Multiresolution Approach to Acceleration of Iterative Image Reconstruction for X-Ray Imaging for Security Applications

TL;DR

This paper introduces a multiresolution wavelet-based method to accelerate iterative 3D X-ray CT image reconstruction in baggage security scans, focusing computational effort on important image features to improve speed and accuracy.

Contribution

The novel approach estimates wavelet coefficients in the transform domain and adaptively expands the wavelet tree during iterations, enhancing reconstruction speed in baggage security imaging.

Findings

Significantly faster convergence compared to traditional methods.

Effective reduction of artifacts from high-attenuating materials.

Validated on real baggage scan data with improved efficiency.

Abstract

Three-dimensional x-ray CT image reconstruction in baggage scanning in security applications is an important research field. The variety of materials to be reconstructed is broader than medical x-ray imaging. Presence of high attenuating materials such as metal may cause artifacts if analytical reconstruction methods are used. Statistical modeling and the resultant iterative algorithms are known to reduce these artifacts and present good quantitative accuracy in estimates of linear attenuation coefficients. However, iterative algorithms may require computations in order to achieve quantitatively accurate results. For the case of baggage scanning, in order to provide fast accurate inspection throughput, they must be accelerated drastically. There are many approaches proposed in the literature to increase speed of convergence. This paper presents a new method that estimates the wavelet coefficients of the images in the discrete wavelet transform domain instead of the image space itself. Initially, surrogate functions are created around approximation coefficients only. As the iterations proceed, the wavelet tree on which the updates are made is expanded based on a criterion and detail coefficients at each level are updated and the tree is expanded this way. For example, in the smooth regions of the image the detail coefficients are not updated while the coefficients that represent the high-frequency component around edges are being updated, thus saving time by focusing computations where they are needed. This approach is implemented on real data from a SureScan (TM) x1000 Explosive Detection System and compared to straightforward implementation of the unregularized alternating minimization of O'Sullivan and Benac [1].