mlsanityData to 7 Oct 2026

AAPM DL-Sparse-View CT Challenge Submission Report: Designing an Iterative Network for Fanbeam-CT with Unknown Geometry

arXiv 2106.00280 ↗First posted 1 Jun 2021cs.LG cs.NA eess.IVcited by 3

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Abstract (arXiv)

This report is dedicated to a short motivation and description of our contribution to the AAPM DL-Sparse-View CT Challenge (team name: "robust-and-stable"). The task is to recover breast model phantom images from limited view fanbeam measurements using data-driven reconstruction techniques. The challenge is distinctive in the sense that participants are provided with a collection of ground truth images and their noiseless, subsampled sinograms (as well as the associated limited view filtered backprojection images), but not with the actual forward model. Therefore, our approach first estimates the fanbeam geometry in a data-driven geometric calibration step. In a subsequent two-step procedure, we design an iterative end-to-end network that enables the computation of near-exact solutions.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 18 of its 31 references that are in the map. First posted on arXiv 1 Jun 2021.

Builds on

18 of 31 references.

References · 18

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6 more
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Citing papers per year: 2022: 1; 2023: 0; 2024: 2.

3 papers.

  1. 2024A study of why we need to reassess full reference image quality assessment with medical imagescited by 16
  2. 2022Deep Learning for Material Decomposition in Photon-Counting CTcited by 2
  3. 2024Continuous Learned Primal Dualcited by 0