Deconvolution methods for mitigation of transverse blurring in optical coherence tomography.

Imaging resolution in optical coherence tomography (OCT) is a key determinant for acquiring clinically useful optical biopsies of tissues. In contrast to light or confocal microscopy, the axial and transverse resolutions in OCT are independent and each can be analyzed individually. A method for mitigating transverse blurring and the apparent loss of transverse resolution in OCT by means of Gaussian beam deconvolution is presented. Such a method provides better representation of a specimen by using known physical parameters of a lens. To implement this method, deconvolution algorithms based on a focal-dependent kernel are investigated. First, the direct inverse problem is investigated using two types of regularization, truncated singular value decomposition, and Tikhonov. Second, an iterative expectation maximization algorithm, the Richardson-Lucy algorithm, with a beam-width-dependent iteration scheme is developed. A dynamically iterative Richardson-Lucy algorithm can reduce transverse blurring by providing an improvement in the transverse point-spread-function for sparse scattering samples in regions up to two times larger than the confocal region of the lens. These deblurring improvements inside and outside of the confocal region, which are validated experimentally, are possible without introducing new optical imaging hardware or acquiring multiple images of the same specimen. Implementation of this method in sparse scattering specimens, such as engineered tissues, has the potential to improve cellular detection and categorization.