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Your search keyword '"Guoqiang Yu"' showing total 3 results
Start Over Author "Guoqiang Yu" Topic blood flow Journal applied physics letters
3 results on '"Guoqiang Yu"'

1. Three-dimensional flow contrast imaging of deep tissue using noncontact diffuse correlation tomography

Author

Daniel Irwin, Yu Lin, Yu Shang, Lian He, Guoqiang Yu, and Chong Huang

Subjects
Flow visualization, Materials science, Physics and Astronomy (miscellaneous), medicine.diagnostic_test, business.industry, Blood flow, Iterative reconstruction, Laser, Imaging phantom, law.invention, Optics, law, Region of interest, medicine, Photonics and Optoelectronics, Tomography, Optical tomography, business
Abstract

This study extended our recently developed noncontact diffuse correlation spectroscopy flowmetry system into noncontact diffuse correlation tomography (ncDCT) for three-dimensional (3-D) flow imaging of deep tissue. A linear array of 15 photodetectors and two laser sources connected to a mobile lens-focusing system enabled automatic and noncontact scanning of flow in a region of interest. These boundary measurements were combined with a finite element framework for DCT image reconstruction implemented into an existing software package. This technique was tested in computer simulations and using a tissue-like phantom with anomaly flow contrast design. The cylindrical tube-shaped anomaly was clearly reconstructed in both simulation and phantom. Recovered and assigned flow contrast changes in anomaly were found to be highly correlated: regression slope = 1.00, R2 = 1.00, and p

Published
2013

2. A Nth-order linear algorithm for extracting diffuse correlation spectroscopy blood flow indices in heterogeneous tissues

Author

Yu Shang and Guoqiang Yu

Subjects
Physics, Diffusion equation, Physics and Astronomy (miscellaneous), Quantitative Biology::Tissues and Organs, Physics::Medical Physics, Autocorrelation, Feature extraction, Monte Carlo method, Linear model, Contrast (statistics), Blood flow, Cerebral blood flow, Statistical physics, Biological system
Abstract

Conventional semi-infinite analytical solutions of correlation diffusion equation may lead to errors when calculating blood flow index (BFI) from diffuse correlation spectroscopy (DCS) measurements in tissues with irregular geometries. Very recently, we created an algorithm integrating a Nth-order linear model of autocorrelation function with the Monte Carlo simulation of photon migrations in homogenous tissues with arbitrary geometries for extraction of BFI (i.e., αDB ). The purpose of this study is to extend the capability of the Nth-order linear algorithm for extracting BFI in heterogeneous tissues with arbitrary geometries. The previous linear algorithm was modified to extract BFIs in different types of tissues simultaneously through utilizing DCS data at multiple source-detector separations. We compared the proposed linear algorithm with the semi-infinite homogenous solution in a computer model of adult head with heterogeneous tissue layers of scalp, skull, cerebrospinal fluid, and brain. To test the capability of the linear algorithm for extracting relative changes of cerebral blood flow (rCBF) in deep brain, we assigned ten levels of αDB in the brain layer with a step decrement of 10% while maintaining αDB values constant in other layers. Simulation results demonstrate the accuracy (errors

Published
2014

3. Extraction of diffuse correlation spectroscopy flow index by integration of Nth-order linear model with Monte Carlo simulation.

Author

Yu Shang, Ting Li, Lei Chen, Yu Lin, Toborek, Michal, and Guoqiang Yu

Subjects
BLOOD flow, MONTE Carlo method, STATISTICAL correlation, LINEAR statistical models, ALGORITHMS, RANDOM noise theory, MATHEMATICAL models
Abstract

Conventional semi-infinite solution for extracting blood flow index (BFI) from diffuse correlation spectroscopy (DCS) measurements may cause errors in estimation of BFI (αDB) in tissues with small volume and large curvature. We proposed an algorithm integrating Nth-order linear model of autocorrelation function with the Monte Carlo simulation of photon migrations in tissue for the extraction of αDB. The volume and geometry of the measured tissue were incorporated in the Monte Carlo simulation, which overcome the semi-infinite restrictions. The algorithm was tested using computer simulations on four tissue models with varied volumes/geometries and applied on an in vivo stroke model of mouse. Computer simulations shows that the high-order (N≥5) linear algorithm was more accurate in extracting αDB (errors<±2%) from the noise-free DCS data than the semi-infinite solution (errors: -5.3% to -18.0%) for different tissue models. Although adding random noises to DCS data resulted in αDB variations, the mean values of errors in extracting αDB were similar to those reconstructed from the noise-free DCS data. In addition, the errors in extracting the relative changes of αDB using both linear algorithm and semi-infinite solution were fairly small (errors<±2.0%) and did not rely on the tissue volume/geometry. The experimental results from the in vivo stroke mice agreed with those in simulations, demonstrating the robustness of the linear algorithm. DCS with the high-order linear algorithm shows the potential for the inter-subject comparison and longitudinal monitoring of absolute BFI in a variety of tissues/organs with different volumes/geometries. [ABSTRACT FROM AUTHOR]

Published
2014
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