Tail artifacts removal of three-dimensional optical coherence tomography angiography with common parts extraction method

Size correction and deep image optimization in optical coherence tomography angiography with structural image-assisted common parts extraction method

Tail artifact elimination is essential in optical coherence tomography angiography (OCTA) for the artifacts will prevent the reconstruction of the 3D vessel image. The tail artifacts of superficial vessels obscure the deep vascular signals and cause the signals at different depths to mix with each other. Most tail artifact elimination methods can shorten the tails but have difficulty in determining the lower boundary of the vessels. In this letter, we introduce a technique to extract vascular signals with more accurate vascular boundaries. With the help of structural image, our method can reconstruct the 3D image of the vascular network more precisely and perform better in deep areas. The images of vessels of palm are used to compare our new technique with previous common parts extraction method experimentally. The results show that our method removes the tail artifacts more thoroughly and has a significant advantage in imaging deep vessels.

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

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.