  Sparse-view CT is a promising strategy for reducing the radiation dose of
traditional CT scans, but reconstructing high-quality images from incomplete
and noisy data is challenging. Recently, 3D Gaussian has been applied to model
complex natural scenes, demonstrating fast convergence and better rendering of
novel views compared to implicit neural representations (INRs). Taking
inspiration from the successful application of 3D Gaussians in natural scene
modeling and novel view synthesis, we investigate their potential for
sparse-view CT reconstruction. We leverage prior information from the
filtered-backprojection reconstructed image to initialize the Gaussians; and
update their parameters via comparing difference in the projection space.
Performance is further enhanced by adaptive density control. Compared to INRs,
3D Gaussians benefit more from prior information to explicitly bypass learning
in void spaces and allocate the capacity efficiently, accelerating convergence.
3D Gaussians also efficiently learn high-frequency details. Trained in a
self-supervised manner, 3D Gaussians avoid the need for large-scale paired
data. Our experiments on the AAPM-Mayo dataset demonstrate that 3D Gaussians
can provide superior performance compared to INR-based methods. This work is in
progress, and the code will be publicly available.
