Single-angle plane wave (SAPW) ultrasound imaging has gained significant attention in ultrafast and wearable ultrasound systems due to its high frame rate and hardware efficiency. However, the lack of transmit diversity leads to severe degradation in image quality, limiting its clinical utility. In contrast, Coherent Plane Wave Compounding (CPWC) improves spatial resolution and contrast by aggregating data from multiple transmission angles, but at the expense of a substantially reduced frame rate. To overcome this trade-off, we propose TF-Fusion, a novel Time-Frequency Domain Feature Fusion framework that enhances SAPW imaging by jointly exploiting both time- and frequency-domain features extracted from raw in-phase/quadrature (IQ) data. Specifically, our model utilizes a dual-branch encoder to learn compact and complementary representations from each domain, followed by a learnable fusion module that adaptively integrates the multi-domain information. This design facilitates effective noise and artifact suppression while preserving fine anatomical structures. We evaluate TF-Fusion on two public benchmarks—PICMUS and CUBDL—and demonstrate that our method significantly improves image resolution and contrast, achieving performance comparable to multi-angle CPWC methods. Notably, TF-Fusion maintains the high temporal resolution of SAPW, making it well-suited for real-time and resource-constrained ultrasound imaging scenarios.

Left ventricular segmentation in echocardiographic videos plays a crucial role in assessing various heart functions and disease diagnoses. However, due to the dynamic nature of echocardiography, maintaining consistent target segmentation across context shifts between frames poses a significant challenge. This task becomes even more difficult in the presence of noise interference in ultrasound images. In this paper, we propose DiffSegMem, which leverages the generative advantages of conditional diffusion model and a contextual memory storage architecture to enhance the accuracy of cross-frame segmentation in dynamic echocardiographic videos. Specifically, we introduce a noise-frequency domain aware dual-branch conditional encoding that establishes noise-resistant conditions at each sampling step, providing a reliable mask for the first frame of the echocardiographic sequence. As a result, our method does not require any prompting for the first video frame. Additionally, we propose a dynamic memory propagation strategy that utilizes memory transfer switches to extract memory from deeply linked video frames using a positional switch, with memory blocks carrying contextual clues prompting segmentation frame by frame. We evaluate our method on publicly available echocardiographic segmentation datasets and demonstrate state-of-the-art performance compared to existing models, outperforming current supervised methods for prompt-based segmentation.

In the treatment of coronary artery disease, the stent implantation strategy (SIS) plays a decisive role in both the procedural success rate and the long-term prognosis of patients. However, SIS determination is primarily based on the experience of the doctors, visual evaluation based on coronary angiography, and the real-time interpretation of intravascular ultrasound (IVUS) during the procedure. To improve the rationality of stent implantation and reduce postoperative adverse events, we propose an objective, automated, and efficient method to evaluate the rationality of SIS. Specifically, a time-series chunk encoder is constructed to extract temporal features from IVUS video, enabling the model to handle long-sequence feature extraction while accommodating input videos of varying lengths. Furthermore, a frequency domain feature encoder is employed to extract spectral characteristics from IVUS video. Meanwhile, stent, balloon, and placement information is input into the model in text form, with feature extraction performed using two pretrained BERT models in medical Chinese and medical English. In order to ensure the discriminability of text features, a text memory bank and text matching loss function are designed to participate in model training in conjunction with other commonly used loss functions. Finally, two Transformer Layers integrate the temporal, spectral, and textual modalities, and a fully connected layer is used to determine the rationality of the SIS. Validation in a private rationality evaluation dataset for SIS demonstrates that our method effectively performs rationality evaluation tasks, providing valuable auxiliary recommendations for clinical decision-making.

Intravascular ultrasound (IVUS) not only reveals changes within the vascular lumen but also illustrates the cross-sectional structure, encompassing aspects such as plaques, vessel wall thickness, morphology, and composition. However, during the image acquisition process, ultrasound imaging of vessels can lead to intraluminal misalignment due to motion, resulting in inaccurate measurement outcomes. Current methods for motion artifact removal in IVUS face the following challenges: (a) Gating, which extracts key gating frames to form a new artifact-free sequence, but often results in the loss of substantial useful information; and (b) Direct artifact removal, which requires lumen segmentation followed by registration, where the accuracy of registration is highly dependent on segmentation accuracy. To address these challenges, this paper proposes a robust direct artifact removal method based on spatiotemporal representations. Specifically, to address the issue of information loss in gating methods, a spatiotemporal representation network is introduced, which primarily relies on temporal granularity normalization and spatial position compensation. To tackle the problem of direct artifact removal methods heavily relying on segmentation accuracy, this paper integrates the segmentation network with the artifact removal network, allowing for mutual supervision. This approach ensures effective motion artifact removal even when segmentation accuracy is not optimal. Experimental results show that our method not only achieves state-of-the-art (SOTA) performance in both quantitative and qualitative evaluations but also maintains robust motion artifact removal even when the segmentation network is replaced with one of lower performance.

Clinical workflow of cardiac assessment on 2D echocardiography requires both accurate segmentation and quantification of the Left Ventricle (LV) from paired apical 4-chamber and 2-chamber. Moreover, uncertainty estimation is significant in clinically understanding the performance of a model. However, current research on 2D echocardiography ignores this vital task while joint segmentation with quantification, hence motivating the need for a unified optimization method. In this paper, we propose a multitask model with Task Relation Spatial co-Attention (referred as TRSA-Net) for joint segmentation, quantification, and uncertainty estimation on paired 2D echo. TRSA-Net achieves multitask joint learning by novelly exploring the spatial correlation between tasks. The task relation spatial co-attention learns the spatial mapping among task-specific features by non-local and co-excitation, which forcibly joints embedded spatial information in the segmentation and quantification. The Boundary-aware Structure Consistency (BSC) and Joint Indices Constraint (JIC) are integrated into the multitask learning optimization objective to guide the learning of segmentation and quantification paths. The BSC creatively promotes structural similarity of predictions, and JIC explores the internal relationship between three quantitative indices. We validate the efficacy of our TRSA-Net on the public CAMUS dataset. Extensive comparison and ablation experiments show that our approach can achieve competitive segmentation performance and highly accurate results on quantification.