Developing heart sound classification algorithms for murmur identification is critical for early screening of heart diseases. However, identifying murmurs in long-duration heart sound signals can be challenging due to their weak features and interference from noise. Considering the periodic patterns of heart sounds and murmurs, periodic priors can be introduced to enhance murmur identification, an approach that remains underutilized in current methods. In this study, we propose a novel Dual-level Periodic Pattern-Aware Transformer (DPPAT) to implicitly leverage the periodic priors of heart sound signals without requiring cycle segmentation. In the regional-level, an Adaptive Period-Aligned Window Selection algorithm is designed for the model to extract periodic components while suppressing random noise using a Periodic Pattern Attention module. In the global-level, the model further integrates these periodic features in global-modeling to enhance the identification of murmur-discriminative features. Validated on the dataset from 2022 George B. Moody PhysioNet Challenge, our proposed method achieves a weighted accuracy of 84.27% and an F1-score of 70.38% through 10-fold cross-validation. The generalizability of DPPAT is further verified on two additional public datasets, including both heart sound and respiratory sound signals. Furthermore, attention visualizations provide a clear understanding of the focus of the model, highlighting the decision-making basis for murmur identification.

Real-time guidewire tracking and position estimation are crucial for intraoperative navigation during percutaneous coronary intervention (PCI). Compared with other interventional procedures, PCI employs smaller-sized guidewires that are extremely difficult to distinguish from surrounding anatomical structures due to their slenderness and complex anatomical backgrounds. Moreover, guidewires typically exhibit low signal-to-noise ratios (SNR) in fluoroscopic video sequences. Additionally, complex motion artifacts caused by patient breathing and cardiac movements further complicate real-time guidewire localization. To address these challenges, we propose a novel end-to-end framework for guidewire segmentation and localization. Given that background information in PCI images predominantly occupies low-frequency components, we designed a multi-scale feature aggregation module based on low-frequency suppression to enhance the network's capability for extracting slender structures such as guidewires. Furthermore, to handle discontinuities caused by low SNR and structural occlusions in preliminary segmentation results, we incorporated a lightweight reconstruction network along with a generative adversarial network (GAN) to repair broken regions. The two networks are trained jointly, and their outputs are ultimately fused to achieve accurate guidewire segmentation. Extensive experiments conducted on multi-center private datasets demonstrate the superior performance of our approach, with F1 score of 76.45% and a localization precision of 0.18 mm. With a compact model size of only 12.64 M parameters and a real-time processing speed of 46 FPS, our method offers a highly promising solution for future clinical applications.

The registration of coronary artery structures from preoperative coronary computed tomography angiography to intraoperative coronary angiography is of great interest to improve guidance in percutaneous coronary interventions. However, non-rigid deformation and discrepancies in both dimensions and topology between the two imaging modalities present a challenge in the 2D/3D coronary artery registration. In this study, we address this problem by formulating it as a centerline feature matching task and propose a GNN-based vessel matching network (GVM-Net) to establish dense correspondence between different image modalities in an end-to-end manner. GVM-Net considers centerline points as nodes in graphs and effectively models the complex topological relationships between them through attention mechanisms and message passing. Furthermore, by incorporating redundant rows and columns into the matching matrix, GVM-Net can effectively handle inconsistencies in vascular structures. We also introduce the query-based nodes grouping module, which clusters nodes in the feature space to further explore the topological relationships. GVM-Net achieves an average F1-score of 89.74% with a mean pixel distance of 0.48 pixels on the synthetic dataset with 276 data pairs and an average F1-score of 83.35% with a mean error of 1.52 mm in 55 manually labeled clinical cases, both exceeding existing feature matching methods.

