The RoboCT system enables the exploration of questions that are not feasible with traditional rotating table or gantry setups. By placing the source and detector freely around the object, non-standard trajectories (e.g., circle, helix) can be obtained, which is essential for various reasons such as complex objects, ROI reconstructions, limited angle imaging, etc. However, reconstructing data acquired with these trajectories using an FBP-based algorithm like FDK is not straightforward. Currently, deviations from the circular trajectory are reconstructed using an algebraic reconstruction technique (ART). The parameterization of ART significantly affects the reconstruction quality, especially under challenging conditions such as limited angle, sparse sampling, or truncation artefacts. ART also has drawbacks, including longer computation time compared to FBP and the need to start the iterative process after data acquisition.
Theoretical descriptions exist for FBP-based reconstruction for general trajectories [1, 2]. However, the filtering step cannot be performed with shift-invariant filter kernels like in FDK. Instead, trajectory-specific filter kernels must be derived and determined, making the process complex and time-consuming.
This master’s thesis aims to investigate the possibility of learning invariant or variant filters specific to trajectories using Known Operator Learning [3]. Building on previous work in filter learning [4,5], the implementation will be carried out using the Pyronn framework [6]. The study will also explore whether these filters can be learned purely from simulated data using a specially created phantom and their generalization to real data with different objects under the specified trajectory, based on previous research [5].
[1] Defrise, Michel, and Rolf Clack. “A cone-beam reconstruction algorithm using shift-variant filtering and cone-beam backprojection.” IEEE transactions on medical imaging 13.1 (1994): 186-195.
[2] Oeckl, Steven. “Rekonstruktionsverfahren mit der approximativen Inversen und einer neuen Formel zur Inversion der Röntgen-Transformation.” (2014).
[3] Maier, Andreas K., et al. “Learning with known operators reduces maximum error bounds.” Nature machine intelligence 1.8 (2019): 373-380.
[4] Syben, Christopher, et al. “Precision learning: reconstruction filter kernel discretization.” arXiv preprint arXiv:1710.06287 (2017).
[5] Syben, Christopher, et al. “Known operator learning enables constrained projection geometry conversion: Parallel to cone-beam for hybrid MR/X-ray imaging.” IEEE Transactions on Medical Imaging 39.11 (2020): 3488-3498.
[6] Syben, Christopher, et al. “PYRO-NN: Python reconstruction operators in neural networks.” Medical physics 46.11 (2019): 5110-5115.