Among numerous medical imaging modalities, diffusion weighted imaging (DWI) is extremely sensitive to acute ischemic stroke lesions, especially small infarcts. However, magnetic resonance imaging is time-consuming and expensive, and it is also prone to interference from metal implants. Therefore, the aim of this study is to design a medical image synthesis method based on generative adversarial network, Stroke-p2pHD, for synthesizing DWI images from computed tomography (CT). Stroke-p2pHD consisted of a generator that effectively fused local image features and global context information (Global_to_Local) and a multi-scale discriminator (M ²Dis). Specifically, in the Global_to_Local generator, a fully convolutional Transformer (FCT) and a local attention module (LAM) were integrated to achieve the synthesis of detailed information such as textures and lesions in DWI images. In the M ²Dis discriminator, a multi-scale convolutional network was adopted to perform the discrimination function of the input images. Meanwhile, an optimization balance with the Global_to_Local generator was ensured and the consistency of features in each layer of the M ²Dis discriminator was constrained. In this study, the public Acute Ischemic Stroke Dataset (AISD) and the acute cerebral infarction dataset from Yantaishan Hospital were used to verify the performance of the Stroke-p2pHD model in synthesizing DWI based on CT. Compared with other methods, the Stroke-p2pHD model showed excellent quantitative results (mean-square error = 0.008, peak signal-to-noise ratio = 23.766, structural similarity = 0.743). At the same time, relevant experimental analyses such as computational efficiency verify that the Stroke-p2pHD model has great potential for clinical applications.
Humans ; Tomography, X-Ray Computed/methods* ; Diffusion Magnetic Resonance Imaging/methods* ; Cerebral Infarction/diagnostic imaging* ; Stroke/diagnostic imaging* ; Neural Networks, Computer ; Image Processing, Computer-Assisted/methods* ; Algorithms

In this study, we propose an automatic contour outlining method to measure the spatial resolution of homemade automatic tube current modulation (ATCM) phantom by outlining the edge contour of the phantom image, selecting the region of interest (ROI), and measuring the spatial resolution characteristics of computer tomography (CT) phantom image. Specifically, the method obtains a binarized image of the phantom outlined by an automated fast region convolutional neural network (AFRCNN) model, measures the edge spread function (ESF) of the CT phantom with different tube currents and layer thicknesses, and differentiates the ESF to obtain the line spread function (LSF). Finally, the values passing through the zeros are normalized by the Fourier transform to obtain the CT spatial resolution index (RI) for the automatic measurement of the modulation transfer function (MTF). In this study, this algorithm is compared with the algorithm that uses polymethylmethacrylate (PMMA) to measure the MTF of the phantom edges to verify the feasibility of this method, and the results show that the AFRCNN model not only improves the efficiency and accuracy of the phantom contour outlining, but also is able to obtain a more accurate spatial resolution value through automated segmentation. In summary, the algorithm proposed in this study is accurate in spatial resolution measurement of phantom images and has the potential to be widely used in real clinical CT images.
Phantoms, Imaging ; Tomography, X-Ray Computed/instrumentation* ; Algorithms ; Neural Networks, Computer ; Image Processing, Computer-Assisted/methods* ; Humans ; Polymethyl Methacrylate

Alveolar bone reconstruction simulation is an effective means for quantifying orthodontics, but currently, it is not possible to directly obtain human alveolar bone material models for simulation. This study introduces a prediction method for the equivalent shear modulus of three-dimensional random porous materials, integrating the first-order Ogden hyperelastic model to construct a computed tomography (CT) based porous hyperelastic Ogden model (CT-PHO) for human alveolar bone. Model parameters are derived by combining results from micro-CT, nanoindentation experiments, and uniaxial compression tests. Compared to previous predictive models, the CT-PHO model shows a lower root mean square error (RMSE) under all bone density conditions. Simulation results using the CT-PHO model parameters in uniaxial compression experiments demonstrate more accurate prediction of the mechanical behavior of alveolar bone under compression. Further prediction and validation with different individual human alveolar bone samples yield accurate results, confirming the generality of the CT-PHO model. The study suggests that the CT-PHO model proposed in this paper can estimate the material properties of human alveolar bone and may eventually be used for bone reconstruction simulations to guide clinical treatment.
Humans ; Tomography, X-Ray Computed/methods* ; Porosity ; Alveolar Process/physiology* ; Bone Density ; Computer Simulation ; Elasticity ; X-Ray Microtomography ; Stress, Mechanical ; Finite Element Analysis ; Models, Biological

