Spectrally Grouped Total Variation Reconstruction for Scatter Imaging Using ADMM

TL;DR

This paper introduces a novel spectrally grouped regularization method for scatter imaging reconstruction, leveraging ADMM for efficient parallel optimization, resulting in improved spectral and spatial image quality from multiplexed measurements.

Contribution

It proposes a new spectral grouping regularization approach and an ADMM-based parallel optimization algorithm for scatter imaging reconstruction, enhancing image quality.

Findings

Improved spectral and spatial image quality demonstrated on real data.

Spectral grouping regularization outperforms traditional edge-preserving methods.

Parallel ADMM algorithm accelerates convergence and computation.

Abstract

We consider X-ray coherent scatter imaging, where the goal is to reconstruct momentum transfer profiles (spectral distributions) at each spatial location from multiplexed measurements of scatter. Each material is characterized by a unique momentum transfer profile (MTP) which can be used to discriminate between different materials. We propose an iterative image reconstruction algorithm based on a Poisson noise model that can account for photon-limited measurements as well as various second order statistics of the data. To improve image quality, previous approaches use edge-preserving regularizers to promote piecewise constancy of the image in the spatial domain while treating each spectral bin separately. Instead, we propose spectrally grouped regularization that promotes piecewise constant images along the spatial directions but also ensures that the MTPs of neighboring spatial bins are similar, if they contain the same material. We demonstrate that this group regularization results in improvement of both spectral and spatial image quality. We pursue an optimization transfer approach where convex decompositions are used to lift the problem such that all hyper-voxels can be updated in parallel and in closed-form. The group penalty introduces a challenge since it is not directly amendable to these decompositions. We use the alternating directions method of multipliers (ADMM) to replace the original problem with an equivalent sequence of sub-problems that are amendable to convex decompositions, leading to a highly parallel algorithm. We demonstrate the performance on real data.