Optical imaging has evolved far beyond simply looking at the images captured by the camera. The amount of information that can be extracted from the images captured by our setups can be maximized by harnessing the computational techniques on the images post-acquisition. Researchers have utilized both mathematical models of image formation and advances in artificial intelligence and machine learning to not only enhance the quality of images captured, but also to automate the translation these images to meaningful biological information.
In this section, we describe the various computational techniques to not only enhance the resolution and the overall quality of OCT images but also methods to maximize the information discerned from them. We describe the scattering problem in optical coherence tomography and how interferometric synthetic aperture microscopy restores focal-plane resolution across the entire volume.
Computational dispersion correction and k-space resampling in OCT
Beam-divergence, scattering, and aberrations
Figure 2.2. Illustration for the effect of OCT system’s NA on the expected OCT images. Each row corresponds to a specific numerical aperture of the system from very low NA (≈0), low NA (≈0-0.2), and high NA (> 0.5). The OCT images shown in the right column are not actual images but are cartoon illustrations. The axes are as indicated in the image.
Figure 2.3. Zernike Polynomials from orders N =0 to 6. Polynomials in a row have the same order N. Commonly observed aberrations such as defocus, astigmatism, and spherical are indicated.
Wavefront sampling and forward problem
Image formation in OCT
Description in space domain
Description in spatial-frequency domain
Approximations for a practical system
Adding aberrations to the model
Summary of the forward problem
Solving the scattering problem the traditional way
Solving the inverse scattering problem with resampling
Algorithm for ISAM and CAO
Results from just wavefront resampling and aberration correction