Optimized Spatial-Spectral CT for Multi-Material Decomposition
Matthew Tivnan, Wenying Wang, Steven Tilley, Jeffrey H. Siewerdsen, J., Webster Stayman

TL;DR
This paper proposes a novel spatial-spectral filter design for spectral CT, demonstrating through simulations that filter width significantly impacts material decomposition performance, while filter order has minimal effect.
Contribution
It introduces an alternative spatial-spectral filter design for spectral CT and analyzes how design parameters affect material decomposition, enabling optimized physical configurations.
Findings
Narrower filter widths improve material decomposition performance.
Filter order has minimal impact on performance.
Wider filter widths are feasible with only slight performance loss.
Abstract
Spectral CT is an emerging modality that uses a data acquisition scheme with varied spectral responses to provide enhanced material discrimination in addition to the structural information of conventional CT. Existing clinical and preclinical designs with this capability include kV-switching, split-filtration, and dual-layer detector systems to provide two spectral channels of projection data. In this work, we examine an alternate design based on a spatial-spectral filter. This source-side filter is made up a linear array of materials that divide the incident x-ray beam into spectrally varied beamlets. This design allows for any number of spectral channels; however, each individual channel is sparse in the projection domain. Model-based iterative reconstruction methods can accommodate such sparse spatial-spectral sampling patterns and allow for the incorporation of advanced…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
