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This report is dedicated to a short motivation and description of ourcontribution to the AAPM DL-Sparse-View CT Challenge (team name:"robust-and-stable"). The task is to recover breast model phantom images fromlimited view fanbeam measurements using data-driven reconstruction techniques.The challenge is distinctive in the sense that participants are provided with acollection of ground truth images and their noiseless, subsampled sinograms (aswell as the associated limited view filtered backprojection images), but notwith the actual forward model. Therefore, our approach first estimates thefanbeam geometry in a data-driven geometric calibration step. In a subsequenttwo-step procedure, we design an iterative end-to-end network that enables thecomputation of near-exact solutions.

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