Spectral data completion for dual-source x-ray CT
In the context of x-ray CT, data completion is the process of augmenting truncated projection data to avoid artifacts during reconstruction. Data completion is commonly employed in dual-source CT where physical or hardware constraints limit the field of view covered by one of the two imaging chains. Practically, data completion is accomplished by extrapolating missing data based on the imaging chain with the full field of view, including some reweighting to approximate any spectral differences. While this approach works well in clinical applications, there are applications which would benefit from improved spectral estimation over the full field of view, including model-based iterative reconstruction, contrastenhanced abdominal imaging of large patients, and combined temporal and spectral imaging. Additionally, robust spectral data completion methods could provide an alternative to interior tomography for dose management in cardiac and spectral CT applications. To illustrate challenges with and potential machine-learning (ML) solutions for the spectral data completion problem, we present two realistic simulation experiments. A circular, cone-beam experiment disambiguates three contrast materials with dual-energy data and uses a generative network to inject 3D geometric information into a 2D, image-domain completion problem. A second clinical MDCT experiment uses a sophisticated variational network based on the split Bregman method and is structured to integrate directly into existing analytical reconstruction pipelines. While further work is required to establish performance limits and expectations, the results of both experiments strongly recommend the use of ML in spectral data completion problems.
