Slice Consistent Three Dimensional Modeling for CT Reconstruction

Authors

  • Michael Chen Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA 94720, USA Author
  • Daniel Rodriguez Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA 94720, USA Author
  • Emily Johnson Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA 94720, USA Author

DOI:

https://doi.org/10.71465/mrcis217

Keywords:

3D CT reconstruction, volumetric denoising, convolution–attention networks, medical imaging

Abstract

Most CT denoising methods operate on individual slices and do not explicitly exploit inter-slice continuity. Based on hybrid convolution–attention designs exemplified by CTLformer, this paper proposes a slice-consistent reconstruction approach that extends convolutional and self-attention operations to three-dimensional contexts. Local convolutions capture intra-slice texture, while attention mechanisms aggregate information across adjacent slices. The model is evaluated on volumetric CT datasets containing 7,200 3D scans and approximately 180,000 slices. Compared with 2D CNNs, 3D CNNs, and transformer-based volumetric models, the proposed method improves volumetric PSNR by 1.0–1.6 dB and reduces inter-slice intensity discontinuities

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Published

2026-02-28

Issue

Vol. 6 No. 2 (2026): Multidisciplinary Research in Computing Information Systems

Section

Articles

License

Copyright (c) 2026 Michael Chen, Daniel Rodriguez, Emily Johnson (Author)

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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