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1. WO2020210746 - IMAGING SYSTEM FOR DETECTION OF INTRAOPERATIVE CONTRAST AGENTS IN TISSUE

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[ EN ]

CLAIMS

WHAT IS CLAIMED IS:

1. A system for imaging a tissue specimen comprising:

a) a first optical sub-system configured to acquire high-resolution images of a distribution of a cell-associated contrast agent within the tissue specimen using a first optical- sectioning imaging modality;

b) a second optical sub-system configured to acquire high-resolution images of tissue specimen morphology using a second optical-sectioning imaging modality, wherein the first and second optical sub-systems are configured to image the same optical plane within the tissue specimen; and

c) a processor configured to run an image interpretation algorithm that processes the images acquired using either or both of the first and second optical-sectioning imaging modalities to identify individual cells and determine their locations, and outputs a quantitative measure of a signal derived from the cell-associated contrast agent by measuring the signal at the locations corresponding to those of the individual cells in the images acquired using the first imaging modality.

2. The system of claim 1, wherein the first optical-sectioning imaging modality comprises two-photon fluorescence microscopy, confocal fluorescence microscopy, light sheet microscopy, or structured illumination microscopy.

3. The system of claim 1 or claim 2, wherein the second optical-sectioning imaging modality comprises stimulated Raman scattering microscopy, coherent anti-Stokes Raman scattering microscopy, confocal reflection microscopy, second harmonic generation microscopy, or third harmonic generation microscopy.

4. The system of any one of claims 1 to 3, wherein the first and second optical sub-systems are configured to have an axial resolution smaller than 10 pm.

5. The system of any one of claims 1 to 4, wherein the first and second optical sub-systems are configured to have a lateral resolution smaller than 5 pm.

6. The system of any one of claims 1 to 5, wherein the first optical sub-system is further configured to acquire high resolution images in two or more detection wavelength ranges.

7. The system of claim 6, wherein:

a first detection wavelength range of the two or more detection wavelength ranges includes an emission peak of the cell-associated contrast agent;

a second detection wavelength range of the two or more detection wavelength ranges excludes the emission peak of the cell-associated contrast agent; and

the image interpretation algorithm further processes images acquired using the first detection wavelength to identify individual cells and determine their locations, and outputs a quantitative measure of the signal derived from the cell-associated contrast agent by measuring the signal at the locations of the individual cells and correcting the signal by a background value measured at locations corresponding to those of the individual cells in the images acquired using the second detection wavelength range.

8. The system of any one of claims 3 to 7, wherein the second optical-sectioning imaging modality comprises stimulated Raman scattering microscopy, and the images are acquired at a wavenumber of 2,850 cm 1 corresponding to the CLb-vibration of lipid molecules.

9. The system of claim 8, wherein images are also acquired at a wavenumber of 2,930 cm 1 corresponding to the CLL-vibration of protein and nucleic acid molecules.

10. The system of any one of claims 1 to 9, wherein the cell-associated contrast agent comprises fluorescein, 5-ALA, BLZ-100, or LUM015.

11. The system of any one of claims 6 to 10, wherein the cell-associated contrast agent comprises 5-aminolevulinic acid (5-ALA), wherein the first emission wavelength range includes 640 nm light and the second emission wavelength includes wavelengths shorter than 600 nm.

12. The system of any one of claims 1 to 11, wherein the image interpretation algorithm detects individual cells based on image feature size, shape, pattern, intensity, or any combination thereof.

13. The system of any one of claims 1 to 12, wherein the image interpretation algorithm comprises a supervised machine learning algorithm, an unsupervised machine learning algorithm, a semi-supervised machine learning algorithm, or any combination thereof.

14. The system of claim 13, wherein the machine learning algorithm is trained using a training data set comprising imaging data acquired for archived histopathological tissue samples, imaging date acquired for fresh histopathological tissue samples, or any combination thereof.

15. The system of claim 14, wherein the training data set is continuously, periodically, or randomly updated with imaging data acquired by two or more systems that have been deployed for use at the same or different sites.

16. The system of any one of claims 1 to 15, wherein the image interpretation algorithm determines a total or an average intensity of the signal derived from the cell -associated contrast agent.

