the development of an Eclipse scripting application programming interface (ESAPI)-based tool that automates these tasks within a single workflow. Methods: The in-house script was developed using ESAPI v15.5 on Varian’s T-BOX, with Microsoft Visual Studio 2019 and Eclipse TPS. The graphical user interface was built using Windows Presentation Foundation to allow users to select structures easily. The core logic was written in C#. Development and initial testing were performed on the T-BOX, followed by implementation in the clinical workflow. Results: Cropping time was reduced from several minutes to seconds. The script also eliminated common user errors such as incorrect cropping or overwriting. It allowed multiple structure selection and cropped only overlapping regions, removing the need for manual identification. A maximum of 0.95% variation was found between the volumes of structures created manually and using the script, which is clinically negligible for auxiliary planning structures. Conclusion: This technical note presents a practical ESAPI implementation that demonstrated substantial time savings and improved accuracy, minimizing manual steps and reducing error risk.
Background: The human biofield serves as an indicator of an individual’s physical and emotional health status. Biofield-based therapeutic techniques, also known as complementary and alternative medicine (CAM) techniques such as Reiki, Therapeutic Touch, and Pranic Healing, leverage this information in the preliminary assessment phase before treatment initiation. These modalities are increasingly integrated as complementary methods within health diagnostic frameworks. Among the techniques employed for biofield visualization, gas discharge visualization (GDV) and polycontrast interference photography (PIP) are the predominant imaging methodologies. Notably, the majority of scientific investigations and empirical studies have primarily utilized GDV-derived images, with comparatively fewer studies focusing on PIP-based data. Purpose: The primary objective of this study is to identify energy imbalances within the pancreatic region using biofield imaging and to utilize these patterns for classifying subjects as diabetic or nondiabetic. This work emphasizes the relevance of biofield information in health assessment and evaluates its potential for supporting energy-based diagnostic approaches. Materials and Methods: Color-based clustering methods were applied for segmentation. A transfer learning-based ensemble framework was developed using pretrained convolutional neural network (CNN) architectures ConvNeXtBase and ResNet50 to classify biofield images into diabetic and nondiabetic categories. Grid search optimization identified the optimal hyperparameters, which were applied during fine-tuning to improve feature learning. Ensemble model was evaluated, with the ConvNeXtBase + ResNet50 combination achieving the highest accuracy of 99.12%. Robust performance validation was ensured using 5-fold cross-validation to minimize sampling bias and enhance generalization. Results: The ensemble of ResNet50 and ConvNeXtBase achieved the highest accuracy of 99.12%, outperforming individual models (ConvNeXtBase: 97.93% and ResNet50: 96.28%). Receiver operating characteristic analysis confirmed strong reliability with area under the curve values above 0.99 for both classes (diabetic and nondiabetic). The 5-fold cross-validation analysis further demonstrated the robustness of the proposed ensemble model, achieving a mean accuracy of 97.45%, indicating highly consistent performance across different dataset partitions. Conclusions: The CNN-based models can be trained to classify the biofield images, and this approach can enable automated analysis of biofield images. The approach of using clustering, deep learning, and ensemble modeling as analyzed and described in this study seems to be highly effective. The overall system of biofield imaging and automated clustering can act as a potential noninvasive diagnostic support tool, though further testing with larger datasets and expert validation is necessary for clinical application.
Purpose: The purpose of the study was to optimize the Patient-specific Quality Assurance (PSQA) process using artificial neural networks (ANNs), log file data and complexity indices (CIs) to identify treatment plans susceptible to deliverability issues. Methods: Log file accuracy was validated. CIs and Gamma analysis results from log files were evaluated for their ability to discriminate treatment plans through comparison with experimental gamma results. ANNs were trained to classify plans using log file gamma data and CIs, with experimental gamma results as the reference standard. Results: Log files data demonstrated high accuracy and reproducibility. Gamma passing rates from log files exceeded experimental measurements (99.0% ± 0.8% vs. 95.9% ± 2.8%). However, the correlation between experimental and log file-based gamma results was insufficient for standalone plan classification. Among the evaluated CIs, maximum leaf travel per MU, maximum gantry rotation, and beam delivered energy demonstrated strong predictive performance. In contrast, field irregularity and leaf-gantry synchronization showed limited predictive value, and small field contribution was the least informative. Multi-index ANN analysis achieved an average sensitivity of 0.70 ± 0.10, specificity of 0.68 ± 0.17, and an F1-score of 0.68 ± 0.08 across 20 training sessions. Discussion and Conclusions: The reliability of log file parameters depends on the accuracy of linac calibration and its thorough quality assurance procedures. Although gamma analysis alone has limited capacity to detect delivery issues, integrating CIs and log file data within a multifactorial machine-learning classification significantly enhances predictive accuracy. This approach provides a rational alternative to experimental PSQA verification, improving workflow efficiency.
