GaSpCT: Gaussian Splatting for Novel CT Projection View Synthesis
Emmanouil Nikolakakis Utkarsh Gupta Jonathan Vengosh Justin Bui Razvan Marinescu
We present GaSpCT, a novel view synthesis and 3D scene representation method
used to generate novel projection views for Computer Tomography (CT) scans. We
adapt the Gaussian Splatting framework to enable novel view synthesis in CT
based on limited sets of 2D image projections and without the need for
Structure from Motion (SfM) methodologies. Therefore, we reduce the total
scanning duration and the amount of radiation dose the patient receives during
the scan. We adapted the loss function to our use-case by encouraging a
stronger background and foreground distinction using two sparsity promoting
regularizers: a beta loss and a total variation (TV) loss. Finally, we
initialize the Gaussian locations across the 3D space using a uniform prior
distribution of where the brain's positioning would be expected to be within
the field of view. We evaluate the performance of our model using brain CT
scans from the Parkinson's Progression Markers Initiative (PPMI) dataset and
demonstrate that the rendered novel views closely match the original projection
views of the simulated scan, and have better performance than other implicit 3D
scene representations methodologies. Furthermore, we empirically observe
reduced training time compared to neural network based image synthesis for
sparse-view CT image reconstruction. Finally, the memory requirements of the
Gaussian Splatting representations are reduced by 17% compared to the
equivalent voxel grid image representations.