ArticleBiomedical optics express2018
Approaches to denoise the diffuse optical signals for tissue blood flow measurement.
Article in Biomedical optics express, 2018. 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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Who cites it
7 citing papers in PubMed.
- Quantification of blood flow index in diffuse correlation spectroscopy using a robust deep learning method.Journal of biomedical optics · 2024Article
- Diffuse Correlation Spectroscopy: A Review of Recent Advances in Parallelisation and Depth Discrimination Techniques.Sensors (Basel, Switzerland) · 2023Review
- Comprehensive investigations of cerebral hemodynamic responses in CSVD patients with mental disorders: a pilot study.Frontiers in psychiatry · 2023Article
- Quantification of blood flow index in diffuse correlation spectroscopy using long short-term memory architecture.Biomedical optics express · 2021Article
- Recipes for diffuse correlation spectroscopy instrument design using commonly utilized hardware based on targets for signal-to-noise ratio and precision.Biomedical optics express · 2021Article
- Collimating micro-lens fiber array for noncontact near-infrared diffuse correlation tomography.Biomedical optics express · 2021Article
- Enhancement of diffuse correlation spectroscopy tissue blood flow measurement by acoustic radiation force.Biomedical optics express · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
Various diseases are relevant to the abnormal blood flow in tissue. Diffuse correlation spectroscopy (DCS) is an emerging technology to extract the blood flow index (BFI) from light electric field temporal autocorrelation data. To account for tissue heterogeneity and irregular geometry, we developed an innovative DCS algorithm (i.e., the Nth order linear algorithm, or simply the NL algorithm) previously, in which the DCS signals are fully utilized through iterative linear regressions. Under the framework of NL algorithm, the BFI to be extracted is significantly influenced by the linear regression approach adopted. In this study, three approaches were proposed and evaluated for performing the iterative linear regressions, in order to understand what are the appropriate regression methods for BFI estimation. The three methods are least-squared minimization (L
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Registered trials
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