CT Reconstruction Data for Bayesian Geometry Uncertainty Study

Three datasets accompany the preprint 'A Bayesian Approach to CT Reconstruction with Uncertain Geometry' by Frederik H. Pedersen, Jakob S. Jørgensen, and Martin S. Andersen from the Technical University of Denmark. The datafiles are provided as MATLAB .mat files to reproduce the paper's results and figures. Each file corresponds to one of the three cases presented in the paper.

Use Cases

• Benchmarking Bayesian reconstruction algorithms based on the described geometry uncertainty models.
• Reproducing the results and figures from the associated arXiv preprint.
• Developing new CT reconstruction methods that account for uncertain geometry parameters.
• Teaching Bayesian inference principles in the context of medical imaging inverse problems.

Strengths

• Data is directly tied to a peer-reviewed preprint, providing a clear research context.
• Files are provided in a standard scientific format (MATLAB .mat) for reproducibility.
• Accompanied by code on GitHub, facilitating replication and extension of the work.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and dataset scale are unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Technical University of Denmark

Collection Method

Created for the research paper 'A Bayesian Approach to CT Reconstruction with Uncertain Geometry'.