Background:Computed tomography (CT) is a major source of medical radiation exposure, and radiologic technologists play a central role in applying dose-minimization principles. To date, no nationwide data exist on Moroccan technologists' knowledge, attitudes, and practices regarding CT dose optimization. Objective:To assess awareness of dose-minimization concepts, implementation of optimization techniques, and perceived barriers among radiologic technologists in Morocco, and to identify educational and structural interventions to enhance radiation safety. Materials and Methods:We conducted a cross-sectional, web-based survey from May 2025 to June 2025, targeting certified CT technologists across public and private hospitals in all 12 administrative regions of Morocco. The 22-item questionnaire covered demographics, familiarity with as low as reasonably achievable and dosimetric indices (CT dose index [CTDIvol], dose length product (DLP), size-specific dose estimate), use of dose-modulation techniques, adherence to diagnostic reference levels (DRLs), protocol adjustment practices (including pediatric and obese patients), and training needs. Data from 168 complete responses were analyzed using descriptive statistics and Chi-square tests (SPSS 21.0), with P < 0.05 denoting significance. Results:Of respondents, 66.9% were aged 25-35 years, 57% were female, and 50% had 1-5 years of experience. Although 82.6% reported familiarity with dose-minimization principles, only 46.1% understood dose-modulation technology, and 28.7% fully grasped CTDIvol/DLP metrics. Awareness of national DRLs was low (23.5%), with just 4.2% routinely comparing their practice to reference benchmarks. While 44.3% "always" considered dose reduction when selecting protocols, only 26.3% "regularly" modified exposure parameters. Key barriers included insufficient training (69.1%), technical limitations of existing CT scanners (53.3%), and high workload (39.4%). Half of the participants (50.9%) expressed a strong interest in further dose-optimization training. Conclusion:This first national survey reveals substantial gaps between general awareness and practical mastery of CT dose minimization among Moroccan technologists. To strengthen patient protection, we recommend implementing structured continuous education programs, hands-on optimization workshops, integration of medical physicists for protocol auditing, and development of context-specific DRLs and standardized CT protocols. These measures will be critical for advancing radiation safety and ensuring compliance with international best-practice guidelines.
Purpose: This study aims to develop a prediction model for patient-specific quality assurance (PSQA) outcomes for the institution using established plan complexity metrics (PCMs) from volumetric modulated arc treatment (VMAT) plans delivered on two machines with different characteristics. Materials and Methods: One hundred VMAT plans were created using Eclipse Treatment Planning Systems in a Unique machine and analyzed for various PCMs such as modulation complexity score for VMAT (MCSv), leaf travel modulation complexity score, plan normalized monitor unit (PMU), modulation index for VMAT suggested by Li and Xing (MISPORT), and multileaf collimator speed and acceleration. The study was repeated with 50 clinically used plans in TrueBeam machine. PSQA Gamma Pass Rates (GPRs) were recorded for criteria 3%/3 mm, 3%/2 mm, 3%/1 mm, 2%/3 mm, and 2%/2 mm. The influence of PCMs on GPRs was assessed using Pearson’s correlation, and linear regression models were developed and validated. Results: The validation results demonstrated the predictive potential of the models, with deviation in the Unique falling within 3% for GPRs 3%/3 mm and 2%/3 mm, and in the TrueBeam falling within 1% for GPRs 3%/3 mm, 3%/2 mm, and 3%/1 mm, respectively. Conclusions: The PCM-based prediction tool developed has a high potential to predict PSQA results with < 3% error for the Unique Machine and with <1% error for the TrueBeam Machine. This tool directly computes GPRs, offering a simpler and more efficient evaluation method. The tool more effectively predicts spatial accuracy than dosimetric accuracy and demonstrates its sensitivity to machine-specific characteristics.
