Fan-Beam CT Reconstruction for Unaligned Sparse-View X-ray Baggage Dataset

TL;DR

This paper introduces a novel calibration and reconstruction method for unaligned sparse-view X-ray baggage datasets, leveraging neural attenuation fields and pose optimization to improve 3D imaging in security applications.

Contribution

It presents a new approach combining multi-spectral neural attenuation fields with pose optimization for better 3D reconstruction from unaligned sparse-view baggage X-ray data.

Findings

Enhanced rendering consistency for novel views.

Improved generalization in security baggage inspection.

Effective reconstruction with limited and unaligned data.

Abstract

Computed Tomography (CT) is a technology that reconstructs cross-sectional images using X-ray images taken from multiple directions. In CT, hundreds of X-ray images acquired as the X-ray source and detector rotate around a central axis, are used for precise reconstruction. In security baggage inspection, X-ray imaging is also widely used; however, unlike the rotating systems in medical CT, stationary X-ray systems are more common, and publicly available reconstructed data are limited. This makes it challenging to obtain large-scale 3D labeled data and voxel representations essential for training. To address these limitations, our study presents a calibration and reconstruction method using an unaligned sparse multi-view X-ray baggage dataset, which has extensive 2D labeling. Our approach integrates multi-spectral neural attenuation field reconstruction with Linear pushbroom (LPB) camera model pose optimization, enhancing rendering consistency for novel views through color coding network. Our method aims to improve generalization within the security baggage inspection domain, where generalization is particularly challenging.