mlsanityData to 7 Oct 2026

TD-Net: A Tri-domain network for sparse-view CT reconstruction

arXiv 2311.15369 ↗First posted 26 Nov 2023eess.IV cs.AI cs.CVcited by 0

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Abstract (arXiv)

Sparse-view CT reconstruction, aimed at reducing X-ray radiation risks, frequently suffers from image quality degradation, manifested as noise and artifacts. Existing post-processing and dual-domain techniques, although effective in radiation reduction, often lead to over-smoothed results, compromising diagnostic clarity. Addressing this, we introduce TD-Net, a pioneering tri-domain approach that unifies sinogram, image, and frequency domain optimizations. By incorporating Frequency Supervision Module(FSM), TD-Net adeptly preserves intricate details, overcoming the prevalent over-smoothing issue. Extensive evaluations demonstrate TD-Net's superior performance in reconstructing high-quality CT images from sparse views, efficiently balancing radiation safety and image fidelity. The enhanced capabilities of TD-Net in varied noise scenarios highlight its potential as a breakthrough in medical imaging.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 13 references that are in the map. First posted on arXiv 26 Nov 2023.

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References · 6

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2020Transformers are RNNs: Fast Autoregressive Transformers with Linear Attentioncited by 1,979
  3. 2016Deep Convolutional Neural Network for Inverse Problems in Imagingcited by 431
  4. 2019DuDoNet: Dual Domain Network for CT Metal Artifact Reductioncited by 68
  5. 2019The LoDoPaB-CT Dataset: A Benchmark Dataset for Low-Dose CT Reconstruction Methodscited by 12
  6. 2021Fourier Image Transformercited by 12

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