Med Phys [JOURNAL]
Sun
200 papers
Incremental feature fusion based time series forecasting with cumulative risk constraint for longitudinal overall survival prediction.
Tang Z, Lin J, Li J … +2 more , Pan J, Yan J
Med Phys · 2026 May · PMID 42169480 · Publisher ↗
BACKGROUND: Overall survival (OS) prediction methods usually adopt pre-operative data which lack important prognosis-related information, such as post-operative lesion status and evolution during treatment, leading to un...
BACKGROUND: Overall survival (OS) prediction methods usually adopt pre-operative data which lack important prognosis-related information, such as post-operative lesion status and evolution during treatment, leading to unsatisfactory performance. Incorporating longitudinal data into OS prediction, however, introduces two main challenges: (1) variable time span; and (2) implicit spatiotemporal information. PURPOSE: This study aims to break the limitation of pre-operative data based OS prediction by addressing the aforementioned two main challenges and leveraging longitudinal data to achieve accurate OS prediction. METHODS: We propose a novel longitudinal data based OS prediction method. Specifically, a new incremental feature fusion (IFF) based time series forecasting module is presented to derive accumulated features up to each time point and fill missing time points in longitudinal data. It addresses the challenge of variable time span with high computational efficiency compared to the widely applied decoder-only transformer with causal-attention (DoT-CA). Based on the accumulated features in the IFF module, corresponding survival risks up to each time point are predicted under a cumulative survival risk (CSR) constraint, where the survival risks are encouraged to be monotonically increased over time, effectively exploring the spatiotemporal information. RESULTS: In the experiment, both in-house and public multimodal MR datasets (BraTS2020) containing 1678 patients of diffuse glioma are used to evaluate our method, and the experimental results show that our method outperforms all state-of-the-art (SOTA) methods with statistical significance. Further ablation study shows that both proposed IFF module and CSR constraint are effective in longitudinal OS prediction. Moreover, the proposed IFF module is more efficient than DoT-CA, enabling scalable offline longitudinal analysis on large patient cohorts. CONCLUSION: Longitudinal data contain important spatiotemporal information related to prognosis, based on which more accurate OS prediction can be achieved comparing with existing pre-operative data based methods. For longitudinal data with variable time span, the evolution pattern of lesions can be effectively learned and used to fill up the missing time points. Codes of our method are available at https://github.com/BH-MICom/OSTimes.
Transformer-based synthesis of 7 Tesla-like T1 contrast from routine clinical magnetic resonance imaging.
Eidex Z, Safari M, Wang T … +6 more , Yu DS, Wildman V, Mao H, Middlebrooks E, Kesarwala AH, Yang X
Med Phys · 2026 May · PMID 42169478 · Publisher ↗
BACKGROUND: Ultra-high-field 7 Tesla (7T) magnetic resonance imaging (MRI) provides improved resolution and signal-to-noise ratio (SNR) over standard clinical field strengths (1.5T, 3T). However, 7T scanners are costly,...
BACKGROUND: Ultra-high-field 7 Tesla (7T) magnetic resonance imaging (MRI) provides improved resolution and signal-to-noise ratio (SNR) over standard clinical field strengths (1.5T, 3T). However, 7T scanners are costly, scarce, and introduce additional challenges such as susceptibility artifacts. PURPOSE: We propose an efficient transformer-based model (7T-Restormer) to synthesize 7T-like MRI (quantitative T1 MP2RAGE maps) from routine 1.5T or 3T T1-weighted (T1W) images. METHODS: The proposed method leverages an efficient restoration transformer backbone and spatial attention layers to capture long-range dependencies and generate high-quality 7T-like images. Our model was validated on an institutional dataset, comprised of 35 1.5T and 108 3T T1w MRI paired with corresponding 7T T1 maps of patients with confirmed multiple sclerosis (MS). A total of 141 patient cases (32 128 slices) were randomly divided into 105 (25 1.5T and 80 3T) training cases (19 204 slices), 19 (5 1.5T and 14 3T) validation cases (3476 slices), and 17 (5 1.5T and 12 3T) test cases (3145 slices). The synthetic 7T T1 maps were evaluated by comparing their similarity to the ground truth volumes using peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and normalized mean squared error (NMSE), and were compared against ResViT and ResShift. In addition, a blinded reader study was performed in which three clinicians scored the diagnostic image quality of each method on a 5-point Likert scale (1 = non-acceptable, 5 = excellent). Statistical significance (α = 0.05) was assessed using two-sided paired t-tests with Holm correction for multiple comparisons and Wilcoxon signed-rank tests with Holm correction for reader scores. Effect sizes (Cohen's for paired data) were also computed to quantify practical significance. We interpreted and as small, medium, and large effects. RESULTS: For 1.5T inputs, 7T-Restormer achieved an NMSE of 0.018 ± 0.003, PSNR of 25.0 ± 0.6 dB, and SSIM of 0.870 ± 0.019; for 3T inputs, the corresponding values were 0.019 ± 0.006, 24.5 ± 1.2 dB, and 0.874 ± 0.027. Across both field strengths, 7T-Restormer provided the lowest NMSE and highest PSNR and SSIM among the three methods, reducing NMSE by approximately 5% and 25% relative to ResShift and ResViT at 1.5T and by 14% and 24% at 3T, respectively, and increasing PSNR by 0.5-1.5 dB (Holm-adjusted p < 0.05, paired Cohen's = 1.37-6.84). Training on mixed 1.5T+3T data improved performance for 1.5T inputs compared with 1.5T-only training (e.g., NMSE 0.018 vs. 0.019; p = 0.001, ), without degrading performance for 3T inputs (p = 0.630, for NMSE). In the blinded reader study, 7T-Restormer received higher diagnostic quality scores (3.50 ± 0.28) than ResShift (3.00 ± 0.27) and ResViT (2.33 ± 0.27), corresponding to mean improvements of 0.5-1.2 points on the 5-point scale (Holm-adjusted p = 0.014 and 0.004; paired Cohen's and 3.24, respectively). CONCLUSION: We propose a novel method for predicting quantitative 7T MP2RAGE maps from 1.5T and 3T T1W scans with higher quality than existing state-of-the-art methods. Future development and application of our approach may enhance diagnostic accuracy, treatment planning, and facilitate downstream tasks by making the benefits of 7T MRI more accessible to standard clinical workflows.
A two-stage approach for the identification and classification of osteoporotic vertebral compression fractures.
