Local determination of the attenuation in biological tissues has been the focus of extensive research over the past decades. Its characterization is of major interest for both noninvasive thermal therapies and diagnostic applications. Numerous ultrasound (US)-based methods have been investigated in recent years to assess attenuation invivo. In this article, we aim to map US attenuation in four organs of human volunteers: liver, pancreas, kidney, and breast. We propose to compare the mean attenuation values obtained in each tissue with the corresponding proton density fat fraction (PDFF) derived from quantitative magnetic resonance imaging (qMRI). This comparison allows us to (i) present a direct calculation method of the local US attenuation and (ii) investigate the relationship between the average fat content of each organ and its global US attenuation. The ultrasonic measurement method is in line with techniques originating from the spectral difference method. Here, the attenuation coefficient (AC) is estimated by insonifying tissues with a single plane wave and acquiring the backscattered echoes. This approach avoids the need for a reference medium to compensate for diffraction and focusing effects. The method is characterized and validated on a calibrated phantom and compared with two commonly used techniques on ex vivo liver tissues. Subsequently, attenuation maps and average values obtained from US imaging in healthy volunteers are compared with PDFF values measured by qMRI. Hepatic ( ) renal ( ), pancreatic ( ) and breast ( ) tissues were analyzed. Statistical significance was assessed using a paired t-test. To account for multiple comparisons, a Bonferroni correction was applied, resulting in an adjusted 5% significance threshold of . Effect sizes were also reported using Cohen's parameter. Effect sizes were considered large for . Measurements on the calibrated phantom showed relative errors between the measured mean values and the manufacturer values of 2% and 9%, respectively. Average AC of each organ was included in the confidence interval of the corresponding literature value. The Pearson correlation coefficient between (PDFF) and AC slope is ( ). When each organ was considered separately, no significant correlation was observed between PDFF average values and global US attenuation, as variations between volunteers were found of the same order of magnitude as the standard deviation around each averagevalue. This work presents an alternative method for in vivo characterization of US attenuation based on the emission of a plane wave, and highlights the impact of fat density on inter-organ attenuation variations. Together, these results provide new insights into the relationship between tissue microstructure and USattenuation.

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. 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). A CEM phantom containing simulated breast lesions was positioned on an anthropomorphic thoracic phantom and scanned using a clinical PCCT system at 120kV 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 70keV, 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. Among all PCCT configurations, the UHR mode with a small FOV and either a 512or 1024matrix at 50keV VMI achieved the highest combined CNR across all iodine concentrations. Additionally, the UHR mode with a 512matrix 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 50keV VMIs derived from DECT scans. 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.

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. 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. 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 (IMPTConv and PATConv) or with a 10% prescription dose escalation to the hypoxic sub-volumes of the low- and high-risk target structures (IMPTEsc and PATEsc). 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 (TCPConv) or with consideration of hypoxia-induced radioresistance (TCPHyp). Statistical significance (p < 0.05) of different TCP or mean OAR dose distributions was determined using the Wilcoxon signed-rank test. During IMPT, radiotherapy prescription dose escalation increased TCPConv 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, TCPConv 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 TCPConv 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 TCPHyp 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 TCPHyp 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 IMPTConv, (14.3 ± 10.5)Gy RBE by PATConv, (9.8 ± 10.5)Gy RBE by IMPTEsc, and (10.4 ± 12.8)Gy RBE by PATEsc (all p = 0.002). 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.

Photon-counting CT (PCCT) is the latest technology enabling imaging with reduced noise and inherent spectral separation, with the potential to directly calculate a more accurate tissue stopping power from spectral data. This potential benefit is difficult to quantify in practice and is currently evaluated mainly in phantoms with simplified geometries that only approximate real patient anatomy. In this work, we proposed virtual imaging simulators as an alternative approach to experimental validation of beam range uncertainty in complex patient geometry using a computational model of a human head and a CT system. In addition, we validate the accuracy of stopping power ratio (SPR) calculations on a model of a PCCT scanner using a conventional stoichiometric calibration approach and a prototype software TissueXplorer. A validated CT simulator (DukeSim) was used to generate PCCT projections of a computational head phantom, which were reconstructed with an open-source toolbox (ASTRA). The dose of 2 Gy was delivered through protons in a single fraction to target two different cases of nasal and brain tumors. The ground-truth treatment plan was made directly on the computational phantom using clinical treatment planning software (RayStation). This plan was then recalculated on the corresponding CT images for which SPR values were estimated using both the conventional method and the prototype software TissueXplorer. The resulting dose distributions were subsequently compared against the ground-truth plan to quantify dose differences arising from SPR estimation. The mean percentage difference in estimating the SPR with TissueXplorer in all head tissues inside the scanned volume was 0.28%. SPRs obtained with this method showed smaller dose distribution differences from the ground truth plan than the conventional stoichiometric calibration method on the computational head phantom. Virtual imaging offers an alternative approach to validation of the SPR prediction from CT imaging, as well as its effect on the dose distribution and thus downstream clinical outcomes. According to this simulation study, software solutions that utilize spectral information hold promise for more accurate prediction of the SPR than the conventional stoichiometricapproach.

