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

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