Graphics processing unit accelerated optical coherence tomography processing at megahertz axial scan rate and high resolution video rate volumetric rendering

Abstract

In this report, we describe how to highly optimize a computer unified device architecture based platform to perform real-time processing of optical coherence tomography interferometric data and three-dimensional (3-D) volumetric rendering using a commercially available, cost-effective, graphics processing unit (GPU). The maximum complete attainable axial scan processing rate, including memory transfer and displaying B-scan frame, was 2.24 MHz for 16 bits pixel depth and 2048 fast Fourier transform size; the maximum 3-D volumetric rendering rate, including B-scan, en face view display, and 3-D rendering, was ~23 volumes/second (volume size: 1024×256×200). To the best of our knowledge, this is the fastest processing rate reported to date with a single-chip GPU and the first implementation of real-time video-rate volumetric optical coherence tomography (OCT) processing and rendering that is capable of matching the acquisition rates of ultrahigh-speed OCT.

An improved algorithm of structural image reconstruction with rapid scanning optical delay line for Optical Coherence Tomography

“…This occurs most often in case than MATLAB or LabVIEW computing environment used because most of the inner functions in these packages are single threaded and using all CPU cores means use of additional Toolboxes. Some other algorithms implement parallelization using Graphics Processing Unit (GPU) which allows sufficiently increasing speed of image processing but it means additional money cost for buying GPGPU compatible video card [17]. Programming languages which allow multi-threaded calculation such as C# allows making image processing at video rate speed (24 frames per seconds) using even inexpensive CPU with 2-4 cores onboard.…”

High-speed OCT light sources and systems [Invited]

“…Fortunately, each A-scan can be processed independently, opening the possibility of fully exploiting the parallel processing power of graphics processing units (GPUs). Since MHz OCT systems were not commercially available until recently, the data processing rate was much higher than the data acquisition rate, and processing was applied to prerecorded data sets or at relatively slow speeds [134][135][136]. Apart from data processing, visualization of the results requires a lot of computational power [137,138], especially when combined with additional processing, such as for Doppler-OCT [139,140].…”

Review of intraoperative optical coherence tomography: technology and applications [Invited]

“…GPUs were primarily used for rendering graphics prior to the advent of high level GPU programming languages such as NVIDIA's compute unified device architecture (CUDA) [134]. For OCT in particular, GPU processing became highly desirable as the repetition rate of OCT lasers increased to kilohertz [35,114,[135][136][137][138] and even megahertz [34], and researchers were no longer able to process OCT data on the CPU in real-time. Moreover, GPUs also allowed real-time volume rendering for visualization and manipulation of OCT 3D and 4D (volumes over time) data.…”