Technologies for three-dimensional spectroscopic imaging of tissue properties
Technologies are disclosed for an imaging device that may indicate at least one property of a tissue sample. The device may project light on the tissue sample. The device may control a capture of hyperspectral fluorescence image data of the tissue sample. The device may control a capture of spatial frequency domain imaging (SFDI) image data of the tissue sample. The device may process a first computational overlap of the hyperspectral fluorescence image data and the SFDI image data. The device may determine one or more properties of the first location of the tissue sample based on the first computational overlap of the hyperspectral fluorescence image data and the SFDI image data. The device may generate a 2D or a 3D spatial visual representation of the first location of the tissue sample based on the first computational overlap of the hyperspectral fluorescence image data and the SFDI image data.
1 . An imaging device configured to indicate at least one property of a tissue sample, the imaging device comprising:
a memory;
a light emitting projector;
a camera; and
a processor, the processor configured at least to:
project light on the tissue sample;
control a capture of hyperspectral fluorescence image data of the tissue sample, the hyperspectral fluorescence image data corresponding at least to a first location in the tissue sample;
control a capture of spatial frequency domain imaging (SFDI) image data of the tissue sample, the SFDI image data corresponding at least to the first location in the tissue sample, wherein the hyperspectral fluorescence image data and the SFDI image data correspond to a substantial entirety of the tissue sample;
process a first computational overlap of the hyperspectral fluorescence image data and the SFDI image data, the first computational overlap comprising at least the first location in the tissue sample;
determine one or more properties of at least the first location of the tissue sample based, at least in part, on the first computational overlap of the hyperspectral fluorescence image data and the SFDI image data, wherein determining the one or more properties comprises fitting SFDI-derived optical absorption and reduced scattering maps, derived from the SFDI image data, to a two-layer Monte Carlo look-up table modeling a first layer containing melanin without blood and a second layer containing blood without melanin;
segment the tissue sample into one or more zones based on the hyperspectral fluorescence image data and the SFDI image data; and
generate at least one of: a two-dimensional (2D), or a three-dimension (3D), spatial visual representation comprising at least the first location of the tissue sample based, at least in part, on the first computational overlap of the hyperspectral fluorescence image data and the SFDI image data.
2 . The device of claim 1 , wherein the hyperspectral fluorescence image data further corresponds at least to a second location in the tissue sample, and the SFDI image data further corresponds at least to the second location in the tissue sample, the processor being further configured to:
process a second computational overlap of the hyperspectral fluorescence image data and the SFDI image data, the second computational overlap further comprising at least the second location in the tissue sample; and
determine one or more properties of at least the second location of the tissue sample based, at least in part, on the second computational overlap, the at least one of: the two-dimensional (2D), or the three-dimension (3D), spatial visual representation further comprising at least the second location of the tissue sample, the spatial visual representation being further based, at least in part, on the second computational overlap.
3 . The device of claim 1 , wherein the processor is further configured to:
generate at least a first image of the tissue sample based on the hyperspectral fluorescence image data, the first image of the tissue sample depicting at least the first location in the tissue sample; and
generate at least a second image of the tissue sample based on the SFDI image data, the second image of the tissue sample depicting at least the first location in the tissue sample.
4 . The device of claim 1 , wherein the processor is further configured such that at least one of: the two-dimensional (2D), or the three-dimension (3D), spatial visual representation further comprises an indication of at least the first location in the tissue sample.
5 . The device of claim 1 , wherein the processor is further configured to:
identify at least one condition corresponding to at least the first location in the tissue sample based, at least in part, on the first computational overlap of the hyperspectral fluorescence image data and the SFDI image data.
6 . The device of claim 5 , wherein the processor is further configured such that at least one of: the two-dimensional (2D), or the three-dimension (3D), spatial visual representation further comprises an indication of the at least one condition corresponding to the first location in the tissue sample.
7 . The device of claim 5 , wherein the processor is further configured such that the at least one condition is one or more of: plaque, tooth decay, gingivitis, periodontitis, dehydrated skin, atopic dermatitis, eczema or a tissue condition.
8 . The device of claim 5 , wherein the tissue sample is at least one of: skin tissue, or an oral cavity tissue.
9 . The device of claim 8 , wherein the at least one condition corresponds to a dental disease of the oral cavity tissue.
