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

• US 7,835,786 B2
• Filed: 07/25/2006
• Issued: 11/16/2010
• 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 M.
• Primary Examiner(s): Le; Long V
• Assistant Examiner(s): HOFFA, ANGELA MARIE
• Application Number:
  US11/493,020
• Publication Number:
  US 20070019199A1
• Time in Patent Office: 1,575 Days
• Field of Search: None
• US Class Current: 600/477
• 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

Methods, systems, and computer program products for optimizing a probe geometry for spectroscopic measurement in a turbid medium are provided. A probe geometry comprising one emitting entity and at least on collecting entity is selected. A simulation is performed to generate optical parameter values measured by the probe geometry. 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 known optical properties known 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 differing in at least one property. An optimization algorithm is applied at each iteration to select an optimal probe geometry.

Claims

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

using a computer programmed to perform the following steps;

(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 of a light transport model 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 that would be 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 that produces optical properties corresponding to the measured optical parameter values generated by the simulation of the light transport model as output;

(d) comparing the optical properties produced by the inversion algorithm with the plurality of sets of 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 for the turbid medium 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 consists of from 1 to 5 collecting entities, inclusive.

17. 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

_sis 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>−

l</sup>.

18. The method of claim 17, 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>.

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

at least one processor programmed to implement;

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

(b) an objective function configured to implement an inversion algorithm that is configured to receive as input the measured optical parameter values, configured to produce corresponding optical properties, configured to compare the optical properties produced by the inversion algorithm with the plurality of sets of optical properties known to correspond to the measured optical parameter values, and configured to determine 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 configured to determine a degree of matching between the produced and known optical properties for each geometry; and

(c) a probe selector configured to select one of the geometries as an optimal geometry for the turbid medium based the degree of matching associated with the selected geometry.

20. The system of claim 19 wherein the probe selector is configured 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.

21. The system of claim 20 wherein the optimization algorithm comprises a genetic algorithm.

22. A computer program product comprising computer executable instructions embodied in a non-transitory 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 of a light transport model 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 that would be 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 that produces optical properties corresponding to the measured optical parameter values generated by the simulation of the light transport model as output;

(d) comparing the optical properties produced by the inversion algorithm with the plurality of sets of 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 for the turbid medium based on the degree of matching determined for each geometry in step (d).