Using Monte Carlo and iterative techniques to determine tissue oxygen saturation

• US 10,213,142 B2
• Filed: 05/24/2016
• Issued: 02/26/2019
• Est. Priority Date: 05/03/2012
• Status: Active Grant

• Current Assignee: ViOptix, Inc.
• Original Assignee: ViOptix, Inc.
• Inventors: Bechtel, Kate LeeAnn, Nishihara, H. Keith
• Primary Examiner(s): Winakur, Eric
• Assistant Examiner(s): Fardanesh, Marjan
• Application Number:
  US15/163,565
• Publication Number:
  US 20160262665A1
• Time in Patent Office: 1,008 Days
• Field of Search: None
• CPC Class Codes: A61B 2090/065 for measuring contact or contact pressure
• CPC Class Codes: A61B 2090/306 using optical fibres
• CPC Class Codes: A61B 2090/395 with marking agent for marking skin or other tissue
• CPC Class Codes: A61B 2560/0431 Portable apparatus, e.g. comprising a handle or case
• CPC Class Codes: A61B 2560/0475 Special features of memory means, e.g. removable memory cards
• CPC Class Codes: A61B 2562/0271 Thermal or temperature sensors
• CPC Class Codes: A61B 2562/166 the sensor is mounted on a specially adapted printed circuit board
• CPC Class Codes: A61B 5/0059 using light, e.g. diagnosis by transillumination, diascopy, fluorescence A61B5/0093, A61B5/1455, A61B5/02416 and A61B5/0261 take precedence
• CPC Class Codes: A61B 5/0075 by spectroscopy, i.e. measuring spectra, e.g. Raman spectroscopy, infrared absorption spectroscopy A61B5/0071 takes precedence
• CPC Class Codes: A61B 5/14546 for measuring analytes not otherwise provided for, e.g. ions, cytochromes
• CPC Class Codes: A61B 5/1455 using optical sensors, e.g. spectral photometrical oximeters
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/14552 Details of sensors specially adapted therefor
• CPC Class Codes: A61B 5/1459 invasive, e.g. introduced into the body by a catheter A61B5/1464 takes precedence
• CPC Class Codes: A61B 5/1495 Calibrating or testing of in-vivo probes
• CPC Class Codes: A61B 5/72 Signal processing specially adapted for physiological signals or for diagnostic purposes character recognition, recognising digital ink, document-oriented image-based pattern recognition G06V30/00; recognition or understanding of postal images G06V30/424
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61B 5/74 Details of notification to user or communication with user or patient indicating measured values G01D7/00; user input means input or output arrangements for computers G06F3/00
• CPC Class Codes: A61B 5/7405 using sound
• CPC Class Codes: A61B 5/742 : using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/7475 : User input or interface means, e.g. keyboard, pointing device, joystick
• CPC Class Codes: A61B 90/11 : with guides for needles or instruments, e.g. arcuate slides or ball joints
• CPC Class Codes: A61B 90/39 : Markers, e.g. radio-opaque or breast lesions markers
• CPC Class Codes: A61M 35/003 : Portable hand-held applicators having means for dispensing or spreading integral media hand-held massage devices with liquid delivery A61H7/003

Abstract

A method for determining oxygen saturation includes emitting light from sources into tissue; detecting the light by detectors subsequent to reflection; and generating reflectance data based on detecting the light. The method includes determining a first subset of simulated reflectance curves from a set of simulated reflectance curves stored in a tissue oximetry device for a coarse grid; and fitting the reflectance data points to the first subset of simulated reflectance curves to determine a closest fitting one of the simulated reflectance curves. The method includes determining a second subset of simulated reflectance curves for a fine grid based on the closest fitting one of the simulated reflectance curves; determining a peak of absorption and reflection coefficients from the fine grid; and determining an absorption and a reflectance coefficient for the reflectance data points by performing a weighted average of the absorption coefficients and reflection coefficients from the peak.