Wang Y, Chen S, Zhuang H … +7 more , Yang Y, Kou Y, Xiong J, Zhang P, Zhang D, Bai X, Liang M
Med Phys · 2026 May · PMID 42159178 · Publisher ↗
BACKGROUND: Osteoporotic vertebral compression fractures (OVCFs) are frequently underdiagnosed worldwide due to their subtle radiographic presentation and the inherently low contrast of x-ray images. This diagnostic chal...
BACKGROUND: Osteoporotic vertebral compression fractures (OVCFs) are frequently underdiagnosed worldwide due to their subtle radiographic presentation and the inherently low contrast of x-ray images. This diagnostic challenge is further compounded by the limited availability of high-quality annotated datasets. PURPOSE: In this study, we proposed OVCFinder, a deep-learning-based two-stage cascade model designed for the accurate detection of OVCFs and the classification of old and new fractures in x-ray images. METHODS: The segmentation stage is built upon an enhanced DeepLabv3+ backbone, refined to improve local feature sensitivity by reducing the output stride, replacing atrous convolutions with standard ones to suppress noise, and incorporating multiscale convolutional layers to capture morphological variations. In the classification stage, a model library comprising nine diverse deep learning architectures supports both binary and three-class classification tasks, effectively distinguishing normal, new, and old fractures. RESULTS: Experimental results demonstrate that the OVCFinder's performance exceeds that of single-stage detection frameworks. Furthermore, for binary classification, this cascade model obtained an average weighted accuracy of 62.72%, weighted precision of 72.90%, weighted recall of 62.72%, and weighted F1-score of 66.69%. The best individual model performances were achieved by AlexNet, with an accuracy of 70.49%, and VGG16, with an F1-score of 72.36%. These results notably exceeded the average performance of expert physicians (53.42% weighted accuracy, 53.51% weighted precision, 53.42% weighted recall, and 53.16% weighted F1 score). In the more challenging ternary classification task, this model achieved an average weighted accuracy of 37.05%, weighted precision of 50.16%, weighted recall of 22.91%, and weighted F1 score of 30.62%, with the upper-bound performance (ResNet152 reaching 48.09% accuracy and 39.27% F1 score) substantially surpassing that of human experts (39.74% weighted accuracy and 29.49% weighted F1 score). CONCLUSIONS: This work represents the first application of a segmentation-classification cascade strategy for OVCF analysis, enabling end-to-end vertebral segmentation and fracture characterization directly on x-ray images. By decomposing the task, applying targeted optimizations, and adopting a modular design, OVCFinder achieves notable advantages in recognition accuracy, robustness, and interpretability. The OVCFinder's performance in both tasks demonstrates its clinical applicability and diagnostic superiority over conventional approaches, offering a low-cost, high-reliability auxiliary diagnostic tool for use in primary healthcare settings.
Cone beam computed tomography reconstruction from truncated projections using prior information and transfer learning.
Han Y, Fang C, Huang Y … +4 more , Wu Q, Zheng C, Liu H, Yang Y
Med Phys · 2026 May · PMID 42145070 · Publisher ↗
BACKGROUND: Cone beam computed tomography (CBCT) is widely used in clinical practice and small animal research for image guidance. The reconstruction quality will be compromised by severe truncation-related artifacts whe...
BACKGROUND: Cone beam computed tomography (CBCT) is widely used in clinical practice and small animal research for image guidance. The reconstruction quality will be compromised by severe truncation-related artifacts when the scanned object is not fully covered by the field of view (FOV). PURPOSE: This work aims to develop a Dual-Domain Deep learning-based method for CBCT Reconstruction from Truncated projections (D3CRT) through the guidance of non-truncated prior information. METHODS: The D3CRT comprised sequential procedures in both projection and image domains. First, in projection domain, a Sinogram Generation Network (SG-Net) based on the denoising diffusion probabilistic model (DDPM) was employed to predict the missing projection data outside the FOV. The SG-Net was fine-tuned via transfer learning using non-truncated prior data to achieve object-specific adaptation. FDK reconstruction was subsequently performed using the predicted projections. Second, in image domain, an Image Enhancement Network (IE-Net) was applied to refine the FDK reconstructed images. Compressed sensing (CS) reconstruction was then carried out to enforce data fidelity by incorporating the original projections, followed by a secondary IE-Net for final image quality enhancement. In-vivo small animal experiments were conducted on a micro-CBCT system to validate the D3CRT method, with non-truncated prior data obtained from large-FOV low-resolution scans. Dice similarity coefficient (DSC), structural similarity index measure (SSIM), root mean square error (RMSE) were used for quantitative evaluation. RESULTS: The proposed D3CRT effectively improves the image reconstruction quality under truncated projection conditions. For whole-body and lung regions, D3CRT achieved DSCs of 97.1% and 96.0%, outperforming the low-resolution prior images (DSCs of 96.8% and 89.9%) when compared with the reference region segmentations. Quantitative evaluations within the FOV yielded an average RMSE of 2.95 and an SSIM of 98.1% for D3CRT, demonstrating better performance than the Low Resolution Image Constrained Reconstruction (LRICR) method which directly takes low-resolution prior images as the initial inputs for CS reconstruction (RMSE 3.83 , SSIM 97.4%). CONCLUSION: By leveraging non-truncated prior information and projection-domain transfer learning, the proposed D3CRT effectively improved the overall quality of CBCT reconstruction from truncated projections.
Deep learning-based dual-domain reconstruction for nonstop gated CBCT in respiratory gating lung SBRT.
Yu M, Berry S, Silverberg N … +12 more , Fu Y, Harris W, Cai W, Kuo L, He X, Gelblum D, Mueller B, Cervino L, Li T, Li X, Moran J, Zhang H
Med Phys · 2026 May · PMID 42142397 · Publisher ↗
BACKGROUND: The nonstop gated CBCT (ngCBCT) technique has been proposed as a next-generation replacement to the current inefficient clinical gated CBCT (gCBCT), reducing half-fan scan time from 2-8 min to 1 min on C-arm...