To address the issues of difficulty in preserving anatomical structures, low realism of generated images, and loss of high-frequency image information in medical image cross-modal translation, this paper proposes a medical image cross-modal translation method based on diffusion generative adversarial networks. First, an unsupervised translation module is used to convert magnetic resonance imaging (MRI) into pseudo-computed tomography (CT) images. Subsequently, a nonlinear frequency decomposition module is used to extract high-frequency CT images. Finally, the pseudo-CT image is input into the forward process, while the high-frequency CT image as a conditional input is used to guide the reverse process to generate the final CT image. The proposed model is evaluated on the SynthRAD2023 dataset, which is used for CT image generation for radiotherapy planning. The generated brain CT images achieve a Fréchet Inception Distance (FID) score of 33.159 7, a structure similarity index measure (SSIM) of 89.84%, a peak signal-to-noise ratio (PSNR) of 35.596 5 dB, and a mean squared error (MSE) of 17.873 9. The generated pelvic CT images yield an FID score of 33.951 6, a structural similarity index of 91.30%, a PSNR of 34.870 7 dB, and an MSE of 17.465 8. Experimental results show that the proposed model generates highly realistic CT images while preserving anatomical accuracy as much as possible. The transformed CT images can be effectively used in radiotherapy planning, further enhancing diagnostic efficiency.
Humans ; Tomography, X-Ray Computed/methods* ; Magnetic Resonance Imaging/methods* ; Image Processing, Computer-Assisted/methods* ; Neural Networks, Computer ; Brain/diagnostic imaging* ; Algorithms ; Radiotherapy Planning, Computer-Assisted ; Generative Adversarial Networks

Small animal multimodal biomedical imaging refers to the integration of multiple imaging techniques within the same system or device to acquire comprehensive physiological and pathological information of small animals, such as mice and rats. With the continuous advancement of biomedical research, this cutting-edge technology has attracted extensive attention. Multimodal imaging techniques, based on diverse imaging principles, overcome the limitations of single-modal imaging through information fusion, significantly enhancing the overall system's sensitivity, temporal/spatial resolution, and quantitative accuracy. In the future, the integration of new materials and artificial intelligence will further boost its sensitivity and resolution. Through interdisciplinary innovation, this technology is expected to become the core technology of personalized medicine and expand its applications to drug development, environmental monitoring, and other fields, thus reshaping the landscape of biomedical research and clinical practice. This review summarized the progress on the application and investigation of multimodal biomedical imaging techniques, and discussed its development in the future.
Animals ; Multimodal Imaging/trends* ; Rats ; Mice ; Artificial Intelligence ; Diagnostic Imaging/methods* ; Magnetic Resonance Imaging ; Tomography, X-Ray Computed

Endometrial cancer (EC) is one of the most common gynecological malignancies, with an increasing incidence rate worldwide. Accurate segmentation of lesion areas in computed tomography (CT) images is a critical step in assisting clinical diagnosis. In this study, we propose a novel deep learning-based segmentation model, termed spatial choice and weight union network (SCWU-Net), which incorporates two newly designed modules: the spatial selection module (SSM) and the combination weight module (CWM). The SSM enhances the model's ability to capture contextual information through deep convolutional blocks, while the CWM, based on joint attention mechanisms, is employed within the skip connections to further boost segmentation performance. By integrating the strengths of both modules into a U-shaped multi-scale architecture, the model achieves precise segmentation of EC lesion regions. Experimental results on a public dataset demonstrate that SCWU-Net achieves a Dice similarity coefficient (DSC) of 82.98%, an intersection over union (IoU) of 78.63%, a precision of 92.36%, and a recall of 84.10%. Its overall performance is significantly outperforming other state-of-the-art models. This study enhances the accuracy of lesion segmentation in EC CT images and holds potential clinical value for the auxiliary diagnosis of endometrial cancer.
Humans ; Endometrial Neoplasms/diagnostic imaging* ; Female ; Tomography, X-Ray Computed/methods* ; Deep Learning ; Algorithms ; Image Processing, Computer-Assisted/methods* ; Neural Networks, Computer