Claims

1. A method comprising:

providing a tissue oximetry device comprising a housing, wherein the tissue oximetry device is self-contained and comprises a sensor tip comprising a set of light sources, a plurality of detectors, memory, and a processor, the source and detectors are separated by predetermined distances, and the memory stores a set of simulated reflectance curves, each simulated reflectance curve of the set of simulated reflectance curves is based on a simulation of light reflected from simulated tissue, and simulated data points of simulated reflectance intensities of the simulated reflectance curves are for the predetermined distances between the source and detectors;

transforming electrical signals generated by the processor into light using the light sources;

using the set of light sources of the tissue oximetry device, emitting the light having at least two wavelengths into tissue;

using the plurality of detectors of the tissue oximetry device, detecting light reflected in response to the light emitted into the tissue;

transforming the detected light into electrical signal using the detectors, wherein the electrical signals correspond to reflectance data points for the tissue;

generating the digital reflectance data points for the tissue based on the electrical signals;

from the simulated data points, for the simulated reflectance intensities for the predetermined distances between the source and detectors, for the set of simulated reflectance curves stored in a memory of the tissue oximetry device, selecting, by the processor housed in the housing, a first selected simulated reflectance curve from the set of simulated reflectance curves stored in the memory to form a coarse grid of the set of simulated reflectance curves;

from the simulated data points, for the simulated reflectance intensities for the predetermined distances between the source and detectors, for the set of simulated reflectance curves stored in a memory of the tissue oximetry device, selecting, by the processor housed in the housing, a second selected simulated reflectance curve from the set of simulated reflectance curves stored in the memory to form the coarse grid of the set of simulated reflectance curves, wherein the second selected simulated reflectance curve is a first interval value away from the first simulated reflectance curve;

from the simulated data points, for the simulated reflectance intensities for the predetermined distances between the source and detectors, for the set of simulated reflectance curves stored in a memory of the tissue oximetry device, selecting, by the processor housed in the housing, a third selected simulated reflectance curve from the set of simulated reflectance curves stored in the memory to form the coarse grid of the set of simulated reflectance curves, wherein the third selected simulated reflectance curve is a second interval value away from the second simulated reflectance curve;

forming, by the processor housed in the housing, a first subset of simulated reflectance curves comprising the first, second, and third simulated reflectance curves, wherein the first subset is based on the coarse grid of the set of simulated reflectance curves stored in the memory and having the first and second interval values in the set of simulated reflectance curves stored in the memory;

using the processor of the tissue oximetry device, calculating a closest fitting of the digital reflectance data points to a closest fit curve of the first subset of simulated reflectance curves;

based on the closest fit curve, forming, by the processor housed in the housing, a second subset of simulated reflectance curves from the set of simulated reflectance curves, wherein the second subset is based on a fine grid;

using the closest fit curve and second subset of simulated reflectance curves, calculating a set of absorption coefficients and a set of scattering coefficients for the reflectance data points;

using the processor of the tissue oximetry device, calculating an oxygen saturation value for the tissue based on the set of absorption coefficients and disregarding the scattering coefficients when calculating the oxygen saturation value; and

outputting an indication of the oxygen saturation value at an interface of the tissue oximetry device.

2. The method of claim 1 wherein the interface of the tissue oximetry device comprises a liquid crystal display.

3. The method of claim 1 wherein the interface of the tissue oximetry device comprises light emitting diodes.

4. The method of claim 1 wherein the interface of the tissue oximetry device comprises a speaker.

5. The method of claim 1 wherein the using of the closest fit curve and second subset of simulated reflectance curves, calculating a set of absorption coefficients and a set of scattering coefficients for the reflectance data points comprises:

positioning the closest fit curve a center of the fine grid of the second subset of simulated reflectance curves;

using the fine grid with the closest fit curve at the center, determining a peak surface array of absorption coefficients and reflection scattering coefficients; and

calculating a centroid calculation of the absorption coefficients and scattering coefficients from the peak surface array to determine the set of absorption coefficients and the set of scattering coefficients for the reflectance data points.

