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

21 Apr 2022, 14:19 (edited 04 Jun 2022)MIDL 2022 Short PapersReaders: Everyone
  • Keywords: Sparse view CT, cone beam CT, deep learning, convolutional neural networks
  • TL;DR: Modifies the Primal-Dual UNet to reconstruct volumes from cone beam projections
  • Abstract: 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.
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  • Paper Type: novel methodological ideas without extensive validation
  • Primary Subject Area: Image Acquisition and Reconstruction
  • Secondary Subject Area: Application: Radiology
  • Confidentiality And Author Instructions: I read the call for papers and author instructions. I acknowledge that exceeding the page limit and/or altering the latex template can result in desk rejection.
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