Purpose: This study aims to assess the current prevalence, utilization patterns, and operational challenges of electronic portal imaging device (EPID)-based dosimetry systems in Indian radiotherapy centres. Materials and Methods: A multiple-choice survey was distributed through Google Forms to medical physicists in India. The four-section survey covered treatment techniques, EPID availability and dosimetry, alternative detectors, and future challenges. Responses were limited to one per institution, with follow-up reminders. Results: A total of 237 responses were received with a response rate of 62.2%. Among these, 73% of institutions currently use EPID-based dosimetry systems. Of these users, 42.2% rely on EPID for daily quality assurance (QA), and 62.4% of institutions use EPID for more than 50% of their total QA workflow. The primary tasks performed using EPID systems include patient-specific QA (PSQA) for intensity modulated radiation therapy (100%), periodic machine QA (58.4%), PSQA for stereotactic radiation therapy/stereotactic body radiation therapy (48.6%), machine commissioning (18.5%), and in vivo dosimetry (10.4%). Among institutions not currently using EPID due to nonavailability, 89.1% expressed willingness to adopt it for PSQA in the future. Major challenges in implementing EPID dosimetry were software limitations (34.2%) and maintenance and calibration issues (35.0%). After adopting EPID dosimetry, 79.8% of institutions observed improvements compared to conventional detectors. In addition, 93.7% of institutions believed that EPID dosimetry implementation would reduce physicist workload through automated software and resource optimization. Conclusion: This study reveals the widespread use of EPID systems and highlights that implementation offers certain advantages. However, issues such as software limitations, high initial cost, and the need for affordable or free resources remain barriers to wider adoption.
Background: Accurate prediction of radiation-induced toxicity remains a key challenge in head-and-neck radiotherapy. This retrospective study compared traditional normal-tissue complication probability (NTCP) models, Lyman–Kutcher–Burman and relative seriality, with machine learning (ML) approaches (artificial neural network [ANN] and extreme gradient boosting [XGBoost]) for organ-at-risk (OAR) toxicity prediction for a small cohort (n = 57). Materials and Methods: Fifty-seven patients treated with intensity-modulated radiotherapy, volumetric modulated arc therapy, or hybrid techniques were analyzed across 115 OARs (parotid glands [54], larynx [31], and spinal cord [30]). Post-treatment toxicities were graded using Common Terminology Criteria for Adverse Events v5.0 with a median follow-up of 10 months. Models and ML were implemented using stratified 5-fold cross-validation and assessed using discrimination (area under the receiver operating characteristic curve [AUC]), Brier score, calibration analysis, and SHapley Additive exPlanations values. Results: Grade ≥2 toxicity occurred in 63.0% (34/54) of parotid glands and 45.2% (14/31) of larynges, with no spinal cord events. ML models achieved superior discrimination for parotid glands (ANN: AUC = 0.866, 95% confidence interval [CI]: 0.81–0.93; XGBoost: AUC = 0.847, 95% CI: 0.78–0.91) and larynx (XGBoost: AUC = 0.853, 95% CI: 0.78–0.92) compared to traditional models (all AUC < 0.60, P < 0.001). Calibration analysis revealed Brier scores of 0.135–0.145 for ML models versus 0.276–0.295 for traditional approaches, though calibration slopes (1.37–1.84) indicated systematic under-prediction requiring attention in clinical implementation. Age (P = 0.002, Cohen’s d = 0.908), total dose (P = 0.035), and treatment duration (P = 0.026) were significantly associated with parotid toxicity. Traditional model parameters required substantial adjustment from literature values (parotid tolerance dose for 50% complication: 10.0 vs. 28.4 Gy). Conclusion: ML captured nonlinear interactions between dosimetric and clinical variables more effectively than traditional NTCP models, yielding superior predictive accuracy. However, findings are exploratory given the same-dataset validation with a modest cohort size (n = 57), and external validation in larger, multi-institutional cohorts is essential before clinical implementation.
