ArticleBiomedical optics express2019
Real-time optical properties and oxygenation imaging using custom parallel processing in the spatial frequency domain.
Article in Biomedical optics express, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Development of a statistically standardized optical digital wrist model through integrated MRI-diffuse optical imaging methodology.Journal of biomedical optics · 2025Article
- Development and characterization of a combined fluorescence and spatial frequency domain imaging system for real-time dosimetry of photodynamic therapy.Journal of biomedical optics · 2025Article
- Deep-learning-enabled spatial frequency domain imaging of the spatiotemporal dynamics of skin physiology.Journal of biomedical optics · 2025Article
- Development of a platform for broadband, spectra-fitted, tissue optical phantoms.Journal of biomedical optics · 2023Article
- Measurement of tissue optical properties in a wide spectral range: a review [Invited].Biomedical optics express · 2023Review
- Deep learning in macroscopic diffuse optical imaging.Journal of biomedical optics · 2022Review
- Real-time, wide-field and high-quality single snapshot imaging of optical properties with profile correction using deep learning.Biomedical optics express · 2020Article
- Macroscopic fluorescence lifetime topography enhanced via spatial frequency domain imaging.Optics letters · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
The development of real-time, wide-field and quantitative diffuse optical imaging methods is becoming increasingly popular for biological and medical applications. Recent developments introduced a novel approach for real-time multispectral acquisition in the spatial frequency domain using spatio-temporal modulation of light. Using this method, optical properties maps (absorption and reduced scattering) could be obtained for two wavelengths (665 nm and 860 nm). These maps, in turn, are used to deduce oxygen saturation levels in tissues. However, while the acquisition was performed in real-time, processing was performed post-acquisition and was not in real-time. In the present article, we present CPU and GPU processing implementations for this method with special emphasis on processing time. The obtained results show that the proposed custom direct method using a General Purpose Graphic Processing Unit (GPGPU) and C CUDA (Compute Unified Device Architecture) implementation enables 1.6 milliseconds processing time for a 1 Mega-pixel image with a maximum average error of 0.1% in extracting optical properties.
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Registered trials
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