Spectroscopic tomography systems and methods for noninvasive detection and measurement of analytes using collision computing

• US 9,554,738 B1
• Filed: 08/11/2016
• Issued: 01/31/2017
• Est. Priority Date: 03/30/2016
• Status: Active Grant

• Current Assignee: Zyomed Holdings, Inc.
• Original Assignee: Zyomed Corp.
• Inventors: Gulati, Sandeep, Ruchti, Timothy L., Smith, John L., Van Antwerp, William
• Primary Examiner(s): Kim, Kiho
• Application Number:
  US15/234,449
• Time in Patent Office: 173 Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2562/0238 Optical sensor arrangements for performing transmission measurements on body tissue
• CPC Class Codes: A61B 2562/046 in a matrix array
• CPC Class Codes: A61B 2576/00 Medical imaging apparatus involving image processing or analysis A61B1/00009, A61B6/52 and A61B8/52 take precedence; image processing per se G06T; details of algorithms for analysing medical images G06T7/0012
• 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 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• 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 5/6801 specially adapted to be attached to or worn on the body surface
• CPC Class Codes: G01N 21/3563 for analysing solids; Preparation of samples therefor
• CPC Class Codes: G01N 21/359 using near infrared light
• CPC Class Codes: G01N 2201/12 Circuits of general importance; Signal processing

Abstract

In a noninvasive system for detection/measurement of analytes in tissue, spectra from the medium are deconstructed into features. Conditioned features, which contain frequency components specific to the analytes, are derived from one or more features after modulating a carrier kernel with the feature. The conditioned features are computationally collided with one or more Zyotons that are co-dependent with the conditioned features. One or more collisions amplify the energy absorbed by the analyte from radiation directed to the tissue. The value of the amplified energy is transformed into an accurate estimate of the concentration of the analyte. Depending on the analyte type, a particular tissue region is targeted and/or one or more parameters of the computational collision are selected.

Claims

1. A method for determining at least one of a presence and a concentration of an analyte in a tissue sample, the method comprising the steps of:

(a) obtaining a feature energy change as a result of a collision between two co-dependent waveforms, one of the two co-dependent waveforms being based on a feature waveform corresponding to absorption of energy by the analyte when radiation is directed to the tissue sample; and

(b) transforming, via a processor, the feature energy change to an analyte concentration, wherein the analyte is classified as one of a multi-compartment analyte excluding glucose, a red cell analyte, a plasma analyte, an intracellular analyte, a structural analyte, an exogenous analyte, and an antigen/antibody analyte.

2. The method of claim 1, further comprising at least one of directing radiation to a region and collecting radiation from a region of the tissue sample based on, at least in part, a class of the analyte.

3. The method of claim 1, wherein the feature waveform corresponding to absorption of energy by the analyte:

is based on a portion of a spectral signal, the portion corresponding to a specified wavelength range; and

corresponds to absorption of energy by the analyte within the specified wavelength range.

4. The method of claim 3, wherein the feature further corresponds to at least one of:

(A) energy absorption within the specified wavelength range by at least one confounder in the tissue sample, and (B) loss of energy within the specified wavelength range due to scattering.

5. The method of claim 1, further comprising generating a feature by:

directing near-infrared radiation to the tissue sample;

collecting radiation from the tissue sample;

generating a spectrum from the collected radiation; and

selecting a region of the spectrum bounded by a wavelength range as the feature waveform.

6. The method of claim 5, wherein at least one of directing near-infrared radiation to the tissue sample and collecting radiation from the tissue sample comprises targeting a region of the tissue sample based on, at least in part, a class of the analyte.

7. The method of claim 5, wherein collecting the radiation comprises at least one of receiving diffusely reflected radiation from the tissue sample and receiving radiation transmitted through the tissue sample.

8. The method of claim 1, further comprising generating a conditioned feature by modulating a carrier kernel using the feature waveform.

9. The method of claim 8, wherein:

one of the two co-dependent waveforms comprises an original Zyoton constructed to amplify an amount of energy absorbed by the analyte within the feature region; and

the carrier kernel is constructed such that the original Zyoton and the conditioned feature obtained by modulating the carrier kernel using the feature are co-dependent.

10. The method of claim 9, wherein:

colliding the original Zyoton with the conditioned feature comprises, in a first iteration, colliding the original Zyoton with the conditioned feature to obtain a modified Zyoton; and

obtaining the feature energy change comprises computing a change in energy of the modified Zyoton relative to energy of the original Zyoton.

11. The method of claim 9, wherein obtaining the feature energy change comprises:

colliding the original Zyoton with the conditioned feature comprises, in a first iteration;

colliding the original Zyoton with the conditioned feature to obtain a modified Zyoton;

computing a change in energy of the modified Zyoton, relative to energy of the original Zyoton; and

renormalizing the modified Zyoton to obtain a renormalized Zyoton; and

in each of N4 additional iterations, where −

1>

1;

colliding one of the original Zyoton and a second codependent Zyoton with a renormalized Zyoton from an immediately preceding iteration, to obtain a remodified Zyoton;

computing a change in energy of the remodified Zyoton, relative to energy of one of the original Zyoton and the second codependent Zyoton; and

renormalizing the remodified Zyoton to obtain a renormalized remodified Zyoton.

12. The method of claim 11, wherein obtaining the feature energy change comprises accumulating the energy change computed in each iteration.

13. The method of claim 9, wherein each of the original Zyoton and the carrier kernel comprises:

k analyte-information-representing components; and

at least one of;

(i) m transition components, and (ii) j non-analyte-information-representing components,

wherein at least one of k, m, and j is selected based on, at least in part, a class of the analyte.

