Sparse-view computed tomography (CT) is a practical solution to reduce radiation dose, but the resulting ill-posed inverse problem poses significant challenges for accurate image reconstruction. Although deep learning and diffusion-based methods have shown promising results, they often lack physical interpretability or suffer from high computational costs due to iterative sampling starting from random noise. Recent advances in generative modeling, particularly Poisson Flow Generative Models (PFGM), enable high-fidelity image synthesis by modeling the full data distribution. In this work, we propose Residual Poisson Flow (ResPF) Generative Models for efficient and accurate sparse-view CT reconstruction. Based on PFGM++, ResPF integrates conditional guidance from sparse measurements and employs a hijacking strategy to significantly reduce sampling cost by skipping redundant initial steps. However, skipping early stages can degrade reconstruction quality and introduce unrealistic structures. To address this, we embed a data-consistency into each iteration, ensuring fidelity to sparse-view measurements. Yet, PFGM sampling relies on a fixed ordinary differential equation (ODE) trajectory induced by electrostatic fields, which can be disrupted by step-wise data consistency, resulting in unstable or degraded reconstructions. Inspired by ResNet, we introduce a residual fusion module to linearly combine generative outputs with data-consistent reconstructions, effectively preserving trajectory continuity. To the best of our knowledge, this is the first application of Poisson flow models to sparse-view CT. Extensive experiments on synthetic and clinical datasets demonstrate that ResPF achieves superior reconstruction quality, faster inference, and stronger robustness compared to state-of-the-art iterative, learning-based, and diffusion models.

to evaluate the prognostic role of the marker albumin-myosteatosis (MAM) in Caucasian patients with metastatic colorectal cancer. this study involved 55 consecutive Caucasian patients diagnosed with metastatic colorectal cancer. CT scans at the L3 vertebral level were analyzed to determine skeletal muscle cross-sectional area, skeletal muscle index (SMI), and skeletal muscle density (SMD). Bioelectrical impedance analysis (BIA) (phase angle, reactance, resistance, and SMI-BIA) was used. Albumin and prealbumin were measured. The albumin-myosteatosis marker (AMM = serum albumin (g/dL) × skeletal muscle density (SMD) in Hounsfield units (HU) was calculated. Survival was estimated using the Kaplan-Meier method and comparisons between groups were performed using the log-rank test. the median age was 68.1 ± 9.1 years. Patients were divided into two groups based on the median MAM (129.1 AU for women and 156.3 AU for men). Patients in the low MAM group had significantly reduced values of phase angle and reactance, as well as older age. These patients also had higher rates of malnutrition by GLIM criteria (odds ratio: 3.8; 95 % CI = 1.2-12.9), low muscle mass diagnosed with TC (odds ratio: 3.6; 95 % CI = 1.2-10.9) and mortality (odds ratio: 9.82; 95 % CI = 1.2-10.9). The Kaplan-Meir analysis demonstrated significant differences in 5-year survival between MAM groups (patients in the low median MAM group vs. patients in the high median MAM group), (HR: 6.2; 95 % CI = 1.10-37.5). the marker albumin-myosteatosis (MAM) may function as a prognostic marker of survival in Caucasian patients with metastatic CRC.

The study explores the value of a model constructed by integrating CTA-based carotid plaque radiomic features, clinical risk factors, and plaque imaging characteristics for prognosticating the risk of ischemic stroke. Data from 123 patients with carotid atherosclerosis were analyzed and divided into stroke and asymptomatic groups based on DWI findings. Clinical information was collected, and plaque imaging characteristics were assessed to construct a traditional model. Radiomic features of carotid plaques were extracted using 3D-Slicer software to build a radiomics model. Logistic regression was applied in the training set to establish the traditional model, the radiomics model, and a combined model, which were then tested in the validation set. The prognostic ability of the three models for ischemic stroke was evaluated using ROC curves, while calibration curves, decision curve analysis, and clinical impact curves were used to assess the clinical utility of the models. Differences in AUC values between models were compared using the DeLong test. Hypertension, diabetes, elevated homocysteine (Hcy) concentrations, and plaque burden are independent risk factors for ischemic stroke and were used to establish the traditional model. Through Lasso regression, nine optimal features were selected to construct the radiomics model. ROC curve analysis showed that the AUC values of the three Logistic regression models were 0.766, 0.766, and 0.878 in the training set, and 0.798, 0.801, and 0.847 in the validation set. Calibration curves and decision curve analysis showed that the radiomics model and the combined model had higher accuracy and better fit in prognosticating the risk of ischemic stroke. The radiomics model is slightly better than the traditional model in evaluating the risk of ischemic stroke, while the combined model has the best prognostic performance.