Computed tomography (CT) produces cross-sectional images for medical diagnosis; however, in wide cone-beam CT, conventional CT dose index (CTDI) measurements using a 100-mm ionization chamber (IC) underestimate dose for larger beam widths. This study evaluated and compared CTDI measurements in wide beam using different methods for wide-beam CT, following International Atomic Energy Agency Human Health Report No. 5. Measurements were performed on an Aquilion™ ONE CT scanner (160 mm beam width) using 100-and 300-mm ICs (Radcal 10 × 6–3CT and PTW TM30017). CTDIfree-in-air and weighted CTDI (CTDIw) were obtained under brain and abdomen protocols, both in free air and phantom conditions, at the Advanced Diagnostic Imaging Center, Faculty of Medicine Ramathibodi Hospital, Thailand. For free-in-air measurements at 80 mm beam width, CTDI100 air (no-step) was slightly smaller than CTDI300 air with percentage differences of −1.27% and −1.94%, while CTDI100air (two-step) showed +6.79% and +6.48% differences for brain and abdomen protocols, respectively. At 160 mm beam width, CTDI100 air (no-step) was significantly lower due to incomplete dose coverage, whereas two-and three-step methods yielded slightly higher values. For CTDIw at 80 mm, percentage differences were −17.06% and −15.05% (no-step), −3.29% and +1.88% (two-step), and −8.94% and −3.45% (calculated two-step) for brain and abdomen, respectively. At 160 mm, CTDI100w (no-step) was 41.27% and 36.38% lower than CTDI300W, while CTDI100w (three-step) exceeded CTDI300w due to scattering or overlap. Overall, CTDI measurements using a 100 mm IC underestimate dose for beam widths >NT +40 mm. The two-step technique is sufficient for wide-beam CT dosimetry measurements.
In the current global scenario, human populations remain increasingly susceptible to radiation exposure due to several contributing factors. These include the expanding use of radiation-based technologies to improve quality of life, the growth of nuclear industries and space research activities, geopolitical tensions among nuclear-armed states, and the persistent threat of nuclear terrorism, all of which collectively elevate the risk of large-scale radiation exposure. Biodosimetry, which relies on the detection of radiation-induced cytogenetic and molecular biomarkers, is a globally recognized scientific approach for early triage and absorbed dose assessment following radiation exposure. In India, the biodosimetry facility at the Bhabha Atomic Research Centre (BARC), Mumbai, has pioneered dose-estimation techniques and remains the country’s nodal reference laboratory responsible for conducting biodosimetry for suspected over-exposed individuals in regulatory, emergency, and other critical situations. To strengthen national preparedness, BARC has launched the Indian Biodosimetry Network (IN-BioDoS) for inter-laboratory comparison (ILC) and capacity building. Six laboratories from different regions of India have been included in this network. Currently, more than ten Indian Universities/research institutes are focusing on radiation biology research, contributing to the development of skilled professionals who can carry forward the nation’s biodosimetry program. Over the past few decades, biodosimetry research has seen notable progress. A comprehensive review of literature sourced from databases such as PubMed, ScienceDirect, and Google Scholar reveals that Indian researchers have made significant contributions to the advancement of biological dosimetry. In this context, the aim of this review is to provide a detailed overview of ionizing radiation exposure sources, dose quantification methods, and the application of biodosimetry from an Indian perspective. Furthermore, the review summarizes two decades of research and development carried out by Indian scientists across thematic areas such as basic research, radiation dose assessment, biodosimetry in radiological emergencies, and ILCs. Recent advances and future perspectives are also briefly discussed.
