Suppr超能文献

基于透过率效应差减的光学相干尾影伪像图像中尾影去除。

Tail Artifact Removal via Transmittance Effect Subtraction in Optical Coherence Tail Artifact Images.

机构信息

Faculty of Mathematics and Physics, University of Ljubljana, 1000 Ljubljana, Slovenia.

Jozef Stefan Institute, 1000 Ljubljana, Slovenia.

出版信息

Sensors (Basel). 2023 Nov 21;23(23):9312. doi: 10.3390/s23239312.

Abstract

Optical Coherence Tomography Angiography (OCTA) has revolutionized non-invasive, high-resolution imaging of blood vessels. However, the challenge of tail artifacts in OCTA images persists. In response, we present the Tail Artifact Removal via Transmittance Effect Subtraction (TAR-TES) algorithm that effectively mitigates these artifacts. Through a simple physics-based model, the TAR-TES accounts for variations in transmittance within the shallow layers with the vasculature, resulting in the removal of tail artifacts in deeper layers after the vessel. Comparative evaluations with alternative correction methods demonstrate that TAR-TES excels in eliminating these artifacts while preserving the essential integrity of vasculature images. Crucially, the success of the TAR-TES is closely linked to the precise adjustment of a weight constant, underlining the significance of individual dataset parameter optimization. In conclusion, TAR-TES emerges as a powerful tool for enhancing OCTA image quality and reliability in both clinical and research settings, promising to reshape the way we visualize and analyze intricate vascular networks within biological tissues. Further validation across diverse datasets is essential to unlock the full potential of this physics-based solution.

摘要

光学相干断层扫描血管造影术(OCTA)已经彻底改变了血管的非侵入性、高分辨率成像。然而,OCTA 图像中的尾迹伪影仍然是一个挑战。为此,我们提出了一种通过透射效应减法(TAR-TES)算法来去除尾迹伪影的方法,该方法可以有效地减轻这些伪影。通过一个简单的物理模型,TAR-TES 考虑了血管浅层的透射率变化,从而在血管后面的深层去除了尾迹伪影。与其他校正方法的比较评估表明,TAR-TES 在去除这些伪影的同时,还能很好地保留血管图像的基本完整性。至关重要的是,TAR-TES 的成功与一个权重常数的精确调整密切相关,这强调了针对每个数据集进行参数优化的重要性。总之,TAR-TES 是一种强大的工具,可以提高 OCTA 图像的质量和可靠性,无论是在临床还是研究环境中,有望改变我们观察和分析生物组织内复杂血管网络的方式。进一步在不同数据集上进行验证对于充分发挥这种基于物理的解决方案的潜力至关重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce8f/10708777/4ea7dd543c07/sensors-23-09312-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验