CT colonography (CTC) has been widely used in diagnosis of colon diseases. Computer-aided detection (CAD) automatically detects the locations of suspicious lesions on CTC, and provides radiologists with a second opinion. CAD has the potential to increase radiologists' diagnostic performance in the detection of lesions and to decrease variability of the diagnostic accuracy among readers. The current fundamental scheme, the key techniques used for detection of lesions on CTC, the detection performance, as well as the pitfalls, challenges, and the future of CAD were reviewed in this article.

Objective To evaluate the diagnostic value of tracking movement of normal patellar using volume scan on sensation 320 CT. Method Data of dynamic scans of 30 knees was collected using the motor function of 320 CT and retrospectively analyzed. The data of movement of the patellorfemoral joint was obtained during flexion (from 0° to 120°) within 10-sec by 320 CT from all volunteers. The 3D coordinate of the center of patella was recorded to investigate the dispose relation of patellofemoral joint.Result With the knee angle changed from 0° to 90°, the patella moved rapidly along the Y-axis direction ( sagittal plane) down about (53.87 ± 0. 45 ) mm, and then entered the plateau phase with little change.When the knee flexion reached 10°-30° ,the patellar movement along the X-axis reached the largest range of (2. 31 ±0. 52)-(3.36 ± 0. 43 ) mm, and subsequently moved to the opposite lateral direction with the maximum about (8. 53 ± 0. 44 ) mm at 120°. In the Z axis, the track initially showed plateau, and then presented a rapidly downward trend after 30°. The patellar tracking is like an outward arc during the whole knee flexion. Conclusion The motor functional imaging of 320 CT can pinpoint the patelar tracking in a fast, painless way.

Objective To investigate CT and MRI findings of rare cystic disease of the pancreas.Methods Elven cases with rare cystic disease of the pancreas underwent plain and contrast-enhanced CT and MR imaging before operation were reviewed.The clinical presentations and imaging findings were analysed.Among eleven cases,four were epidermoid,four were lymphepithelial cyst and three were lymphangioma.Results (1) Epidermoids located in the tail of the pancreas with smooth wall,the density of parenchyma of the lesons was the same as spleen at CT plain scan.On contrast-enhanced CT and MRI,the parenchyma of the lesions showed the same enhanced pattern with spleen.(2)Lymphepithelial cysts often occurred in olderly men.The lesions appeared as multilocular masses with definite border,isodensity at CT plain scan,and mixed iso-hyperintensity on both T_1WI and T_2WI images.After injection of contrast medium,the wall and septum of the lesions were enhanced.(3)Lymphangiomas were multilocular cystic lesion in the body-tail of the pancreas,hypodense at CT plain scan,and long T_1 and T_2 signal intensity at MRI plain scan.The septum and wall of the lesions were slightly enhanced on contrast-enhanced images.The lesions were not communicated with the pancreatic duct but pancreatic ducts were compressed and slightly shifted.Conclusion CT and MRI findings of rare neoplastic cystic disease of the pancreas are of certain characteristics.

Three kinds of PEGylated gold nanoparticles (PEG-Au NPs) with different surface charges are prepared by assembly of thiolated polyethylene glycol (HS-PEG) with different terminal groups including methoxy, amino or carboxyl on gold nanoparticle surface through sulfur-gold covalent bond, respectively.The experimental results of cell co-culturing and tail intravenous injection in mouse indicate that the biological behaviors of PEG-Au NPs are affected significantly by their surface charges.The cellular internalization amounts of PEG-Au NPs are following the order, positive charge ＞ neutral charge ＞ negative charge.The PEG-Au NPs are gradually transferred to liver and spleen from main organs through the circulation of blood after tail intravenous injection in mouse.The negatively charged PEG-Au NPs have the slowest hepatic clearance rate while the positively charged PEG-Au NPs can cause the strongest response of immune system in mice.

Objective To investigate the diagnostic value of random forest(RF)model based on CT images and clinical data for preoperative T staging of colorectal cancer. Methods Four hundred and fifty patients with colorectal cancer who were pathologically confirmed by surgery and underwent preoperative CT examinationinthe first hospital of Jilin university from January 2016 to July 2016 were included retrospectively(Stage≤T2,T3,and T4 each has 150 cases).According to the ratio of 2:1,the patients were divided into training set(300 cases)and test set(150 cases,stage ≤T2,T3,and T4 each has 50 cases)by computer random software. Each of 450 patients had one lesion. All the patients underwent preoperative abdominal and pelvic contrast-enhanced CT scan.The clinical,imaging and pathological data[gender,age, carcinoembryonic antigen (CEA) level, carbohydrate antigen 19-9 (CA19-9) expression, intestinal wall deformation, maximum diameter of tumorand thickness of intestinal wall, location, enhancement homogeneity and enhancement rate]of these patients were collected.The correlation between the collected factors and pathological T staging was analyzed by Spearman correlation analysis.The preoperative staging model of colorectal cancer was established by RF algorithm in the training set.Two kinds of methods(model and traditional method)were used to diagnose T stage of the patients in the test set.The accuracy of the two methods was calculated by postoperative pathological staging as the gold standard.The consistency test was used to evaluate the consistency of the RF model results with the pathological results. Results T-staging was positively correlated with CEA, CA19-9, intestinal wall deformation, tumor size and thickness of intestinal wall(r=0.449,0.291,0.624,0.573,0.386;P<0.05).Age,location,enhancement homogeneity and enhancement rate were slightly negatively correlated with T-staging(r=-0.115,-0.245,-0.120 and-0.339;P<0.05).The predictive results of the model in≤T2,T3,and T4 stage cancers were moderately and highly consistent with the standard of pathology,and the Kappa value were 0.769,0.615 and 0.800,respectively.The total accuracy rate of the model andthe traditional method are 80.7%(121/150)and 54.0%(81/150). Conclusion Application of random forest model based on multi-slice spiral CT images and clinical data can improve the diagnostic efficacy of preoperative T stage of colorectal cancer.

