Objective To explore the diagnostic and differential diagnostic value of spiral CT in traumatic wet lung.Methods CT features of traumatic wet lung in 60 cases were retrospectively analyzed and summarized. Results CT features of wet lung included 4 types:(1)the interstitial type in 10 cases,appeared as thicknees and blurring of the shadow of pulmonary blood vessels;(2)the disseminated consolidation type in 15 cases appeared as scattered mottling or small patch foci with slight high density in pulmonary parenchyma;(3)the mist type in 20 cases appeared as slight high density with ground-glass like cloudy shadow;(4)the type of segmental consolidation in 15 cases appeared as high dense foci with large patch in segmental distribution.Conclusion Spiral CT can well detect the site, type, extent of traumatic wet lung and other injury. It is the most optimal means at present for traumatic wet lung.

BACKGROUND: Clinical diagnosis criteria for rotary subluxation of atlanto-axial joint have not been identified. Many clinical diagnoses are suspicious. Therefore, investigating a new diagnostic method and diagnostic evidence is beneficial to enhancing diagnosis accuracy, and is a study hotspot in the current field.OBJECTIVE: This study aimed to observe the relationship between anatomy of normal human atlanto-axial joint and functional positions, and to provide anatomical basis for the diagnosis of atlanto-axial rotary subluxation.DESIGN: Single sample self-control study.SETTING: Department of Imageology, Zhongshan Hospital Affiliated to Xiamen University.PARTICIPANTS: This study was carried out in the CT Room, Department of Imageology, Zhongshan Hospital Affiliated to Xiamen University between June 2003 and November 2004. Fifty-three volunteers, 27 males and 26 females, with an average age of 31.2 years (range 4.5-75 years), without cervical trauma or history of cervical diseases, were randomly recruited in to this study. Written informed consents were obtained from each subject or their guardians.METHODS:CT helical scanning and three-dimensional reconstruction imaging of atlanto-axial joint in the neutral position were performed in all the volunteers and were supplemented in the right and left rotary position in 21 subjects on a helical CT scanner (somatom plus 4, Siemens, Germany), SUN magic view 1000 workstation and three-dimensional reconstruction software.MAIN OUTCOME MEASURES: ① Lateral atlanto-axial articular facet contraposition. ② Lateral atlanto dental space③ Atlanto dental interval. ④ The largest extent of articular facets dislocation in the right and left rotary positions. ⑤ Head's rotary maximal angle in the right and left rotary positions.RESULTS: ① Three-dimensional images with atlanto-axial joint in the functional position of 53 patients showed that the articular surfaces of lateral atlanto-axial joint had basic contraposition, the width of atlanto-dental interval ranged from 1.0 to 3.3 mm, and the lateral atlanto-dental distance devirated in 24 patients (45.28%).②Three-dimensional images with atlanto-axial joint in the rotary position of 21 patients showed that the width of atlanto dental interval had no change, the articular facets of lateral atlanto-axial joint had rotary displacement with the width ranging from 5.76 to 8.18 mm, and anastomotic area of articular facet was decreased by 1/2 to 2/3. The condyles had deflected to the rotary direction in the patients with symmetrical lateral atlanto dental space, and for 15 patients with asymmetrical lateral atlanto dental space, the condyle deflected slightly or had no changes.CONCLUSION: CT-three dimensional imaging can clearly show the anatomical structure of atlanto-axial joint in functional position and the changing rule of atlanto-axial joint movement, providing theoretical evidence for the clinical diagnosis and the treatment of atlanto-axial subluxation.

