AAPM DL-Sparse-View CT Challenge Submission Report: Designing an Iterative Network for Fanbeam-CT with Unknown Geometry
This addresses a specific challenge in medical imaging for CT reconstruction, but it is incremental as it builds on existing data-driven techniques for an unknown geometry scenario.
The paper tackled the problem of reconstructing breast phantom images from limited-view fanbeam CT measurements without known geometry, by first estimating the geometry and then using an iterative network to achieve near-exact solutions.
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.