Processing-Aware Real-Time Rendering for Optimized Tissue Visualization in Intraoperative 4D OCT

Abstract

Intraoperative Optical Coherence Tomography (iOCT) has advanced in recent years to provide real-time high resolution volumetric imaging for ophthalmic surgery. It enables real-time 3D feedback during precise surgical maneuvers. Intraoperative 4D OCT generally exhibits lower signal-to-noise ratio compared to diagnostic OCT and visualization is complicated by instrument shadows occluding retinal tissue. Additional constraints of processing data rates upwards of 6 GB/s create unique challenges for advanced visualization of 4D OCT. Prior approaches for real-time 4D iOCT rendering have been limited to applying simple denoising filters and colorization to improve visualization. We present a novel real-time rendering pipeline that provides enhanced intraoperative visualization and is specifically designed for the high data rates of 4D iOCT. We decompose the volume into a static part consisting of the retinal tissue and a dynamic part including the instrument. Aligning the static parts over time allows temporal compounding of these structures for improved image quality. We employ a translational motion model and use axial projection images to reduce the dimensionality of the alignment. A model-based instrument segmentation on the projections discriminates static from dynamic parts and is used to exclude instruments from the compounding. Our real-time rendering method combines the compounded static information with the latest iOCT data to provide a visualization which compensates instrument shadows and improves instrument visibility. We evaluate the individual parts of our pipeline on pre-recorded OCT volumes and demonstrate the effectiveness of our method on a recorded volume sequence with a moving retinal forceps.