Reconstruct Spine CT from Biplanar X-Rays via Diffusion Learning
This addresses a practical issue for surgical guidance by providing an alternative to intraoperative CT imaging, though it is an incremental improvement over prior reconstruction techniques.
The paper tackles the problem of reconstructing 3D CT scans from biplanar X-rays, which is useful when CT imaging is unavailable, and achieves a 10% higher SSIM of 0.83 and a 25% lower FID of 83.43 compared to existing methods.
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 Fréchet Inception Distance (FID) of 83.43, which represents a relative decrease of 25\%.