Chronic obstructive pulmonary disease (COPD) remains difficult to diagnose reliably due to limitations of conventional spirometry and CT interpretation. Although deep learning has shown promise, CNNs are constrained by local receptive fields, and ViTs are constrained by high computational cost, highlighting the relevance of multimodal integration of CT and clinical data for improving COPD diagnosticaccuracy. In this study, we propose a Hybrid-Mamba Network with Dual Level Attention Fusion for multimodal COPDdiagnosis. We retrospectively enrolled 381 participants (184 with COPD and 197 healthy controls). Clinical data encompassed basic information, respiratory symptoms, blood gas analysis, pulmonary function tests, and blood routine tests. The framework employs a Hybrid-Mamba architecture for efficient CT feature representation, leverages a tailored Hybrid-DWConv-AAS Block for enhanced feature integration, and incorporates a Dual Level Attention Fusion Block to adaptively integrate CT and clinicaldata. On the test set, our proposed network achieved an AUC of 0.985 and an accuracy of 0.947, with an average per-patient inference time of 97.54 ms, while maintaining robust diagnostic performance under simulatedperturbations. These findings indicate that the framework provides an efficient and robust approach for COPDdiagnosis.

Megavoltage computed tomography (MVCT) is an essential imaging modality for verifying patient positioning in helical tomotherapy. However, its clinical application in daily anatomical monitoring and adaptive radiotherapy is hindered by inherent image artifacts and poor soft-tissue contrast. This issue is particularly pronounced in pelvic radiotherapy, where the intra- and interfraction anatomical variations necessitate high-quality image guidance to ensure precise dose delivery. We developed a deep-learning-based framework for MVCT enhancement to improve anatomicalvisualizationand facilitate accurate adaptive treatment planning for cervical cancer. This study analyzed a retrospective cohort of 170 patients with cervical cancer who underwent helical tomotherapy. The proposed deep-learning-based algorithm employed a generative adversarial network (GAN) that integrated deformable convolution and a self-attention mechanism (SADC-EGAN) to improve MVCT image quality. Comparative analyses were conducted against representative baseline methods, including U-Net, Attention U-Net, U-Net++, Swin-UNet, CycleGAN, and Pix2Pix. Model performance was assessed using quantitative metrics, including mean absolute error (MAE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and Fréchet inception distance (FID). The synthetic computed tomography (sCT) images generated by the proposed SADC-EGAN method demonstrated superior Hounsfield unit (HU) accuracy and structural similarity comparedto the original MVCT. Specifically, the MAE between the sCT and kilovoltage computed tomography (kVCT) was reduced to 36.32±6.69 HU, compared with 56.72±9.09 HU for MVCT. In terms of image quality, the sCT images exhibited notable enhancements over MVCT images, with higher PSNR (32.54±2.31 vs. 29.40±1.56dB), improved SSIM (0.93±0.01vs. 0.89±0.02), and substantially lower FID (66.68±22.31vs. 153.52±28.77). The proposed SADC-EGAN framework, integrating deformable convolutions and self-attention, effectively generated high-quality kVCT-like images from MVCT, improving both HU accuracy and image quality. This approachhas clinical potential to enable online adaptive helical tomotherapy for cervical cancer.