17. The system of any one of claims 1 to 16, wherein the image interpretation algorithm determines whether the signal derived from the cell-associated contrast agent for individual cells is above a specified threshold level for contrast-positive cells.

18. The system of claim 17, wherein the image interpretation algorithm outputs a total number of contrast-positive cells within an image of the tissue specimen, a density of contrast-positive cells within an image of the tissue specimen, a percentage of contrast-positive cells within an image of the tissue specimen, or any combination thereof.

19. The system of claim 18, wherein the image interpretation algorithm also outputs a cellularity score based on the images acquired using the second optical-sectioning imaging modality.

20. The system of any one of claims 1 to 19, wherein the images of the tissue specimen are acquired in vivo.

21. The system of any one of claims 1 to 19, wherein the images of the tissue specimen are acquired ex vivo.

22. The system of any one of claims 1 to 21, wherein the system is used during a surgical procedure to identify locations for performing a biopsy or to determine if resection is complete.

23. A method for cellular resolution imaging of a tissue specimen, the method comprising;

a) acquiring high-resolution, optically-sectioned images of a distribution of a cell- associated contrast agent within the tissue specimen using a first imaging modality;

b) acquiring high-resolution, optically-sectioned images of tissue specimen morphology within the same optical focal plane within the tissue specimen as that for (a) using a second imaging modality; and

c) processing the images acquired using either or both of the first and second imaging modalities using an image interpretation algorithm that identifies individual cells and determines their locations, and outputs a quantitative measure of a signal derived from the cell-associated contrast agent at cellular resolution by measuring the signal at

locations corresponding to those of the individual cells in the images acquired using the first imaging modality.

24. The method of claim 23, wherein the first imaging modality comprises two-photon fluorescence microscopy, confocal fluorescence microscopy, light sheet microscopy, or structured illumination microscopy.

25. The method of claim 23 or claim 24, wherein the second imaging modality comprises stimulated Raman scattering microscopy, coherent anti-Stokes Raman scattering microscopy, confocal reflection microscopy, second harmonic generation microscopy, or third harmonic generation microscopy.

26. The method of any one of claims 23 to 25, wherein the images acquired using the first imaging modality and the second imaging modality have an axial resolution smaller than 10 pm.

27. The method of any one of claims 23 to 26, wherein the images acquired using the first imaging modality and the second imaging modality have a lateral resolution smaller than 5 pm.

28. The method of any one of claims 23 to 27, further comprising acquiring images in two or more detection wavelength ranges using the first imaging modality.

29. The method of claim 28, wherein:

a first detection wavelength range of the two or more detection wavelength ranges includes an emission peak of the cell-associated contrast agent;

a second detection wavelength range of the two or more detection wavelength ranges excludes the emission peak of the cell-associated contrast agent; and

the image interpretation algorithm further processes images acquired using the first detection wavelength to identify individual cells and determine their locations, and outputs a quantitative measure of the signal derived from the cell-associated contrast agent by measuring the signal at the locations of the individual cells and correcting the signal by a background value measured at locations corresponding to those of the individual cells in the images acquired using the second emission wavelength range.

30. The method of any one of claims 25 to 29, wherein the second imaging modality comprises stimulated Raman scattering microscopy, and the images are acquired at a wavenumber of 2,850 cm 1 corresponding to the CTh-vibration of lipid molecules.

31. The method of claim 30, wherein images are also acquired at a wavenumber of 2,930 cm 1 corresponding to the CTb-vibration of protein and nucleic acid molecules.

32. The method of any one of claims 23 to 31, wherein the cell-associated contrast agent comprises fluorescein, 5-ALA, BLZ-100, or LUM015.

33. The method of any one of claims 28 to 32, wherein the cell-associated contrast agent comprises 5-aminolevulinic acid (5-ALA), wherein the first emission wavelength range includes 640 nm light and the second emission wavelength includes wavelengths shorter than 600 nm.

34. The method of any one of claims 23 to 33, wherein the image interpretation algorithm detects individual cells based on image feature size, shape, pattern, intensity, or any combination thereof.

35. The method of any one of claims 23 to 34, wherein the image interpretation algorithm comprises a supervised machine learning algorithm, an unsupervised machine learning algorithm, a semi-supervised machine learning algorithm, or any combination thereof.

