Vision-Language Models (VLMs) have great potential for advancing echocardiography (Echo) probe navigation, which is crucial for assisting sonographers in standardized view acquisition. However, the high clinical deployment costs and spurious correlations pose major challenges for VLM-based probe navigation. To address these challenges, we propose Contrastive Language-Image Navigator for Latent Hyperbolic Geometric Knowledge Distillation (CLINav-GKD), a novel VLM-based 6-DOF Echo probe navigation framework. Specifically, Contrastive Language-Image Navigator (CLN) proposes a lightweight VLM-based 6-DOF navigator, reducing deployment costs while improving sensitivity to quality variations. Latent Hy perbolic Geometric Distiller (LGD) models the global geometric-topology between samples, mitigating spurious correlations and enhancing robustness. We train CLINav-GKD on real-world data with probe motion trajectories. Experimental results show that CLINav-GKD outperforms other VLM-based distillation methods by 2.8%, 3.8%, and 3.6% in probe navigation, achieving a superior balance of accuracy, robustness, and deployability for real-world clinical use. Code and data are available at https://github.com/DaisyLi0516/CLINav-GKD.

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.