BACKGROUND: The nonstop gated CBCT (ngCBCT) technique has been proposed as a next-generation replacement to the current inefficient clinical gated CBCT (gCBCT), reducing half-fan scan time from 2-8 min to 1 min on C-arm linear accelerators while substantially lowering imaging dose. However, ngCBCT yields highly non-uniform and under-sampled projections, posing a major challenge for conventional reconstruction methods to achieve high-quality images. PURPOSE: To develop a powerful and efficient Dual-Domain Convolutional Neural Network (DDCNN) tailored to the unique characteristics of ngCBCT projections to achieve high-quality ngCBCT imaging. METHODS: Clinical raw gCBCT projections from 31 free-breathing respiratory-gated lung SBRT patients (77 half-fan and 65 full-fan scans) were retrospectively retrieved and down-sampled based on respiratory signals to emulate ngCBCT acquisitions. The proposed DDCNN integrates a projection-domain network, which completes missing projection data, with an image-domain network that further reduces artifacts. The two domains are linked by the fast FDK algorithm, enabling accurate and efficient reconstruction suitable for real-time clinical use. RESULTS: DDCNN outperformed conventional reconstruction methods and image-domain-only CNN approaches both qualitatively and quantitatively. It achieved high image quality and rapid reconstruction (<1 min) for ngCBCT acquisitions, supporting clinical adoption in pretreatment setup for respiratory gating lung SBRT. CONCLUSION: Pairing the innovative ngCBCT acquisition strategy with the DDCNN framework enables substantial reductions in scan time and imaging dose while maintaining high image quality. This advancement has the potential to improve patients' treatment experience and overall efficiency of respiratory gating lung SBRT, and also paves the way for broader adoption of respiratory gating techniques in other motion-affected tumor sites.
Design of a next-generation conventional scintillator x-ray detector: Improved spatial resolution and fill factor.
Hsieh SS
Med Phys · 2026 May · PMID 42142394 · Full text
BACKGROUND: The most compelling advantage that photon counting detectors (PCDs) currently have over energy integrating detectors (EIDs) for CT applications may be their improved spatial resolution. However, PCDs are diff...
BACKGROUND: The most compelling advantage that photon counting detectors (PCDs) currently have over energy integrating detectors (EIDs) for CT applications may be their improved spatial resolution. However, PCDs are difficult to manufacture. Next-generation EIDs using conventional scintillator sensors could be easier to mass produce, but their spatial resolution is thought to be limited by fill factor considerations. Specifically, each EID pixel comprises a block of scintillator surrounded by inactive, optically reflective septa. The minimum width of optically reflective septa is ∼0.1 mm, and unless this is shrunk, decreases in pixel pitch inevitably cause an increasing fraction of dead space. PURPOSE: To propose a modified system geometry in which the detector pixels are tilted by a few degrees, such that rays that are incident on reflective septa would later encounter scintillator. Because reflective septa are nearly transparent to x-rays compared to scintillator, this geometry restores much of the lost fill factor. METHODS: Tilting can be achieved in several different ways. We discuss three implementation options: individual detector modules may be tilted, with gaps between modules that are seamless from the perspective of the x-ray source; the whole detector can be arranged to point towards a "false" focal spot that is offset slightly from the true focal spot; the scintillator wafer can structured differently, e.g. by sawing it at an angle. The pros and cons of each option are discussed. Tilting introduces blurring, but a smaller pixel size improves resolution, and the combination of both improves both resolution and fill factor. We used Monte Carlo simulations to compare a conventional detector with 1.0 mm, 0.75, or 0.5 mm pixel pitch to a tilted detector with 0.5 mm pixel pitch. We assumed negligible optical crosstalk and electronic noise, that the scintillator was 1.5 mm thick with attenuation similar to gadolinium oxysulfide, that the reflective septa was 0.1 mm wide at standard resolution or 0.08 mm at higher resolutions and composed of a low-attenuating polymer similar to acrylic infused with 20% titanium content. We also compared this to a simple model of a CdTe detector with 1.6 mm thickness, 0.3 mm pixel pitch, and Gaussian charge sharing. RESULTS: Tilt increases the aperture of the pixel in an energy-dependent fashion. With a 1D ASG (85% fill factor) and at 120 kVp, the detection efficiencies (DQE(0)) of a conventional detector at 1.0 mm, conventional detector at 0.5 mm pixel pitch, CdTe detector, and tilted detector at 0.5 mm pixel pitch are 65%, 55%, 66%, and 64%, respectively. With a sparse 2D ASG (1.5 × 3.0 mm openings, 86% fill factor), the DQE(0) of a tilted detector with 0.75 mm is 69%. The DQE(0) of all detectors decreases at higher energy because of punch-through. CONCLUSIONS: By tilting detector modules a few degrees, energy-integrating detectors can provide increases in spatial resolution without the expected losses in fill factor. This architecture could bring the resolution improvements associated with photon counting CT into the domain of energy-integrating detectors equipped with conventional scintillator sensors.
Gamma knife knowledge-based planning with isocenter selection.
Zhang B, Ruschin M, Chan TCY
Med Phys · 2026 May · PMID 42135596 · Full text
BACKGROUND: In Gamma Knife (GK) radiosurgery, effective treatment planning aims to achieve a desired dose distribution while minimizing the overall treatment duration. Clinically, plans are generated either manually thro...
BACKGROUND: In Gamma Knife (GK) radiosurgery, effective treatment planning aims to achieve a desired dose distribution while minimizing the overall treatment duration. Clinically, plans are generated either manually through forward planning, or using inverse planning methods. To address the limitations presented by forward and inverse planning, a knowledge-based planning (KBP) pipeline was recently developed for GK, using 3D dose predictions in combination with inverse optimization. However, this approach still relied on manually selected isocenter locations, which may limit plan quality. PURPOSE: To develop a complete KBP pipeline for GK, combining dose prediction with the simultaneous optimization of isocenter locations and beam-on-times. METHODS: Data for 20 patients were obtained from a local care center. A previously trained deep learning model generated 3D dose predictions for each case. The primary treatment planning model was a mixed-integer model (GK-KBP-OptIso) that combined isocenter selection with beam-on-time optimization. Plans were generated under several different maximum allowable isocenter limits and compared against (1) plans generated using an existing KBP pipeline with fixed, manually selected isocenters (GK-KBP-FixIso) and (2) historical clinical plans. Plan quality was assessed using coverage, selectivity, Paddick conformity, and gradient indices, along with overall treatment time. RESULTS: Under the same isocenter limits, GK-KBP-OptIso achieved comparable conformity to GK-KBP-FixIso ( vs ) while significantly improving dose falloff. It also matched clinical plan quality, though with slightly increased treatment times. However, when allowed to use variable number of isocenters, GK-KBP-OptIso produced plans with a higher average conformity ( ) than clinical plans ( ). CONCLUSIONS: A KBP pipeline that integrates isocenter selection yields plans that are equal or superior to those generated using KBP with manually selected isocenters or manual clinical planning. The presented approach holds the potential to streamline the effort required to generate high-quality GK treatment plans.
Uncertainty-aware gamma interaction localization and reconstruction in PET.
Thull J, Remennik J, Schug D … +3 more , Weissler B, Kuhl Y, Schulz V
Med Phys · 2026 May · PMID 42135595 · Full text
BACKGROUND: Precise localization of gamma-ray interactions inside scintillation detectors is essential for high-resolution positron emission tomography (PET) imaging. Although machine learning methods have demonstrated s...
