Background A relevant proportion of patients with suspected coronary artery disease undergo invasive coronary angiography showing normal or nonobstructive coronary arteries. However, the prevalence of coronary microvascular disease (CMD) and coronary spasm in patients with nonobstructive coronary artery disease remains to be determined. The objective of this study was to determine the prevalence of coronary CMD and coronary vasospastic angina in patients with no obstructive coronary artery disease. Methods and Results A systematic review and meta‐analysis of studies assessing the prevalence of CMD and vasospastic angina in patients with no obstructive coronary artery disease was performed. Random‐effects models were used to determine the prevalence of these 2 disease entities. Fifty‐six studies comprising 14 427 patients were included. The pooled prevalence of CMD was 0.41 (95% CI, 0.36–0.47), epicardial vasospasm 0.40 (95% CI, 0.34–0.46) and microvascular spasm 24% (95% CI, 0.21–0.28). The prevalence of combined CMD and vasospastic angina was 0.23 (95% CI, 0.17–0.31). Female patients had a higher risk of presenting with CMD compared with male patients (risk ratio, 1.45 [95% CI, 1.11–1.90]). CMD prevalence was similar when assessed using noninvasive or invasive diagnostic methods. Conclusions In patients with no obstructive coronary artery disease, approximately half of the cases were reported to have CMD and/or coronary spasm. CMD was more prevalent among female patients. Greater awareness among physicians of ischemia with no obstructive coronary arteries is urgently needed for accurate diagnosis and patient‐tailored management.

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Cardiologists highlight the need for an intraoperative 3D visualization to assist interventions. The intraoperative 2D X-ray/Digital Subtraction Angiography (DSA) images in the standard clinical workflow limit cardiologists' views significantly. Compared with image-to-image registration, model-to-image registration is an essential approach taking advantage of the reuse of pre-operative 3D models reconstructed from Computed Tomography Angiography (CTA) images. Traditional optimized-based registration methods suffer severely from high computational complexity. Moreover, the consequence of lacking ground truth for learning-based registration approaches should not be neglected. To overcome these challenges, we introduce a model-to-image registration framework via deep learning for image-guided endovascular catheterization. This work performs autonomous vessel segmentation from intra-operative fluoroscopy images via a deep residual U-net and a model-to-image matching via a convolutional neural network. For this study, image data were collected from 10 patients who performed Transcatheter Aortic Valve Implantation (TAVI) procedures. It was found that vessel segmentation of test data results in median values of Dice Similarity Coefficient, Precision, and Recall of (0.75, 0.58, 0.67) for femoral artery, and (0.71, 0.56, 0.74) for aortic root. The segmentation network behaves better than manual annotation, and it recognizes part of vessels that were not labeled manually. Image matching between the transformed moving image and the fixed image results in a median value of Recall of 0.90. The proposed approach achieves a good accuracy of vessel segmentation and a good recall value of model-toimage matching.

Background. Cardiac allograft vasculopathy (CAV) remains the Achilles’ heel of long-term survival after heart transplantation (HTx). The severity and extent of CAV is graded with conventional coronary angiography (COR) which has several limitations. Recently, vessel

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