The Gamma Knife is an important treatment unit for stereotactic radiosurgery. The isocenter is well understood from the collective behavior of all its 192 Co60 sources but is not known from the behavior of individual sources. To introduce and validate the "starburst shot," a new quality assurance (QA) technique to better understand the Gamma Knife's isocenter by analyzing the convergence of all individual collimator beam axes. We built cylindrical phantoms to acquire panoramic radiographic films for 4-, 8-, and 16-mm collimator sizes. We developed an algorithm to locate the centers of each entrance and corresponding exit exposure on the panoramic film, map their centers back to the cylinder, and connect the centers with a line segment in 3D. These lines represent the collimators' central axes, and we analyzed the resulting cluster of 192 lines using an optimization algorithm to locate the smallest sphere that simultaneously touches all lines. This sphere's center is an estimate of the isocenter, and the radial distances from the isocenter were scored by the probability of passing within 0.5mm of the isocenter. This optimization method provided a robust determination of the isocenter. For the 4-mm collimators, was 1.00. For the 8-mm collimators, ranged from 0.81 to 0.93, and for the 16-mm collimators, it ranged from 0.74 to 0.92. The isocenters determined for the 4-, 8-, and 16-mm collimators were found to be within 0.2mm of each other. In a double-exposure experiment, the starburst shot accurately found a 1-mm shift introduced between exposures. The starburst shot is a new, effective technique for detailed Gamma Knife isocenter QA, providing a comprehensive analysis of all 192 beams. It successfully verifies the isocenter's position and size with submillimeter accuracy. The starburst shot differs significantly from standard techniques, such as the pinprick tool test, and we propose a tiered system of acceptable values of 0.90 for 4mm, 0.80 for 8mm, and 0.70 for 16mm, as a QA standard for clinical use.

The van Herk margin formula, derived for 3-D conformal radiotherapy with a uniform 3.2 mm dose penumbra and no intrinsic dose buffer, remains widely used to design CTV-to-PTV expansions in contemporary image-guided prostate radiotherapy. However, coplanar VMAT techniques often feature broader penumbrae and explicit PTV-isodose clearances, potentially violating the assumptions underlying the original formulation and leading to overly conservative margins. We investigated whether the widely adopted van Herk margin formula overestimates clinical target volume (CTV)-to-planning target volume (PTV) expansions for contemporary coplanar volumetric modulated arc therapy (VMAT) prostate treatments. Fifty consecutive intact-prostate VMAT radiotherapy plans (two coplanar arcs; clinical margins 3 mm, except for 2 mm posterior) were exported. Direction-specific 90% isodose-to-PTV gaps and penumbra widths were measured. Candidate anisotropic margins were tested by eroding the PTV to create CTV_eval. Monte-Carlo simulations combined systematic (Σ) shifts with Gaussian random (σ') blurring kernels of 0-2 mm were performed. Acceptability criteria of (i) CTV Dmin0.03 cc ≥ 90% Rx in ≥ 90% of simulated scenarios or (ii) population tumor-control probability (TCP) loss < 1 % were used. VMAT plans exhibited intrinsic 90% isodose clearances of 3-5 mm laterally/anteriorly and 1-2 mm superior-inferiorly, while axial-plane penumbras were up to fivefold broader than van Herk's assumption. With a [0, 0, 2] mm (LR/AP/SI) margin, ≥ 90% of patients maintained Dmin0.03 cc ≥ 90% Rx provided Σ lay within an ellipsoid of [2.0, 1.5, 1.8] mm and σ' ≤ [1.5, 2.0, 1.5] mm. Under TCP criteria the safe Σ ellipsoid for high-risk disease was [2.5, 1.9, 2.2] mm, while low-intermediate risk was even less sensitive. For image-guided coplanar VMAT prostate radiotherapy, an anisotropic [0, 0, 2] mm CTV-to-PTV margin is sufficient for target coverage. A modified margin expression that subtracts the measured isodose-to-PTV gap and uses reduced random-error coefficients better reflects modern practice.

