Trajectory-Aware Adaptive Imaging Clue Analysis for Guidewire Artifact Removal in Intravascular Optical Coherence Tomography

Gongning Luo, Xinghua Ma, Jinwen Guo, Mingye Zou, Wei Wang, Yang Cao, Kuanquan Wang, Shuo Li

Abstract: Guidewire Artifact Removal (GAR) involves restoring missing imaging signals in areas of IntraVascular Optical Coherence Tomography (IVOCT) videos affected by guidewire artifacts. GAR helps overcome imaging defects and minimizes the impact of missing signals on the diagnosis of CardioVascular Diseases (CVDs). To restore the actual vascular and lesion information within the artifact area, we propose a reliable Trajectory-aware Adaptive imaging Clue analysis Network (TAC-Net) that includes two innovative designs: (i) Adaptive clue aggregation, which considers both texture-focused original (ORI) videos and structure-focused relative total variation (RTV) videos, and suppresses texture-structure imbalance with an active weight-adaptation mechanism; (ii) Trajectory-aware Transformer, which uses a novel attention calculation to perceive the attention distribution of artifact trajectories and avoid the interference of irregular and non-uniform artifacts. We provide a detailed formulation for the procedure and evaluation of the GAR task and conduct comprehensive quantitative and qualitative experiments. The experimental results demonstrate that TAC-Net reliably restores the texture and structure of guidewire artifact areas as expected by experienced physicians ( e.g. , SSIM: 97.23%). We also discuss the value and potential of the GAR task for clinical applications and computer-aided diagnosis of CVDs.