36. The method of claim 35, wherein the machine learning algorithm is trained using a training data set comprising imaging data acquired for archived histopathological tissue samples, imaging date acquired for fresh histopathological tissue samples, or any combination thereof.

37. The method of claim 36, wherein the training data set is continuously, periodically, or randomly updated with imaging data acquired by two or more systems that have been deployed for use at the same or different sites.

38. The method of any one of claims 23 to 37, wherein the image interpretation algorithm determines a total or an average intensity of the signal derived from the cell -associated contrast agent.

39. The method of any one of claims 23 to 38, wherein the image interpretation algorithm determines whether the signal derived from the cell-associated contrast agent for individual cells is above a specified threshold level for contrast-positive cells.

40. The method of claim 39, wherein the image interpretation algorithm outputs a total number of contrast-positive cells within an image of the tissue specimen, a density of contrast-positive cells within an image of the tissue specimen, a percentage of contrast-positive cells within an image of the tissue specimen, or any combination thereof.

41. The method of claim 40, wherein the image interpretation algorithm also outputs a cellularity score based on the images acquired using the second optical-sectioning imaging modality.

42. The method of any one of claims 23 to 41, wherein the images of the tissue specimen are acquired in vivo.

43. The method of any one of claims 23 to 41, wherein the images of the tissue specimen are acquired ex vivo.

44. The method of any one of claims 23 to 43, wherein the method is used during a surgical procedure to identify locations for performing a biopsy or to determine if resection is complete.

45. A system for imaging a tissue specimen comprising:

a) a high-resolution optical-sectioning microscope configured to acquire images of the tissue specimen; and

b) a processor configured to run an image interpretation algorithm that detects individual cells in the images acquired by the high-resolution optical-sectioning microscope and outputs a quantitative measure of a signal derived from a cell-associated contrast agent.

46. The system of claim 45, wherein the high-resolution optical sectioning microscope comprises a two-photon fluorescence microscope, a confocal fluorescence microscope, a light sheet microscope, or a structured illumination microscope.

47. The system of any one of claims 45 to 46, wherein the high-resolution optical sectioning microscope is configured to acquire images in two or more emission wavelength ranges.

48. The system of claim 47, wherein:

a first emission wavelength range of the two or more detection wavelength ranges includes an emission peak of the cell-associated contrast agent;

a second detection wavelength range of the two or more emission wavelength ranges excludes the emission peak of the cell-associated contrast agent; and

the image interpretation algorithm processes images acquired using the first detection wavelength range to identify individual cells and determine their locations, and outputs a quantitative measure of the signal derived from the cell-associated contrast agent by measuring the signal at the locations of the individual cells and correcting the signal using a background value measured at corresponding locations in images acquired using the second detection wavelength range.

49. The system of any one of claims 45 to 48, wherein the cell-associated contrast agent comprises fluorescein, 5-ALA, BLZ-100, or LUM015.

50. The system of any one of claims 47 to 49, wherein the cell-associated contrast agent comprises 5-aminolevulinic acid (5-ALA), wherein the first emission wavelength range includes 640 nm light and the second emission wavelength includes wavelengths shorter than 600 nm.

51. The system of any one of claims 45 to 50, wherein the image interpretation algorithm detects individual cells based on image feature size, shape, pattern, intensity, or any combination thereof.

52. The system of any one of claims 45 to 51, wherein the image interpretation algorithm comprises a supervised machine learning algorithm, an unsupervised machine learning algorithm, a semi-supervised machine learning algorithm, or any combination thereof.

53. The system of claim 52, wherein the machine learning algorithm is trained using a training data set comprising imaging data acquired for archived histopathological tissue samples, imaging date acquired for fresh histopathological tissue samples, or any combination thereof.

54. The system of any one of claims 45 to 53, wherein the quantitative measure of the signal derived from the cell-associated contrast agent comprises a measure of an amount of contrast agent associated with one or more individual cells in the image.

55. The system of any one of claims 45 to 54, wherein the quantitative measure of the signal derived from the cell-associated contrast agent comprises a measure of a total number of contrast-positive cells within an image, a density of contrast-positive cells within an image, a percentage of contrast agent positive cells within an image, or any combination thereof.

