Open AccessModel-based material decomposition with system blur modeling
Author(s) -
Wenying Wang,
Matthew Tivnan,
Grace J. Gang,
Yiqun Ma,
Qian Cao,
Minghui Lu,
Josh StarLack,
Richard E. Colbeth,
Wojciech Zbijewski,
J. Webster Stayman
Publication year - 2020
Publication title -
medical imaging 2020: physics of medical imaging
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.192
H-Index - 176
pISSN - 0277-786X
DOI - 10.1117/12.2549549
Subject(s) - image resolution , computer science , resolution (logic) , detector , spectral resolution , iterative reconstruction , spectral imaging , artificial intelligence , optics , computer vision , algorithm , physics , spectral line , telecommunications , astronomy
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.