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Proceedings Paper

Model-based material decomposition with system blur modeling
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Paper Abstract

In this work, we present a novel model-based material decomposition (MBMD) approach for x-ray CT that includes system blur in the measurement model. Such processing has the potential to extend spatial resolution in material density estimates - particularly in systems where different spectral channels exhibit different spatial resolutions. We illustrate this new approach for a dual-layer detector x-ray CT and compare MBMD algorithms with and without blur in the reconstruction forward model. Both qualitative and quantitative comparisons of performance with and without blur modeling are reported. We find that blur modeling yields images with better recovery of high-resolution structures in an investigation of reconstructed line pairs as well as lower cross-talk bias between material bases that is ordinarily found due to mismatches in spatial resolution between spectral channels. The extended spatial resolution of the material decompositions has potential application in a range of high-resolution clinical tasks and spectral CT systems where spectral channels exhibit different spatial resolutions.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 113123Q (16 March 2020); doi: 10.1117/12.2549549
Show Author Affiliations
Wenying Wang, Johns Hopkins Univ. (United States)
Matthew Tivnan, Johns Hopkins Univ. (United States)
Grace J. Gang, Johns Hopkins Univ. (United States)
Yiqun Ma, Johns Hopkins Univ. (United States)
Qian Cao, Johns Hopkins Univ. (United States)
Minghui Lu, Varex Imaging Corp. (United States)
Josh Star-Lack, Varex Imaging Corp. (United States)
Richard E. Colbeth, Varex Imaging Corp. (United States)
Wojciech Zbijewski, Johns Hopkins Univ. (United States)
J. Webster Stayman, Johns Hopkins Univ. (United States)

Published in SPIE Proceedings Vol. 11312:
Medical Imaging 2020: Physics of Medical Imaging
Guang-Hong Chen; Hilde Bosmans, Editor(s)

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