BACKGROUND: Precise localization of gamma-ray interactions inside scintillation detectors is essential for high-resolution positron emission tomography (PET) imaging. Although machine learning methods have demonstrated strong performance in gamma interaction positioning, most existing approaches do not quantify event-level uncertainty, leaving valuable information unused. PURPOSE: This study quantifies event-wise positional uncertainties in gamma interaction localization and demonstrates its utility for improving PET image quality through uncertainty-aware filtering and weighting. METHODS: We employ machine learning to perform gamma interaction positioning in a semi-monolithic scintillation detector block with overall dimensions of , corresponding to the planar-segmented, planar-monolithic, and depth-of-interaction (DOI) directions. We train multilayer perceptrons (MLPs) and convolutional neural networks (CNNs) using silicon photomultiplier (SiPM) light spread measurements as network inputs. Regression models were trained using a Gaussian negative log-likelihood to jointly estimate gamma interaction coordinates and event-wise positional variance. Classifiers were evaluated, which inferred event-level uncertainty from the variance of the predicted spatial probability distribution. These uncertainty estimates primarily reflect variability in detector response and photon statistics, corresponding to aleatoric uncertainty. Regression and classification objectives were explored through task-specific hyperparameter optimizations. All 24 semi-monolithic detectors of a proof-of-concept PET system were calibrated in the segmented, monolithic, and depth-of-interaction (DOI) dimensions using collimated fan-beam irradiations of each detector. For each detector, a dataset of 880,000 events per spatial dimension was acquired and randomly split into 60% training, 20% validation, and 20% test sets, with models trained and evaluated independently across seven detectors. The resulting calibration models were then applied to reprocess measurements of imaging phantoms. Performance was evaluated using the mean absolute error (MAE) on the fan-beam dataset and the median distance between reconstructed lines of response (LORs) and known point-source locations measured within the scanner. Predicted variances were further integrated into the time-of-flight ordered-subsets expectation maximization (TOF-OSEM) reconstruction via event-level filtering and LOR weighting to assess spatial resolution and noise propagation. RESULTS: Across architectures, uncertainty-aware models achieved high positioning accuracy. CNN classifiers provided the best planar performance, while CNN regressors performed best for depth-of-interaction (DOI) estimation. Variance- and energy-based filtering substantially improved positioning accuracy, reducing the MAE to in the monolithic and in the DOI dimension. Variance- and energy-aware filtering also improved the LOR precision, reducing the median LOR distance to as low as . In image reconstruction, filtering and weighting improved image quality, with filtering providing the strongest gains and enabling visualization of rods with a peak-to-valley ratio (PVR) of 1.184. These approaches increased the signal-to-noise ratio (SNR) but also the coefficient of variation (COV), consistent with reduced effective sensitivity and amplified Poisson noise. CONCLUSIONS: Event-level uncertainty estimation enables meaningful filtering and weighting strategies that improve interaction positioning and reconstructed PET image quality. Despite the trade-off between enhanced spatial resolution and increased noise, the uncertainty-aware framework introduces a new reconstruction parameter that can be exploited to improve image quality and potentially support more reliable quantitative PET imaging.
Direct electrical measurement of tube-voltage waveforms in mammography systems using ALDEN-type high-voltage connectors.
Negishi T, Shinsho K, Abe S … +1 more , Ogura I
Med Phys · 2026 May · PMID 42135578 · Full text
BACKGROUND: Accurate tube-voltage characterization is essential for mammography quality assurance and estimation of mean glandular dose (MGD). In mammography systems equipped with ALDEN-type high-voltage connectors, dire...
BACKGROUND: Accurate tube-voltage characterization is essential for mammography quality assurance and estimation of mean glandular dose (MGD). In mammography systems equipped with ALDEN-type high-voltage connectors, direct electrical access to the tube-voltage circuit has historically been restricted, limiting evaluation primarily to non-invasive measurement methods. PURPOSE: To develop and evaluate a direct-connection adapter enabling invasive acquisition of tube-voltage waveforms in mammography systems using ALDEN-type high-voltage connectors and to compare waveform characteristics with those obtained using a non-invasive multimeter. METHODS: A direct-connection adapter-based measurement system was developed to provide electrical access to the tube-voltage circuit without modifying the primary conduction path. Tube-voltage measurements were performed using a commercial tube-voltage/tube-current meter incorporating a fixed high-voltage divider (1:20 000). Tube-voltage waveforms were measured in three clinical mammography systems under multiple target/filter configurations using both the invasive method and a commercially available non-invasive multimeter (RaySafe X2). Tube-voltage ripple was quantified from time-resolved voltage data obtained under identical exposure conditions. RESULTS: Mean tube-voltage values obtained using invasive and non-invasive approaches showed approximately linear agreement with preset voltage. However, ripple magnitudes differed substantially under certain beam conditions. The invasive method yielded ripple values ranging from 2.5% to 6.2%, whereas non-invasive measurements reached up to 48.5% under specific configurations. CONCLUSIONS: Direct electrical measurement of tube-voltage waveforms in mammography systems using ALDEN-type high-voltage connectors was demonstrated using the proposed adapter. Because the invasive method directly measures the electrical high-voltage waveform without signal reconstruction, it provides an electrical reference for evaluating potential variability in non-invasive waveform measurements and may support generator characterization and waveform-based investigations in research and equipment evaluation settings.
Dose prediction for head-and-neck cancer using a fusion of lightweight 3D multi-scale feature enhancement modules.
Wei L, Yue Y, Shang H … +2 more , Dai Z, Zhang X
Med Phys · 2026 May · PMID 42135557 · Publisher ↗
BACKGROUND: Radiotherapy is a cornerstone of head-and-neck cancer (HNC) treatment, but traditional radiation therapy planning remains time-consuming, experience-dependent, and prone to inconsistent quality. Deep learning...