6. The method of claim 5 wherein the absorption coefficients and the scattering coefficients for the reflectance data points are independent from each other.

7. The method of claim 1 wherein the simulated reflectance curves were previously determined, before the digital reflectance data points are determined.

8. The method of claim 1 comprising disregarding the set of scattering coefficients when calculating the oxygen saturation value for the tissue.

9. The method of claim 1 wherein the absorption coefficients and the scattering coefficients for the reflectance data points are independent from each other when the scattering coefficients and the scattering coefficients for the reflectance data points are determined.

10. The method of claim 1 wherein at a first frequency repetitively performing transforming the detected light into electrical signal using the detectors,

wherein at a second frequency repetitively performing:

from the simulated data points, for the simulated reflectance intensities for the predetermined distances between the source and detectors, for the set of simulated reflectance curves stored in the memory of the tissue oximetry device, selecting, by the processor housed in the housing, the first selected simulated reflectance curve from the set of simulated reflectance curves stored in the memory to form the coarse grid of the set of simulated reflectance curves;

from the simulated data points, for the simulated reflectance intensities for the predetermined distances between the source and detectors, for the set of simulated reflectance curves stored in the memory of the tissue oximetry device, selecting, by the processor housed in the housing, the second selected simulated reflectance curve from the set of simulated reflectance curves stored in the memory to form the coarse grid of the set of simulated reflectance curves, wherein the second selected simulated reflectance curve is the first interval value away from the first simulated reflectance curve;

from the simulated data points, for the simulated reflectance intensities for the predetermined distances between the source and detectors, for the set of simulated reflectance curves stored in the memory of the tissue oximetry device, selecting, by the processor housed in the housing, the third selected simulated reflectance curve from the set of simulated reflectance curves stored in the memory to form the coarse grid of the set of simulated reflectance curves, wherein the third selected simulated reflectance curve is the second interval value away from the second simulated reflectance curve;

forming, by the processor housed in the housing, the first subset of simulated reflectance curves comprising the first, second, and third simulated reflectance curves, wherein the first subset is based on the coarse grid of the set of simulated reflectance curves stored in the memory and having the first and second interval values in the set of simulated reflectance curves stored in the memory;

using the processor of the tissue oximetry device, calculating the closest fitting of the digital reflectance data points to the closest fit curve of the first subset of simulated reflectance curves;

based on the closest fit curve, forming, by the processor housed in the housing, the second subset of simulated reflectance curves from the set of simulated reflectance curves, wherein the second subset is based on the fine grid;

using the closest fit curve and second subset of simulated reflectance curves, calculating the set of absorption coefficients and the set of scattering coefficients for the reflectance data points; and

using the processor of the tissue oximetry device, calculating the oxygen saturation value for the tissue based on the set of absorption coefficients and disregarding the scattering coefficients when calculating the oxygen saturation value, and

wherein at a third frequency repetitively performing outputting the indication of the oxygen saturation value at the interface of the tissue oximetry device, and the first, second, and third frequencies are different frequencies.

11. The method of claim 10 wherein the first frequency is greater than the second and third frequencies and the second frequency is greater than the third frequency.

12. The method of claim 1 wherein the processor of the tissue oximeter device is not used to calculate the simulated reflectance curves.

13. A method comprising:

providing a tissue oximetry device comprising a housing, wherein the tissue oximetry device is self-contained and comprises a sensor tip comprising a set of light sources, a plurality of detectors, memory, and a processor, the source and detectors are separated by predetermined distances, and the memory stores a set of simulated reflectance curves, each simulated reflectance curve of the set of simulated reflectance curves is based on a simulation of light reflected from simulated tissue, and simulated data points of simulated reflectance intensities of the simulated reflectance curves are for the distances between the source and detectors;

using the set of light sources of the tissue oximetry device, emitting light having at least a two wavelengths into tissue;