Lung cancer is a leading cause of cancer-related deaths worldwide, with its high mortality rate primarily attributed to delayed diagnosis. Radiomics, by extracting abundant quantitative features from medical images, offers novel possibilities for early diagnosis and precise treatment of lung cancer. This article reviewed the latest advancements in radiomics for lung cancer management, particularly its integration with artificial intelligence (AI) to optimize diagnostic processes and personalize treatment strategies. Despite existing challenges, such as non-standardized image acquisition parameters and limitations in model reproducibility, the incorporation of AI significantly enhanced the precision and efficiency of image analysis, thereby improving the prediction of disease progression and the formulation of treatment plans. We emphasized the critical importance of standardizing image acquisition parameters and discussed the role of AI in advancing the clinical application of radiomics, alongside future research directions.
Humans ; Lung Neoplasms/diagnosis* ; Artificial Intelligence ; Early Detection of Cancer/methods* ; Precision Medicine ; Image Processing, Computer-Assisted/methods* ; Tomography, X-Ray Computed ; Radiomics

OBJECTIVES: To develop and validate a preoperative clinical-radiomics model for predicting overall survival (OS) and disease-free survival (DFS) in patients with extrahepatic cholangiocarcinoma (eCCA) undergoing radical resection. METHODS: In this retrospective study, consecutive patients with pathologically-confirmed eCCA who underwent radical resection at our institution from 2015 to 2022 were included. The patients were divided into a training cohort and a validation cohort according to the chronological order of their CT examinations. Least absolute shrinkage and selection operator (LASSO)-Cox regression was employed to select predictive radiomic features and clinical variables. The selected features and variables were incorporated into a Cox regression model. Model performance for 1-year OS and DFS prediction was assessed using calibration curves, area under receiver operating characteristic curve (AUC), and concordance index (C-index). RESULTS: This study included 123 patients (mean age 64.0 ± 8.4 years, 85 males/38 females), with 86 in the training cohort and 37 in the validation cohort. The OS-predicting model included four clinical variables and four radiomic features. It achieved a training cohort AUC of 0.858 (C-index = 0.800) and a validation cohort AUC of 0.649 (C-index = 0.605). The DFS-predicting model included four clinical variables and four other radiomic features. It achieved a training cohort AUC of 0.830 (C-index = 0.760) and a validation cohort AUC of 0.717 (C-index = 0.616). CONCLUSIONS The preoperative clinical-radiomics models show promise as a tool for predicting 1-year OS and DFS in eCCA patients after radical surgery.
Humans ; Male ; Female ; Retrospective Studies ; Middle Aged ; Cholangiocarcinoma/mortality* ; Prognosis ; Bile Duct Neoplasms/mortality* ; Tomography, X-Ray Computed/methods* ; Aged ; Radiomics

OBJECTIVE: To explore the reliability and accuracy of the arbitrary point method for measuring the anteversion angle of acetabular prosthesis after total hip arthroplasty (THA) based on pelvic X-ray films. METHODS: The clinical data of 23 patients (25 hips) who underwent THA between December 2018 and September 2023 and met the selection criteria were retrospectively analyzed. Among them, there were 16 males and 7 females, with an average age of 57.6 years (range, 34-81 years); 13 hips had THA on the left side and 12 on the right side. There were 19 cases (21 hips) of osteonecrosis of the femoral head, 2 cases (2 hips) of femoral neck fractures, 1 case (1 hip) of developmental dysplasia of the hip, and 1 case (1 hip) of osteoarthritis. After THA, all patients underwent X-ray examination and CT scan. Three physicians measured the anteversion angle of acetabular prosthesis using the arbitrary point method and the CT measurement method respectively, and repeated the measurements three times. The results of the two measurement methods were compared, and the intraclass correlation coefficient (ICC) was employed to assess the reproducibility of the methods. RESULTS: The anteversion angles of acetabular prosthesis were (15.87±7.73)° measured by the arbitrary point method, and (15.31±7.89)° measured by CT measurement method. There was no significant difference between the two methods ( t=1.515, P=0.143). The ICC of the measurement results by the arbitrary point method for the three physicians were 0.97 ( P<0.001), 0.96 ( P<0.001), and 0.96 ( P<0.001), respectively; and the ICC of the measurement results by CT method were 0.93 ( P<0.001), 0.93 ( P<0.001), and 0.94 ( P<0.001), respectively. CONCLUSION The arbitrary point method for measuring the anteversion angle of acetabular prosthesis after THA based on pelvic X-ray film is easy to operate, accurate, and has high reproducibility.
Humans ; Arthroplasty, Replacement, Hip/methods* ; Male ; Female ; Aged ; Middle Aged ; Hip Prosthesis ; Acetabulum/surgery* ; Aged, 80 and over ; Retrospective Studies ; Adult ; Reproducibility of Results ; Feasibility Studies ; Tomography, X-Ray Computed

