Methods, systems, and computer program products for optimization of probes for spectroscopic measurement in turbid media

• US 20070019199A1
• Filed: 07/25/2006
• Published: 01/25/2007
• Est. Priority Date: 07/25/2005
• Status: Active Grant

• Current Assignee: Duke University, Wisconsin Alumni Research Foundation (University of Wisconsin System)
• Original Assignee: Duke University, Wisconsin Alumni Research Foundation (University of Wisconsin System)
• Inventors: Ramanujam, Nirmala, Palmer, Gregory
• Granted Patent: US 7,835,786 B2
• Time in Patent Office: Days
• US Class Current: 356/446
• CPC Class Codes: G01N 2021/4742 comprising optical fibres
• CPC Class Codes: G01N 2021/4752 Geometry
• CPC Class Codes: G01N 21/4738 Diffuse reflection precedence is given to G01N21/55 - G01N21/57 if specular component is taken into consideration, e.g. also for testing fluids, fibrous materials
• CPC Class Codes: G01N 2201/1296 using neural networks

Abstract

The presently disclosed subject matter provides methods, systems, and computer program products for optimizing a probe geometry for spectroscopic measurement in a turbid medium. According to one method, a probe geometry comprising one emitting entity for emitting electromagnetic radiation into a turbid medium and at least on collecting entity for collecting the electromagnetic radiation that has interacted with the turbid medium is selected. A simulation is performed with inputs of the probe geometry and a plurality of sets of optical property values associated with the turbid medium to generate output comprising optical parameter values measured by the probe geometry for each set of input optical property values. The measured optical parameter values are input to an inversion algorithm to produce corresponding optical properties as output. The produced optical properties are compared with optical properties known to correspond to the measured optical parameter values and a degree of matching between the produced optical properties and the known optical properties is determined. The simulation and inversion steps are repeated for a plurality of additional probe geometries. Each additional probe geometry differs from the previously tested probe geometry in at least one property. The property may be a quantity of collecting entities, a diameter of at least one emitting or collecting entity, a linear between the emitting and collecting entities, or combinations thereof. An optimization algorithm is applied at each iteration to select a probe geometry such that the resulting degree of matching will converge to an optimum value. An optimal geometry is selected based on the degree of matching determined for each geometry.

Claims

1. A method for optimizing a probe geometry for spectroscopic measurement in a turbid medium, the method comprising:

(a) selecting a probe geometry comprising at least one emitting entity for emitting electromagnetic radiation into a turbid medium and at least one collecting entity for collecting the electromagnetic radiation that has interacted with the turbid medium;

(b) performing a simulation with inputs of the probe geometry and a plurality of sets of optical property values associated with the turbid medium to generate output comprising optical parameter values measured by the probe geometry for each set of input optical property values;

(c) providing the measured optical parameter values as input to an inversion algorithm and thereby producing corresponding optical properties as output;

(d) comparing the produced optical properties with optical properties known to correspond to the measured optical parameter values and determining a degree of matching between the produced and known optical properties;

(e) repeating steps (b)-(d) for a plurality of additional probe geometries, wherein each additional probe geometry differs from the probe geometry of step (a) in at least one property selected from the group consisting of a quantity of collecting entities, a diameter of at least one collecting entity, a linear distance between the emitting entity and the collecting entity, and combinations thereof, wherein repeating steps (b)-(d) comprises, at each iteration, applying an optimization algorithm to select a probe geometry such that the resulting degree of matching will converge to an optimum value; and

(f) selecting from among the different probe geometries, an optimal geometry based on the degree of matching determined for each geometry in step (d).

2. The method of claim 1, wherein the emitting entity is not a collecting entity.

3. The method of claim 1 wherein the emitting and collecting entities comprise optical fibers.

4. The method of claim 1, wherein the simulation employs a Monte Carlo model.

5. The method of claim 1, wherein the sets of optical properties include scattering coefficients (μ

_s), absorption coefficients (μ

_a), and anisotropy factors (g).

6. The method of claim 1, wherein the optical parameter values are selected from the group consisting of diffuse reflectance values, sensing depths of a probe geometry, sensing volume or spatial resolution of a probe geometry, measurements of a physical property of a tissue, measurements of a fluorescent property of a tissue, measurement of Raman scattering, and combinations thereof.

7. The method of claim 6, wherein the optical parameter values are diffuse reflectance values.

8. The method of claim 1, wherein each emitting and collecting entity of the probe has a diameter selected from the group consisting of 50, 100, 150, 200, 300, 400, and 500 μ

m.

9. The method of claim 1, wherein the turbid medium comprises a group of cells or a tissue in a subject or isolated from a subject.

10. The method of claim 9, wherein the group of cells or the tissue comprises a tumor or a tumor biopsy.

11. The method of claim 1, wherein the method simultaneously and/or consecutively optimizes the probe in terms of fiber diameter and distance from the emitting entity to each collecting entity.

12. The method of claim 1, wherein the inversion algorithm is executed by a neural network.

13. The method of claim 12 comprising training the neural network using a training set of optical parameter values and the corresponding known optical properties

14. The method of claim 1, wherein the optimization algorithm comprises a genetic algorithm.

15. The method of claim 14, wherein the genetic algorithm comprises a component selected from the group consisting of a crossover operator, a mutation operator, and combinations thereof.