Objective To evaluate the clinical value of CT three-dimensional(3-D) imaging on diagnosing of bone and joint diseases.Methods The imaging findings of 357 cases proved by operation or clinic were analyzed retrospectively by the 3-D imaging.The diagnosis accuracy of 3-D imaging was compared with that of routine CT.3-D images were obtained by surface shade display(SSD),multi-planar reconstruction(MPR),maximum intensity project(MIP) and volume rendering(VR).Results In 357 patients,257 cases were traumatic disease,100 cases were non-traumatic disease.The diagnosis accuracy of 3-D imaging was 98.3%,which was higher than that of routine CT(P

Objective To evaluate the techniques of separating, fusing, opacifying and falsecoloring- volume rendering( SFOF-VR)3D imaging on the bone and blood vessels in the region of atlantoaxial joint(AAJ). Methods SFOF-VR 3D imaging was performed on 35 cases, including normal (n = 6),variations and lesions( n =29), which were selected from cases with the head-neck CTA examination. With the original scanning data, the VR imaging was performed, then SFOF-VR imaging. Comparisons were made between the features of 3D images of SFOF-VR and VR in showing the anatomy, diagnosing the variation and lesion of AAJ and VA, and the results were analyzed by using the Fisher exact test. Results The results of 35 cases included 6 normal cases in the region of atlantoaxial joint and VA, 16 variations in atlantoaxial joint or VA, 6 cases with arteriosclerosis in VA and 7 cases with fracture or dislocation of atlantoaxial joint. All SFOF-VR imaging are satisfactory. SFOF-VR imaging can show the atlas, epistropheus and VA independently or jointly, cut or color the structures freely and get 3D measurements in any direction.Comparisons showed that SFOF-VR images were much clearer and more comprehensive than VR in displaying the anatomy, variation and lesions of AAJ and VA. Statistical differences were found in showing the spatial structure, blocking structure, space measurement, variation and disease and image edge ( there are25,7,3,0, 31,3,1,0, 26,3,6,0, 23,7,5,0, 0,10,20,5 cases for SFOF-VR image with excellent,good, ordinary and poor quality, and 2,5,24,4, 0,2,8,25, 0,7,25,3, 5,6,23,1, 29,4,2,0 cases for VR;χ2 values 40. 259, 61.444, 42.245, 24.220, 51.299, P<0. 01 ). Conclusions SFOF-VR is a new 3D imaging technique which can clearly and directly show the bones and blood vessels in the region of AAJ.It can provide anatomical information for imaging diagnosis and surgical operation.

Objective:To establish and verify the prediction model of benign or malignant of solitary pulmonary nodules (SPNs≤20 mm) based on artificial intelligence.Methods:Totally 338 SPNs (≤20 mm) from 279 patients, confirmed by operation and pathology, were selected in Zhongshan Hospital Xiamen University from November 2018 to May 2020. Clinical data (age, gender, smoking history, individual and family history of malignancy), image features (maximum diameter, minimum diameter, solid proportion, volume, lobulation sign, burr sign, vacuole sign, cavity sign, pleural indentation sign, and radiomic features (maximum CT value, minimum CT value, average CT value, median CT value, CT value standard deviation, skewness, peak, energy, entropy) were analyzed retrospectively. All the data of patients were randomly divided into training set (271 SPNs) and test set (67 SPNs). In the training set, the clinical, image features and radiomic features were first selected by the least absolute shrinkage and selection operator (LASSO) regression, then the independent risk factors of SPN (≤20 mm) were screened out by multi-variate logistic regression analysis, and the nomogram prediction models were constructed. Finally, the data of test set were used to verify the prediction model by the ROC curve and calibration curve (CC).Results:In the training set of 271 SPNs, 81 SPNs were benign and 190 malignant. After analysis of LASSO regression and multi-factor logistics regression, the independent predictors of benign or malignant SPN were age, gender, largest diameter, vacuole sign and solid proportion. The prediction model was P=e x/(1+e x), x=-2.583+0.027×age+1.519×gender+0.127×maximum diameter-2.132×solid proportion+1.720×vacuole sign. The results of the model showed that the area under curve (AUC) of ROC was 0.850, and the sensitivity was 73.7%, specificity was 82.7% and accuracy was 82.3%. In the test set of 67 SPNs, 22 SPNs were benign and 45 malignant. The results showed that the AUC of ROC was 0.882, and the sensitivity was 82.2%, specificity was 81.8% and accuracy was 85.1%. The calibration nomogram of prediction model showed that CC from training set or test set well coincided with its individual ideal curve ( Ptraining=0.688, Ptest=0.618). Conclusion:Prediction model of benign or malignant SPN ≤20 mm is established based on AI; it can obtain the prediction probability and has good diagnostic efficiency.