Enhanced Leaf Modelling (ELM), introduced in Varian Eclipse v18, provides a refined representation of MLC transmission by explicitly modelling physical leaf geometry, interleaf leakage, and leaf-tip effects for Halcyon, replacing the traditional dosimetric leaf gap approximation. Unlike C-arm systems, Halcyon employs no collimator jaws, making out-of-field dose more exclusively dependent on MLC transmission characteristics and increasing the relative importance of accurate MLC modelling. Local commissioning supported the implementation of ELM on Halcyon and demonstrated improved agreement between calculated and measured dose distributions in the commissioning tests. These commissioning findings were separate form the present analysis, which characterises the dosimetric impact of ELM in clinically representative multi-metastatic stereotactic radiotherapy (MMSRT) plans rather than independently validating their physical accuracy. Ten MMSRT patients (2–6 lesions) were replanned using two calculation models: the standard Acuros XBv16 and v18 (with ELM). For each, two optimisation strategies were compared, manual and automatic normal tissue optimisation (NTO), to assess robustness across planning methods. The low-dose valley, defined as [Brain-PTVs], represented normal brain between targets. Dose-volume metrics were extracted across V50
This study aimed to determine the optimal scheduling of radiotherapy (RT) and anti-PD-1/CTLA-4 immune checkpoint inhibitors (ICIs) in a murine model of mesothelioma. BALB/c mice inoculated with a syngeneic mesothelioma tumour on the flank were treated with combination RT and ICI, and tumour growth was measured. An extension of an existing mechanistic model of the tumour microenvironment was fitted to the data, estimating growth, clearing of RT-damaged tumour cells, release of antigens, and activation of T-cells. This model was further extended to investigate the effects of tumour hypoxia on treatment efficacy. Each treatment schedule was analysed to calculate the highest tumour control probability (TCP). Combining ICIs with RT increases the TCP, consistent with other pre- clinical studies. Anti-PD-1 was more effective than anti-CTLA-4 at enhancing tumour control in our model. However, administering anti-PD-1 alone was less effective compared to combined anti-CTLA-4 and anti-PD-1. The TCP was robust to changes in the interval between ICI treatment days and delaying anti-CTLA-4 treatment. However, TCP was sensitive to delays in RT treatment and increases in the interval between RT treatment days. This study supports the synergistic effect between RT and ICIs. Further validation of the model on an external dataset is required before the model can be used to inform optimal treatment scheduling for future human clinical trials.
Cone-beam computed tomography (CBCT) is widely used in image-guided radiotherapy and adaptive radiotherapy, but its direct quantitative use is limited by scatter artifacts, low soft-tissue contrast, and inaccurate Hounsfield unit (HU) values. Diffusion models provide stable image generation capabilities for CBCT-to-CT synthesis. However, in medical image synthesis tasks where patient-specific anatomical fidelity is more important than generation diversity, the stochastic sampling capability of diffusion models may conflict with task requirements. We propose a fidelity-oriented Anatomical Information-Attention Diffusion Model (AIADM) for pelvic CBCT-to-CT synthesis to generate synthetic CT (sCT) images. Unlike conventional diffusion models that primarily predict noise during reverse denoising, AIADM directly predicts the image state at the preceding diffusion time step, aiming to reduce unnecessary generation variation. A Fourier-based anatomical information fusion module integrates frequency-domain features of the current diffusion state with CBCT structural features, while a Vision Transformer attention module captures global pelvic structural dependencies. The diffusion process loss and anatomical information consistency loss constrain the intensity residuals of stepwise prediction and the target edge responses, respectively. Experiments on an internal pelvic dataset comprising 86 patients show that AIADM outperforms the GAN-based, VAE-based, and diffusion-based methods compared in this study across the four evaluated metrics. Using the registered reference pCT as the evaluation reference, AIADM achieves an MAE of 14.25 ± 1.05 HU, an RMSE of 26.42 ± 5.70 HU, a PSNR of 43.87 ± 1.89 dB, and an SSIM of 0.96 ± 0.01. Visual comparisons and HU profiles show that the generated results are closer to the reference pCT, with fewer visible streak artifacts in multiplanar and three-dimensional reconstruction views. AIADM introduces image-domain supervision and anatomical feature fusion into a conditional diffusion framework for pelvic CBCT-to-CT synthesis. The proposed framework reduces intensity errors relative to the registered pCT, increases image similarity, and improves the visual continuity of reconstruction views, providing a basis for subsequent image synthesis and dosimetric evaluation.
Automatic segmentation of lung fields in chest X-ray (CXR) images is an essential part of the diagnostic process. However, the lungs’ diverse shapes and irregular appearance caused by disease make this task challenging. Most previous studies have relied on individual networks, each with its own strengths and weaknesses. In this study, a stacking-based ensemble learning approach incorporating multiple individual networks was developed to leverage their complementary strengths and improve these challenges. To evaluate the generalizability and reproducibility of the results, three public datasets, namely Montgomery, Shenzhen, and JSRT, were utilized. The outputs of the three models, FAT-Net, UNet3+, and DeepLabv3+, on the validation data were used as inputs to a convolutional neural network acting as a meta-learner. Finally, the performance of the proposed method was evaluated on the test data. The proposed method achieved improved performance across the evaluated metrics, with the two key segmentation metrics, IoU and Dice score, reaching 94.49
Routine computed tomography (CT) quality control (QC) is used to verify adequate image quality, support radiation-dose optimisation, and identify changes in scanner performance. Commonly used test objects include the ACR CT Phantom, the AAPM CT Performance Phantom, and the Catphan 600. The AFISMI CT Phantom is a locally manufacturable, water-filled prototype developed for routine constancy testing. This study provides preliminary single-scanner benchmarking of the AFISMI phantom using the IndoQCT automated analysis platform. The AFISMI phantom comprises a hollow polymethyl methacrylate (PMMA) shell (16 cm diameter and 10 cm length), a nominal 1.0-mm aluminium sheet fixed at 45° for slice-thickness measurement, and acrylic and Teflon inserts. Each phantom was scanned in three separate acquisitions on a Philips Ingenuity 128 CT scanner using 120 kVp, 300 mAs, a 5.0-mm slice thickness, and a 200-mm field of view; each acquisition was analysed independently. IndoQCT assessed uniformity in homogeneous water-filled or water-equivalent regions, Hounsfield unit (HU) accuracy, noise power spectrum (NPS), modulation transfer function (MTF), slice thickness, and signal-difference-to-noise ratio (SDNR). Raw-noise magnitude was not analysed separately because it is not included in the Indonesian national CT Compliance Test protocol used to define the QC scope. Under the tested scan and reconstruction conditions, the AFISMI phantom produced broadly comparable results for uniformity, water CT-number accuracy, MTF, and slice-thickness measurement relative to the commercial reference phantoms. NPS magnitude and shape differed among the phantoms. The AFISMI phantom had a higher mean SDNR than the AAPM phantom under the tested conditions, but this result cannot be used to claim better low-contrast performance because phantom geometry, target contrast, and background materials differed. Scalar outcomes are reported as mean ± standard deviation from three acquisitions and compared descriptively; no inferential statistical tests were performed. The AFISMI phantom and IndoQCT showed feasibility for automated CT constancy measurements under the tested single-scanner conditions. The findings represent preliminary benchmarking rather than independent validation or evidence of comparative advantage. Longitudinal baseline testing, multi-scanner evaluation, production-unit reproducibility assessment, and formal cost analysis are required before broad QA applicability or economic advantage can be established.
