mlsanityData to 14 Oct 2026

Swap-Net: A Memory-Efficient 2.5D Network for Sparse-View 3D Cone Beam CT Reconstruction

arXiv 2410.10836 ↗First posted 29 Sep 2024eess.IV cs.CVcited by 3

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Abstract (arXiv)

Reconstructing 3D cone beam computed tomography (CBCT) images from a limited set of projections is an important inverse problem in many imaging applications from medicine to inertial confinement fusion (ICF). The performance of traditional methods such as filtered back projection (FBP) and model-based regularization is sub-optimal when the number of available projections is limited. In the past decade, deep learning (DL) has gained great popularity for solving CT inverse problems. A typical DL-based method for CBCT image reconstruction is to learn an end-to-end mapping by training a 2D or 3D network. However, 2D networks fail to fully use global information. While 3D networks are desirable, they become impractical as image sizes increase because of the high memory cost. This paper proposes Swap-Net, a memory-efficient 2.5D network for sparse-view 3D CBCT image reconstruction. Swap-Net uses a sequence of novel axes-swapping operations to produce 3D volume reconstruction in an end-to-end fashion without using full 3D convolutions. Simulation results show that Swap-Net consistently outperforms baseline methods both quantitatively and qualitatively in terms of reducing artifacts and preserving details of complex hydrodynamic simulations of relevance to the ICF community.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 30 of its 80 references that are in the map. First posted on arXiv 29 Sep 2024.

Builds on

30 of 80 references.

References · 30

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18 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Learning robust parameter inference and density reconstruction in flyer plate impact experimentscited by 2
  2. 2025Clinical Metadata Guided Limited-Angle CT Image Reconstructioncited by 0
  3. 2026Material Identification using Multi-Modal Intrinsic Radiation and Radiographycited by 0