16. The method of claim 1, wherein the probe geometry comprises from 1 to collecting entities, inclusive.

17. The method of claim 16, wherein each of the plurality of additional probe geometries differs from the probe geometry of step (a) in at least one property selected from the group consisting of a quantity of collecting entities, a diameter of at least one emitting or collecting entity, a linear distance between the emitting and collecting entities, and combinations thereof.

18. The method of claim 1, wherein determining a degree of matching between the produced and known optical properties comprises:

(a) determining a root mean square error (RMSE) for a parameter selected from the group consisting of absorption coefficient (μ

_a) and reduced scattering coefficient (μ

_s′

;

=μ

_s×

(1-g), wherein μ

_s is a scattering coefficient and g is an anisotropy factor) for each probe geometry; and

(b) choosing a probe geometry for which the RMSE for each of μ

_a and μ

_s′

is less than or equal to 0.5 cm<sup>−

1</sup>.

19. The method of claim 18, wherein choosing a probe geometry includes choosing a probe geometry for which the RMSE for each of μ

_a and μ

_s′

is less than or equal to 0.45 cm<sup>−

1</sup>

20. A probe comprising a probe geometry selected using the method of claim 1.

21. A probe for generating and collecting electromagnetic radiation that interacts with a turbid medium, the probe comprising:

(a) at least one emitting entity for emitting electromagnetic radiation into a turbid medium; and

(b) at least two collecting the electromagnetic radiation emitted by the emitting entity that has interacted with the turbid medium,

wherein a linear distance between the emitting entity and each collecting entity does not exceed 1.5 millimeters and wherein the linear distance is determined using an optimization algorithm that optimizes measurement accuracy of the probe with regard to at least one optical property of a turbid medium.

22. The probe of claim 21, wherein the probe comprises between 2 and 6 collecting entities, inclusive.

23. The probe of claim 22, wherein the probe comprises between 2 and 5 collecting entities, inclusive, and the emitting entity is not also a collecting entity.

24. The probe of claim 21 wherein the emitting and collecting entities comprise optical fibers.

25. The probe of claim 21, wherein:

(i) the collecting entity comprises a first collection fiber and a second collection fiber;

(ii) the emitting entity comprises an illumination fiber having a diameter of about 500 μ

m, the first collection fiber has a diameter of about 200 μ

m, and the second collection fiber has a diameter of about 400 μ

m; and

(iii) a linear distance from the center of the illumination fiber to the center of the first collection fiber is about 380 μ

m and a linear distance from the center of the illumination fiber to the center of the second collection fiber is about 1360 μ

m.

26. The probe of claim 25, wherein a linear distance from the center of the first collection fiber to the center of the second collection fiber is between about 980 μ

m and about 1740 μ

m.

27. The probe of claim 21, wherein the optimization algorithm includes a genetic algorithm usable to select a probe geometry such that a degree of matching between a value of the optical property measured by the fiber optic probe and a known value for the optical property will converge to an optimum value.

28. A system for selecting an optimal geometry for a probe for spectroscopic measurement in turbid media, the system comprising:

(a) a light transport model for receiving as inputs a probe geometry and a plurality of sets of optical properties of a turbid medium and for producing as output optical parameter values that would be measured by the probe geometry for each set of input optical properties;

(b) an objective function for implementing an inversion algorithm for receiving as input the measured optical parameter values, for producing corresponding optical properties, for comparing the produced optical properties with optical properties known to correspond to the measured optical parameter values, and for determining a degree of matching between the produced and known optical properties for the given probe geometry, wherein the light transport model and the inversion algorithm are adapted to test a plurality of different probe geometries and wherein the inversion algorithm is adapted to determine a degree of matching between the produced and known optical properties for each geometry; and

(c) a probe selector for selecting one of the geometries as an optimal geometry based the degree of matching associated with the selected geometry.

29. The system of claim 28 wherein the probe selector is adapted to iteratively select new probe geometries to be tested by the light transport model and the inversion algorithm and, at each iteration, to apply an optimization algorithm to select a probe geometry such that the resulting degree of matching will converge to an optimum value.

30. The system of claim 29 wherein the optimization algorithm comprises a genetic algorithm.

31. A computer program product comprising computer executable instructions embodied in computer readable medium for performing steps comprising:

(a) selecting a probe geometry comprising at least one emitting entity for emitting electromagnetic radiation into a turbid medium and at least one collecting entity for collecting the electromagnetic radiation that has interacted with the turbid medium;

;

(b) performing a simulation with inputs of the probe geometry and a plurality of sets of optical property values associated with the turbid medium to generate output comprising optical parameter values measured by the probe geometry for each set of input optical property values;

(c) providing the measured optical parameter values as input to an inversion algorithm and thereby producing corresponding optical properties as output;

(d) comparing the produced optical properties with optical properties known to correspond to the measured optical parameter values and determining a degree of matching between the produced and known optical properties;

(e) repeating steps (b)-(d) for a plurality of additional probe geometries, wherein each additional probe geometry differs from the probe geometry of step (a) in at least one property selected from the group consisting of a quantity of collecting entities, a diameter of at least one emitting or collecting entity, a linear distance between the emitting and collecting entities, and combinations thereof, wherein repeating steps (b)-(d) comprises, at each iteration, applying an optimization algorithm to select a probe geometry such that the resulting degree of matching will converge to an optimum value; and

(f) selecting, from among the different probe geometries, an optimal geometry based on the degree of matching determined for each geometry in step (d).