Browsing by Author "Miller, Donald T."

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    Adaptive-optics Optical Coherence Tomography Processing Using a Graphics Processing Unit
    (Institute of Electrical and Electronics Engineers, 2014) Shafer, Brandon A.; Kriske, Jeffery E.; Kocaoglu, Omer P.; Turner, Timothy L.; Liu, Zhuolin; Lee, John J.; Miller, Donald T.; Department of Engineering Technology, School of Engineering and Technology
    Graphics processing units are increasingly being used for scientific computing for their powerful parallel processing abilities, and moderate price compared to super computers and computing grids. In this paper we have used a general purpose graphics processing unit to process adaptive-optics optical coherence tomography (AOOCT) images in real time. Increasing the processing speed of AOOCT is an essential step in moving the super high resolution technology closer to clinical viability.
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    Imaging and quantifying ganglion cells and other transparent neurons in the living human retina
    (National Academy of Sciences, 2017-11-28) Liu, Zhuolin; Kurokawa, Kazuhiro; Zhang, Furu; Lee, John J.; Miller, Donald T.; Engineering Technology, School of Engineering and Technology
    Ganglion cells are the primary building block of retinal neural circuitry, but have been elusive to observe and quantify in the living human eye. Here, we show a light microscopy modality that reveals not only the somas of these cells, but also their 3D packing geometry, primary subtypes, and spatial projection to other neurons. The method provides a glimpse of the rich tapestry of neurons, glia, and blood vessels that compose the retina, thus exposing the anatomical substrate for neural processing of visual information. Clinically, high-resolution images of retinal neurons in living eyes hold promise for improved diagnosis and assessing treatment of ganglion cell and other neuron loss in retinal disease., Ganglion cells (GCs) are fundamental to retinal neural circuitry, processing photoreceptor signals for transmission to the brain via their axons. However, much remains unknown about their role in vision and their vulnerability to disease leading to blindness. A major bottleneck has been our inability to observe GCs and their degeneration in the living human eye. Despite two decades of development of optical technologies to image cells in the living human retina, GCs remain elusive due to their high optical translucency. Failure of conventional imaging—using predominately singly scattered light—to reveal GCs has led to a focus on multiply-scattered, fluorescence, two-photon, and phase imaging techniques to enhance GC contrast. Here, we show that singly scattered light actually carries substantial information that reveals GC somas, axons, and other retinal neurons and permits their quantitative analysis. We perform morphometry on GC layer somas, including projection of GCs onto photoreceptors and identification of the primary GC subtypes, even beneath nerve fibers. We obtained singly scattered images by: (i) marrying adaptive optics to optical coherence tomography to avoid optical blurring of the eye; (ii) performing 3D subcellular image registration to avoid motion blur; and (iii) using organelle motility inside somas as an intrinsic contrast agent. Moreover, through-focus imaging offers the potential to spatially map individual GCs to underlying amacrine, bipolar, horizontal, photoreceptor, and retinal pigment epithelium cells, thus exposing the anatomical substrate for neural processing of visual information. This imaging modality is also a tool for improving clinical diagnosis and assessing treatment of retinal disease.
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    Multi-reference global registration of individual A-lines in adaptive optics optical coherence tomography retinal images
    (Elsevier, 2021) Kurokawa, Kazuhiro; Crowell, James A.; Do, Nhan; Lee, John J.; Miller, Donald T.; Engineering Technology, Purdue School of Engineering and Technology
    Significance: Adaptive optics optical coherence tomography (AO-OCT) technology enables non-invasive, high-resolution three-dimensional (3D) imaging of the retina and promises earlier detection of ocular disease. However, AO-OCT data are corrupted by eye-movement artifacts that must be removed in post-processing, a process rendered time-consuming by the immense quantity of data. Aim: To efficiently remove eye-movement artifacts at the level of individual A-lines, including those present in any individual reference volume. Approach: We developed a registration method that cascades (1) a 3D B-scan registration algorithm with (2) a global A-line registration algorithm for correcting torsional eye movements and image scaling and generating global motion-free coordinates. The first algorithm corrects 3D translational eye movements to a single reference volume, accelerated using parallel computing. The second algorithm combines outputs of multiple runs of the first algorithm using different reference volumes followed by an affine transformation, permitting registration of all images to a global coordinate system at the level of individual A-lines. Results: The 3D B-scan algorithm estimates and corrects 3D translational motions with high registration accuracy and robustness, even for volumes containing microsaccades. Averaging registered volumes improves our image quality metrics up to 22 dB. Implementation in CUDA™ on a graphics processing unit registers a 512 × 512 × 512 volume in only 10.6 s, 150 times faster than MATLAB™ on a central processing unit. The global A-line algorithm minimizes image distortion, improves regularity of the cone photoreceptor mosaic, and supports enhanced visualization of low-contrast retinal cellular features. Averaging registered volumes improves our image quality up to 9.4 dB. It also permits extending the imaging field of view (∼2.1 × ) and depth of focus (∼5.6 × ) beyond what is attainable with single-reference registration. Conclusions: We can efficiently correct eye motion in all 3D at the level of individual A-lines using a global coordinate system.
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    Parallel Processing For Adaptive Optics Optical Coherence Tomography (AO-OCT) Image Registration Using GPU
    (2016-07-08) Do, Nhan Hieu; Lee, John Jaehwan; Miller, Donald T.; King, Brian; Salama, Paul
    Adaptive Optics Optical Coherence Tomography (AO-OCT) is a high-speed, high-resolution ophthalmic imaging technique offering detailed 3D analysis of retina structure in vivo. However, AO-OCT volume images are sensitive to involuntary eye movements that occur even during steady fixation and include tremor, drifts, and micro-saccades. To correct eye motion artifacts within a volume and to stabilize a sequence of volumes acquired of the same retina area, we propose a stripe-wise 3D image registration algorithm with phase correlation. In addition, using several ideas such as coarse-to-fine approach, spike noise filtering, pre-computation caching, and parallel processing on a GPU, our approach can register a volume of size 512 x 512 x 512 in less than 6 seconds, which is a 33x speedup as compared to an equivalent CPU version in MATLAB. Moreover, our 3D registration approach is reliable even in the presence of large motions (micro-saccades) that distort the volumes. Such motion was an obstacle for a previous en face approach based on 2D projected images. The thesis also investigates GPU implementations for 3D phase correlation and 2D normalized cross-correlation, which could be useful for other image processing algorithms.