Simultaneous reconstruction and displacement estimation for spectral-domain optical coherence elastography

TL;DR

This paper introduces a novel framework for improving image quality and displacement estimation in spectral-domain optical coherence elastography by combining motion compensated denoising, resolution enhancement, and refined displacement re-estimation, resulting in up to 33% accuracy improvement.

Contribution

The work presents a combined approach integrating BM4D denoising, ISAM resolution enhancement, and sub-pixel cross correlation for more accurate elastography displacement mapping.

Findings

Up to 33% improvement in displacement accuracy.

Enhanced image quality through motion compensated denoising.

Effective resolution enhancement away from the focal plane.

Abstract

Optical coherence elastography allows the characterization of the mechanical properties of tissues, and can be performed through estimating local displacement maps from subsequent acquisitions of a sample under different loads. This displacement estimation is limited by noise in the images, which can be high in dynamic systems due to the inability to perform long exposures or B-scan averaging. In this work, we propose a framework for simultaneously enhancing both the image quality and displacement map for elastography, by motion compensated denoising with the block-matching and 4D filtering (BM4D) method, followed by a re-estimation of displacement. We adopt the interferometric synthetic aperture microscopy (ISAM) method to enhance the lateral resolution away from the focal plane, and use sub-pixel cross correlation block matching for non-uniform deformation estimation. We validate this approach on data from a commercial spectral domain optical coherence tomography system, whereby we observe an enhancement of both image and displacement accuracy of up to 33% over a standard approach.