Fan-Beam CT Reconstruction for Unaligned Sparse-View X-ray Baggage Dataset
This addresses the challenge of obtaining 3D labeled data for security baggage inspection, where stationary X-ray systems are common, but it appears incremental as it builds on existing neural field and optimization techniques.
The study tackled the problem of reconstructing 3D CT images from unaligned sparse-view X-ray baggage data, which lacks large-scale labeled datasets, by developing a calibration and reconstruction method that integrates multi-spectral neural attenuation fields with pose optimization, resulting in improved rendering consistency for novel views.
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.