10 . The device of claim 1 , wherein the one or more properties of at least the first location of the tissue sample is at least one of: the SFDI-derived optical absorption, optical scattering, body oil concentration, blood oxygenation, tissue oxygenation, tissue hydration, peeling, collagen density, lipid concentration, acne, burn severity, sun damage, wrinkles, rosacea, bacterial concentration, carcinogenesis, collagen structure, sub-surface angiography, rash severity, or skin pigmentation.
11 . The device of claim 1 , wherein the camera is a hyperspectral camera.
12 . The device of claim 1 , wherein the light projector comprises at least one of: a light emitting diode (LED) projector, a broadband illumination source, a digital micromirror device (DMD) array, or a digital light processing (DLP) array.
13 . The device of claim 1 , wherein the processor is further configured to:
determine at least one of: one or more properties of each of the one or more zones, or one or more conditions of each of the one or more zones, based, at least in part, on the hyperspectral fluorescence image data and the SFDI image data; and
assign a score to each of the one or more zones using machine learning, the assigned score being based on at least one of: the one or more properties of each of the one or more zones, or the one or more conditions of each of the one or more zones.
14 . The device of claim 13 , wherein the device is in communication with the Internet, and the processor is further configured to:
initiate an Internet-based search of one or more treatment products corresponding to at least some of the one or more zones based, at least in part, on the score of the at least some of the one or more zones.
15 . The device of claim 1 , wherein the processor is configured such that the capture of at least one of: the hyperspectral fluorescence image data of the tissue sample, or the SFDI image data of the tissue sample, is an in-vivo capture.
16 . A method for indicating at least one property of a tissue sample performed by an imaging device, the imaging device comprising a memory, a light emitting projector, a camera, and a processor, the method comprising:
projecting, via the projector, light on the tissue sample;
controlling, via the processor, a capture of hyperspectral fluorescence image data of the tissue sample, the hyperspectral fluorescence image data corresponding at least to a first location in the tissue sample;
controlling, via the processor, a capture of spatial frequency domain imaging (SFDI) image data of the tissue sample, the SFDI image data corresponding at least to the first location in the tissue sample, wherein the hyperspectral fluorescence image data and the SFDI image data correspond to a substantial entirety of the tissue sample;
processing, via the processor, a first computational overlap of the hyperspectral fluorescence image data and the SFDI image data, the first computational overlap comprising at least the first location in the tissue sample;
determining, via the processor, one or more properties of at least the first location of the tissue sample based, at least in part, on the first computational overlap of the hyperspectral fluorescence image data and the SFDI image data, wherein determining the one or more properties comprises fitting SFDI-derived optical absorption and reduced scattering maps, derived from the SFDI image data, to a two-layer Monte Carlo look-up table modeling a first layer containing melanin without blood and a second layer containing blood without melanin;
segmenting, via the processor, the tissue sample into one or more zones based on the hyperspectral fluorescence image data and the SFDI image data; and
generating, via the processor, at least one of: a two-dimensional (2D), or a three-dimension (3D), spatial visual representation comprising at least the first location of the tissue sample based, at least in part, on the first computational overlap of the hyperspectral fluorescence image data and the SFDI image data.
17 . The method of claim 16 , wherein the hyperspectral fluorescence image data further corresponds at least to a second location in the tissue sample, and the SFDI image data further corresponds at least to the second location in the tissue sample, the method further comprising:
processing, via the processor, a second computational overlap of the hyperspectral fluorescence image data and the SFDI image data, the second computational overlap further comprising at least the second location in the tissue sample; and
determining, via the processor, one or more properties of at least the second location of the tissue sample based, at least in part, on the second computational overlap, the at least one of: the two-dimensional (2D), or the three-dimension (3D), spatial visual representation further comprising at least the second location of the tissue sample, the spatial visual representation being further based, at least in part, on the second computational overlap.
18 . The method of claim 16 , further comprising:
generating, via the processor, at least a first image of the tissue sample based on the hyperspectral fluorescence image data, the first image of the tissue sample depicting at least the first location in the tissue sample; and
generating, via the processor, at least a second image of the tissue sample based on the SFDI image data, the second image of the tissue sample depicting at least the first location in the tissue sample.
19 . The method of claim 16 , further comprising:
generating, via the processor, an indication of at least the first location in the tissue sample on at least one of: the two-dimensional (2D), or the three-dimension (3D), spatial visual representation.
20 . The method of claim 16 , further comprising:
identifying, via the processor, at least one condition corresponding to at least the first location in the tissue sample based, at least in part, on the first computational overlap of the hyperspectral fluorescence image data and the SFDI image data.