Background: Carotid plaque morphology and tissue composition help assess risk stratification of stroke events. Many post-processing image techniques based on CT and MR images have been widely used in related research, such as image segmentation, 3D reconstruction, and computer fluid dynamics. However, the criteria for the 3D numerical model of carotid plaque established by CT and MR angiographic image data remain open to questioning.Method: We accurately duplicated the geometry and simulated it using computer software to make a 3D numerical model. The initial images were obtained by CTA and TOF-MRA. MIMICS (Materialize’s interactive medical image control system) software was used to process the images to generate three-dimensional solid models of blood vessels and plaques. The subsequent output was exported to the ANSYS software to generate finite element simulation results for the further hemodynamic study.Results: The 3D models of carotid plaque of TOF-MRA and CTA were simulated by using computer software. CTA has a high-density resolution for carotid plaque, the boundary of the CTA image is obvious, and the main component of which is a calcified tissue. However, the density resolution of TOF-MRA for the carotid plaque and carotid artery was not as good as that of CTA. The results show that there is a large deviation between the TOF-MRA and CTA 3D model of plaque in the carotid artery due to the unclear recognition of plaque boundary during 3D reconstruction, and this can further affect the simulation results of hemodynamics.Conclusion: In this study, two-dimensional images and three-dimensional models of carotid plaques obtained by two angiographic techniques were compared. The potential of these two imaging methods in clinical diagnosis and fluid dynamics of carotid plaque was evaluated, and the selectivity of image post-processing analysis to original medical image acquisition was revealed.

BACKGROUND Primary pancreatic paraganglioma is exceedingly rare. Most patients with pancreatic paraganglioma lack a typical clinical presentation, and the tumor is difficult to accurately differentiate from other pancreatic neuroendocrine tumors, making the misdiagnosis rate extremely high. Surgical excision is the primary treatment modality but is considered high risk. Because of its rich vascularity, the tumor easily bleeds during surgery, especially malignant paragangliomas invading large blood vessels. Thus, a thorough preoperative evaluation of the tumor is necessary. Here, we report a primary malignant pancreatic paraganglioma, the second such case in a young patient that was successfully resected surgically. CASE SUMMARY A 26-year-old female patient was admitted to the hospital with unexplained abdominal pain. Dual-layer spectral-detector computed tomography (DLCT) revealed a mixed density mass in the pancreatic body and tail. The patient was transferred to our hospital after previous failed surgical resection at other hospitals. The patient and her family strongly desired surgery. After a thorough preoperative evaluation and adequate preparation, a large mass with the greatest dimension of 8.0 cm was successfully resected. The final pathological diagnosis was malignant paraganglioma. The patient was discharged in good condition 2 wk postoperatively. CONCLUSION The rare malignant pancreatic paraganglioma reported here was difficult to diagnose preoperatively. Early filling of the draining vein may be a crucial diagnostic imaging feature. DLCT can provide more precise information for surgical resection through dual-energy imaging.

IntroductionPreoperative diagnosis of benign and malignant thyroid nodules is crucial for appropriate clinical treatment and individual patient management. In this study, a double-layer spectral detector computed tomography (DLCT)-based nomogram for the preoperative classification of benign and malignant thyroid nodules was developed and tested. MethodsA total of 405 patients with pathological findings of thyroid nodules who underwent DLCT preoperatively were retrospectively recruited. They were randomized into a training cohort (n=283) and a test cohort (n=122). Information on clinical features, qualitative imaging features and quantitative DLCT parameters was collected. Univariate and multifactorial logistic regression analyses were used to screen independent predictors of benign and malignant nodules. A nomogram model based on the independent predictors was developed to make individualized predictions of benign and malignant thyroid nodules. Model performance was evaluated by calculating the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis(DCA). ResultsStandardized iodine concentration in the arterial phase, the slope of the spectral hounsfield unit(HU) curves in the arterial phase, and cystic degeneration were identified as independent predictors of benign and malignant thyroid nodules. After combining these three metrics, the proposed nomogram was diagnostically effective, with AUC values of 0.880 for the training cohort and 0.884 for the test cohort. The nomogram showed a better fit (all p > 0.05 by Hosmer−Lemeshow test) and provided a greater net benefit than the simple standard strategy within a large range of threshold probabilities in both cohorts. DiscussionThe DLCT-based nomogram has great potential for the preoperative prediction of benign and malignant thyroid nodules. This nomogram can be used as a simple, noninvasive, and effective tool for the individualized risk assessment of benign and malignant thyroid nodules, helping clinicians make appropriate treatment decisions.