OBJECTIVE: To conduct a comprehensive analysis of proximal humeral anatomical characteristics in the Chinese population utilizing three-dimensional reconstruction technology, thereby establishing an evidence base for the enhancement of shoulder hemiarthroplasty procedures and the development of domestically manufactured prostheses. METHODS: The study cohort comprised 30 patients (60 shoulders) presenting with cervicoscapular pain between July 2023 and June 2025, with equal gender distribution (15 males and 15 females); age distribution ranged from 20 to 75 years (mean, 53.7 years). Data acquisition was performed via high-resolution CT imaging (technical parameters: slice thickness 0.625 mm, voltage 120 kV, current 150 mA, matrix 512×512). Subsequently, CT datasets were processed in DICOM format using Mimics17.0 software for three-dimensional reconstruction, followed by quantitative assessment via Imageware12.0 software to evaluate key proximal humeral parameters: humeral head dimensions (coronal diameter, sagittal diameter, surface curvature diameter, thickness), angular measurements [neck-shaft angle, retroversion angle (retroversion angle 1 was the angle between the humeral head axis and the line connecting the medial and lateral condyles, and retroversion angle 2 was the angle between the humeral head axis and the tangent of the trochlea)], and positional metrics (medial offset, posterior offset). Statistical analysis incorporated Pearson correlation coefficients to determine parameter relationships, with comparative evaluations conducted across demographic variables including gender, height, body mass, and age. RESULTS: Quantitative analysis yielded the following measurements: humeral head coronal diameter (41.8±3.6) mm, sagittal diameter (39.1±4.1) mm, surface curvature diameter (44.9±4.6) mm, thickness (17.2±1.8) mm, neck-shaft angle (128.4±4.2)°, retroversion angle 1 (16.9±8.9)°, retroversion angle 2 (21.4±11.3)°, medial offset (3.8±1.7) mm, and posterior offset (5.1±1.6) mm. Correlation analysis demonstrated the most pronounced positive relationship between humeral head surface curvature diameter and thickness ( r=0.966, P=0.001), with additional significant positive correlations observed between surface curvature diameter and coronal diameter ( r=0.842, P=0.001), posterior offset and retroversion angle 1 ( r=0.766, P=0.001), and coronal diameter and thickness ( r=0.727, P=0.001). Demographic analysis revealed significantly greater dimensions in males compared to females for humeral head surface curvature diameter, coronal diameter, sagittal diameter, and thickness ( P<0.05), with these parameters demonstrating progressive increases corresponding to height ( P<0.05). With the exception of neck-shaft angle, all parameters exhibited a positive correlation with body mass. No significant age-related differences were detected across parameters ( P>0.05). CONCLUSION The proximal humeral morphology in the Chinese population exhibits substantial variability, necessitating optimization of prosthetic designs based on population-specific anatomical metrics to enhance the efficacy of personalized clinical interventions.
Humans ; Middle Aged ; Male ; Female ; Aged ; Imaging, Three-Dimensional/methods* ; Adult ; Tomography, X-Ray Computed/methods* ; Humerus/diagnostic imaging* ; Shoulder Joint/surgery* ; Hemiarthroplasty/methods* ; Humeral Head/anatomy & histology* ; Young Adult ; Clinical Relevance