Methods and systems for enhancing optical image quality
Systems and methods for enhancing quality of a flow image of a sample are provided. An image is obtained from a plurality of optical image scans of a sample comprising blood perfused tissue. A vector comprising tissue components, flow components, and noise is then generated, and eigenvectors and eigenvalues are estimated from the vector. From the eigenvectors and eigenvalues, an eigen regression filter is applied to isolate flow components from the tissue components in the sample. The isolated tissue components may then be removed from the image to enhance visualization and quantification of a flow of dynamic moving particles within the sample.
1. A method for enhancing quality of a flow image of a sample comprising:
acquiring a first flow image from a plurality of optical image scans of a sample, wherein the sample comprises a biological tissue having fluid flow therethrough, the fluid flow being a blood flow;
generating a vector from the plurality of optical image scans, the vector comprising tissue components, flow components, and noise;
estimating eigenvectors and eigenvalues from the vector;
isolating the flow components from the tissue components by application of an eigen regression filter for a determined number of the eigenvectors;
removing the tissue components in the first flow image; and
quantifying blood perfusion within the biological tissue,
wherein quantifying blood perfusion within the biological tissue comprises calculating a mean capillary flow velocity and capillary flow heterogeneity.
2. The method of claim 1 , wherein acquiring the first flow image from the plurality of optical image scans of the sample comprises:
acquiring repeated optical image scans at a single location on the sample; and
generating a time series of the optical image scans.
3. The method of claim 1 , wherein the vector is a two-dimensional vector.
4. The method of claim 1 , wherein generating the vector from the plurality of optical image scans comprises modeling signals from the plurality of optical image scans as a sum of at least three independent Gaussian processes comprising the tissue components, the flow components, and the noise.
5. The method of claim 1 , wherein the determined number of the eigenvectors is based on the number of optical image scans performed.
6. The method of claim 1 , wherein the eigenvectors that comprise the determined number of the eigenvectors represent the flow components.
7. The method of claim 1 , wherein the plurality of optical image scans are generated by optical coherence tomography and are optical microangiography (OMAG) scans.
8. The method of claim 7 , wherein acquiring the first flow image from the plurality of optical image scans of the sample comprises generating the first flow image from a plurality of repeated 8-scans of the sample.
9. The method of claim 1 , wherein the plurality of optical image scans are generated by one of laser speckle imaging, laser Doppler imaging, confocal microscopy, multiphoton microscopy, holography, motion contrast photography, and fluorescence imaging.
10. The method of claim 1 , wherein removing the tissue components in the first flow image contrasts blood flow from static tissue within blood-perfused tissue.
11. A system for in vivo imaging, comprising:
an optical micro-angiography (OMAG) apparatus configured to generate images from living tissue; and
a non-transitory computer-readable medium having stored therein instructions executable to cause a computing device to carry out the method of claim 1 .
12. An optical micro-angiography (OMAG) apparatus configured to generate images from living tissue; and
a non-transitory computer-readable medium having stored therein instructions executable by a computing device to cause the computing device to perform functions to extract tissue motion from the generated images, the functions comprising:
acquiring a first flow image from a plurality of OMAG scans of the sample, wherein the sample comprises a biological tissue having fluid flow therethrough, the fluid flow being a blood flow;
generating a vector from the plurality of OMAG scans, the vector comprising tissue components, flow components, and noise;
estimating eigenvectors and eigenvalues from the vector;
isolating the flow components from the tissue components by application of an eigen
regression filter for a determined number of the eigenvectors; and
removing the tissue components in the flow image; and
quantifying blood perfusion within the biological tissue,
wherein quantifying blood perfusion within the biological tissue comprises calculating a mean capillary flow velocity and capillary flow heterogeneity.
13. The apparatus of claim 12 , wherein the plurality of OMAG scans of the sample comprises a plurality of adjacent 8 -scans of the sample.
14. The apparatus of claim 12 , wherein acquiring the first flow image from the plurality of OMAG scans of the sample comprises:
acquiring repeated OMAG scans at a single location on the sample; and
generating a time series of the OMAG scans.
15. The apparatus of claim 12 , wherein the determined number of the eigenvectors is based on the number of OMAG scans performed.
16. The apparatus of claim 12 , wherein generating the vector from the plurality of OMAG scans comprises modeling signals from the plurality of OMAG scans as a sum of three independent Gaussian processes comprising the tissue components, the flow components, and the noise.
17. The apparatus of claim 12 , wherein the vector is a two-dimensional vector.
18. The apparatus of claim 12 , wherein the sample comprises one of the following: a brain, an eye, a retina, a tumor, a heart, skin, a kidney, an oral cavity, a gastroenterology tract, a respiratory tract, a reproductive system, and a cochlea.