Synchronizing an optical coherence tomography system
Methods for synchronizing an Optical Coherence Tomography (OCT) system including detection when a plurality of A-line scans obtained from reflected light of a cantilever scanning fiber within a probe oscillating along a scanning path that increases in amplitude over time are no longer being obtained at a point along the oscillating scanning path when the scanning fiber reaches a minimum speed, determining a value by which a phase angle of the oscillating scanning path is out of synchronization with the plurality of A-line scans, and adjusting a trigger clock for the obtaining the plurality of A-line scans based on the value.
1. A method for synchronizing an Optical Coherence Tomography (OCT) system, comprising:
detecting when a plurality of A-line scans obtained from reflected light of a cantilever scanning fiber within a probe oscillating along a scanning path that increases in amplitude over time are no longer being obtained at a point along the oscillating scanning path when the scanning fiber reaches a minimum speed;
determining a value by which a phase angle of the oscillating scanning path is out of synchronization with the plurality of A-line scans; and
adjusting a trigger clock for the obtaining the plurality of A-line scans based on the value.
2. The method of claim 1 , wherein the detecting includes:
obtaining a series of samples of at least one A-line scan among the plurality of A-lines scans just before, during and after the scanning fiber reaches the minimum speed;
generating a Line-to-Line (L-L) correlation between at least one sample among the series of samples and nearest neighbor samples to the at least one sample among the series of samples; and
generating a Neighbor-to-Neighbor (N-N) correlation between the at least one sample and two nearest neighbor samples; and
wherein determining includes adjusting the value to minimize the L-L correlation and to maximize the N-N correlation.
3. The method of claim 2 , wherein the detecting includes:
inputting the plurality of A-line scans to an L-L processor and to an N-N processor operating in parallel with the L-L processor in order to generate the L-L correlation and the N-N correlation; and
wherein the determining includes outputting the L-L correlation and the N-N correlation to a trained Artificial Intelligence (AI) engine to adjust the value in real-time.
4. The method of claim 2 , wherein the plurality of A-line scans are obtained from reflected light from a flat surface, wherein detecting includes determining if a B-line scan image constructed from the plurality of A-line scans is a straight line or a non-straight line, and wherein when the image is a non-straight line the determining includes further or alternatively adjusting the value until the non-straight line becomes the straight line.
5. The method of claim 4 , wherein the detecting includes:
inputting the plurality of A-line scans to an L-L processor and to an N-N processor operating in parallel with the L-L processor in order to generate the L-L correlation and the N-N correlation;
constructing B-line scan images from a series of the plurality of A-line scans; and
outputting the B-line scan images to a processor operating in parallel with the L-L processor and the N-N processor for analyzing whether each image among the B-line scan images is the straight line or the non-straight line; and
wherein the determining includes outputting the L-L correlation, the N-N correlation, and a straight/non-straight line determination to a trained Artificial Intelligence (AI) engine to determine how to adjust the value in real-time.
6. The method of claim 1 , wherein the plurality of A-line scans are obtained from reflected light from a flat surface, wherein detecting includes determining if a B-line scan image constructed from the plurality of A-line scans is a straight line or a non-straight line, and wherein when the image is a non-straight line the determining includes adjusting the value until the non-straight line becomes the straight line.
7. The method of claim 6 , wherein the detecting includes:
constructing B-line scan images from a series of the plurality of A-line scans; and
outputting the B-line scan images to a processor for analyzing whether each image among the B-line scan images is the straight line or the non-straight line; and
wherein the determining includes outputting a straight/non-straight line determination to a trained Artificial Intelligence (AI) engine to adjust the value in real-time.
8. The method of claim 1 , wherein the plurality of A-line scans are obtained from reflected light from a surface that is not a flat surface, wherein detecting includes determining if B-line scan images constructed from the plurality of A-line scans are in focus or out of focus, and wherein when the B-line scan images are out of focus the determining includes adjusting the value until one of the B-line scan images becomes in focus.
9. The method of claim 8 , wherein the determining includes:
generating Fast Fourier Transforms (FFTs) of a series of B-line scan images when the plurality of A-line scans are at various phase angles around an optimum phase angle; and
analyzing the FFTs to determine if the B-line scan images are in focus or out of focus; and
wherein the determining includes outputting an in focus/out of focus determination to a trained Artificial Intelligence (AI) engine to adjust the value in real-time.
10. The method of claim 8 , wherein the detecting includes:
obtaining a series of samples of at least one A-line scan among the plurality of A-lines scans just before, during and after the scanning fiber reaches the minimum speed;
generating a Line-to-Line (L-L) correlation between at least one sample among the series of samples and nearest neighbor samples to the at least one sample among the series of samples; and
generating a Neighbor-to-Neighbor (N-N) correlation between the at least one sample and two nearest neighbor samples;
and wherein the determining includes further or alternatively adjusting the value to minimize the L-L correlation and to maximize the N-N correlation.
11. The method of claim 10 , wherein the plurality of A-line scans are obtained from reflected light from a flat surface, wherein detecting includes determining if a B-line scan image constructed from the plurality of A-line scans is a straight line or a non-straight line, and wherein when the image is a non-straight line the determining includes further or alternatively adjusting the value until the non-straight line becomes the straight line.
12. The method of claim 11 , wherein the detecting includes:
generating Fast Fourier Transforms (FFTs) of a series of B-line scan images when the plurality of A-line scans are at various phase angles around an optimum phase angle;
analyzing with a lateral FFT processor the FFTs to determine if the B-line scan images are in focus or out of focus;
inputting the plurality of A-line scans to an L-L processor and to an N-N processor operating in parallel with the L-L processor in order to generate the L-L correlation and the N-N correlation;
constructing B-line scan images from a series of the plurality of A-line scans; and
outputting the B-line scan images to a processor operating in parallel with the L-L processor, the N-N processor and the lateral FFT processor for analyzing whether each image among the B-line scan images is the straight line or the non-straight line; and
wherein the determining includes outputting the L-L correlation, the N-N correlation, a straight/non-straight line determination and an in focus/out of focus determination to a trained Artificial Intelligence (AI) engine to determine how to adjust the value in real-time.
13. The method of claim 8 , wherein the plurality of A-line scans are obtained from reflected light from a flat surface, wherein detecting includes determining if a B-line scan image constructed from the plurality of A-line scans is a straight line or a non-straight line, and wherein when the image is a non-straight line the determining includes further or alternatively adjusting the value until the non-straight line becomes the straight line.
14. The method of claim 11 , wherein the detecting includes:
generating Fast Fourier Transforms (FFTs) of a series of B-line scan images when the plurality of A-line scans are at various phase angles around an optimum phase angle;
analyzing with a lateral FFT processor the FFTs to determine if the B-line scan images are in focus or out of focus; and
outputting the B-line scan images to a processor operating in parallel with the lateral FFT processor for analyzing whether each image among the B-line scan images is the straight line or the non-straight line; and
wherein the determining includes outputting an in focus/out of focus determination and a straight/non-straight line determination to a trained Artificial Intelligence (AI) engine to determine how to adjust the value in real-time.