14. The method of claim 1, further comprising obtaining a plurality of energy changes, wherein transforming the feature energy change to an analyte concentration comprises:

selecting a projector curve from a plurality of projector curves according to an absorption gradient based on the plurality of energy changes; and

projecting an energy change value derived from the feature energy change to the analyte concentration using the selected projector curve.

15. A system for determining at least one of a presence and a concentration of an analyte in a tissue sample, comprising a processor that is at least one of programmed in part and programmable in part to:

(a) obtain a feature energy change obtained from a collision between two co-dependent waveforms, one of the two co-dependent waveforms being based on a feature waveform corresponding to absorption of energy by the analyte when radiation is directed to the tissue sample; and

(b) obtain a transformation of the feature energy change to an analyte concentration,

wherein the analyte is classified as one of a multi-compartment analyte excluding glucose, a red cell analyte, a plasma analyte, an intracellular analyte, a structural analyte, an exogenous analyte, and an antigen/antibody analyte.

16. The system of claim 15, further comprising:

an illumination subsystem for directing radiation to the tissue sample; and

a detector for collecting at least one of;

(i) radiation diffusily reflected from the tissue sample, and (ii) radiation transmitted through the tissue sample,

wherein at least one of;

(i) the illumination subsystem is configured to direct the radiation to a region of the tissue sample selected based on, at least in part, a class of the analyte, and (ii) the detector is configured to collect the radiation from a region of the tissue sample selected based on, at least in part, the class of the analyte.

17. The system of claim 16, wherein:

the feature waveform corresponding to absorption of energy by the analyte;

(i) is based on a portion of a spectral signal, the portion corresponding to a specified wavelength range, and (ii) corresponds to absorption of energy at least in part by the analyte within the specified wavelength range; and

the processor is at least one of programmed in part and programmable in part to generate the feature waveform using radiation collected by the detector.

18. The system of claim 16, wherein at least one of:

the illumination subsystem is adjustable to direct radiation having a specified wavelength range; and

the detector is adjustable to collect radiation having a specified wavelength length.

19. The system of claim 16, wherein:

at least one of the processor, the illumination subsystem, and the detector is encased within a wearable device;

the wearable device has a volume of no more than 100 cubic centimeters; and

the processor determines at least one of the presence and the concentration of the analyte within about 500 milliseconds after the illumination subsystem directs the radiation.

20. The system of claim 15, wherein the processor is at least one of programmed in part and programmable in part to generate a conditioned feature by modulating a carrier kernel using the feature waveform.

21. The system of claim 20, wherein the processor is at least one of programmed in part and programmable in part to receive the carrier kernel.

22. The system of claim 15, wherein one of the two co-dependent waveforms comprises an original Zyoton constructed to amplify an amount of energy absorbed by the analyte within the feature region, the system further comprising a memory unit to store the original Zyoton.

23. The system of claim 15, wherein:

one of the two co-dependent waveforms comprises an original Zyoton constructed to amplify an amount of energy absorbed that is indicated by the feature; and

the processor is at least one of programmed in part and programmable in part to receive the original Zyoton.

24. The system of claim 15, wherein:

to perform a first iteration of the collision, the processor is at least one of programmed in part and programmable in part to;

collide an original Zyoton constructed to amplify an amount of energy absorbed that is indicated by the feature with a conditioned feature obtained by modulating a carrier kernel using the feature waveform to obtain a modified Zyoton; and

to obtain the feature energy change, compute a change in energy of the modified Zyoton relative to energy of the original Zyoton.

25. The system of claim 24, wherein each of the original Zyoton and the carrier kernel comprises:

k analyte-information-representing components; and

at least one of;

(i) m transition components, and (ii) j non-analyte-information-representing components,

wherein at least one of k, m, and j is selected based on, at least in part, a class of the analyte.

26. The system of claim 15, wherein:

to perform a first iteration of the collision, the processor is at least one of programmed in part and programmable in part to;

collide an original Zyoton constructed to amplify an amount of energy absorbed that is indicated by the feature with a conditioned feature obtained by modulating a carrier kernel using the feature waveform to obtain a modified Zyoton;

to obtain the feature energy change, compute a change in energy of the modified Zyoton relative to energy of the original Zyoton; and

renormalize the modified Zyoton to obtain a renormalized Zyoton; and

to perform −

1 additional iterations of the collision, where −

1≧

1, the processor is at least one of programmed in part and programmable in part to;

collide one of the original Zyoton and a second codependent Zyoton with a renormalized Zyoton from an immediately preceding iteration, to obtain a remodified Zyoton;

to obtain the feature energy change, further compute a change in energy of the remodified Zyoton relative to energy of one of the original Zyoton and the second codependent Zyoton; and

renormalize the remodified Zyoton to obtain a renormalized remodified Zyoton.

27. The system of claim 26, wherein to obtain the feature energy change the processor is at least one of programmed in part and programmable in part to accumulate the energy change computed in each iteration.

28. The system of claim 15, further comprising a memory for storing instructions for programming the processor to obtain the energy change and to obtain the transformation to the analyte concentration.

29. The system of claim 15, wherein the processor comprises at least one of an application specific integrated circuit (ASIC) and a field programmable gate array (FPGA) processor to at least one of:

(i) obtain the energy change, and (ii) obtain the transformation to the analyte concentration.

30. The system of claim 15, wherein:

the processor is at least one of programmed in part and programmable in part to obtain a plurality of energy changes comprising the feature energy change; and

to transform the feature energy change to an analyte concentration the processor is at least one of programmed in part and programmable in part to;

select a projector curve from a plurality of projector curves according to an absorption gradient based on the plurality of energy changes; and

project an energy change value derived from the feature energy change to the analyte concentration using the selected projector curve.