Paper
16 March 2020 Model-based material decomposition with system blur modeling
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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.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Wenying Wang, Matthew Tivnan, Grace J. Gang, Yiqun Ma, Qian Cao, Minghui Lu, Josh Star-Lack, Richard E. Colbeth, Wojciech Zbijewski, and J. Webster Stayman "Model-based material decomposition with system blur modeling", Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 113123Q (16 March 2020); https://doi.org/10.1117/12.2549549
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Cited by 2 scholarly publications.
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KEYWORDS
Systems modeling

Iodine

Scintillators

Imaging systems

X-rays

Computed tomography

Dual energy imaging

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