To facilitate implementation of plan-of-the-day (POTD) selection for treating locally advanced cervical cancer (LACC), we developed a POTD assessment tool for CBCT-guided radiotherapy (RT). A female pelvis segmentation model (U-Seg3) is combined with a quantitative standard operating procedure (qSOP) to identify optimal and acceptable plans. 

Approach: The planning CT[i], corresponding structure set[ii], and manually contoured CBCTs[iii] (n=226) from 39 LACC patients treated with POTD (n=11) or non-adaptive RT (n=28) were used to develop U-Seg3, an algorithm incorporating deep-learning and deformable image registration techniques to segment the low-risk clinical target volume (LR-CTV), high-risk CTV (HR-CTV), bladder, rectum, and bowel bag. A single-channel input model (iii only, U-Seg1) was also developed. Contoured CBCTs from the POTD patients were (a) reserved for U-Seg3 validation/testing, (b) audited to determine optimal and acceptable plans, and (c) used to empirically derive a qSOP that maximised classification accuracy. 

Main Results: The median [interquartile range] DSC between manual and U-Seg3 contours was 0.83 [0.80], 0.78 [0.13], 0.94 [0.05], 0.86[0.09], and 0.90 [0.05] for the LR-CTV, HR-CTV, bladder, rectum, and bowel bag. These were significantly higher than U-Seg1 in all structures but bladder. The qSOP classified plans as acceptable if they met target coverage thresholds (LR-CTV≧99%, HR-CTV≧99.8%), with lower LR-CTV coverage (≧95%) sometimes allowed. The acceptable plan minimising bowel irradiation was considered optimal unless substantial bladder sparing could be achieved. With U-Seg3 embedded in the qSOP, optimal and acceptable plans were identified in 46/60 and 57/60 cases. 

Significance: U-Seg3 outperforms U-Seg1 and all known CBCT-based female pelvis segmentation models. The tool combining U-Seg3 and the qSOP identifies optimal plans with equivalent accuracy as two observers. In an implementation strategy whereby this tool serves as the second observer, plan selection confidence and decision-making time could be improved whilst simultaneously reducing the required number of POTD-trained radiographers by 50%.

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Previous studies have highlighted the prominent role of age in lung cancer risk, with signs of lung aging visible in computed tomography (CT) imaging. This study aims to characterize lung aging using quantitative radiomic features extracted from five delineated lung lobes and explore how age contributes to lung cancer development through these features. We analyzed baseline CT scans from the Wenling lung cancer screening cohort, consisting of 29,810 participants. Deep learning-based segmentation method was used to delineate lung lobes. A total of 1,470 features were extracted from each lobe. The minimum redundancy maximum relevance algorithm was applied to identify the top 10 age-related radiomic features among 13,137 never smokers. Multiple regression analyses were used to adjust for confounders in the association of age, lung lobar radiomic features, and lung cancer. Linear, Cox proportional hazards, and parametric accelerated failure time models were applied as appropriate. Mediation analyses were conducted to evaluate whether lobar radiomic features mediate the relationship between age and lung cancer risk. Age was significantly associated with an increased lung cancer risk, particularly among current smokers (hazard ratio = 1.07, P = 2.81 × 10^- 13). Age-related radiomic features exhibited distinct effects across lung lobes. Specifically, the first order mean (mean attenuation value) filtered by wavelet in the right upper lobe increased with age (β = 0.019, P = 2.41 × 10^- 276), whereas it decreased in the right lower lobe (β = -0.028, P = 7.83 × 10^- 277). Three features, namely wavelet_HL_firstorder_Mean of the right upper lobe, wavelet_LH_firstorder_Mean of the right lower lobe, and original_shape_MinorAxisLength of the left upper lobe, were independently associated with lung cancer risk at Bonferroni-adjusted P value. Mediation analyses revealed that density and shape features partially mediated the relationship between age and lung cancer risk while a suppression effect was observed in the wavelet first order mean of right upper lobe. The study reveals lobe-specific heterogeneity in lung aging patterns through radiomics and their associations with lung cancer risk. These findings may contribute to identify new approaches for early intervention in lung cancer related to aging. Not applicable.