BACKGROUND: Radiotherapy is a cornerstone of head-and-neck cancer (HNC) treatment, but traditional radiation therapy planning remains time-consuming, experience-dependent, and prone to inconsistent quality. Deep learning-based dose prediction has emerged as a promising solution, yet existing models struggle with insufficient long-range spatial correlation capture and imbalanced prediction accuracy across high- and low-dose regions. Thus, there is an urgent need for a tailored framework to address these clinical challenges. PURPOSE: This study proposes a U-Net-based encoder-decoder model fused with lightweight 3D multi-scale feature enhancement modules for 3D HNC radiotherapy dose prediction, aiming to improve target dose precision and organs-at-risk (OARs) sparing. METHODS: A public OpenKBP dataset consisting of 340 HNC patients (200 for training, 40 for validation, 100 for testing) undergoing 6MV IMRT was utilized. Data were preprocessed (including CT truncation, normalization, multimodal integration) and augmented (random flipping, translation, rotation) to enhance generalization. The proposed model integrates an eight-layer Transformer for global feature extraction, 3D multi-scale convolutional blocks (MSCBs) for fine-grained feature capture, and an efficient multi-scale convolutional attention decoding (EMCAD) module for optimized feature fusion. A baseline DOSE-PYFER model, GAN-based models, and a cascade Transformer-based model were used for comparative analysis. Performance was evaluated using Dose score, DVH score, and gamma passing rate. RESULTS: The proposed model exhibited superior accuracy compared to comparative models. It achieved a Dose score of 2.704 Gy and a DVH score of 1.611 Gy, outperforming the baseline and cascade Transformer-based model. The gamma passing rate reached 92.76%, indicating excellent spatial consistency with ground truth. No overfitting was observed, with stable training and validation loss curves. The model efficiently generates 3D dose distributions, supporting rapid clinical workflow. CONCLUSION: The proposed model, integrating Transformer-driven global feature extraction and EMCAD-based multi-scale attention decoding, outperforms conventional models in HNC dose prediction. It effectively resolves long-range correlation capture and dose region balance issues, and can be clinically deployed to streamline radiotherapy planning, enhance plan consistency, and support timely adaptive radiotherapy.
Applying a portable pocket handheld ultrasound scanner to monitor tumor volume in preclinical studies.
Melemenidis S, Soto LA, Kaffas ANE … +3 more , Ashraf RM, Loo BW, Graves EE
Med Phys · 2026 May · PMID 42130097 · Publisher ↗
BACKGROUND: Calipers remain the most common method for monitoring tumor volume in preclinical studies, despite known geometric limitations that can lead to substantial measurement error, particularly for irregularly shap...
BACKGROUND: Calipers remain the most common method for monitoring tumor volume in preclinical studies, despite known geometric limitations that can lead to substantial measurement error, particularly for irregularly shaped tumors. Portable handheld ultrasound (US) devices have recently become more accessible and may provide a practical alternative; however, their quantitative performance in routine small-animal workflows has not been fully characterized. PURPOSE: To evaluate whether a low-cost handheld US probe is suitable for routine preclinical tumor-volume measurement and to quantify its accuracy relative to calipers using cone-beam computed tomography (CBCT)-derived volumes as the reference standard. METHODS: Five BALB/c and five C57BL/6J mice (n = 10) bearing subcutaneous CT26 (colorectal) or LLC1 (lung) tumors were measured on day 14 post-inoculation using digital calipers and a handheld US probe. Tumor volumes were estimated using caliper-derived length × width (L×W_Cal), US-derived length × width (L×W_US), US-derived length × width × depth (L×W×D_US), and an US-based region-of-interest method (ROI_US). Excised tumors were imaged with CBCT and segmented to obtain reference volumes. Volumes were normalized to CBCT and analyzed using nonparametric repeated-measures statistics (Friedman test with Holm-Bonferroni-corrected Wilcoxon signed-rank tests; significance defined as adjusted p < 0.05). Effect sizes were calculated using the rank-biserial correlation coefficient. RESULTS: CBCT tumor volumes ranged from 30-170 mm (82.8 ± 49.6 mm). Caliper-derived L×W_Cal volumes significantly overestimated CBCT (median 196% of reference; adjusted p = 0.0078; large effect size, r = 1.0). US-based methods demonstrated substantially reduced bias and variability. L×W_US, L×W×D_US, and ROI_US volumes were not significantly different from CBCT after correction, with ROI_US showing the lowest median absolute error (9.6%). CONCLUSIONS: In this preclinical cohort, handheld US reduced systematic volumetric overestimation relative to calipers and demonstrated improved agreement with CBCT-derived reference volumes. ROI-based US analysis yielded the lowest absolute error. These findings support the use of portable US as a more accurate alternative to calipers for tumor-volume monitoring in small-animal studies.
MRI-informed hypoxia-based proton radiotherapy dose escalation for head-and-neck cancer-a proof-of-concept.
Tattenberg S, Tanneau N, Dandachly W … +7 more , Leporq B, Allignet B, Bouyer C, Pilleul F, Gregoire V, Biston MC, Beuf O
Med Phys · 2026 May · PMID 42130094 · Publisher ↗
BACKGROUND: Partially as a result of hypoxia-induced radioresistance, rates of treatment failure for head-and-neck cancer patients receiving radiotherapy can be considerable. Clinical trials utilizing positron emission t...
BACKGROUND: Partially as a result of hypoxia-induced radioresistance, rates of treatment failure for head-and-neck cancer patients receiving radiotherapy can be considerable. Clinical trials utilizing positron emission tomography (PET) to image tumor hypoxia and escalate the prescription dose in hypoxic sub-volumes are being pursued in response, with current clinical prescription doses of 70 Gy generally escalated to 77-78 Gy. Instead utilizing magnetic resonance imaging (MRI) for hypoxia-based prescription dose escalation would be associated with a variety of advantages, including not requiring an additional imaging-related radiation dose to be delivered to the patient and allowing for a variety of other functional maps to be extracted from the same patient imaging session, in addition to tumor hypoxia information. PURPOSE: The purpose of this study is to investigate the benefits of MRI-informed hypoxia-based radiotherapy dose escalation for head-and-neck cancer patients treated with proton radiotherapy. METHODS: Ten patients with head-and-neck cancer scheduled to undergo photon therapy underwent a multi-parametric MRI protocol based on which tumor hypoxia maps were computed for every patient using a quantitative blood oxygenation level dependent (BOLD) approach. Four proton therapy treatment plans were then created for each patient, consisting of intensity-modulated proton therapy (IMPT) and proton arc therapy (PAT) treatment planning performed according to current clinical standards (IMPT and PAT) or with a 10% prescription dose escalation to the hypoxic sub-volumes of the low- and high-risk target structures (IMPT and PAT). The generated treatment plans were then analyzed with respect to target and organ-at-risk (OAR) doses and normal tissue complication probabilities (NTCPs) as well as tumor control probabilities (TCPs) calculated according to conventional models (TCP) or with consideration of hypoxia-induced radioresistance (TCP). Statistical significance (p < 0.05) of different TCP or mean OAR dose distributions was determined using the Wilcoxon signed-rank test. RESULTS: During IMPT, radiotherapy prescription dose escalation increased TCP in the nominal scenario by (5.9 ± 6.3) percentage points (pp) in the normoxic (p < 0.001) and (5.2 ± 9.0) pp in the hypoxic target volumes (p = 0.006). In the worst-case scenario, TCP was increased by (5.6 ± 4.5) pp (p < 0.001) and (5.3 ± 6.4) pp (p = 0.003). Dose escalation during PAT improved TCP by (3.1 ± 3.3) pp (p < 0.001) and (2.1 ± 5.4) pp (p = 0.015) in the nominal scenario and (3.3 ± 3.1) pp (p < 0.001) and (3.3 ± 4.4) pp (p < 0.001) in the worst-case scenario. When hypoxia-induced radioresistance was considered, dose escalation elevated TCP in the nominal scenario by (7.6 ± 4.5) pp (p < 0.001) during IMPT and (6.4 ± 4.1) pp (p < 0.001) during PAT and TCP in the worst-case scenario by (6.3 ± 3.8) pp (p < 0.001) during IMPT and (6.3 ± 3.1) pp (p < 0.001) during PAT. Compared to the patients' clinical photon therapy treatment plans in the nominal scenario, mean OAR doses were reduced by (13.5 ± 9.3)Gy RBE by IMPT, (14.3 ± 10.5)Gy RBE by PAT, (9.8 ± 10.5)Gy RBE by IMPT, and (10.4 ± 12.8)Gy RBE by PAT (all p = 0.002). CONCLUSIONS: MRI-based hypoxia-informed radiotherapy prescription dose escalation during both IMPT and PAT significantly increased calculated TCPs while significantly reducing doses delivered to nearby healthy organs compared to the patients' clinical photon therapy treatment plans. MRI-based hypoxia-informed prescription dose escalation is therefore considered feasible and may help partially address hypoxia-induced radioresistance.
