Efficient Image-to-Image Schr\"odinger Bridge for CT Field of View Extension

TL;DR

The paper introduces an efficient image-to-image Schrödinger Bridge diffusion model for CT field of view extension, significantly improving reconstruction accuracy and speed over existing diffusion methods.

Contribution

It proposes a novel I$^2$SB diffusion framework that learns direct mappings between limited and extended FOV images, enhancing interpretability and clinical applicability.

Findings

Achieves RMSE of 49.8 HU on simulated data and 152.0 HU on real data, outperforming other diffusion models.

Reconstructs images in 0.19 seconds per slice, over 700 times faster than cDDPM.

Outperforms state-of-the-art diffusion models in accuracy and efficiency.

Abstract

Computed tomography (CT) is a cornerstone imaging modality for non-invasive, high-resolution visualization of internal anatomical structures. However, when the scanned object exceeds the scanner's field of view (FOV), projection data are truncated, resulting in incomplete reconstructions and pronounced artifacts near FOV boundaries. Conventional reconstruction algorithms struggle to recover accurate anatomy from such data, limiting clinical reliability. Deep learning approaches have been explored for FOV extension, with diffusion generative models representing the latest advances in image synthesis. Yet, conventional diffusion models are computationally demanding and slow at inference due to their iterative sampling process. To address these limitations, we propose an efficient CT FOV extension framework based on the image-to-image Schr\"odinger Bridge (I$^2$SB) diffusion model. Unlike traditional diffusion models that synthesize images from pure Gaussian noise, I$^2$SB learns a direct stochastic mapping between paired limited-FOV and extended-FOV images. This direct correspondence yields a more interpretable and traceable generative process, enhancing anatomical consistency and structural fidelity in reconstructions. I$^2$SB achieves superior quantitative performance, with root-mean-square error (RMSE) values of 49.8 HU on simulated noisy data and 152.0 HU on real data, outperforming state-of-the-art diffusion models such as conditional denoising diffusion probabilistic models (cDDPM) and patch-based diffusion methods. Moreover, its one-step inference enables reconstruction in just 0.19 s per 2D slice, representing over a 700-fold speedup compared to cDDPM (135 s) and surpassing DiffusionGAN (0.58 s), the second fastest. This combination of accuracy and efficiency indicates that I$^2$SB has potential for real-time or clinical deployment.