Temporomandibular disorders (TMDs) are frequently associated with posterior condylar displacement; however, early prediction of this displacement remains a significant challenge. Therefore, in this study, we aimed to develop and evaluate a predictive model for bilateral posterior condylar displacement. In this retrospective observational study, 166 cone-beam computed tomography images were examined and categorized into two groups based on condyle positions as observed in the sagittal images of the joint space: those with bilateral posterior condylar displacement and those without. Three machine-learning algorithms-Random Forest, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and Extreme Gradient Boosting (XGBoost)-were used to identify risk factors and establish a risk assessment model. Calibration curves, receiver operating characteristic curves, and decision curve analyses were employed to evaluate the accuracy of the predictions, differentiation, and clinical usefulness of the models, respectively. Articular eminence inclination (AEI) and age were identified as significant risk factors for bilateral posterior condylar displacement. The area under the curve values for the LASSO and Random Forest models were both > 0.7, indicating satisfactory discriminative ability of the nomogram. No significant differences were observed in the differentiation and calibration performance of the three models. Clinical utility analysis revealed that the LASSO regression model, which incorporated age, AEI, A point-nasion-B point (ANB) angle, and facial height ratio (S-Go/N-Me), demonstrated superior net benefit compared to the other models when the probability threshold exceeded 45%. Patients with a steeper AEI, insufficient posterior vertical distance (S-Go/N-Me), an ANB angle ≥ 4.7°, and older age are more likely to experience bilateral posterior condylar displacement. The prognostic nomogram developed and validated in this study may assist clinicians in assessing the risk of bilateral posterior condylar displacement.

Osteoporosis is a prevalent skeletal disorder characterized by reduced bone mineral density (BMD) and structural deterioration, resulting in increased fracture risk. Early diagnosis is crucial to prevent fractures and improve patient outcomes. This study investigates the diagnostic utility of morphometric and cortical indices derived from cone-beam computed tomography (CBCT) for identifying osteoporotic postmenopausal women who were candidates for dental implant therapy, with dual-energy X-ray absorptiometry (DXA) used as the reference standard. This cross-sectional study included 71 postmenopausal women, aged 50-79 years, who underwent CBCT imaging at the Oral and Maxillofacial Radiology Department of Hamadan University of Medical Sciences between 2022 and 2024. Participants with systemic conditions affecting bone metabolism were excluded. The morphometric indices-Computed Tomography Mandibular Index (CTMI), Computed Tomography Index Superior (CTI(S)), Computed Tomography Index Inferior (CTI(I)), and Computed Tomography Cortical Index (CTCI)-were measured at the mental foramen and antegonial regions using OnDemand3D Dental software. Bone mineral density (BMD) was assessed by DXA scans of the lumbar spine and femoral neck. In addition to traditional statistical analyses (Pearson's correlation and one-way ANOVA with LSD test), a multilayer perceptron (MLP) neural network model was employed to evaluate the diagnostic power of CBCT indices. DXA results based on the femoral neck T-scores categorized 38 patients as normal, 32 as osteopenic, and one as osteoporotic, while lumbar spine T-scores identified 38 normal, 22 osteopenic, and 11 osteoporotic patients. Significant differences (p < 0.05) were observed in most CBCT-derived indices, with the CTMI index demonstrating the most marked variation, especially between normal and osteoporotic groups (p < 0.001). Moreover, significant positive correlations were found between the CBCT indices and DXA T-scores across the lumbar spine, femoral neck, and total hip regions. The neural network model achieved an overall diagnostic accuracy of 75%, with the highest predictive importance attributed to antegonial CTCI and CTMI indices. This study highlights the significant correlation between CBCT-derived radiomorphometric indices such as CTMI, CTI(S), CTI(I), and CTCI at the mental foramen and antegonial regions and bone mineral density (BMD) in postmenopausal women. CBCT, particularly the CTMI index in the antegonial region, offers a cost-effective, non-invasive method for early osteoporosis detection, providing a valuable alternative to traditional screening methods.

