System and method for embedded diffuse correlation spectroscopy

An apparatus to perform diffuse correlation spectroscopy (DCS) to measure blood perfusion in a biological tissue, the apparatus comprising:

• a hardware DCS correlator;
• and
• a hardware DCS analyzer coupled with the DCS correlator and embedded therewith on an integrated circuit chip, the DCS analyzer comprising:
• a memory, the memory storing autocorrelation values, model parameters used in solving an autocorrelation equation of scattered light, fitting parameters, and simulated correlation values obtained from a theoretical DCS model, the scattered light being detected by a photodetector from the blood perfusion in the biological tissue;
• a hardware mean square error (MSE) module configured to compute a mean square error between theoretical autocorrelation values computed from the model parameters and measured autocorrelation values;
• a hardware sorting module configured to sort a latest three MSE values obtained from the MSE module and to generate indexes of largest, medium, and smallest MSE values, the indexes being stored in the memory as a subset of the fitting parameters;
• a hardware convergence checking module configured to determine whether convergence is reached in solving the autocorrelation equation;
• a hardware search module configured to calculate αDB and β values at reflection, extension, contraction, and shrink locations, the αDB and β values being calculated in parallel;
• a hardware comparison module configured to compare two latest MSE values and facilitate finding new αDB and β values to replace those values associated with a largest MSE;
• a state controller coupled with the memory and modules comprising the MSE module, the sorting module, the convergence checking module, the search module, and the comparison module to control an operation thereof;
• and
• an output buffer to present, as an output of the DCS analyzer, a fitted solution of the autocorrelation equation, facilitating real-time quantification of the blood perfusion in the biological tissue.

An embedded diffuse correlation spectroscopy (DCS) analyzer to measure blood perfusion in a biological tissue, the analyzer comprising:

• a memory, the memory storing autocorrelation values, model parameters used in solving an autocorrelation equation of scattered light, fitting parameters, and simulated correlation values obtained from a theoretical DCS model, the scattered light being detected by a photodetector from the blood perfusion in the biological tissue;
• a hardware mean square error (MSE) module configured to compute a mean square error between theoretical autocorrelation values computed from the model parameters and measured autocorrelation values;
• a hardware sorting module configured to sort a latest three MSE values obtained from the MSE module and to generate indexes of largest, medium, and smallest MSE values, the indexes being stored in the memory as a subset of the fitting parameters;
• a hardware convergence checking module configured to determine whether convergence is reached in solving the autocorrelation equation;
• a hardware search module configured to calculate αDR and β values at reflection, extension, contraction, and shrink locations, the αDB and β values being calculated in parallel;
• a hardware comparison module configured to compare two latest MSE values and to facilitate finding new αDB and β values to replace those values associated with a largest MSE;
• a state controller coupled with the memory and modules comprising the MSE module, the sorting module, the convergence checking module, the search module, and the comparison module to control an operation thereof;
• and
• an output buffer configured to present, as an output of the DCS analyzer, a fitted solution of the autocorrelation equation, facilitating real-time quantification of the blood perfusion in the biological tissue, wherein based on feedback from the comparison module, the state controller is configured to:
• select a smallest MSE value (MSEx) as a first input and MSE at a reflection location (MSRR) as a second input;
• determine whether MSER is less than MSEX;
• select MSE at an extension location (MSEE) as an input when MSER is less than the current MSE value and determine whether MSEE is less than a current MSER;
• replace αDB and β values related to a largest MSE value (MSEZ) with αDB and β values related to MSEE when MSEE is less than MSER;
• replace αDB and β values related to MSEZ with αDB and β values related to MSER when MSEE is not less than MSER;
• select MSE at a contraction location (MSEC) as an input when MSER is not less than MSEX and determine whether MSEC is less than a current MSER;
• replace αDB and, β values related to MSEZ with αDB and, β values related to MSEC when MSEC is less than MSER;
• select MSEZ as an input when MSEC is not less than MSER and determine whether MSEZ is less than a MSEC;
• when MSEZ is less than MSEC, replace αDB and β values related to a medium MSE value (MSEY) with αDB and β values related to MSE in a first shrink location (MSESY), and replace αDB and β values related to MSEZ with αDB and β values related to MSE in a second shrink location (MSESZ);
• and
• when MSEZ is not less than MSEC, replace αDB and β values related to MSEZ with αDB and β values related to MSEC.