56. The system of any one of claims 45 to 55, wherein the images of the tissue specimen are acquired in vivo.

57. The system of any one of claims 45 to 55, wherein the images of the tissue specimen are acquired ex vivo.

58. The system of any one of claims 45 to 57, wherein the system is used during a surgical procedure to identify locations for performing a biopsy or to determine if resection is complete.

59. A method for imaging a tissue specimen, the method comprising:

a) acquiring high-resolution, optically sectioned images of the tissue specimen; and b) processing the images using an image interpretation algorithm that detects individual cells in the images and outputs a quantitative measure of a signal derived from a cell- associated contrast agent at a location of one or more individual cells.

60. The method of claim 59, wherein the high-resolution, optically-sectioned images are acquired using two-photon fluorescence microscopy, confocal fluorescence microscopy, light sheet microscopy, or structured illumination microscopy.

61. The method of any one of claims 59 to 60, wherein the first imaging modality comprises two-photon fluorescence microscopy, confocal fluorescence microscopy, light sheet microscopy, or structured illumination microscopy.

62. The method of any one of claims 59 to 61, wherein the cell-associated contrast agent comprises fluorescein, 5-ALA, BLZ-100, or LUM015.

63. The method of any one of claims 59 to 62, wherein the image interpretation algorithm detects individual cells based on image feature size, shape, pattern, intensity, or any combination thereof.

64. The method of any one of claims 59 to 63, wherein the image interpretation algorithm comprises an artificial intelligence or machine learning algorithm.

65. The method of claim 64, wherein the machine learning algorithm comprises a supervised machine learning algorithm, an unsupervised machine learning algorithm, a semi-supervised machine learning algorithm, or any combination thereof.

66. The method of any one of claims 64 to 65, wherein the machine learning algorithm is trained using a training data set comprising imaging data acquired for archived histopathological tissue samples, imaging date acquired for fresh histopathological tissue samples, or any combination thereof.

67. The method of any one of claims 59 to 66, wherein the image interpretation algorithm determines an average intensity of the signal derived from the cell-associated contrast agent.

68. The method of any one of claims 59 to 67, wherein the image interpretation algorithm determines whether the signal for each individual cell is above a specified threshold level for contrast-positive cells.

69. The method of claim 68, wherein the image interpretation algorithm outputs a total number of contrast-positive cells within an image, a density of contrast-positive cells within an image, a percentage of contrast-positive cells within an image, or any combination thereof.

70. The method of any one of claims 59 to 69, wherein the image interpretation algorithm outputs a cellularity score.

71. The method of any one of claims 59 to 70, wherein the images of the tissue specimen are acquired in vivo.

72. The method of any one of claims 59 to 71, wherein the images of the tissue specimen are acquired ex vivo.

73. The method of any one of claims 59 to 72, wherein the method is used during a surgical procedure to identify locations for performing a biopsy or to determine if resection is complete.

74. A method for detection of a cell-associated optical contrast agent in a tissue specimen, the method comprising:

a) acquiring a first high-resolution, optically-sectioned image of the tissue specimen at a first emission wavelength range that includes an emission peak of the cell-associated contrast agent,

b) acquiring a second high-resolution, optically-sectioned image of the tissue specimen at a second emission wavelength range that excludes the emission peak of the cell- associated contrast agent; and

c) applying a pseudo-color algorithm to the first and second images to generate a multi color image of the tissue specimen that facilitates human interpretation.

75. The method of claim 74, wherein the first and second high-resolution, optically-sectioned images are acquired using two-photon fluorescence microscopy, confocal fluorescence microscopy, light sheet microscopy, or structured illumination microscopy.

76. The method of claim 74 or claim 75, wherein the image acquired using the first emission wavelength range is further processed by an image interpretation algorithm to identify individual cells and their locations, and outputs a total number of contrast-positive cells within the image, a density of contrast-positive cells within the image, a percentage of contrast-positive cells within the image, or any combination thereof.

77. The method of any one of claims 74 to 76, wherein the images of the tissue specimen are acquired in vivo.

78. The method of any one of claims 74 to 77, wherein the images of the tissue specimen are acquired ex vivo.

79. The method of any one of claims 74 to 78, wherein the system is used during a surgical procedure to identify locations for performing a biopsy or to determine if resection is complete.