Triple collaborative consistency with Mamba for semi-supervised 3D medical image segmentation.
Gao Y, Liu B, Li Q … +2 more , Zhang Y, Shi Y
Med Phys · 2026 May · PMID 42130057 · Publisher ↗
BACKGROUND: Current semi-supervised segmentation methods face the following challenges: (1) Cross-branch collaboration: Existing methods typically rely on single-branch pseudo-label generation or simple multi-view fusion...
BACKGROUND: Current semi-supervised segmentation methods face the following challenges: (1) Cross-branch collaboration: Existing methods typically rely on single-branch pseudo-label generation or simple multi-view fusion strategies, failing to fully exploit the interaction between local details and global structures. This limitation leads to suboptimal performance in the boundary segmentation of complex anatomical structures. (2) Inefficiency in long-range modeling: While Transformer-based methods can capture global dependencies, they suffer from quadratic growth in computational complexity and the risk of overfitting when applied to high-resolution data (e.g., 3D medical images), making it difficult to balance efficiency and accuracy. PURPOSE: To address the above challenges, this article proposes a Tri-branch Collaborative Consistency model based on Mamba long-range modeling (TCC-Mamba), which aims to reduce reliance on annotations while improving segmentation accuracy in complex regions of medical images. METHODS: TCC-Mamba consists of a shared encoder and a tri-branch decoder. Specifically, a tri-branch collaborative supervision mechanism is introduced where three decoders form a closed-loop learning system through cross-pseudo-label supervision, enabling collaborative optimization and information sharing. Additionally, a geometric consistency loss function is incorporated to enhance boundary awareness. Furthermore, we integrate the SpatialTriMamba module, leveraging the efficient long-range dependency modeling of state-space models to achieve dynamic fusion of global context and local features, thereby improving segmentation accuracy for complex boundaries. RESULTS: We conducted experiments on three public datasets: Left Atrium (LA), Pancreas CT, and ACDC, using 10%, 20%, and 30% labeled data. The results demonstrate that our method outperforms the six current advanced semi-supervised methods, achieving better segmentation performance. CONCLUSIONS: The TCC-Mamba introduces novel methodologies in medical image segmentation tasks. This model combines the SpatialTriMamba module to capture long-range features and utilizes signed distance maps to enhance the use of geometric information, leading to exceptional results in handling complex anatomical structures. It provides an efficient and reliable solution for semi-supervised medical image segmentation.
Dosimetric impact of clinical planning methodology for Yttrium-90 microsphere radioembolization.
Moretti T, Roa D, Olguin E … +1 more , Leon S
Med Phys · 2026 May · PMID 42121336 · Publisher ↗
BACKGROUND: Some patients receive glass Yttrium-90 microsphere radioembolization for treatment of hepatocellular carcinoma. Traditional dosimetry uses a partition model to calculate doses to relevant structures, but this...
BACKGROUND: Some patients receive glass Yttrium-90 microsphere radioembolization for treatment of hepatocellular carcinoma. Traditional dosimetry uses a partition model to calculate doses to relevant structures, but this model has serious limitations; it assumes uniform perfusion and no cross-compartmental dose. PURPOSE: This work aims to assess several methods for performing dosimetry for these patients and compare them, with special attention paid to the differences of these methods from the partition model for the tumor, non-tumorous liver, and lungs. By performing this comparison, the partition model can be assessed for its shortcomings and dosimetric precision relative to other models. METHODS: Ten patients receiving Y had their procedures simulated in Monte Carlo and doses tallied using three different source models, with two based on pre- and posttreatment imaging, and one meant to mimic the assumptions of the partition model. RESULTS: When comparing mean dose within the prescribed tumor volume, the partition and pretreatment imaging models agreed to within 9.7%. Several patients showed lung doses above predicted doses from current standard practice, demonstrating the importance of cross-compartmental doses. Additionally, patients who received lobectomy often had high differential uptake of microspheres in the tumor, which were missed in prescription. However, the partition model missed high doses (> 20 Gy) to the stomach in two patients which were noted in simulation. CONCLUSIONS: Overall, the partition model is appropriate for calculation of mean tumor doses if the information used in treatment planning is accurate, but caution should be used when calculating doses outside the liver, as cross-compartmental effects are often observed.
Advancing iris melanoma brachytherapy: Eye plaque models for Monte Carlo simulations and 3D dosimetric datasets.
Djedouani M, Viner A, Fletcher EM … +1 more , Thomson RM
Med Phys · 2026 May · PMID 42121324 · Full text
PURPOSE: To develop and benchmark eye plaque models for Monte Carlo (MC) simulation of iris melanoma brachytherapy and to develop a database of 3D dose distributions for the iris plaques containing , , and seeds. ACQ...
