Reconstruct Spine CT from Biplanar X-Rays via Diffusion Learning

TL;DR

This paper presents a novel diffusion-based method for reconstructing 3D spine CT images from biplanar X-rays, outperforming existing techniques in quality and accuracy with innovative loss functions.

Contribution

Introduces a diffusion model for 3D CT reconstruction from biplanar X-rays, incorporating a new projection loss to enhance structural fidelity.

Findings

Achieves higher SSIM of 0.83, a 10% increase over previous methods.

Reduces FID to 83.43, a 25% improvement.

Outperforms state-of-the-art benchmarks in image quality and metrics.

Abstract

Intraoperative CT imaging serves as a crucial resource for surgical guidance; however, it may not always be readily accessible or practical to implement. In scenarios where CT imaging is not an option, reconstructing CT scans from X-rays can offer a viable alternative. In this paper, we introduce an innovative method for 3D CT reconstruction utilizing biplanar X-rays. Distinct from previous research that relies on conventional image generation techniques, our approach leverages a conditional diffusion process to tackle the task of reconstruction. More precisely, we employ a diffusion-based probabilistic model trained to produce 3D CT images based on orthogonal biplanar X-rays. To improve the structural integrity of the reconstructed images, we incorporate a novel projection loss function. Experimental results validate that our proposed method surpasses existing state-of-the-art benchmarks in both visual image quality and multiple evaluative metrics. Specifically, our technique achieves a higher Structural Similarity Index (SSIM) of 0.83, a relative increase of 10\%, and a lower Fr\'echet Inception Distance (FID) of 83.43, which represents a relative decrease of 25\%.