Aims: This study evaluates the feasibility of deep learning-generated synthetic computed tomography (sCT) for proton dose calculation. Materials and Methods: The sCT images were generated from T2-weighted magnetic resonance imaging (MRI) of 10 retrospectively collected prostate cancer patients using MRI Planner (Spectronic Medical, Sweden). The sCT images were compared with CT images to assess image quality, proton range, and dosimetric evaluation. Image quality was evaluated using mean Hounsfield unit (HU) difference, mean absolute error (MAE), and mean error. Geometric agreement between CT and sCT images was measured using the dice similarity coefficient (DSC). Ground truth CT images were employed to generate single-field uniform dose (SFUD) and intensity-modulated proton therapy plans for each patient. The proton range shift (RS) between CT and sCT images was calculated at the central 80% distal dose falloff in each SFUD plan. Dosimetric evaluations were performed using dose–volume histogram comparison and gamma index analysis. Results: The mean MAE values for the body, bone, and soft tissue were 43.42 ± 3.96, 118.40 ± 9.70, and 33.90 ± 4.04 HU, respectively. DSC values revealed good geometric agreement between sCT and CT images. All RS values fell within clinically acceptable criteria, with a relative RS ranging from 0.02%–0.67%. The relative dose differences for all clinical target volume metrics were less than ±1.0%, and gamma pass rates exceeded 90% at 3%/3 mm, 3%/2 mm, and 2%/2 mm criteria. Conclusion: sCT images generated by MRI Planner were feasibly used for proton dose calculation in prostate cancer and could potentially be used for implementing MRI-only proton therapy.
Background/Purpose: Small-lesion detectability in positron emission tomography-computed tomography (PET/CT) depends on reconstruction parameters such as postreconstruction smoothing and matrix size, but practical guidance for selecting these parameters is limited. This work quantifies how iterative reconstruction settings affect contrast recovery coefficient (CRC, %), background variability (BV), and contrast-to-noise ratio (CNR) using a standardized NEMA IEC body phantom. This phantom-only study quantifies how iterative reconstruction and postreconstruction filtering affect CRC, %, BV, and CNR in a standardized NEMA IEC body phantom. Materials and Methods: A NEMA IEC body phantom with six spheres (10, 13, 17, 22, 28, and 37 mm) and a lung insert was filled to achieve nominal 4:1 and 8:1 sphere-to-background activity ratios using ¹3F. Four spheres were prepared as hot spheres and two as cold spheres (4 hot/2 cold). The phantom was scanned on a Philips Gemini PET/CT system. Images were reconstructed using ordered-subsets expectation maximization (3 iterations/16 subsets) while varying (i) Gaussian postreconstruction smoothing kernels of 3 mm, 5 mm, and 7 mm full width at half maximum, (ii) alternative filters (Hann, Butterworth, median), and (iii) matrix size (128 × 128 vs. 168 × 168). For each condition, we measured CRC, BV (expressed as % standard deviation of background VOIs), and CNR. Results: CRC increased with sharper filtering and with the larger matrix. At a 4:1 activity ratio, the 10 mm sphere reached 15% CRC with a 3 mm Gaussian filter, 12% with a 5 mm filter, and 10% with a 7 mm filter; the 37 mm sphere remained around 55% CRC across filters. At an 8:1 ratio, CRC rose overall: the 10 mm sphere reached 17% (3 mm), 14% (5 mm), and 12% (7 mm), while larger spheres maintained CRC ≥60% (for example, the 28 mm sphere reached 69% CRC with 3 mm Gaussian smoothing). Increasing matrix size from 128 × 128 to 168 × 168 improved CRC by approximately 2–7 absolute percentage points depending on sphere size (13 mm: 24% → 28%; 37 mm: 50% → 57%). These CRC gains were accompanied by increased BV and changes in CNR, which are now quantified in this revision. BV and CNR values represent within-scan spatial variability across background VOIs, not variability from repeated acquisitions. Conclusions: Image quality optimization is a trade-off. A 168 × 168 matrix combined with a 3 mm Gaussian filter maximizes CRC and CNR in the smallest spheres (10–13 mm) but increases BV. A 5 mm Gaussian filter provides a more clinically acceptable CRC–BV balance for spheres ≥17 mm (for example, CRC ≈40–46% for the 17 mm sphere) while moderating BV compared with the sharpest setting. A 7 mm filter suppresses BV further, but at the cost of CRC for the smallest spheres. Reporting CRC alone can exaggerate the benefit of aggressive reconstruction; BV and CNR must be considered together. A 168 × 168 matrix with a 3–5 mm Gaussian improved CRC for ≤13 mm spheres but at a BV penalty; for ≥17 mm spheres, a 5 mm Gaussian provided a more balanced CRC–BV profile, with similar or improved CNR relative to 3 mm. These findings are specific to this NEMA IEC phantom and may not translate directly to patient imaging. All BV and CNR values reflect spatial noise within a single acquisition rather than scan-to-scan variability.