using the plurality of detectors of the tissue oximetry device, detecting light reflected in response to the light emitted into the tissue;

generating the digital reflectance data points for the tissue based on the detected light;

from the simulated data points, for the simulated reflectance intensities for the predetermined distances between the source and detectors, for the set of simulated reflectance curves stored in a memory of the tissue oximetry device, selecting, by the processor housed in the housing, a first selected simulated reflectance curve from the set of simulated reflectance curves stored in the memory;

from the simulated data points, for the simulated reflectance intensities for the predetermined distances between the source and detectors, for the set of simulated reflectance curves stored in a memory of the tissue oximetry device, selecting, by the processor housed in the housing, a second selected simulated reflectance curve from the set of simulated reflectance curves stored in the memory, wherein the second selected simulated reflectance curve is a first interval value away from the first simulated reflectance curve;

from the simulated data points, for the simulated reflectance intensities for the predetermined distances between the source and detectors, for the set of simulated reflectance curves stored in a memory of the tissue oximetry device, selecting, by the processor housed in the housing, a third selected simulated reflectance curve from the set of simulated reflectance curves stored in the memory, wherein the third selected simulated reflectance curve is a second interval value away from the second simulated reflectance curve;

forming, by the processor housed in the housing, a first subset of simulated reflectance curves comprising the first, second, and third simulated reflectance curves, wherein the first subset is based on the coarse grid of the set of simulated reflectance curves stored in the memory and having the first and second interval values in the set of simulated reflectance curves stored in the memory;

using the processor of the tissue oximetry device, calculating a closest fitting of the digital reflectance data points to a closest fit curve of the first subset of simulated reflectance curves;

based on the closest fit curve, forming, by the processor housed in the housing, a second subset of simulated reflectance curves from the set of simulated reflectance curves, wherein the second subset is based on a fine grid;

positioning, by the processor housed in the housing, the closest fit curve at a center of the fine grid of the second subset of simulated reflectance curves;

using the fine grid with the closest fit curve at the center, determining a peak surface array of absorption coefficients and reflection scattering coefficients; and

using the processor, calculating a centroid calculation of the absorption coefficients and scattering coefficients from the peak surface array to determine the set of absorption coefficients for the reflectance data points;

using the processor of the tissue oximetry device, calculating an oxygen saturation value for the tissue based on the set of absorption coefficients; and

outputting an indication of the oxygen saturation value at an interface of the tissue oximetry device.

14. The method of claim 13 wherein the simulated reflectance curves were previously determined, before the digital reflectance data points are determined.

15. The method of claim 13 comprising disregarding the set of scattering coefficients when calculating the oxygen saturation value for the tissue.

16. The method of claim 13 wherein the absorption coefficients and the scattering coefficients for the reflectance data points are independent from each other when the scattering coefficients and the scattering coefficients for the reflectance data points are determined.

17. The method of claim 13 wherein the processor of the tissue oximeter device is not used to calculate the simulated reflectance curves.

18. The method of claim 13 wherein at a first frequency repetitively performing:

generating the digital reflectance data points for the tissue based on the electrical signals;

from the simulated data points, for the simulated reflectance intensities for the predetermined distances between the source and detectors, for the set of simulated reflectance curves stored in the memory of the tissue oximetry device, selecting, by the processor housed in the housing, the first selected simulated reflectance curve from the set of simulated reflectance curves stored in the memory;

from the simulated data points, for the simulated reflectance intensities for the predetermined distances between the source and detectors, for the set of simulated reflectance curves stored in the memory of the tissue oximetry device, selecting, by the processor housed in the housing, the second selected simulated reflectance curve from the set of simulated reflectance curves stored in the memory, wherein the second selected simulated reflectance curve is the first interval value away from the first simulated reflectance curve;

from the simulated data points, for the simulated reflectance intensities for the predetermined distances between the source and detectors, for the set of simulated reflectance curves stored in the memory of the tissue oximetry device, selecting, by the processor housed in the housing, the third selected simulated reflectance curve from the set of simulated reflectance curves stored in the memory, wherein the third selected simulated reflectance curve is the second interval value away from the second simulated reflectance curve;