BackgroundIndependent verification of the manufacturer‐provided dosimetry data for 106Ru ophthalmic brachytherapy applicators is crucial for safe and accurate treatment, yet a standardized, traceable method for clinical absolute dosimetry has been lacking.PurposeThis work establishes a complete framework for traceable absorbed dose to water measurements of 106Ru eye plaques in absolute units of Gray, complementing the high‐precision BetaCheck‐106™ setup with a robust detector calibration methodology independent of the source manufacturer.MethodsThree microSilicon diode detectors were calibrated in traceable 60Co and 6 MeV electron reference beams. Depth‐dose measurements for four CCB‐type 106Ru plaques were performed in water using the BetaCheck‐106™ setup. Monte Carlo (MC) simulations were employed to calculate depth‐dependent beam quality correction factors kQ,Q0 which account for the detector's response in the 106Ru field relative to the calibration beams. The method was validated against measurements performed at the National Physical Laboratory (NPL) using alanine dosimetry. MC simulations were also used to investigate the water‐equivalence of the NPL alanine/PMMA phantom setup.ResultsThe MC‐calculated correction factors for the diodes showed a significant depth‐dependence, underscoring the necessity of such corrections in the steep 106Ru depth‐dose gradient. The dose rates determined with the calibrated diodes were in agreement with the NPL alanine results. Both methods yielded dose rates systematically lower than those provided in the manufacturer's certificates, though generally within the stated uncertainties. The MC simulations revealed substantial non‐water equivalence correction factors for the alanine/PMMA phantom, highlighting the advantage of direct measurements in water.ConclusionsWe present a novel, comprehensive methodology for independent and traceable absolute dosimetry of 106Ru applicators. By combining a dedicated water phantom setup with diode detectors calibrated against external beam standards and MC‐derived correction factors, this framework empowers clinical users to perform robust verification measurements, filling a critical gap in the quality assurance of ocular brachytherapy.

Noninvasive assessment of diastolic dysfunction relies on multiple echocardiographic indicators, including measurements from both standard B-mode images and Doppler, obtained at various cardiac locations such as the mitral annulus, tricuspid annulus, left ventricle, and left atrium. The diagnostic process is complex and subject to interobserver variability, making accurate and rapid evaluation challenging. Automated semantic segmentation of key cardiac structures, such as the left atrium, left ventricle, and mitral valve annulus, offers a potential solution by capturing temporal changes throughout the cardiac cycle. This study aims to improve the accuracy of segmenting the left atrium, left ventricle, and mitral valve annulus in echocardiographic images and to leverage the resulting temporal segmentation features for more reliable identification of diastolic dysfunction. This study presents Diff-TransUNet, a novel segmentation model incorporating a noise-robust Differential Transformer module. Evaluations on private (1137 training images, 135 validation images, and 88 test images), CAMUS (1400 training images, 200 validation images, and 200 test images), and EchoNet-Dynamic (5000 training images, 2546 validation images, and 2528 test images) datasets demonstrate improved performance over state-of-the-art methods, assessed by Dice coefficient (Dice), Intersection-over-Union (IoU), and 95th percentile Hausdorff Distance (HD95) metrics. Statistical analysis was performed to compare Diff-TransUNet with baseline methods across evaluation metrics. To control for errors arising from multiple comparisons, p-values were adjusted using the Benjamini-Hochberg false discovery rate (FDR) correction. Statistical significance was assessed at a 95% confidence level. In addition to p-values, Cohen's d effect size was computed to quantify the practical significance of performance differences. The proposed Diff-TransUNet achieved a Dice of 87.49%, IoU of 79.07%, and HD95 of 1.48 on the private dataset. Compared with state-of-the-art models, Dice improved by 1.35%-4.30% (p<0.05, Cohen's d=0.32-0.90), IoU by 1.97%-5.67% (p<0.05, Cohen's d=0.37-1.03), and HD95 by 0.16-0.83 (p<0.05, Cohen's d=0.21-0.90). On the CAMUS dataset, the model achieved a Dice of 88.74%, IoU of 80.58%, and HD95 of 2.83, showing improvements of 1.07%-4.96% (p<0.05, Cohen's d=0.18-0.63) in Dice, 1.55%-6.89% (p<0.05, Cohen's d=0.19-0.71) in IoU, and 0.41-2.85 (p<0.05, Cohen's d=0.12-0.46) in HD95 compared to advanced models. On the EchoNet-Dynamic dataset, the model obtained a Dice of 92.25%, IoU of 85.87%, and HD95 of 1.65, outperforming other methods by 0.42%-2.00% (p<0.05, Cohen's d=0.10-0.40) in Dice, 0.69%-3.21% (p<0.05, Cohen's d=0.10-0.43) in IoU, and 0.21-1.12 (p<0.05, Cohen's d=0.09-0.34) in HD95. Furthermore, by extracting volumetric segmentation features, the proposed method achieved an accuracy of 88.95% (95 % CI 87.15% to 90.08%) in identifying diastolic dysfunction. The proposed Diff-TransUNet model achieves significant improvements in ultrasound segmentation. Features extracted from the left ventricle, left atrium, and mitral annulus segmented by Diff-TransUNet can be effectively used for the identification of diastolic dysfunction.