80. A method for guiding a surgical resection, the method comprising;

a) acquiring high-resolution, optically-sectioned images of a distribution of a cell- associated contrast agent within a tissue specimen using two photon fluorescence microscopy;

b) acquiring high-resolution, optically-sectioned images of tissue specimen morphology within the same optical focal plane of the tissue specimen using stimulated Raman scattering microscopy; and

c) processing the images acquired using stimulated Raman scattering using an image interpretation algorithm that identifies individual cells and determines their locations, and outputs a quantitative measure of a signal derived from the cell-associated contrast agent at cellular resolution by measuring the signal at locations corresponding to those of the individual cells in the images acquired using two photon fluorescence.

81. A method for guiding a surgical resection, the method comprising:

a) acquiring high-resolution, optically sectioned images of a tissue specimen using two photon fluorescence; and

b) processing the images using an image interpretation algorithm that detects individual cells in the images and outputs a quantitative measure of a signal derived from a cell- associated contrast agent with cellular resolution at a location of one or more individual cells.

82. A method for guiding a surgical resection, the method comprising:

a) acquiring a first high-resolution, optically-sectioned two photon fluorescence image of the tissue specimen at a first emission wavelength range that includes an emission peak of a cell-associated contrast agent,

b) acquiring a second high-resolution, optically-sectioned two photon fluorescence image of the tissue specimen at a second emission wavelength range that excludes the emission peak of the cell-associated contrast agent; and

c) applying a pseudo-color algorithm to the first and second two photon fluorescence images to generate a multi-color image of the tissue specimen that facilitates human interpretation.

83. The method of any one of claims 80 to 82, wherein images for at least one imaging modality are acquired using confocal fluorescence microscopy, light sheet microscopy, or structured illumination microscopy instead of two photon fluorescence microscopy.

84. The method of any one of claims 80 to 83, wherein images acquired using at least a second imaging modality are acquired using coherent anti-Stokes Raman scattering microscopy,

confocal reflection microscopy, second harmonic generation microscopy, or third harmonic generation microscopy instead of stimulated Raman scattering microscopy.

85. The method of any one of claims 80 to 84, wherein the tissue specimen is a brain tissue specimen, breast tissue specimen, lung tissue specimen, pancreatic tissue specimen, or prostate tissue specimen.

86. The method of any one of claims 80 to 85, wherein the images of the tissue specimen are acquired in vivo.

87. The method of any one of claims 80 to 86, wherein the images of the tissue specimen are acquired ex vivo.

88. The method of any one of claims 80 to 87, wherein an image interpretation algorithm used to process images acquired using two photon fluorescence outputs a total number of contrast positive cells within the image, a density of contrast-positive cells within the image, a percentage of contrast-positive cells within the image, or any combination thereof.

89. The method of any one of claims 80 to 88, wherein an image interpretation algorithm used to process the images acquired using two photon fluorescence or stimulated Raman scattering comprises a machine learning algorithm.

90. The method of claim 89, wherein the machine learning algorithm comprises a supervised machine learning algorithm, an unsupervised machine learning algorithm, a semi-supervised machine learning algorithm, or any combination thereof.

91. The method of claim 89 or claim 90, wherein the machine learning algorithm comprises an artificial neural network algorithm, a deep convolutional neural network algorithm, a deep recurrent neural network, a generative adversarial network, a support vector machine, a hierarchical clustering algorithm, a Gaussian process regression algorithm, a decision tree algorithm, a logistical model tree algorithm, a random forest algorithm, a fuzzy classifier algorithm, a k-means algorithm, an expectation-maximization algorithm, a fuzzy clustering algorithm, or any combination thereof.

92. The method of any one of claims 89 to 91, wherein the machine learning algorithm is trained using a training data set comprising imaging data acquired for archived histopathological tissue samples, imaging date acquired for fresh histopathological tissue samples, or any combination thereof.

93. The method of claim 92, wherein the training data set is continuously, periodically, or randomly updated with imaging data acquired by two or more systems that have been deployed for use at the same or different sites.

94. The method of claim 93, wherein the two or more systems are deployed at different sites, and the training data set is continuously, periodically, or randomly updated via an internet connection.