forming, by the processor housed in the housing, the first subset of simulated reflectance curves comprising the first, second, and third simulated reflectance curves, wherein the first subset is based on the coarse grid of the set of simulated reflectance curves stored in the memory and having the first and second interval values in the set of simulated reflectance curves stored in the memory;

using the processor of the tissue oximetry device, calculating the closest fitting of the digital reflectance data points to the closest fit curve of the first subset of simulated reflectance curves;

based on the closest fit curve, forming, by the processor housed in the housing, the second subset of simulated reflectance curves from the set of simulated reflectance curves, wherein the second subset is based on the fine grid;

positioning, by the processor housed in the housing, the closest fit curve at the center of the fine grid of the second subset of simulated reflectance curves;

using the fine grid with the closest fit curve at the center, determining the peak surface array of absorption coefficients and reflection scattering coefficients; and

using the processor, calculating the centroid calculation of the absorption coefficients and scattering coefficients from the peak surface array to determine the set of absorption coefficients for the reflectance data points; and

using the processor of the tissue oximetry device, calculating the oxygen saturation value for the tissue based on the set of absorption coefficients, and

wherein at a second frequency repetitively performing outputting the indication of the oxygen saturation value at the interface of the tissue oximetry device, and the first and second frequencies are different frequencies.

19. The method of claim 18 wherein the first frequency is greater than the second frequency.

23. The method of claim 18 wherein the first frequency is greater than the second and third frequencies.

24. The method of claim 23 wherein the second frequency is greater than the third frequency.

20. A method comprising:

providing a tissue oximetry device, which is self-contained, comprising a housing, a processor housed by the housing, a memory housed by the housing, and a sensor tip, housed by the housing, comprising a set of light sources and a plurality of detectors, wherein the source and detectors are separated by predetermined distances, the memory stores a set of simulated reflectance curves, each simulated reflectance curve of the set of simulated reflectance curves is a simulation of light reflected from simulated tissue, and each simulated reflectance curve of the set of simulated reflectance curves is associated with a reflectance coefficient for the simulated tissue;

continuously monitoring patient tissue comprising repetitively performing;

transforming electrical signals generated by the processor into light using the light sources;

using the set of light sources of the tissue oximetry device, emitting the light having at least a first wavelength and a second wavelength into the tissue;

using the plurality of detectors of the tissue oximetry device, detecting light reflected in response to the light emitted into the tissue;

at a first frequency repetitively performing transforming the detected light into electrical signal using the detectors, wherein the electrical signals correspond to digital reflectance data points for the tissue;

at a second frequency repetitively performing;

selecting, by the processor housed in the housing, a first selected simulated reflectance curve from the set of simulated reflectance curves stored in the memory to form a coarse grid of the set of simulated reflectance curves;

selecting, by the processor housed in the housing, a second selected simulated reflectance curve from the set of simulated reflectance curves stored in the memory to form the coarse grid of the set of simulated reflectance curves, wherein the second selected simulated reflectance curve is a first interval value away from the first simulated reflectance curve;

selecting, by the processor housed in the housing, a third selected simulated reflectance curve from the set of simulated reflectance curves stored in the memory to form the coarse grid of the set of simulated reflectance curves, wherein the third selected simulated reflectance curve is a second interval value away from the second simulated reflectance curve;

forming, by the processor housed in the housing, a first subset of simulated reflectance curves comprising the first, second, and third simulated reflectance curves, wherein the first subset is a subset of the coarse grid of the set of simulated reflectance curves stored in the memory and having the first and second interval values in the set of simulated reflectance curves stored in the memory;

using the processor of the tissue oximetry device, calculating a closest fitting of the digital reflectance data points to a closest fit curve of the first subset of simulated reflectance curves;

based on the closest fit curve, forming, by the processor housed in the housing, a second subset of simulated reflectance curves from the set of simulated reflectance curves, wherein the second subset is based on a fine grid;

using the closest fit curve and second subset of simulated reflectance curves, calculating a set of absorption coefficients for the reflectance data points; and

using the processor of the tissue oximetry device, calculating an oxygen saturation value for the tissue based on the set of absorption coefficients; and

outputting an indication of the oxygen saturation value at an interface of the tissue oximetry device, wherein the first and second frequencies are different frequencies.

