GPU-accelerated OCT imaging: Real-time data processing and artifact suppression for enhanced monitoring of 3D bioprinted tissues and vascular-like networks

Abstract

Optical coherence tomography (OCT) imaging technology has significant advantages in in situ and noninvasive monitoring of biological tissues. However, it still faces the following challenges: including data processing speed, image quality, and improvements in three-dimensional (3D) visualization effects. OCT technology, especially functional imaging techniques like optical coherence tomography angiography (OCTA), requires a long acquisition time and a large data size. Despite the substantial increase in the acquisition speed of swept source optical coherence tomography (SS-OCT), it still poses significant challenges for data processing. Additionally, during in situ acquisition, image artifacts resulting from interface reflections or strong reflections from biological tissues and culturing containers present obstacles to data visualization and further analysis. Firstly, a customized frequency domain filter with anti-banding suppression parameters was designed to suppress artifact noises. Then, this study proposed a graphics processing unit (GPU)-based real-time data processing pipeline for SS-OCT, achieving a measured line-process rate of 800[Formula: see text]kHz for 3D fast and high-quality data visualization. Furthermore, a GPU-based real-time data processing for CC-OCTA was integrated to acquire dynamic information. Moreover, a vascular-like network chip was prepared using extrusion-based 3D printing and sacrificial materials, with sacrificial material being printed at the desired vascular network locations and then removed to form the vascular-like network. OCTA imaging technology was used to monitor the progression of sacrificial material removal and vascular-like network formation. Therefore, GPU-based OCT enables real-time processing and visualization with artifact suppression, making it particularly suitable for in situ noninvasive longitudinal monitoring of 3D bioprinting tissue and vascular-like networks in microfluidic chips.