ObjectiveTo investigate the diagnostic value of spectral saturation inversion recovery, gradient-echo chemical shift MRI， and proton magnetic resonance spectroscopy in quantifying hepatic fat content. MethodsConventional T1-weighted and T2-weighted scanning (without fat saturation and with fat saturation)， gradient-echo T1W in-phase （IP） and opposedphase （OP） images and 1H-MRS were performed in 31 healthy volunteers and 22 patients who were candidates for liver surgery. Signal intensities of T1WI amp; T1WIFS (SInonfat1, SIfat1), T2WI amp; T2WI-FS (SInonfat2, SIfat2), and IP amp; OP (SIin, SIout) were measured respectively, the relative signal intensity one (RSI1), relative signal intensity two (RSI2)， and fat index (FI) were calculated. Peak values and the area under peak of 1H-MRS were measured, and the relative lipid content of liver cells (RLC ) were calculated. Twenty-two patients accepted liver resection and histological examination after MRI scanning, the proportion of fatty degenerative cells were calculated by image analysis software. Results①Hepatic steatosis group showed higher average values of RSI1, FI， and RLC to non-hepatic steatosis group (Plt;0.05), while there was no significant difference in RSI2 between two groups (Pgt;0.05). ②There was a statistical significant difference in RLC among different histopathological grades of hepatic steatosis, and RLC increased in parallel with histopathological grade (Plt;0.05).There was no significant difference in RSI2, RSI1, and FI among different histopathological grades, although the latter two had a tendency of increasing concomitant with histopathological grade (Pgt;0.05). ③The values of FI and RLC were positively correlated with the PFDC （r=0468, P=0.027； r＝0771， Plt;0.000 1）, while they were not in RSI1 and RSI2 (r=0.411, P=0.057； r=0.191, P=0.392). ConclusionsSPIR, Gradient-echo chemical shift MRI and 1H-MRS can help to differentiate patients with hepatic steatosis from normal persons, the latter also can help to classify hepatic steatosis. In quantifying hepatic fat content, 1H-MRS is superior to gradient-echo chemical shift MRI, while SPIR’s role is limited.
ST segment morphology is closely related to cardiovascular disease. It is used not only for characterizing different diseases, but also for predicting the severity of the disease. However, the short duration, low energy, variable morphology and interference from various noises make ST segment morphology classification a difficult task. In this paper, we address the problems of single feature extraction and low classification accuracy of ST segment morphology classification, and use the gradient of ST surface to improve the accuracy of ST segment morphology multi-classification. In this paper, we identify five ST segment morphologies: normal, upward-sloping elevation, arch-back elevation, horizontal depression, and arch-back depression. Firstly, we select an ST segment candidate segment according to the QRS wave group location and medical statistical law. Secondly, we extract ST segment area, mean value, difference with reference baseline, slope, and mean squared error features. In addition, the ST segment is converted into a surface, the gradient features of the ST surface are extracted, and the morphological features are formed into a feature vector. Finally, the support vector machine is used to classify the ST segment, and then the ST segment morphology is multi-classified. The MIT-Beth Israel Hospital Database (MITDB) and the European ST-T database (EDB) were used as data sources to validate the algorithm in this paper, and the results showed that the algorithm in this paper achieved an average recognition rate of 97.79% and 95.60%, respectively, in the process of ST segment recognition. Based on the results of this paper, it is expected that this method can be introduced in the clinical setting in the future to provide morphological guidance for the diagnosis of cardiovascular diseases in the clinic and improve the diagnostic efficiency.