PURPOSE: To develop and benchmark eye plaque models for Monte Carlo (MC) simulation of iris melanoma brachytherapy and to develop a database of 3D dose distributions for the iris plaques containing , , and seeds. ACQUISITION AND VALIDATION METHODS: Five iris plaque models are developed with egs_brachy using published dimensions and material data; previously benchmarked seed models are used. Three plaque models are based on those used by the Mayo Clinic, consisting of a modification of the COMS 22 mm plaque design with a 10 mm void in the center surrounded by an inner collimating lip; plaques span , , and arcs. Two additional plaques are modeled: a modified Iris-270 plaque with no collimating lips; a partially-loaded COMS 22 mm plaque with no insert. Plaques are simulated in a water phantom with dose scored in (0.05 voxels. Simulations under TG-43 conditions are also carried out. Doses are compared to previously published data for validation. DATA FORMAT AND USAGE NOTES: The eye plaque models will be distributed with egs_brachy on GitHub (https://github.com/clrp-code/egs_brachy), along with an input file to facilitate custom simulations. The dosimetric database (https://doi.org/10.5281/zenodo.14776641) is comprised of 3D dose distributions for each plaque type, simulation condition, and radionuclides. POTENTIAL APPLICATIONS: The iris plaque models enable custom simulations with the open-access egs_brachy code. The database of 3D dose distributions supports advanced dose evaluations, as recommended by AAPM Task Group 221 on Ocular Brachytherapy. Overall, this work supports adoption of model-based dose evaluations for brachytherapy as recommended by TG-186.
Correction to "Experimental investigation of real-time 3D beam's eye view image-guided radiotherapy for prostate SBRT".
Med Phys · 2026 May · PMID 42121317 · Publisher ↗
Abstract loading — click title to view on PubMed.
Phantom evaluation of spectral performance in photon-counting CT for breast cancer imaging.
Ren L, Xi Y, Ananthakrishnan L … +7 more , Soesbe T, Lewis M, Seiler S, Ataei A, Liu H, Li Y, Ahn RW
Med Phys · 2026 May · PMID 42121306 · Publisher ↗
BACKGROUND: Contrast enhancement is the most sensitive indicator for detecting breast malignancies. Computed tomography (CT) has had a limited role for the locoregional staging of breast tumors due to low soft tissue con...
BACKGROUND: Contrast enhancement is the most sensitive indicator for detecting breast malignancies. Computed tomography (CT) has had a limited role for the locoregional staging of breast tumors due to low soft tissue contrast. PURPOSE: To evaluate and optimize the performance of clinical photon-counting computed tomography (PCCT) for breast cancer imaging using a contrast-enhanced mammography (CEM) phantom and to compare its imaging performance with dual-source dual-energy CT (DS-DECT). METHODS: A CEM phantom containing simulated breast lesions was positioned on an anthropomorphic thoracic phantom and scanned using a clinical PCCT system at 120 kV in multi-energy mode and a DS-DECT system with two kV pairs of 70/Sn150 kV and 90/Sn150 kV. PCCT scanner variables included scan mode [standard resolution (SR) and ultra-high resolution (UHR)], field of view (FOV) size (large and small), matrix size (512 and 1024), and type of image used for analysis [low-energy threshold images, virtual monoenergetic images (VMIs) at 50, 60, and 70 keV, and iodine maps]. Quantitative analysis was performed using circular regions of interest (ROIs) placed on iodine-containing lesions and background within the phantom. For each ROI, mean CT numbers or iodine concentrations and standard deviations were measured across the central five slices and three independent scans. Contrast-to-noise ratio (CNR) and circularity were evaluated across all PCCT configurations and image types and compared with those obtained from DS-DECT. RESULTS: Among all PCCT configurations, the UHR mode with a small FOV and either a 512 or 1024 matrix at 50 keV VMI achieved the highest combined CNR across all iodine concentrations. Additionally, the UHR mode with a 512 matrix and either small or large FOV yielded the highest combined circularity values. The optimal PCCT configuration achieved higher CNR, and higher or comparable circularity compared with 50 keV VMIs derived from DECT scans. CONCLUSIONS: This phantom study demonstrated that optimal spectral performance for potential breast cancer imaging with PCCT is achieved using UHR mode, low-keV VMIs, a regular matrix size, and dedicated reconstruction FOVs, outperforming DECT.
Automated extraction of the plane of minimal hiatal dimensions and mid-sagittal plane from 3D transperineal ultrasound.
Szentimrey Z, Ameri G, Hong CX … +3 more , Cheung RYK, Eltahawi A, Ukwatta E
Med Phys · 2026 May · PMID 42108227 · Full text
BACKGROUND: Transperineal ultrasound (TPUS) is a valuable imaging tool for evaluating patients with a variety of pelvic floor disorders, including pelvic organ prolapse (POP). Currently, calculating measurements of anato...
BACKGROUND: Transperineal ultrasound (TPUS) is a valuable imaging tool for evaluating patients with a variety of pelvic floor disorders, including pelvic organ prolapse (POP). Currently, calculating measurements of anatomical structures and relationships as well as extracting the mid-sagittal (MS) plane of 2D and 3D ultrasound images are obtained manually, which is a time-consuming process and requires a reviewer with prior training in pelvic floor US interpretation. The need for manual analysis of ultrasound images has limited the broader adoption of TPUS for evaluating pelvic floor disorders in both research and clinical practice. An automated segmentation and plane extraction method would improve the ability to easily quantify pelvic anatomy relevant to pelvic floor disorders and improve the efficiency and reproducibility of POP diagnosis and treatment. PURPOSE: To develop a fast, reproducible, and automated method of acquiring the MS plane, plane of minimal hiatal dimensions (PMHD), and segmentations of the pelvic floor organs from 3D TPUS images. METHODS: Our method used a nnU-Net segmentation model to segment structures of interest in the 3D TPUS images. The model segmented the pubis symphysis (PS), urethra, bladder, rectum, rectal ampulla, and anorectal angle (ANA). The segmented output was then fed into a heuristics-based method to determine the PS and ANA to extract the MS plane and PMHD automatically. We used a dataset consisting of 161 3D TPUS images from 104 patients. 89 of the volumes were acquired in a resting state and 72 during the Valsalva maneuver. The segmentation and plane extraction algorithms were evaluated by comparing the results with manual segmentations and manual plane extraction methods using the dice similarity coefficients (DSC), mean absolute surface distance (MAD), and absolute angle difference (AAD), respectively. The Wilcoxon-signed rank statistical test was used with Bonferroni-correction to p < 0.01. Cohen effect size was used for comparing model results. RESULTS: The nnU-Net segmentation model reported an average DSC(%) of 70.4%, 58.5%, 57.1%, 48.9%, 39.0%, and 19.8% for bladder, rectum, PS, urethra, ANA, and rectal ampulla respectively. The nnU-Net segmentation model achieved significantly higher DSC (p < 0.01) for the urethra and rectum than all other tested models. Across all metrics, the nnU-Net segmentation model achieved an average effect size of 0.3, 0.5, 0.7, and 0.8 compared to a 3D ResNet34 + U-Net, 3D U-Net, 2D U-Net, and Attention 3D U-Net model, respectively. The average AADs between the automatically calculated plane slices and manually estimated planes dataset for the MS plane and PMHD were 3.8° and 2.4°, respectively. The PS and ANA segmentation centroids were used to calculate the MS plane and PMHD and they had distance errors of 3.6 mm and 4.4 mm. CONCLUSIONS: We developed an automated 3D segmentation and multiple plane extraction method of female pelvic floor 3D US images. Our method extracts the MS plane and PMHD from 3D US images. The proposed algorithm pipeline can improve the efficiency and reproducibility of TPUS analysis for pelvic floor disorder diagnosis and treatment.
