Objective To evaluate the application value of spiral CT virtual endoscopy and three dimensional imaging in fiberoptic bronchoscopic balloon dilation in patients with benign tracheobronchial stenosis. Methods Thirty-three cases of benign tracheobronchial stenosis from June 2004 to November 2008 were checked by spiral CT with airway tracheobronchial reconstruction. For the patients with indications, balloon dilatation was performed under fiberoptic bronchoscope. The three-dimensional reconstruction images were compared with the findings under bronchoscopy. And the preoperative and postoperative three-dimensional reconstruction images were compared for airway diameter. Results Three cases were found stenosis of middle lobe by CT virtual endoscopy and did not undergo balloon dilatation. The remaining 30 cases were confirmed by bronchoscopy findings similar to the images by tracheobronchial reconstruction with CT, with consistent rate of 100% . Immediate postoperative three-dimensional CTreconstruction of tracheal bronchus revealed that diameter of stenotic bronchus increased from ( 2. 7 ±1. 3) mm to ( 6. 9 ±1. 6) mmafter operation. Conclusion Multislice spiral CT virtual endoscopy is helpful in fiberoptic bronchoscopic balloon dilation in patients with benign tracheobronchial stenosis and postoperative follow-up.
Choroidal vascularity index （CVI）, as a new biological parameter to quantitatively evaluate the state of choroidal vessels, has shown great potential in the diagnosis and treatment of ophthalmic diseases in recent years. CVI primarily calculated from images obtained via optical coherence tomography (OCT) and OCT angiography, demonstrates enhanced accuracy, stability, and clinical value with the advancement of three-dimensional imaging and artificial intelligence technologies. Compared with two-dimensional CVI, three-dimensional CVI comprehensively reflects the spatial distribution and structural changes of choroidal blood vessels by constructing three-dimensional choroidal models through ultra-widefield scanning. In various ophthalmic diseases, including age-related macular degeneration, central serous chorioretinopathy, diabetic retinopathy, and pathological myopia, CVI exhibits characteristic changes that not only contribute to understanding disease pathogenesis but also serve as indicators for early screening, individualized treatment, and efficacy monitoring. The application of artificial intelligence and deep learning technology improves the efficiency of automated CVI analysis, while integration with multimodal imaging further optimizes disease evaluation. Future efforts should focus on establishing standardized measurement protocols and quality control systems to promote its broader application and development in ophthalmology.