OBJECTIVE: To summarize the clinical features and management of two brothers affected with X-linked inhibitor of apoptosis protein (XIAP) deficiency. METHODS: This study retrospectively analyzed the clinical presentations, treatment, and follow-up of two brothers with XIAP deficiency diagnosed at Shanghai Children's Hospital in 2020, and summarized similar cases recorded in databases such as PubMed, Wanfang, Chinese Medical Association Journals, and WIP from January 2006 to November 2024. This study was approved by the Medical Ethics Committee of our hospital (Ethics No.: 2025R128-E01). RESULTS: Patient 1 was the younger brother, who presented at 8 years of age with growth retardation, folliculitis, erythema nodosum, and perineal abscess. Sequencing revealed that he has carried a hemizygous c.566T>C (p.Leu189Pro) variant of the XIAP gene, which was inherited from his mother. He was allergic to infliximab treatment and underwent allogeneic stem cell transplantation (HSCT) in January 2021. During a follow-up of 3 years and 10 months post-transplantation, he showed no gastrointestinal symptoms and had a good outcome. Patient 2 was the elder brother, who presented at 10 years and 6 months of age with growth retardation, rash, and anal fistula. Genetic testing revealed the same variant. He was treated with oral azathioprine but did not have regular follow-ups. At 14-years-and-6-months of age, he had developed severe gastrointestinal infection and hemophagocytic lymphohistiocytosis, which was alleviated after treatment with antibiotics, glucocorticoids, immunoglobulin, and rituximab. He is currently being prepared for HSCT. A total of 13 publications were retrieved, which involved 64 patients from 23 families, with 23 different variants identified. The main clinical manifestations included splenomegaly (34 cases, 53.1%), hemophagocytic lymphohistiocytosis (27 cases, 42.2%), and inflammatory bowel disease or colitis (20 cases, 31.8%). There were significant phenotypic differences among patients from the same family. Thirteen patients (20.3%) underwent HSCT, with a survival rate of 61.5%. CONCLUSION For male children with early onset, poor treatment response, especially those with unexplained splenomegaly and IBD-like symptoms, early genetic testing is recommended. HSCT is a safe and effective treatment for XIAP deficiency. For patients with developmental delay, early onset, and severe IBD phenotype, early transplantation is recommended.
Humans ; Male ; X-Linked Inhibitor of Apoptosis Protein/deficiency* ; Child ; Genetic Diseases, X-Linked/therapy* ; Phenotype ; Siblings ; Retrospective Studies ; Hematopoietic Stem Cell Transplantation

Introduction: A negative computed tomography (CT) scan does not preclude an insulinoma, as small lesions frequently remain undetected on conventional imaging. This case highlights the indispensable role of endoscopic ultrasound (EUS)— not just for localizing occult tumors, but as a definitive, minimally invasive therapeutic alternative to high-risk surgical resection. Case: A 39-year-old female with underlying hypertension presented with a 5-month history of predominantly fasting hypoglycemia (glucose <3.0 mmol/L) and neuroglycopenic symptoms, fulfilling Whipple’s triad. A supervised 72-hour fast confirmed endogenous hyperinsulinemic hypoglycemia at 31 hours, with a nadir glucose of 1.4 mmol/L, insulin 116 pmol/L, and C-peptide 821 pmol/L. Notably, contrastenhanced CT of the pancreas was reported as normal. To overcome this, EUS was performed, successfully identifying a hidden 19 × 18 mm lesion in the head of the pancreas, intimately abutting the main pancreatic duct. Despite medical therapy with diazoxide and strict dietary modifications, her hypoglycemia remained refractory. Given the tumor’s proximity to the main pancreatic duct, surgical enucleation carried a prohibitively high risk of complications. Consequently, she underwent EUS-guided radiofrequency ablation (RFA). Immediate post-procedure outcomes demonstrated near-complete resolution of the hypoglycemic episodes. Diazoxide was subsequently stopped. Outpatient continuous glucose monitoring confirmed sustained normoglycemia and marked symptom resolution, with no procedure-related complications. Conclusion The absence of a pancreatic lesion on CT demands persistent clinical suspicion in cases of biochemically proven hypoglycemia. EUS remains paramount for detecting occult lesions missed by standard imaging. Importantly, EUSRFA serves as a highly effective, tissue-sparing alternative to surgical resection for insulinomas, especially when conventional surgery poses prohibitive anatomical risks.
Insulinoma ; Tomography, X-Ray Computed

