Primal-Dual UNet for Sparse View Cone Beam Computed Tomography Volume Reconstruction
This is an incremental improvement for medical imaging, specifically CT reconstruction, but limited to low-resolution proof-of-concept applications.
The paper tackles sparse view cone beam CT volume reconstruction by modifying a Primal-Dual UNet to handle entire volumes instead of slices, achieving a 10dB PSNR increase over direct FDK reconstruction and nearly 3dB over the original method with only 23 projections.
In this paper, the Primal-Dual UNet for sparse view CT reconstruction is modified to be applicable to cone beam projections and perform reconstructions of entire volumes instead of slices. Experiments show that the PSNR of the proposed method is increased by 10dB compared to the direct FDK reconstruction and almost 3dB compared to the modified original Primal-Dual Network when using only 23 projections. The presented network is not optimized wrt. memory consumption or hyperparameters but merely serves as a proof of concept and is limited to low resolution projections and volumes.