A \u3cem\u3eN\u3c/em\u3eth-Order Linear Algorithm for Extracting Diffuse Correlation Spectroscopy Blood Flow Indices in Heterogeneous Tissues

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 \u3c 3%) of high-order (N ≥ 5) linear algorithm in extracting BFIs in different tissue layers and rCBF in deep brain. By contrast, the semi-infinite homogenous solution resulted in substantial errors in rCBF (34.5% ≤ errors ≤ 60.2%) and BFIs in different layers. The Nth-order linear model simplifies data analysis, thus allowing for online data processing and displaying. Future study will test this linear algorithm in heterogeneous tissues with different levels of blood flow variations and noises

Clinical Applications of Near-Infrared Diffuse Correlation Spectroscopy and Tomography for Tissue Blood Flow Monitoring and Imaging

Objective. Blood flow is one such available observable promoting a wealth of physiological insight both individually and in combination with other metrics. Approach. Near-infrared diffuse correlation spectroscopy (DCS) and, to a lesser extent, diffuse correlation tomography (DCT), have increasingly received interest over the past decade as noninvasive methods for tissue blood flow measurements and imaging. DCS/DCT offers several attractive features for tissue blood flow measurements/imaging such as noninvasiveness, portability, high temporal resolution, and relatively large penetration depth (up to several centimeters). Main results. This review first introduces the basic principle and instrumentation of DCS/DCT, followed by presenting clinical application examples of DCS/DCT for the diagnosis and therapeutic monitoring of diseases in a variety of organs/tissues including brain, skeletal muscle, and tumor. Significance. Clinical study results demonstrate technical versatility of DCS/DCT in providing important information for disease diagnosis and intervention monitoring

A New Approach to Linear/Nonlinear Distributed Fusion Estimation Problem

Disturbance noises are always bounded in a practical system, while fusion estimation is to best utilize multiple sensor data containing noises for the purpose of estimating a quantity--a parameter or process. However, few results are focused on the information fusion estimation problem under bounded noises. In this paper, we study the distributed fusion estimation problem for linear time-varying systems and nonlinear systems with bounded noises, where the addressed noises do not provide any statistical information, and are unknown but bounded. When considering linear time-varying fusion systems with bounded noises, a new local Kalman-like estimator is designed such that the square error of the estimator is bounded as time goes to $\infty$. A novel constructive method is proposed to find an upper bound of fusion estimation error, then a convex optimization problem on the design of an optimal weighting fusion criterion is established in terms of linear matrix inequalities, which can be solved by standard software packages. Furthermore, according to the design method of linear time-varying fusion systems, each local nonlinear estimator is derived for nonlinear systems with bounded noises by using Taylor series expansion, and a corresponding distributed fusion criterion is obtained by solving a convex optimization problem. Finally, target tracking system and localization of a mobile robot are given to show the advantages and effectiveness of the proposed methods.Comment: 9 pages, 3 figure

Noncontact Three-Dimensional Diffuse Optical Imaging of Deep Tissue Blood Flow Distribution

The present invention provides for three-dimensional reflectance diffuse optical imaging of deep tissue blood flow distribution that removes the need for probe-tissue contact, thereby allowing for such technology to be applied to sensitive, vulnerable, damaged, or reconstructive tissue. The systems utilize noncontact application and detection of near-infrared light through optical lens and detection through a linear array or two-dimensional array of avalanche photodiodes or a two-dimensional array of detectors provided by charge-coupled-device (CCD). Both further feature a finite-element-method (FEM) based facilitation to provide for three-dimensional flow image reconstruction in deep tissues with arbitrary geometries

Noninvasive Optical Quantification of Absolute Blood Flow, Blood Oxygenation, and Oxygen Consumption Rate in Exercising Skeletal Muscle

This study investigates a method using novel hybrid diffuse optical spectroscopies [near-infrared spectroscopy (NIRS) and diffuse correlation spectroscopy (DCS)] to obtain continuous, noninvasive measurement of absolute blood flow (BF), blood oxygenation, and oxygen consumption rate (V̇O2) in exercising skeletal muscle. Healthy subjects (n=9) performed a handgrip exercise to increase BF and V̇O2 in forearm flexor muscles, while a hybrid optical probe on the skin surface directly monitored oxy-, deoxy-, and total hemoglobin concentrations ([HbO2], [Hb], and THC), tissue oxygen saturation (StO2), relative BF (rBF), and relative oxygen consumption rate (rV̇O2). The rBF and rV̇O2 signals were calibrated with absolute baseline BF and V̇O2 obtained through venous and arterial occlusions, respectively. Known problems with muscle-fiber motion artifacts in optical measurements during exercise were mitigated using a novel gating algorithm that determined muscle contraction status based on control signals from a dynamometer. Results were consistent with previous findings in the literature. This study supports the application of NIRS/DCS technology to quantitatively evaluate hemodynamic and metabolic parameters in exercising skeletal muscle and holds promise for improving diagnosis and treatment evaluation for patients suffering from diseases affecting skeletal muscle and advancing fundamental understanding of muscle and exercise physiology