We present a Graph Cut based image segmentation that was implemented on a Graphics Processing Unit for acceleration of processing retinal images acquired with OCT. We applied this work to generate a retinal thickness map, and for retinal layer segmentation to enhance the visualization of vasculature networks from distinct retinal capillary beds during acquisition using speckle variance OCT.
Transgenic mouse models have been instrumental in the elucidation of the molecular mechanisms behind many genetically based cardiovascular diseases such as Marfan syndrome (MFS). However, the characterization of their cardiac morphology has been hampered by the small size of the mouse heart. In this report, we adapted optical coherence tomography (OCT) for imaging fixed adult mouse hearts, and applied tools from computational anatomy to perform morphometric analyses. The hearts were first optically cleared and imaged from multiple perspectives. The acquired volumes were then corrected for refractive distortions, and registered and stitched together to form a single, high-resolution OCT volume of the whole heart. From this volume, various structures such as the valves and myofibril bundles were visualized. The volumetric nature of our dataset also allowed parameters such as wall thickness, ventricular wall masses, and luminal volumes to be extracted. Finally, we applied the entire acquisition and processing pipeline in a preliminary study comparing the cardiac morphology of wild-type mice and a transgenic mouse model of MFS.
In this report, we describe a graphics processing unit (GPU)-accelerated processing platform for real-time acquisition and display of flow contrast images with Fourier domain optical coherence tomography (FDOCT) in mouse and human eyes in vivo. Motion contrast from blood flow is processed using the speckle variance OCT (svOCT) technique, which relies on the acquisition of multiple B-scan frames at the same location and tracking the change of the speckle pattern. Real-time mouse and human retinal imaging using two different custom-built OCT systems with processing and display performed on GPU are presented with an in-depth analysis of performance metrics. The display output included structural OCT data, en face projections of the intensity data, and the svOCT en face projections of retinal microvasculature; these results compare projections with and without speckle variance in the different retinal layers to reveal significant contrast improvements. As a demonstration, videos of real-time svOCT for in vivo human and mouse retinal imaging are included in our results. The capability of performing real-time svOCT imaging of the retinal vasculature may be a useful tool in a clinical environment for monitoring disease-related pathological changes in the microcirculation such as diabetic retinopathy.
KEYWORDS: Optical coherence tomography, Video acceleration, Video, Video processing, Visualization, OpenGL, Data processing, Data acquisition, Image processing, Volume rendering
In this report, we describe how to highly optimize a CUDA based platform to perform real time optical coherence tomography data processing and 3D volumetric rendering using commercially-available cost-effective graphic processing units (GPUs). The maximum complete attainable axial scan processing rate (including memory transfer and rendering frame) was 2.2 megahertz for 16 bits pixel depth and 2048 pixels/A-scan, the maximum 3D volumetric rendering speed is 23 volumes/second (size:1024×256×200). To the best of our knowledge, this is the fastest processing rate reported to date with single-chip GPU and the first implementation of real time video rate volumetric OCT processing and rendering that is capable of matching the ultrahigh-speed OCT acquisition rates.
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.
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