Comparison of real-time visualization of volumetric OCT data sets by CPU-enabled slicing and GPU-supported ray casting

Alfred R. Fullera, Robert J. Zawadzkib*, Bernd Hamanna and John S. Wernerb

aVisualization and Computer Graphics Research Group, Inst. for Data Analysis and Visualization (IDAV) and Dept. of Computer Science, UC Davis, One Shields Avenue, Davis, CA 95616;

bVision Science and Advanced Retinal Imaging Laboratory (VSRI) and Dept. of Ophthalmology & Vision Science, UC Davis, 4860 Y Street, Suite 2400, Sacramento, CA USA 95817

ABSTRACT

We describe and compare two volume visualization methods for optical coherence tomography (OCT) retinal data sets. One method that was described in an earlier paper is used in our visualization system; one method is CPU-enabled slicing and the other is GPU-supported ray casting. Several metrics including image quality, performance and depth perception are used to grade each method. Feasibility of both visualization schemes for clinical application as well as potential further improvements are discussed.

Keywords: Optical coherence tomography; Ophthalmology; Imaging system; Medical optics instrumentation; Volumetric visualization;

INTRODUCTION Recent progress in OCT, including its Fourier-domain extension, has made possible its successful implementation in commercial clinical instruments by several companies. Acquisition speed of these systems permits measurements of volumetric retinal structures within two to ten seconds, depending on the system’s speed and lateral sampling density, resulting in a large amount of data produced even during a single imaging session. This technological evolution creates a growing demand for fast volume visualization and manipulation software. Over the past three years, our group has been actively developing custom volume visualization software slicing enabled by the computer’s central processing unit (CPU). Recent advances in computer graphics architecture opened the possibility of implementing methods for real-time ray casting supported by today's graphics processing unit (GPU). Both CPU-slicing and GPU-ray casting are based on evenly spaced sampling volume reconstruction; however, the method by which the positions of these samples are calculated varies. CPU-slicing generates two-dimensional slices of the volume whose normal vectors are parallel to the line of sight (view vector) on the CPU and reconstructs the volume using alpha blending on the GPU. GPU-ray casting calculates the vector from the eye point to the volume and steps along this ray on the GPU. It also performs alpha blending on the GPU to reconstruct the volume. The principal advantages of GPU-ray casting are (1) more accurate sampling in perspective renderings and (2) greater flexibility in the lighting of the volumes. The use of either method has many implications with regard to their viability in a clinical setting. We have compared these methods quantitatively and describe a robust solution that utilizes both methods to maximize the performance of our overall system.

As an example, we discuss the visualization of two retinal structures rendered by both volume reconstruction methods. Figure 1 shows a visualization of the optic nerve head (ONH) (acquired over a region of size 6mm x 6mm with our Fd-OCT system) of a healthy 30-year-old volunteer using CPU-slicing as shown on the left and GPU-ray casting on the right side. Please note the improved depth perception as well as topographic features of the top retinal layer with GPU-ray casting due to realistic shadowing effects. *rjzawadzki@ucdavis.edu; phone 1 916 734-5839; fax 1 916 734-4543; http://vsri.ucdavis.edu/

Fig. 1. Visualization of Optical Nerve Head (ONH) region of healthy volunteer. Left: CPU-slicing; right: GPU ray casting

Figure 2 shows another visualization of retinal structures. In this case, a retinal volume acquired over the foveal region covering a 6 mm x 6 mm area of a patient with a retinal detachment. Again, CPU-slicing is shown on the left and GPU-ray casting on the right side. Similarly to the previous visualization, GPU-ray casting results in improved depth perception as well as better visibility of topographic features.

Fig. 2. Visualization of retinal detachment. Left: CPU-slicing; right: GPU ray casting.

The feasibility of both visualization schemes for clinical application as well as potential further improvements are discussed.

ACKNOWLEDGEMENTS

We gratefully acknowledge the contributions of Joseph A. Izatt, from the Dept of Biomedical Engineering, Duke University Durham, NC. The help of Bioptigen Inc. for providing OCT data acquisition software is appreciated. Alfred Fuller has been supported by a Student Employee Graduate Research Fellowship (SEGRF) via Lawrence Livermore National Laboratory. This research was supported by the National Eye Institute (EY 014743).