3D Reconstruction of unstained cells from a single defocused hologram

TL;DR

This paper presents a novel method for 3D reconstruction of unstained red blood cells from a single defocused hologram using a weighted mean gradient descent optimization that leverages amplitude contrast information.

Contribution

It introduces a new weighting scheme based on amplitude contrast to improve 3D volume localization in holographic reconstruction of phase objects.

Findings

Successfully reconstructed 3D volumes of healthy and malaria-infected RBCs.

Method provides an approximate tomographic solution consistent with hologram data.

Approach is simple to implement and effective with single hologram data.

Abstract

We investigate the problem of 3D complex field reconstruction corresponding to unstained red blood cells (RBCs) with a single defocused off-axis digital hologram. We employ recently introduced mean gradient descent (MGD) optimization framework, to solve the 3D recovery problem. While investigating volume recovery problem for a continuous phase object like RBC, we came across an interesting feature of the back-propagated field that it does not show clear focusing effect. Therefore the sparsity enforcement within the iterative optimization framework given the single hologram data cannot effectively restrict the true object volume. For phase objects, it is known that the amplitude contrast of the back-propagated object field at the focus plane is minimum and it increases at the defocus planes. We therefore use this information available in the detector field data to device weights as a function of inverse of amplitude contrast. This weight function is employed in the iterative steps of the optimization algorithm to assist the object volume localization. The experimental illustrations of 3D volume reconstruction of the healthy as well as the malaria infected RBCs are presented. The proposed methodology is simple to implement experimentally and provides an approximate tomographic solution which is axially restricted and is consistent with the object field data.