Digital subtraction angiography (DSA) is an essential diagnostic tool for analyzing and diagnosing cardiovascular diseases. However, patient movement during image acquisition can introduce motion artifacts in DSA images, and this degradation in image quality always hinders accurate vessel identification and surgical treatment. Recently, some deep learning-based studies have been presented to address the artifact problem in DSA images by leveraging a generative model to produce high-quality DSA images directly from contrast images. Motionless data (paired contrast and artifact-free DSA images) is always required for these methods to train a model in a supervised manner. However, we face a dilemma that motionless DSA data is hard to acquire in clinical practice, most of which contain varying degrees of artifacts. This raises issues of insufficient motionless data and imperfect motion data for training effective deep generative models. To address this problem, we propose a new Artifact-aware DSA image generation method (denoted as AaDSA), which aims to generate high-quality DSA images with decreased artifacts using only motion-induced data. Specifically, a Gradient Field Transformation-based (GFT-based) method is introduced to obtain an artifact mask that identifies the artifact regions in a DSA image with minimal manual labeling costs. We then train an AaDSA model using the artifact mask as guidance, avoiding the adverse effect of artifact regions for model training. In the inference phase, the proposed AaDSA model can automatically generate a DSA-like image with decreased artifacts from a single contrast image without any human intervention. Experimental results on a real head-and-neck DSA dataset demonstrate the superiority of our method compared to state-of-the-art methods and its potential for clinical use.

Automatic delineation of the lumen and vessel contours in intravascular ultrasound (IVUS) images is crucial for the subsequent IVUS-based analysis. Existing methods usually address this task through mask-based segmentation, which cannot effectively handle the anatomical plausibility of the lumen and external elastic lamina (EEL) contours and thus limits their performance. In this article, we propose a contour encoding based method called coupled contour regression network (CCRNet) to directly predict the lumen and EEL contour pairs. The lumen and EEL contours are resampled, coupled, and embedded into a low-dimensional space to learn a compact contour representation. Then, we employ a convolutional network backbone to predict the coupled contour signatures and reconstruct the signatures to the object contours by a linear decoder. Assisted by the implicit anatomical prior of the paired lumen and EEL contours in the signature space and contour decoder, CCRNet has the potential to avoid producing unreasonable results. We evaluated our proposed method on a large IVUS dataset consisting of 7204 cross-sectional frames from 185 pullbacks. The CCRNet can rapidly extract the contours at 100 fps. Without any post-processing, all produced contours are anatomically reasonable in the test 19 pullbacks. The mean Dice similarity coefficients of our CCRNet for the lumen and EEL are 0.940 and 0.958, which are comparable to the mask-based models. In terms of the contour metric Hausdorff distance, our CCRNet achieves 0.258 mm for lumen and 0.268 mm for EEL, which outperforms the mask-based models.

Accurate coronary lumen segmentation on coronary-computed tomography angiography (CCTA) images is crucial for quantification of coronary stenosis and the subsequent computation of fractional flow reserve. Many factors including difficulty in labeling coronary lumens, various morphologies in stenotic lesions, thin structures and small volume ratio with respect to the imaging field complicate the task. In this work, we fused the continuity topological information of centerlines which are easily accessible, and proposed a novel weakly supervised model, Examinee-Examiner Network (EE-Net), to overcome the challenges in automatic coronary lumen segmentation. First, the EE-Net was proposed to address the fracture in segmentation caused by stenoses by combining the semantic features of lumens and the geometric constraints of continuous topology obtained from the centerlines. Then, a Centerline Gaussian Mask Module was proposed to deal with the insensitiveness of the network to the centerlines. Subsequently, a weakly supervised learning strategy, Examinee-Examiner Learning, was proposed to handle the weakly supervised situation with few lumen labels by using our EE-Net to guide and constrain the segmentation with customized prior conditions. Finally, a general network layer, Drop Output Layer, was proposed to adapt to the class imbalance by dropping well-segmented regions and weights the classes dynamically. Extensive experiments on two different data sets demonstrated that our EE-Net has good continuity and generalization ability on coronary lumen segmentation task compared with several widely used CNNs such as 3D-UNet. The results revealed our EE-Net with great potential for achieving accurate coronary lumen segmentation in patients with coronary artery disease. Code at http://github.com/qiyaolei/Examinee-Examiner-Network.