Image processing to extend effective OCT penetration depth in tissue

Abstract

Innovative approaches in tissue imaging in an in vivo setting have included the use of optical coherence tomography (OCT) as a substrate for providing high resolution images at depths approaching 1.5 mm. This technology has offered the possibility of analyzing many tissues that are presently only evaluated using histologic methods after excision or biopsy. Despite the relatively high penetration depths of OCT, it is unclear whether the images acquired approximately 0.5 mm beyond the tissue surface maintain sufficient resolution and signal-to-noise ratio to provide useful information. Furthermore, there are relatively few studies that evaluate whether advanced image processing can be harnessed to improve the effective depth capabilities of OCT in tissue. We tested a tissue phantom designed to mimic the prostate as a model system, and independently modulated its refractive index and transmittance. Using dynamic focusing, and with the aid of an image analysis paradigm designed to improve signal detection in a model of tissue, we tested potential improvements in the ability to resolve structures at increasing penetration depths. We found that co-registered signal averaging and wavelet denoising improved overall image quality. B-spline interpolation made it possible to integrate dynamic focus images in a way that improved the effective penetration depth without significant loss in overall image quality. These results support the notion that image processing can refine OCT images for improved diagnostic capabilities to support in vivo microscopy.