ArticleBiomedical optics express2018
Nth-order linear algorithm for diffuse correlation tomography.
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 8 papers.
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Who cites it
8 citing papers in PubMed.
- AI-assisted diffuse correlation tomography for identifying breast cancer.Journal of biomedical optics · 2025Article
- Diffuse correlation tomography: a technique to characterize tissue blood flow abnormalities in benign and malignant breast lesions.Biomedical optics express · 2024Article
- Comprehensive investigations of cerebral hemodynamic responses in CSVD patients with mental disorders: a pilot study.Frontiers in psychiatry · 2023Article
- High-density diffuse correlation tomography with enhanced depth localization and minimal surface artefacts.Biomedical optics express · 2022Article
- Collimating micro-lens fiber array for noncontact near-infrared diffuse correlation tomography.Biomedical optics express · 2021Article
- Diffuse optical assessment of cerebral-autoregulation in older adults stratified by cerebrovascular risk.Journal of biophotonics · 2020Article
- Recovery of the diffuse correlation spectroscopy data-type from speckle contrast measurements: towards low-cost, deep-tissue blood flow measurements.Biomedical optics express · 2019Article
- Approaches to denoise the diffuse optical signals for tissue blood flow measurement.Biomedical optics express · 2018Article
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Authors and funding
5 authors.
Funding
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Abstract
The current approaches to imaging the tissue blood flow index (BFI) from diffuse correlation tomography (DCT) data are either an analytical solution or a finite element method, both of which are unable to simultaneously account for the tissue heterogeneity and fully utilize the DCT data. In this study, a new imaging concept for DCT, namely NL-DCT, was created by us in which the medical images are combined with light Monte Carlo simulation to provide geometrical and heterogeneous information in tissue. Moreover, the DCT data at multiple delay time are fully utilized via iterative linear regression. The unique merit of NL-DCT in utilizing the medical images as prior information, when combined with a split Bregman algorithm for total variation minimization (Bregman-TV), was validated on a realistic human head model. Computer simulation outcomes demonstrate the accuracy and robustness of NL-DCT in localizing and separating the flow anomalies as well as the capability to preserve edges of anomalies.
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