As a leading global cause of mortality, coronary ischemia requires accurate diagnostics for effective management. The combining coronary angiography with fractional flow reserve (FFR) offers structural and functional assessment of coronary stenosis to guide revascularization. However, traditional FFR measurements are invasive, requiring pressure wire placement. Image-based FFR estimation methods integrate vascular morphology with biomechanics but face challenges in modelling the complex fluid-structure interaction (FSI) of coronary flow and vessel walls. Therefore, we propose a physics-guided variational domain progressing method (PVDPM) for non-invasive FFR estimation through FSI system. PVDPM employs the principle of virtual work to model FSI system. This approach can improve the modelling of interdependent physical processes, enabling accurate FFR estimation based on coronary angiography-derived vascular morphology. The PVDPM demonstrates 91% accuracy in clinical datasets and offers solution for diagnosing coronary ischemia based on coronary angiography.

Vessel border detection in IVUS images is essential for coronary disease diagnosis. It helps to obtain the clinical indices on the inner vessel morphology to indicate the stenosis. However, the existing methods suffer the challenge of scale-dependent interference. Early methods usually rely on the hand-crafted features, thus not robust to this interference. The existing deep learning methods are also ineffective to solve this challenge, because these methods aggregate multi-scale features in the top-down way. This aggregation may bring in interference from the non-adjacent scale. Besides, they only combine the features in all scales, and thus may weaken their complementary information. We propose the scale mutualized perception to solve this challenge by considering the adjacent scales mutually to preserve their complementary information. First, the adjacent small scales contain certain semantics to locate different vessel tissues. Then, they can also perceive the global context to assist the representation of the local context in the adjacent large scale, and vice versa. It helps to distinguish the objects with similar local features. Second, the adjacent large scales provide detailed information to refine the vessel boundaries. The experiments show the effectiveness of our method in 153 IVUS sequences, and its superiority to ten state-of-the-art methods.

Estimation of the fractional flow reserve (FFR) pullback curve from invasive coronary imaging is important for the intraoperative guidance of coronary intervention. Machine/deep learning has been proven effective in FFR pullback curve estimation. However, the existing methods suffer from inadequate incorporation of intrinsic geometry associations and physics knowledge. In this paper, we propose a constraint-aware learning framework to improve the estimation of the FFR pullback curve from invasive coronary imaging. It incorporates both geometrical and physical constraints to approximate the relationships between the geometric structure and FFR values along the coronary artery centerline. Our method also leverages the power of synthetic data in model training to reduce the collection costs of clinical data. Moreover, to bridge the domain gap between synthetic and real data distributions when testing on real-world imaging data, we also employ a diffusion-driven test-time data adaptation method that preserves the knowledge learned in synthetic data. Specifically, this method learns a diffusion model of the synthetic data distribution and then projects real data to the synthetic data distribution at test time. Extensive experimental studies on a synthetic dataset and a real-world dataset of 382 patients covering three imaging modalities have shown the better performance of our method for FFR estimation of stenotic coronary arteries, compared with other machine/deep learning-based FFR estimation models and computational fluid dynamics-based model. The results also provide high agreement and correlation between the FFR predictions of our method and the invasively measured FFR values. The plausibility of FFR predictions along the coronary artery centerline is also validated.

The rapid development of the Internet of Things (IoT) widely supports the smart healthcare system. IoT-based smart health has significant importance for the diagnosis of cardiovascular disease (CVD) in clinical practice. Combined with advanced artificial intelligence techniques, IoT-based smart health provides valuable and accurate diagnosis information remotely for cardiovascular disease. The functional assessment of CVD is an essential task in clinical practice. It aims to determine the extent of myocardial ischemia through the measurement of the hemodynamic parameters of the coronary artery. However, the clinical adoption of the hemodynamic parameters is limited due to the potential risks and high health costs during measurements. Recent advances in artificial intelligence have enabled the computation of hemodynamic parameters based on the anatomical features of coronary arteries. However, the existing methods still lack explainability in the prediction. To address this issue, we present a physics-guided deep learning network for the functional assessment of CVD in an IoT-based manner. We specifically design an attentive network to determine the effective features by considering the importance of coronary artery anatomy features and artery segments. To obtain the functional assessment with explainability, we incorporate physical knowledge related to the blood flow into the loss function. It can ensure that functional assessment follows the physical laws. Extensive experiments are performed on a synthetic data set and a real-world clinical data set. The results show that our approach can achieve accurate and physically consistent assessment. Moreover, our method promotes deeper adoption of IoT and deep learning in the field of smart health.