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Objective To evaluate the diagnostic value of cardiac magnetic resonance (CMR) in patients with severe connective tissue disease-associated pulmonary hypertension (CTD-PAH). Methods A total of 48 patients diagnosed with CTD-PAH by right heart catheterization (RHC) in Tianjin Medical University General Hospital from June 2018 to July 2021 were retrospectively included. The parameters of right ventricular (RV) morphology, function and ventricular septum (IVS) were obtained by manual delineation on CMR images and corrected by body surface area. The late gadolinium enhancement (LGE) myocardial mass (MM) and its percentage in left ventricular (LV) MM were manually sketched and calculated on LGE images. The patients were divided into the mild-moderate group and the severe group according to mean pulmonary arterial pressure (mPAP). CMR parameters were compared between the two groups. The diagnostic value of CMR for severe CTDPAH was analyzed by receiver operating characteristic (ROC) curve. Results A total of 48 patients with CTD-PAH were included in this study. The curvature of interventricular septum (CIVS) and RV ejection fraction (EF) were lower in the severe group than those in the mild-moderate group, and the time proportion of IVS deformation, RV end-diastolic volume index (EDVI), RV end-systolic volume index (ESVI) and RV MM were higher than those in the mild-moderate group (P＜ 0.05). ROC curve analysis showed that RV MM, time proportion of IVS deformation and RV ESVI had better diagnostic efficacy in severe CTD-PAH patients (AUC was 0.792, 0.766 and 0.731, respectively). The combined AUC of the three parameters was 0.840, specificity was 85.7% and sensitivity was 79.4%. Conclusion The parameters of RV and IVS measured by CMR can effectively evaluate patients with severe CTD-PAH and directly reflect serverity of cardiac impairment in patients with severe CTD-PAH from the morphological and functional perspective. [ABSTRACT FROM AUTHOR]

Because of the similarity of texture of different lithology images, the accuracy of lithology identification based on a single two-dimensional image is low. In order to solve this problem, an intelligent lithology identification method based on image depth information was studied. Firstly, the depth information was fused with the image data by using four image fusion methods: channel superposition, intensity,hue,saturation( IHS) transform, wavelet transform and multi-modal fusion. Then, based on the converted image data fused with depth information, the intelligent identification method of clastic lithology was studied by using deep convolutional neural network DeepLabv3 + technology. The results of intelligent lithology identification by different fusion methods and manual interpretation were compared and analyzed. The results show that different fusion methods has different lithology identification effects. In the experimental area, the lithology identification accuracy based on multi-modal fusion images is the highest, with Kappa coefficient up to 76. 17%, and the overall identification accuracy up to 91. 05% . The analysis shows that the intelligent lithology identification method taking into account image depth information can significantly improve the identification effect of the gravel with uneven rock surface and large height difference, but the identification effect of the mudstone and sandstone with flat surface and not obvious height difference needs to be improved. The research results provide a new technical idea for rapid lithology identification of clastic rock outcrop in a large area in the field [ABSTRACT FROM AUTHOR]

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