Objective To study the application value of 1.5T MRI whole-body PET-like imaging in the chest,abdominal and pelvic primary occupying lesions.Methods Whole-body PET-like imaging were performed in 5 7 patients with chest,abdominal and pelvic primary occupying lesions.The detection rate of whole-body PET-like in chest,abdominal and pelvis primary occupying lesions were compared.Apparent diffusion coefficient (ADC)values for benign and malignant lesions were compared and analyzed.Results (1) All of 71 cases of chest,abdominal and pelvic primary occupying lesions,67 lesions (94.4%)were diagnosed by whole-body PET-like imaging. Among them,31(93.9%)of malignant lesions were detected and 36(94.7%)benign lesions were detected.There was not statistically significant of detection rate between benign and malignant lesions(P>0.05).(2)The range of ADC value:malignant tumor was (0.83-1.57)× 10-3mm2/s,among which 27 lesions ADC value less than 1.1×10-3mm2/s,accounting for 87.1%;The benign lesion was (1.41-3.16)× 10-3mm2/s,among which the total 32 lesion ADC values of liver cyst(13),hepatic hemangioma(9)and renal cyst(10)were greater than 2.0×10-3mm2/s,accounting for 88.9%.There was significantly different of ADC values between benign and malignant lesions (P<0.05).Conclusion There is higher detection rate on primary chest,abdominal and pelvic space occupying lesions for whole-body PET-like imaging,which is suitable for tumor screening in healthy population.It is helpful to differentiate benign and malignant tumors by quantitative analysis of ADC value.

Objective To explore the accuracy and clinical application value of artificial intelligence(AI)-based coronary computed tomography angiography(CCTA)in the evaluation of coronary artery stenosis in patients with acute coronary syndrome(ACS).Methods Fifty-four patients with suspected ACS who underwent CCTA examination and invasive coronary angiography(ICA)within 72 h were retrospectively selected.The CCTA images of all patients were processed by AI(AI group)and manual post-pro-cessing(manual group),respectively.The image quality,work efficiency and detection rate of coronary artery stenosis were compared between AI group and manual group.With ICA results as the gold standard,the sensitivity,specificity,positive predictive value,negative predictive value and accuracy of AI in the diagnosis of ACS patients with coronary artery stenosis(≥50％)in CCTA were analyzed,and the consistency of AI and ICA examination results was tested.Results The image quality of CCTA in AI group(grade Ⅰ 27.8％)was better than that in manual group(grade Ⅰ 14.8％),but there was no statistical difference between the two groups(X2=2.707,P＞0.05).The average diagnosis time of AI group(89.67 s±33.21 s)was significantlyshorter than that of manual group(813.33 s±301.84 s)and the difference was statistically significant(t=-17.512,P＜0.001),and the average time gain rate was 88.97％.There was no statistical difference in the detection rate of coronary artery stenosis(≥50％)between AI group and manual group(x2=0.003,P＞0.05).The sensitivity,specificity,positive predictive value,negative predictive value,and accuracy of AI in diagnosis of ACS were 87.60％,96.44％,80.30％,97.92％,and 95.19％,respectively,which were significantly consistent with the results of ICA examina-tion(Kappa=0.810,P＜0.05).Conclusion AI-assisted diagnosis can correctly identify the coronary artery tree with better image,significantly shorten the diagnosis time of CCTA in ACS patients with high accuracy,and can provide a strong basis for the early treat-ment of patients with acute chest pain.