SRMAR: Spatiotemporal Representation for Motion Artifact Removal in Intravascular Ultrasound

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.