Aim: This work presents a multi-institutional image comparison between novel O-ring linac cone-beam computed tomography (CBCT) and fan-beam computed tomography simulator (FBCT), among five institutions. Materials and Methods: A phantom was sent to five institutions with Ethos and Halcyon units equipped with HyperSight CBCT (HS-CBCT). HyperSight used a 125 kVp/176 mAs protocol and an iterative-CBCT reconstruction algorithm with scatter correction. FBCT imaging protocols used 125 kVp and exposure from 176 to 379 mAs. Results: Linear fitting of relative electron density versus Hounsfield unit (RED-vs-HU) curves was determined for RED ≤ 1 and RED ≥ 1. The contrast was evaluated with regard to solid water. Noise and contrast-to-noise ratio (CNR) were evaluated with and without exposure normalization. The RED-vs-HU curve for RED ≤ 1 of FBCT shows a 4% greater slope than HS-CBCT (P = 0.024), while for the RED ≥ 1 case, the FBCT slope is 15% larger compared to (P = 0.00004). HS-CBCT has a larger contrast as RED differs from 1, although at RED > 1, the difference is significant, up to 14%. However, noise is larger for HS-CBCT, especially for RED > 1, up to 10 times. Even with exposure correction, noise is more significant for HS-CBCT, which leads to a CNR ten times smaller than FBCT (RED = 1.78). Conclusion: This multi-institutional analysis of HS-CBCT showed reduced slopes of the RED-vs-HU curves compared to FBCT, which results in a greater sensitivity of the HS-CBCT to RED changes. HS-CBCT shows better contrast performance, although the broader beam leads to noise up to 10 times larger noise, even with the scatter correction. The CNR of the FBCT is up to one order of magnitude greater than that for HS-CBCT.
Purpose: Dosimetric comparison of conical collimator (CC) supplied by Varian Medical System on a TrueBeam (TB) for 6MV-flattening filter-free (FFF) beams versus CC on CyberKnife-G4 (CK) by Accuray Inc. Methods: 5 cones with nominal diameters of 5 mm, 7.5 mm, 10 mm, 12.5 mm, and 15 mm were considered in our study. Percentage depth dose (PDD), off-axis ratio (OAR), tissue maximum ratio (TMR), and output factor (OF) were presented and compared. Results: PDD comparisons between TB and CK cones show good agreement across the range of cones; the mean difference of the % dose values, for all cones, was − 0.9% ±1.2% across the five considered depths along the curve. The agreement between CK and TB cones is poorer for TMR; the discrepancies between CK and TB values increase with depth and lightly decrease with increased cone size. OAR profiles are in agreement, although CK cones tend to overestimate the dose between 80% and 5% dose; consequently, the FHWM (full width at half maximum) for CK cones is slightly larger. Except for the 5-mm cone with a difference percentage of −3.7% between CK and Varian cones, CK cones show the largest output factors, with a maximum difference percentage was 1.9% for the 7.5-mm cone. Conclusion: CK and TB cones show similar dosimetric characteristics. The observed differences suggest that the 6MV-FFF beams from TB cones would be slightly “softer” than the 6MV-FFF beams from CK cones; Varian cones may potentially provide better sparing of organs at risk.