Introduction: Insulinoma is a rare functioning pancreatic neuroendocrine tumor and the most common cause of endogenous hyperinsulinemic hypoglycemia. Although biochemical confirmation is usually straightforward, tumor localization may be difficult when lesions are small and not detected on conventional cross-sectional imaging. In such cases, endoscopic ultrasound (EUS) plays an important role in identifying occult lesions and facilitating definitive treatment. Case: A 52-year-old female was admitted in April 2025 with recurrent seizures secondary to hypoglycemia for 3 years, with increasing frequency over time. She fulfilled Whipple’s triad, with documented capillary glucose of 1.8 mmol/L during an episode and symptom resolution following glucose administration. A supervised prolonged fasting test confirmed endogenous hyperinsulinemic hypoglycemia, with plasma glucose 1.4 mmol/L, insulin 122 pmol/L, and C-peptide 1,010 pmol/L. Short Synacthen test demonstrated adequate adrenal reserve. Due to persistent hypoglycemia, she required high-dose diazoxide. Contrast-enhanced computed tomography abdomen did not reveal a pancreatic lesion but incidentally detected a right ovarian teratoma. She underwent total abdominal hysterectomy and bilateral salpingo-oophorectomy in June 2025, with histopathology confirming a mature cystic teratoma without malignancy. However, hypoglycemic episodes persisted. Further evaluation with EUS in July 2025 identified a highly vascular isoechoic 8 × 8 mm lesion in the pancreatic body. Fine-needle biopsy confirmed a well-differentiated neuroendocrine tumor (WHO grade 1) with Ki-67 index of 2%. She subsequently underwent EUSguided radiofrequency ablation in August 2025. Follow-up EUS in December 2025 showed post-ablation change, and her hypoglycemic episodes resolved completely, allowing diazoxide to be discontinued. Conclusion This case highlights the diagnostic challenge of occult insulinoma in the presence of negative conventional imaging. EUS was pivotal for tumor localization and tissue diagnosis, while EUS-guided radiofrequency ablation provided effective minimally invasive treatment in a carefully selected patient.
Insulinoma ; Tomography, X-Ray Computed

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

In order to investigate the mechanical response of lumbar vertebrae during gait cycle in adolescents with idiopathic scoliosis (AIS), the present study was based on computed tomography (CT) data of AIS patients to construct model of the left support phase (ML) and model of the right support phase (MR), respectively. Firstly, material properties, boundary conditions and load loading were set to simulate the lumbar vertebra-pelvis model. Then, the difference of stress and displacement in the lumbar spine between ML and MR was compared based on the stress and displacement cloud map. The results showed that in ML, the lumbar stress was mostly distributed on the convex side, while in MR, it was mostly distributed on the concave side. The stress of the two types of stress mainly gathered near the vertebral arch plate, and the stress of the vertebral arch plate was transmitted to the vertebral body through the pedicle with the progress of gait. The average stress of the intervertebral tissue in MR was greater than that in ML, and the difference of stress on the convex and convex side was greater. The displacement of lumbar vertebrae in ML decreased gradually from L1 to L5. The opposite is true in MR. In conclusion, this study can accurately quantify the stress on the lumbar spine during gait, and may provide guidance for brace design and clinical decision making.
Humans ; Lumbar Vertebrae/diagnostic imaging* ; Scoliosis/diagnostic imaging* ; Adolescent ; Gait/physiology* ; Biomechanical Phenomena ; Tomography, X-Ray Computed ; Stress, Mechanical ; Female ; Male

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

Accurate detection of cephalometric landmarks is crucial for orthodontic diagnosis and treatment planning. Current landmark detection methods are mainly divided into heatmap-based and regression-based approaches. However, these methods often rely on parallel computation of multiple models to improve accuracy, significantly increasing the complexity of training and deployment. This paper presented a novel regression method that can simultaneously detect all cephalometric landmarks in high-resolution X-ray images. By leveraging the encoder module of Transformer, a dual-encoder model was designed to achieve coarse-to-fine localization of cephalometric landmarks. The entire model consisted of three main components: a feature extraction module, a reference encoder module, and a fine-tuning encoder module, responsible for feature extraction and fusion of X-ray images, coarse localization of cephalometric landmarks, and fine localization of landmarks, respectively. The model was fully end-to-end differentiable and could learn the intercorrelation relationships between cephalometric landmarks. Experimental results showed that the successful detection rate (SDR) of our algorithm was superior to other existing methods. It attained the highest 2 mm SDR of 89.51% on test set 1 of the ISBI2015 dataset and 90.68% on the test set of the ISBI2023 dataset. Meanwhile, it reduces memory consumption and enhances the model's popularity and applicability, providing more reliable technical support for orthodontic diagnosis and treatment plan formulation.
Cephalometry/methods* ; Humans ; Algorithms ; Anatomic Landmarks/diagnostic imaging* ; Image Processing, Computer-Assisted/methods* ; X-Rays