FaCT-GS: Fast and Scalable CT Reconstruction with Gaussian Splatting

Abstract

Gaussian Splatting (GS) has emerged as a dominating technique for image rendering and has quickly been adapted for the X-ray Computed Tomography (CT) reconstruction task. However, despite being on par or better than many of its predecessors, the benefits of GS are typically not substantial enough to motivate a transition from well-established reconstruction algorithms. This paper addresses the most significant remaining limitations of the GS-based approach by introducing FaCT-GS, a framework for fast and flexible CT reconstruction. Enabled by an in-depth optimization of the voxelization and rasterization pipelines, our new method is significantly faster than its predecessors and scales well with projection and output volume size. Furthermore, the improved voxelization enables rapid fitting of Gaussians to pre-existing volumes, which can serve as a prior for warm-starting the reconstruction, or simply as an alternative, compressed representation. FaCT-GS is over 4X faster than the State of the Art GS CT reconstruction on standard 512x512 projections, and over 13X faster on 2k projections. Implementation available at: https://github.com/PaPieta/fact-gs.

Figures (12)

• Figure 1: Reconstruction results of select baselines and FaCT-GS given a fixed time for a certain problem size. FaCT-GS provides superior CT reconstruction capabilities and scales exceptionally well with increasing projection size.
• Figure 2: FaCT-GS optimization pipeline. We propose two initialization schemes: one, based on the gradient of the FDK reconstruction from collected projections, and one based on the Gaussians fitted directly to a prior volume. We accelerate the core components of the pipeline: rasterization, voxelization, and compute-intensive loss functions.
• Figure 3: Effective acceleration of the rasterization and voxelization components enabled through CUDA kernel optimization. Outlined relationships follow a power law. Plotted in log scale, baselines sourced from $R^2$-Gaussian zha2024r.
• Figure 4: Impact of increasing volume and projection size on the performance of chosen methods. For larger data sizes, FaCT-GS performs exceptionally well, providing faster and better reconstruction than both FISTA and R$^2$-Gaussian at a fraction of the execution time. R$^2$-Gaussian iteration speed scales better with data size, but FISTA reaches convergence quicker.
• Figure 5: Comparison of the output of reconstruction initialization strategies on four examples with a similar prior volume available. Gradient-based sampling better outlines the high-frequency features than intensity sampling. FaCT-GS rapid volume fitting demonstrates a similar impact as using the prior directly -- a standard approach in algebraic methods.