Purpose: To quantify inter-fractional translational and rotational setup errors in head-and-neck cancer patients undergoing radiotherapy with daily cone-beam computed tomography (CBCT) and to derive anisotropic clinical-to-planning target volume (CTV-PTV) margins for improved precision and organ-at-risk (OAR) sparing. Materials and Methods: A retrospective analysis of 1160 daily CBCT scans from 40 patients treated with curative-intent head-and-neck radiotherapy was conducted. Translational (X, Y, and Z) and rotational (pitch, roll, and yaw) displacements were determined by rigid registration of daily CBCT to planning CT. Systematic (Σ) and random (σ) errors were computed, and CTV-PTV margins were derived using the ICRU 62, Stroom, and van Herk margin recipes. The frequency of positional deviations exceeding 3 mm and 5 mm and correlations with clinical factors such as weight loss were analyzed. Results: Mean systematic errors were 1.5 mm (X), 1.6 mm (Y), and 1.7 mm (Z), while random errors were 0.5 mm, 1.2 mm, and 1.1 mm, respectively. Mean rotational displacements were 0.6° (pitch), 0.5° (roll), and 0.7° (yaw). Margins derived using van Herk’s formula ranged from 4.10 to 5.02 mm. Weight loss showed a moderate correlation with cranio-caudal setup variability (r = 0.42, P = 0.01). Most fractions remained within 3 mm laterally, with greater deviations observed in the cranio-caudal and antero-posterior directions. Conclusion: Daily CBCT allows the precise quantification of inter-fractional setup errors and supports direction-specific margin adaptation. Incorporating clinical parameters such as weight loss and rotational deviation into adaptive workflows can enhance treatment precision, OAR sparing, and overall treatment safety in head-and-neck radiotherapy.
Infrared thermography (IRT) is an emerging noninvasive imaging modality that provides real-time, contactless assessment of skin surface temperature, reflecting underlying vascular perfusion. This narrative review explores the principles, clinical utility, advantages, limitations, and future potential of IRT in vascular diagnostics and monitoring. IRT has demonstrated diagnostic relevance across a spectrum of vascular conditions, including peripheral arterial disease, diabetic foot complications, venous insufficiency, Raynaud’s phenomenon, and postoperative vascular monitoring. Its key benefits – such as radiation-free imaging, portability, and dynamic functional assessment – make it especially valuable for use in vulnerable populations and resource-limited settings. However, challenges such as environmental sensitivity, lack of standardized imaging protocols, and limited specificity necessitate further validation. With the integration of artificial intelligence and wearable technology, IRT holds significant promise as a complementary tool in modern vascular medicine.
Purpose: The study aims to synthesize nickel–cobalt ferrite (NiCoFe2O4) nanoparticles with controlled Co/Ni precursor ratios and to evaluate how structural, surface, and electronic modifications influence their antioxidant and anticancer performance. Materials and Methods: NiCoFe2O4 nanoparticles were synthesized via a co-precipitation route using varying Ni2⁺/Co2⁺ concentrations (0.025–0.1 M). Structural, morphological, and surface analyses were carried out using X-Ray Diffraction, fourier transform infrared spectroscopy, Raman, scanning electron microscopy, transmission electron microscopy, energy-dispersive X-Ray spectroscopy, Brunauer–Emmett–Teller, and X-Ray photoelectron spectroscopy techniques. Biological functionality was assessed using 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging assay for antioxidant activity and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay on MCF-7 breast cancer and L929 fibroblast cells for cytotoxicity evaluation. Results: All samples exhibited a single-phase cubic spinel structure with tunable crystallite size, lattice strain, and mesoporosity. Increasing Co/Ni concentration enhanced cation redistribution, mixed valence states, and pore-volume characteristics, improving redox-active surface behavior. Among all compositions, NC4 showed the highest DPPH radical scavenging efficiency and the strongest anticancer activity, reducing MCF-7 viability to ~ 20%–30% at 100 μg/mL while maintaining >80% viability in normal L929 cells. Conclusion: Tailoring the Co/Ni precursor ratio effectively modulates the structural and surface characteristics of NiCoFe2O4 nanoparticles, leading to enhanced antioxidant capacity and selective anticancer activity. These findings establish NiCoFe2O4 as a promising candidate for biomedical applications, particularly in oxidative stress management and targeted cancer therapeutics.
