No abstract

Background: Determination of disease activity in Takayasu arteritis (TAK) is crucial for clinical management but challenging. The value of different magnetic resonance imaging (MRI) characteristics for the assessment of disease activity remains unclear. This study investigated the imaging findings of the thoracic aortic wall and elasticity by using a comprehensive 3.0 T MRI protocol. Methods: We prospectively enrolled 52 consecutive TAK patients. TAK activity was recorded according to the ITAS2010. All the patients underwent thoracic aortic MRI. The luminal morphology of the thoracic aorta and its main branches were quantitatively evaluated using a contrast-enhanced magnetic resonance angiography (MRA) sequence. The maximum wall thickness of the thoracic aorta, postcontrast enhancement ratio, and aortic wall edema were analyzed in each patient through pre-and post-enhanced T1-weighted and T2-weighted imaging. Pulse-wave velocity (PWV) of the thoracic aorta was calculated using a four-dimensional flow technique. Results: The majority of the 52 patients had type V disease (34.62%, 18/52). Among all the MRI indicators of the thoracic aorta, the area under the curve was the largest for the maximal wall thickness (0.804, 95% confidence interval [CI] = 0.667-0.941). The maximal wall thickness (93.33%, 95% CI = 68.1%-99.8%) exhibited the highest sensitivity with a cutoff value of 3.12 mm. Wall edema (84.00%, 95% CI = 63.9%-95.5%) presented the highest specificity. A positive correlation was noted between PWV and patients' age (r = 0.54, p < 0.001), disease duration (r = 0.52, p < 0.001), and the maximum wall thickness (r = 0.45, p = 0.001). Conclusions: MRI enabled the comprehensive assessment of aortic wall morphology and functional markers for TAK disease activity. Aortic maximal wall thickness was the most accurate indicator of TAK activity. The early phase was superior to the delay phase for aortic wall enhancement analysis for assessing TAK activity.

The aim of this study was to investigate the impact of nitroglycerin (NTG) on the assessment of computed tomography-derived fractional flow reserve (CT-FFR).Materials and Methods: Seventy-seven patients with suspected coronary artery disease were recruited, and they underwent computed tomography angiography (CCTA) before and after NTG administration. The CT-FFRs were compared at 2 CCTAs. The difference was compared using the Wilcoxon signed rank test. Patients were divided into normal and stenosis groups according to CCTA results. Vessels in the stenosis group were further divided into different groups based on coronary artery calcium score (CACS) and stenosis degree. The poststenotic CT-FFR differences before and after NTG (D CT-FFR ) were calculated to evaluate the impact of stenosis degree and CACS. Terminal CT-FFRs derived from CCTAs before and after NTG in total and vessel-specific levels were compared in the normal group.Results: Of 47 patients in the stenosis group, poststenotic CT-FFR was significantly increased after NTG at per-vessel level. By taking CT-FFR of 0.75 or lower as the threshold, 5 and 4 patients showed abnormal CT-FFR before and after NTG, respectively. No significant differences were noted among the various stenosis degree and CACS groups regarding D CT-FFR . Of 30 patients in the normal group, terminal CT-FFR was significantly increased after NTG in total level and vessel-specific level of left anterior descending and right coronary artery, but not in the left circumflex.Conclusions: Both post lesion and distal vessel CT-FFR significantly improved after the administration of GTN with the degree of change not affected by stenosis severity or CACS.

Background: Right heart catheterization is the gold standard for evaluating hemodynamic parameters of pulmonary circulation, especially pulmonary artery pressure (PAP) for diagnosis of pulmonary hypertension (PH). However, the invasive and costly nature of RHC limits its widespread application in daily practice. Purpose: To develop a fully automatic framework for PAP assessment via machine learning based on computed tomography pulmonary angiography (CTPA). Materials and Methods: A machine learning model was developed to automatically extract morphological features of pulmonary artery and the heart on CTPA cases collected between June 2017 and July 2021 based on a single center experience. Patients with PH received CTPA and RHC examinations within 1 week. The eight substructures of pulmonary artery and heart were automatically segmented through our proposed segmentation framework. Eighty percent of patients were used for the training data set and twenty percent for the independent testing data set. PAP parameters, including mPAP, sPAP, dPAP, and TPR, were defined as ground-truth. A regression model was built to predict PAP parameters and a classification model to separate patients through mPAP and sPAP with cut-off values of 40 mm Hg and 55 mm Hg in PH patients, respectively. The performances of the regression model and the classification model were evaluated by analyzing the intraclass correlation coefficient (ICC) and the area under the receiver operating characteristic curve (AUC). Results: Study participants included 55 patients with PH (men 13; age 47.75 ± 14.87 years). The average dice score for segmentation increased from 87.3% ± 2.9 to 88.2% ± 2.9 through proposed segmentation framework. After features extraction, some of the AI automatic extractions (AAd, RVd, LAd, and RPAd) achieved good consistency with the manual measurements. The differences between them were not statistically significant (t = 1.222, p = 0.227; t = −0.347, p = 0.730; t = 0.484, p = 0.630; t = −0.320, p = 0.750, respectively). The Spearman test was used to find key features which are highly correlated with PAP parameters. Correlations between pulmonary artery pressure and CTPA features show a high correlation between mPAP and LAd, LVd, LAa (r = 0.333, p = 0.012; r = −0.400, p = 0.002; r = −0.208, p = 0.123; r = −0.470, p = 0.000; respectively). The ICC between the output of the regression model and the ground-truth from RHC of mPAP, sPAP, and dPAP were 0.934, 0.903, and 0.981, respectively. The AUC of the receiver operating characteristic curve of the classification model of mPAP and sPAP were 0.911 and 0.833. Conclusion: The proposed machine learning framework on CTPA enables accurate segmentation of pulmonary artery and heart and automatic assessment of the PAP parameters and has the ability to accurately distinguish different PH patients with mPAP and sPAP. Results of this study may provide additional risk stratification indicators in the future with non-invasive CTPA data.