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

The implementation of our paper "Trajectory-Aware Adaptive Imaging Clue Analysis for Guidewire Artifact Removal in Intravascular Optical Coherence Tomography".

Abstract

Guidewire Artifact Removal (GAR) involves restoring missing imaging signals in areas of IntraVascular Optical Coherence Tomography (IVOCT) videos affected by guidewire artifacts. We propose a reliable Trajectory-aware Adaptive imaging Clue analysis Network (TAC-Net) to restore the actual vascular and lesion information within the artifact area

Overview

• TAC-Net provides high-fidelity image reconstruction, improving the negative effects of guidewire artifacts, addressing the imaging defects in IVOCT, and eliminating the impact of missing signals.

• Adaptive clue aggregation balances texture-structure clues using active weight control in a parallel architecture, resulting in the realistic restoration of subtle textures and variable structures.

• Trajectory-aware Transformer effectively extracts highly artifact-related features by mining self-attention distributions, while avoiding the interference of unpredictable artifact trajectories.

Requirements

Python 3.6, PyTorch 1.6 and other common packages are listed in requirements.txt

Citation

Please consider citing the project in your publications if it helps your research. The following is a BibTeX reference. The BibTeX entry requires the url LaTeX package.

@article{luo2023trajectory,
  title={Trajectory-aware Adaptive Imaging Clue Analysis for Guidewire Artifact Removal in Intravascular Optical Coherence Tomography},
  author={Luo, Gongning and Ma, Xinghua and Guo, Jinwen and Zou, Mingye and Wang, Wei and Cao, Yang and Wang, Kuanquan and Li, Shuo},
  journal={IEEE Journal of Biomedical and Health Informatics},
  year={2023},
  publisher={IEEE}
}

Acknowledgment

https://github.com/guchengxi1994/RTV-in-Python