ArticleJournal of biomedical optics2014
Parallelized multi-graphics processing unit framework for high-speed Gabor-domain optical coherence microscopy.
Article in Journal of biomedical optics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Model-based optical coherence tomography angiography enables motion-insensitive vascular imaging.Biomedical optics express · 2021Article
- Quantitative assessment of human donor corneal endothelium with Gabor domain optical coherence microscopy.Journal of biomedical optics · 2019Article
- Ten Years of Gabor-Domain Optical Coherence Microscopy.Applied sciences (Basel, Switzerland) · 2019Article
- Capabilities of Gabor-domain optical coherence microscopy for the assessment of corneal disease.Journal of biomedical optics · 2019Article
- Optical Assessment of Soft Contact Lens Edge-Thickness.Optometry and vision science : official publication of the American Academy of Optometry · 2016Article
- Assessing microstructures of the cornea with Gabor-domain optical coherence microscopy: pathway for corneal physiology and diseases.Optics letters · 2015Article
- Measurement of a multi-layered tear film phantom using optical coherence tomography and statistical decision theory.Biomedical optics express · 2014Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Gabor-domain optical coherence microscopy (GD-OCM) is a volumetric high-resolution technique capable of acquiring three-dimensional (3-D) skin images with histological resolution. Real-time image processing is needed to enable GD-OCM imaging in a clinical setting. We present a parallelized and scalable multi-graphics processing unit (GPU) computing framework for real-time GD-OCM image processing. A parallelized control mechanism was developed to individually assign computation tasks to each of the GPUs. For each GPU, the optimal number of amplitude-scans (A-scans) to be processed in parallel was selected to maximize GPU memory usage and core throughput. We investigated five computing architectures for computational speed-up in processing 1000×1000 A-scans. The proposed parallelized multi-GPU computing framework enables processing at a computational speed faster than the GD-OCM image acquisition, thereby facilitating high-speed GD-OCM imaging in a clinical setting. Using two parallelized GPUs, the image processing of a 1×1×0.6 mm3 skin sample was performed in about 13 s, and the performance was benchmarked at 6.5 s with four GPUs. This work thus demonstrates that 3-D GD-OCM data may be displayed in real-time to the examiner using parallelized GPU processing.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.