Learning coronary artery calcium scoring in coronary CTA from non-contrast CT using unsupervised domain adaptation

Deep learning methods have demonstrated the ability to perform accurate coronary artery calcium (CAC) scoring. However, these methods require large and representative training data hampering applicability to diverse CT scans showing the heart and the coronary arteries. Training methods that accurately score CAC in cross-domain settings remains challenging. To address this, we present an unsupervised domain adaptation method that learns to perform CAC scoring in coronary CT angiography (CCTA) from non-contrast CT (NCCT). To address the domain shift between NCCT (source) domain and CCTA (target) domain, feature distributions are aligned between two domains using adversarial learning. A CAC scoring convolutional neural network is divided into a feature generator that maps input images to features in the latent space and a classifier that estimates predictions from the extracted features. For adversarial learning, a discriminator is used to distinguish the features between source and target domains. Hence, the feature generator aims to extract features with aligned distributions to fool the discriminator. The network is trained with adversarial loss as the objective function and a classification loss on the source domain as a constraint for adversarial learning. In the experiments, three data sets were used. The network is trained with 1,687 labeled chest NCCT scans from the National Lung Screening Trial. Furthermore, 200 labeled cardiac NCCT scans and 200 unlabeled CCTA scans were used to train the generator and the discriminator for unsupervised domain adaptation. Finally, a data set containing 313 manually labeled CCTA scans was used for testing. Directly applying the CAC scoring network trained on NCCT to CCTA led to a sensitivity of 0.41 and an average false positive volume 140 mm3/scan. The proposed method improved the sensitivity to 0.80 and reduced average false positive volume of 20 mm3/scan. The results indicate that the unsupervised domain adaptation approach enables automatic CAC scoring in contrast enhanced CT while learning from a large and diverse set of CT scans without contrast. This may allow for better utilization of existing annotated data sets and extend the applicability of automatic CAC scoring to contrast-enhanced CT scans without the need for additional manual annotations. The code is publicly available at https://github.com/qurAI-amsterdam/CACscoringUsingDomainAdaptation

Ephedrine, pseudoephedrine, and amphetamine prevalence in college hockey players : most report performance-enhancing use

BACKGROUND: Patients with Marfan syndrome (MFS) have a highly variable occurrence of aortic complications. Aortic tortuosity is often present in MFS and may help to identify patients at risk for aortic complications. METHODS: 3D-visualization of the total aorta by MR imaging was performed in 211 adult MFS patients (28% with prior aortic root replacement) and 20 controls. A method to assess aortic tortuosity (aortic tortuosity index: ATI) was developed and reproducibility was tested. The relation between ATI and age, and body size and aortic dimensions at baseline was investigated. Relations between ATI at baseline and the occurrence of a clinical endpoint (aortic dissection, and/or aortic surgery) and aortic dilatation rate during 3years of follow-up were investigated. RESULTS: ATI intra- and interobserver agreements were excellent (ICC: 0.968 and 0.955, respectively). Mean ATI was higher in 28 age-matched MFS patients than in the controls (1.92+/-0.2 vs. 1.82+/-0.1, p=0.048). In the total MFS cohort, mean ATI was 1.87+/-0.20, and correlated with age (r=0.281, p1.95 had a 12.8 times higher probability of meeting the combined endpoint (log rank-test, p<0.001) and a 12.1 times higher probability of developing an aortic dissection (log rank-test, p=0.003) compared to patients with an ATI<1.95. CONCLUSIONS: Increased ATI is associated with a more severe aortic phenotype in MFS patients