21. The method of claim 20 wherein the interface of the tissue oximetry device comprises a liquid crystal display.

22. The method of claim 20 comprising at the third frequency repetitively performing outputting the indication of the oxygen saturation value at the interface of the tissue oximetry device comprises updating the output on the display of the indication of the oxygen saturation value, wherein the first, second, and third frequencies are different frequencies.

25. The method of claim 20 wherein the processor of the tissue oximeter device is not used to calculate the simulated reflectance curves.

26. The method of claim 20 wherein the blood oxygen value includes a blood oxygen saturation value.

27. The method of claim 20 wherein the set of light sources of the tissue oximetry device comprises light emitting diodes.

28. The method of claim 20 wherein the simulated reflectance curves were previously determined, before the digital reflectance data points are determined.

29. The method of claim 20 wherein the using of the closest fit curve and second subset of simulated reflectance curves, calculating a set of absorption coefficients for the reflectance data points comprises:

positioning the closest fit curve at a center of the fine grid of the second subset of simulated reflectance curves;

using the fine grid with the closest fit curve at the center, determining a peak surface array of absorption coefficients and reflection scattering coefficients; and

performing a centroid calculation of the absorption coefficients and scattering coefficients from the peak surface array to determine the set of absorption coefficients and a set of scattering coefficients for the reflectance data points.

30. The method of claim 29 comprising disregarding the set of scattering coefficients when calculating the oxygen saturation value for the tissue.

31. The method of claim 29 wherein the absorption coefficients and the scattering coefficients for the reflectance data points are independent from each other when the scattering coefficients and the scattering coefficients for the reflectance data points are determined.

32. The method of claim 29 wherein selecting, by the processor housed in the housing, the first selected simulated reflectance curve from the set of simulated reflectance curves stored in the memory comprises from simulated data points, for simulated reflectance intensities for the predetermined distances between the source and detectors, for the set of simulated reflectance curves stored in the memory of the tissue oximetry device, selecting, by the processor housed in the housing, the first selected simulated reflectance curve from the set of simulated reflectance curves stored in the memory,

wherein selecting, by the processor housed in the housing, the second selected simulated reflectance curve from the set of simulated reflectance curves stored in the memory comprises from the simulated data points, for the simulated reflectance intensities for the predetermined distances between the source and detectors, for the set of simulated reflectance curves stored in the memory of the tissue oximetry device, selecting, by the processor housed in the housing, the second selected simulated reflectance curve from the set of simulated reflectance curves stored in the memory, wherein the second selected simulated reflectance curve is the first interval value away from the first simulated reflectance curve,

wherein selecting, by the processor housed in the housing, the third selected simulated reflectance curve from the set of simulated reflectance curves stored in the memory comprises from the simulated data points, for the simulated reflectance intensities for the predetermined distances between the source and detectors, for the set of simulated reflectance curves stored in the memory of the tissue oximetry device, selecting, by the processor housed in the housing, the third selected simulated reflectance curve from the set of simulated reflectance curves stored in the memory, wherein the third selected simulated reflectance curve is the second interval value away from the second simulated reflectance curve, and

wherein forming, by the processor housed in the housing, the first subset of simulated reflectance curves comprises forming, by the processor housed in the housing, the first subset of simulated reflectance curves comprising the first, second, and third simulated reflectance curves, wherein the first subset is based on the coarse grid of the set of simulated reflectance curves stored in the memory and having the first and second interval values in the set of simulated reflectance curves stored in the memory.