The pancreas is a gland in the abdomen that helps to produce hormones and digest food. The irregular development of tissues in the pancreas is termed as pancreatic cancer. Identification of pancreatic tumors early is significant for enhancing survival rate and providing appropriate treatment. Thus, an efficient Secretary Wolf Bird Optimization (SeWBO)_Efficient DenseNet is presented for pancreatic tumor detection using Computed Tomography (CT) scans. Firstly, the input pancreatic CT image is accumulated from a database and subjected to image preprocessing using a bilateral filter. After this, lesion is segmented by utilizing Parallel Reverse Attention Network (PraNet), and hyperparameters of PraNet are enhanced by using the proposed SeWBO. The SeWBO is designed by incorporating Wolf Bird Optimization (WBO) and the Secretary Bird Optimization Algorithm (SBOA). Then, features like Complete Local Binary Pattern (CLBP) with Discrete Wavelet Transformation (DWT), statistical features, and Shape Local Binary Texture (SLBT) are extracted. Finally, pancreatic tumor detection is performed by SeWBO_Efficient DenseNet. Here, Efficient DenseNet is developed by combining EfficientNet and DenseNet. Moreover, the proposed SeWBO_Efficient DenseNet achieves better True Negative Rate (TNR), accuracy, and True Positive Rate (TPR), of 93.596%, 94.635%, and 92.579%.

We extend the Hybrid Unet Transformer (HUT) foundation model, which combines the advantages of the CNN and Transformer architectures with a noisy self-supervised approach, and demonstrate it in an ischemic stroke lesion segmentation task. We introduce a self-supervised approach using a noise anchor and show that it can perform better than a supervised approach under a limited amount of annotated data. We supplement our pre-training process with an additional unannotated CT perfusion dataset to validate our approach. Compared to the supervised version, the noisy self-supervised HUT (HUT-NSS) outperforms its counterpart by a margin of 2.4% in terms of dice score. HUT-NSS, on average, gained a further margin of 7.2% dice score and 28.1% Hausdorff Distance score over the state-of-the-art network USSLNet on the CT perfusion scans of the Ischemic Stroke Lesion Segmentation (ISLES2018) dataset. In limited annotated data sets, we show that HUT-NSS gained 7.87% of the dice score over USSLNet when we used 50% of the annotated data sets for training. HUT-NSS gained 7.47% of the dice score over USSLNet when we used 10% of the annotated datasets, and HUT-NSS gained 5.34% of the dice score over USSLNet when we used 1% of the annotated datasets for training. The code is available at https://github.com/vicsohntu/HUTNSS_CT .

This study investigates the use of list-mode (LM) maximum a posteriori (MAP) expectation maximization (EM) incorporating prior information predicted by a convolutional neural network for image reconstruction in fast neutron (FN)-based proton therapy range verification.&#xD;Approach. A conditional generative adversarial network (pix2pix) was trained on progressively noisier data, where detector resolution effects were introduced gradually to simulate realistic conditions. FN data were generated using Monte Carlo simulations of an 85 MeV proton pencil beam in a computed tomography (CT)-based lung cancer patient model, with range shifts emulating weight gain and loss. The network was trained to estimate the expected two-dimensional (2D) ground truth FN production distribution from simple back-projection images. Performance was evaluated using mean squared error (MSE), structural similarity index (SSIM), and the correlation between shifts in predicted distributions and true range shifts. &#xD;Main results. Our results show that pix2pix performs well on noise-free data but suffers from significant degradation when detector resolution effects are introduced. Among the LM-MAP-EM approaches tested, incorporating a mean prior estimate into the reconstruction process improved performance, with LM-MAP-EM using a mean prior estimate outperforming naïve LM maximum likelihood EM (LM-MLEM) and conventional LM-MAP-EM with a smoothing quadratic energy function in terms of SSIM. &#xD;Significance. Findings suggest that deep learning techniques can enhance iterative reconstruction for range verification in proton therapy. However, the effectiveness of the model is highly dependent on data quality, limiting its robustness in high-noise scenarios.&#xD.