mlsanityData to 14 Oct 2026

Primal-Dual UNet for Sparse View Cone Beam Computed Tomography Volume Reconstruction

arXiv 2205.07866 ↗First posted 11 May 2022eess.IV cs.CV cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 3 of its 5 references that are in the map. First posted on arXiv 11 May 2022.

Builds on

3 of 5 references.

References · 3

  1. 2016Deep Convolutional Neural Network for Inverse Problems in Imagingcited by 433
  2. 2017Learned Primal-dual Reconstructioncited by 251
  3. 2021Sinogram upsampling using Primal-Dual UNet for undersampled CT and radial MRI reconstructioncited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026SONAR: A Structure-Consistent Neural Operator for Null-Space-Aware Sparse View CT Reconstructioncited by 0