Time-resolved point dosimetry for spread-out Bragg-peak proton FLASH using fibre-coupled scintillators.
Steenholdt SR, Johansen JG, Kanouta E … +1 more , Poulsen PR
Med Phys · 2026 May · PMID 42108226 · Full text
BACKGROUND: In FLASH radiotherapy, the dose is delivered using ultra-high dose rates (UHDR), which are approximately 100 times higher than those used for conventional (CONV) treatments. This has shown promise in sparing...
BACKGROUND: In FLASH radiotherapy, the dose is delivered using ultra-high dose rates (UHDR), which are approximately 100 times higher than those used for conventional (CONV) treatments. This has shown promise in sparing normal tissue while maintaining tumour control. Proton beams, particularly in the spread-out Bragg peak (SOBP), offer a favourable depth-dose profile for sparing healthy tissue. However, quality assurance for FLASH requires time-resolved dosimetry to capture the temporal structure of pencil beam scanning delivery. Fibre-coupled scintillating detectors have been used both in electron and photon UHDR beams, and have been applied in the proton beam entrance plateau. Extending their use to the SOBP requires careful calibration to address quenching and water-inequivalent response near the Bragg peak. PURPOSE: To calibrate and validate a fibre-coupled inorganic scintillator detector system for accurate, time-resolved point dosimetry in the SOBP for UHDR proton beams, which will enable preclinical and in-vivo FLASH studies with robust dosimetric and geometric verification. METHODS: Experiments were conducted using a clinical proton PBS beam line. A 2D range modulator generated a 5 cm SOBP from a mono-energetic beam. Four ZnSe:O scintillator probes coupled to optical fibres were read out by silicon photomultipliers at 50 kHz. An ionisation chamber provided reference dose measurements. The calibration included determining a signal dependent saturation factor of the silicon photomultiplier , measuring the absolute calibration factor , and characterising the correction for the depth-dependent under-response . A calibration validation was performed in the SOBP across a range of UHDR beam currents, evaluating both dosimetric accuracy and probe positional stability. The calibrated system was then used to characterise SOBP beam spot profiles, in terms of full width at half-maximum and dose rate variation with depth. RESULTS: A saturation multiplier of up to 55% was observed across all four probes. The depth-dependent under-response reached up to 12% at the distal SOBP edge. Both effects were successfully corrected for through fitting simple functions. The validation in the SOBP demonstrated that the calibration achieved positional stability within 0.1 mm and agreement between the measured and absolute doses within 0.5% for all probes. Beam characterisation revealed full-width at half-maximum broadening from 8.3 mm at shallow depth to 21.5 mm near the range end, with spot profiles comprising two Gaussian cores and a Lorentzian tail. The maximum instantaneous dose rate in the UHDR beam fell from 800 Gy/s in the entrance plateau to 280 Gy/s in the SOBP. CONCLUSIONS: The developed calibration method enables accurate, time-resolved dosimetry in UHDR proton SOBP beams, allowing for the separation of saturation and quenching corrections. The fibre-coupled scintillator system demonstrated high precision in both dose and geometry, making it suitable for quality assurance in preclinical FLASH studies. This approach streamlines recalibration, reducing beam time requirements, and supports routine monitoring of PBS-delivered proton FLASH treatments in complex depth-dose scenarios.
Ion recombination correction in reference dosimetry for pencil beam scanned proton beams.
Gan JK, Lew KS, Chua CGA … +7 more , Koh CWY, Lee KH, Yagi M, Lew WS, Lee JCL, Park SY, Tan HQ
Med Phys · 2026 May · PMID 42108225 · Publisher ↗
BACKGROUND: The 2024 IAEA TRS-398 revision updated recommendations for reference dosimetry and ion recombination corrections in pencil beam scanned (PBS) proton beams. PURPOSE: This study evaluates the revised ion recomb...
BACKGROUND: The 2024 IAEA TRS-398 revision updated recommendations for reference dosimetry and ion recombination corrections in pencil beam scanned (PBS) proton beams. PURPOSE: This study evaluates the revised ion recombination methods for monoenergetic synchrotron-based PBS proton system across different energies, monitor units (MU), and ionization chamber types. METHODS: Reference-field measurements were performed using a synchrotron system at 70.2, 150.2, and 228.7 MeV and at 6, 50, and 200 MU. Charge-collection data were acquired using PTW Farmer and Advanced Markus chambers across 20-400 V. Ion recombination correction factors ( ) were determined using the Jaffé plot extrapolation method and the TRS-398 two-voltage method (TVM) under different time structure assumptions. Charge multiplication in the chamber was addressed using both low voltage linear fitting and a semiempirical exponential model. RESULTS: For low energy, low MU fields, and TVM yielded values within ∼1% of the Jaffé extrapolation. For high-energy, high-MU fields, maximum differences of 6.25% (Farmer) and 1.62% (Advanced Markus) were observed. The synchrotron beam exhibited energy, MU, and chamber dependent time structure behavior, producing pulsed-like or continuous-like characteristics. Misclassification of the time structure resulted in additional deviations of up to 2.49% (Farmer) and 0.59% (Advanced Markus). Charge multiplication was observed in the Advanced Markus chamber at voltages > 150 V. The exponential fitting successfully modeled this response and produced values agreeing with low voltage fits within 1.5%, while avoiding subjective voltage cutoff selection. CONCLUSION: The revised TRS-398 provides accurate ion recombination corrections for monoenergetic PBS fields at low energies and low MU. However, accuracy of ion recombination correction decreases at higher energies and MU, particularly when the time structure was ambiguous or chamber dependent. Charge multiplication in small volume chambers presents an additional source of uncertainty not fully addressed by TRS-398. Incorporating charge multiplication fitting methods may improve the robustness of reference dosimetry in synchrotron-based PBS proton therapy.
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