Diagnostic Benchmarking of Pseudo-Volumetric Representations for Biplanar X-ray-to-CT Reconstruction

Abstract

The reconstruction of volumetric computed tomography (CT) from two orthogonal X-ray projections is an inverse problem that is difficult to solve as only limited depth information can be obtained from the two-dimensional (2D) projections of three dimensional (3D) anatomies. The majority of previous work has concentrated on improving the reconstruction model, whereas the goal of this work is to improve the input representation before the 3D reconstruction stage, we introduce a diagnostic benchmarking framework to obtain intermediate pseudo-volumetric diagnostic inputs before CT prediction from the digitally reconstructed radiographs (DRRs) in both anterior-posterior (AP) and lateral (LAT) views. This validates the pipeline by experimentation: CT-to-CT; simplified DRR-to-CT and Plastimatch-based DRR-to-CT. Then, it tests one-channel, three-channel and five-channel pseudo-volumetric representations on top of a 3D U-Net-based reconstruction backbone. The peak signal-to-noise ratio (PSNR) and three-dimensional structural similarity index measure (SSIM3D) were used to quantify the evaluation. For multi-patient experiments, enriching the pseudo-volumetric representation was found to yield better validation PSNR from 17.24 dB with pseudol to 18.55 dB with pseudo5, and a mean PSNR of 19.27 dB during validation inference. The results demonstrate that input representation quality and projection consistency are two important factors for biplanar X-ray-to-CT reconstruction. The proposed framework is thus designed as a way to analyze input representations and is not a clinically deployable CT replacement system.