# A Splitting-Based Iterative Algorithm for GPU-Accelerated Statistical Dual-Energy X-Ray CT Reconstruction

## TL;DR

This paper introduces a GPU-accelerated, splitting-based iterative algorithm for statistical dual-energy CT reconstruction, significantly improving speed and quality in baggage material classification tasks.

## Contribution

It presents a novel splitting-based ADMM algorithm that separates reconstruction and decomposition, enabling faster convergence in dual-energy CT imaging.

## Key findings

- Significant reduction in reconstruction time demonstrated on synthetic and real baggage data.
- Improved image quality over traditional methods.
- Effective handling of high dynamic range in material properties.

## Abstract

When dealing with material classification in baggage at airports, Dual-Energy Computed Tomography (DECT) allows characterization of any given material with coefficients based on two attenuative effects: Compton scattering and photoelectric absorption. However, straightforward projection-domain decomposition methods for this characterization often yield poor reconstructions due to the high dynamic range of material properties encountered in an actual luggage scan. Hence, for better reconstruction quality under a timing constraint, we propose a splitting-based, GPU-accelerated, statistical DECT reconstruction algorithm. Compared to prior art, our main contribution lies in the significant acceleration made possible by separating reconstruction and decomposition within an ADMM framework. Experimental results, on both synthetic and real-world baggage phantoms, demonstrate a significant reduction in time required for convergence.