ArticleBiomedical optics express2023
Deep-learning-based separation of shallow and deep layer blood flow rates in diffuse correlation spectroscopy.
Article in Biomedical optics express, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- Diffuse Correlation Spectroscopy for Noninvasive Monitoring of Peripheral Perfusion During Cardiogenic Shock and VA-ECMO Support: An Experimental Study.Shock (Augusta, Ga.) · 2026Article
- Pressure-dependent coupling between arterial pressure and intercostal muscle blood-flow index during acute hypotension in humans.Experimental physiology · 2026Article
- Cerebral blood flow monitoring using a deep learning implementation of the two-layer diffuse correlation spectroscopy analytical model with a 512 × 512 SPAD array.Neurophotonics · 2025Article
- Non-invasive monitoring of microcirculation dynamics in hypovolemic shock: a novel application of diffuse correlation spectroscopy.Intensive care medicine experimental · 2025Article
- Beneath the surface: revealing deep-tissue blood flow in human subjects with massively parallelized diffuse correlation spectroscopy.Neurophotonics · 2025Article
- Fast blood flow index reconstruction of diffuse correlation spectroscopy using a back-propagation-free data-driven algorithm.Biomedical optics express · 2025Article
- A Device-on-Chip Solution for Real-Time Diffuse Correlation Spectroscopy Using FPGA.Biosensors · 2024Article
- Correlation of diabetic renal hypoperfusion with microvascular responses of the skeletal muscle: a rat model study using diffuse correlation spectroscopy.Biomedical optics express · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diffuse correlation spectroscopy faces challenges concerning the contamination of cutaneous and deep tissue blood flow. We propose a long short-term memory network to directly quantify the flow rates of shallow and deep-layer tissues. By exploiting the different contributions of shallow and deep-layer flow rates to auto-correlation functions, we accurately predict the shallow and deep-layer flow rates (RMSE = 0.047 and 0.034 ml/min/100 g of simulated tissue, R
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Registered trials
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