mlsanityData to 14 Oct 2026

Limited-Angle Tomography Reconstruction via Deep End-To-End Learning on Synthetic Data

arXiv 2309.06948 ↗First posted 13 Sep 2023eess.IV cs.CVcited by 5

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Abstract (arXiv)

Computed tomography (CT) has become an essential part of modern science and medicine. A CT scanner consists of an X-ray source that is spun around an object of interest. On the opposite end of the X-ray source, a detector captures X-rays that are not absorbed by the object. The reconstruction of an image is a linear inverse problem, which is usually solved by filtered back projection. However, when the number of measurements is small, the reconstruction problem is ill-posed. This is for example the case when the X-ray source is not spun completely around the object, but rather irradiates the object only from a limited angle. To tackle this problem, we present a deep neural network that is trained on a large amount of carefully-crafted synthetic data and can perform limited-angle tomography reconstruction even for only 30deg or 40deg sinograms. With our approach we won the first place in the Helsinki Tomography Challenge 2022.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 15 of its 45 references that are in the map. First posted on arXiv 13 Sep 2023.

Builds on

15 of 45 references.

References · 15

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3 more
  1. 2018Learning The Invisible: A Hybrid Deep Learning-Shearlet Framework for Limited Angle Computed Tomographycited by 29
  2. 2022Near-Exact Recovery for Tomographic Inverse Problems via Deep Learningcited by 13
  3. 2018High-Resolution Limited-Angle Phase Tomography of Dense Layered Objects Using Deep Neural Networkscited by 9

Built on by

Cited by year

Citing papers per year: 2024: 1; 2025: 4.

5 papers.

  1. 2025Data-Efficient Limited-Angle CT Using Deep Priors and Regularizationcited by 1
  2. 2025PWD: Prior-Guided and Wavelet-Enhanced Diffusion Model for Limited-Angle CTcited by 1
  3. 2024Robustness of data-driven approaches in limited angle tomographycited by 0
  4. 2025Two-stage deep learning framework for the restoration of incomplete-ring PET imagescited by 0
  5. 2025The method of the approximate inverse for limited-angle CTcited by 0