Advances in virtual and augmented reality technology have shown its potential for medical education and clinical application. The purpose of this review is to summarize how medical colleges and universities have explored in virtual and augmented reality technology for Medical Imaging in China and other countries. Applications of virtual and augmented reality technology for diagnostic radiology education can enrich the teaching contents, expand the space and time of teaching, and help students in analysis and diagnosis. In interventional radiology education, students can experience the medical environment which is close to the real one, simulate the surgical process, optimize the teaching resources and improve the teaching quality. With the comprehensive utilization of the Internet, big data, artificial intelligence and new computer technologies, virtual and augmented reality technology will bring a new revolution to future medical education.

Objective:To explore the current status of the artificial intelligence (AI) developments in medical imaging in China, and to provide data for the development of AI.Methods:In May 2022, the Radiology Branch of the Chinese Medical Association and the China Medical Imaging AI Industry-University-Research Innovation Alliance jointly launched a nationwide survey on the application status and development needs of medical imaging AI in China in the form of a questionnaire. This survey was carried out for different groups of people, focusing on the clinical applications of medical imaging AI, enterprise development, and educational needs in colleges and universities, with the descriptive statistical analysis performed.Results:China′s medical imaging AI has made great progress in clinical applications, in enterprise developments, as well as in the education and teaching areas. In terms of clinical application, 90.8% (5 765/6 347) of the survey respondents had a preliminary understanding of AI. There were 62.1% (3 798/6 119) doctors confirmed the applications medical imaging AI products in their departments. AI products were applied in the whole process of medical imaging examination, especially in assistance of the diagnosis. The application of pulmonary nodules screening accounted for 89.5% (3 401/3 798) of all medical imaging AIs. The main factors restricting the rapid development of medical imaging AI included lack of experts [47.3% (3 002/6 347)], poor data quality [45.7% (2 898/6 347)] and imperfect function of the products [40.4% (2 566/6 347)]; in terms of enterprises, there were 65.4% enterprises with a scale of less than 100 employees (17/26), and 34.6% with a scale of more than 100 employees (9/26). The main group of the customers were the hospitals above the second level, accounting for about 92.3% (24/26); in terms of education, the number and quality of AI courses, practical operations and lectures currently carried out by schools vary between different levels. The AI courses for graduated students accounted for about 22.5% (86/381), which were the largest in number; while the proportion of AI courses for junior college students, undergraduates and regular trainees were less than 15%. More than 60% of the students thought it necessary for schools to establish AI courses. Among all the students, the master′s and doctoral candidates had the greatest demand for additional AI courses [84.8% (323/381)].Conclusions:The development and popularization of medical imaging AI in China continues to prosper, with opportunities and challenges coexisting. It is necessary to adhere to the orientation of clinical needs, and to realize the coordinated development of clinical application, enterprise development, as well as education and teaching.

Objective:To establish a clinical diagnostic scoring model for preoperative predicting hepatocellular carcinoma (HCC) microvascular invasion (MVI) based on gadolinium-ethoxybenzyl-diethylenetriamine pentacetic acid (Gd-EOB-DTPA) enhanced MRI, and verify its effectiveness.Methods:From January 2014 to December 2020, a total of 251 cases with pathologically confirmed HCC from Tianjin First Central Hospital and Jilin University First Hospital were retrospectively collected to serve as the training set, while 57 HCC patients from Tianjin Medical University Cancer Hospital were recruited as an independent external validation set. The HCC patients were divided into MVI positive and MVI negative groups according to the pathological results. The tumor maximum diameters and apparent diffusion coefficient (ADC) values were measured. On the Gd-EOB-DTPA MRI images, tumor morphology, peritumoral enhancement, peritumoral low intensity (PTLI), capsule, intratumoral artery, intratumoral fat, intratumoral hemorrhage, and intratumoral necrosis were observed. Univariate analysis was performed using the χ 2 test or the independent sample t-test. The independent risk factors associated with MVI were obtained in the training set using a multivariate logistic analysis. Points were assigned to each factor according to the weight value to establish a preoperative score model for predicting MVI. The receiver operating characteristic (ROC) curve was used to determine the score threshold and to verify the efficacy of this scoring model in predicting MVI in the independent external validation set. Results:The training set obtained 98 patients in the MVI positive group and 153 patients in the MVI negative group, while the external validation set obtained 16 patients in the MVI positive group and 41 patients in the MVI negative group. According to logistic analysis, tumor maximum diameter>3.66 cm (OR 3.654, 95%CI 1.902-7.018), hepatobiliary PTLI (OR 9.235, 95%CI 4.833-16.896) and incomplete capsule (OR 6.266, 95%CI 1.993-9.345) were independent risk factors for MVI in HCC, which were assigned scores of 3, 4 and 2, respectively. The total score ranged from 0 to 9. In the external validation set, ROC curve analysis showed that the area under the curve of the scoring model was 0.918 (95%CI 0.815-0.974, P=0.001). When the score>4 was used as the threshold, the accuracy, sensitivity, and specificity of the model in predicting MVI were 84.2%, 81.3%, and 85.4%, respectively. Conclusions:A scoring model based on Gd-EOB-DTPA-enhanced MRI provided a convenient and reliable way to predict MVI preoperatively.