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.

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.

Wavefront sensorless adaptive optics optical coherence tomography (WSAO-OCT) is a novel imaging technique for in vivo highresolution depth-resolved imaging that mitigates some of the challenges encountered with the use of sensor-based adaptive optics designs. This technique replaces the Hartmann Shack wavefront sensor used to measure aberrations with a depth-resolved image-driven optimization algorithm, with the metric based on the OCT volumes acquired in real-time. The custom-built ultrahigh-speed GPU processing platform and fast modal optimization algorithm presented in this paper was essential in enabling real-time, in vivo imaging of human retinas with wavefront sensorless AO correction. WSAO-OCT is especially advantageous for developing a clinical high-resolution retinal imaging system as it enables the use of a compact, low-cost and robust lens-based adaptive optics design. In this report, we describe our WSAO-OCT system for imaging the human photoreceptor mosaic in vivo. We validated our system performance by imaging the retina at several eccentricities, and demonstrated the improvement in photoreceptor visibility with WSAO compensation.

This report describes a novel, non-invasive and label-free optical imaging technique, speckle variance optical coherence tomography (svOCT), for visualising blood flow within human retinal capillary networks. This imaging system uses a custom-built swept source OCT system operating at a line rate of 100 kHz. Real-time processing and visualisation is implemented on a consumer grade graphics processing unit. To investigate the quality of microvascular detail acquired with this device we compared images of human capillary networks acquired with svOCT and fluorescein angiography. We found that the density of capillary microvasculature acquired with this svOCT device was visibly greater than fluorescein angiography. We also found that this svOCT device had the capacity to generate en face images of distinct capillary networks that are morphologically comparable with previously published histological studies. Finally, we found that this svOCT device has the ability to non-invasively illustrate the common manifestations of diabetic retinopathy and retinal vascular occlusion. The results of this study suggest that graphics processing unit accelerated svOCT has the potential to non-invasively provide useful quantitative information about human retinal capillary networks. Therefore svOCT may have clinical and research applications for the management of retinal microvascular diseases, which are a major cause of visual morbidity worldwide.

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.

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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.