Model-based material decomposition with a penalized nonlinear least-squares CT reconstruction algorithm

• Published in: Physics in medicine & biology Vol. 64; no. 3; p. 035005
• Main Authors: Tilley, Steven, Zbijewski, Wojciech, Stayman, J Webster
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 22.01.2019
• Subjects: Algorithms; contrast; gadolinium; Image Processing, Computer-Assisted - methods; iodine; kV-switching; Least-Squares Analysis; multi-energy; Nonlinear Dynamics; Phantoms, Imaging; spectral; split filter; Tomography, X-Ray Computed
• ISSN: 0031-9155; 1361-6560; 1361-6560
• DOI: 10.1088/1361-6560/aaf973

Spectral information in CT may be used for material decomposition to produce accurate reconstructions of material density and to separate materials with similar overall attenuation. Traditional methods separate the reconstruction and decomposition steps, often resulting in undesirable trade-offs (e.g. sampling constraints, a simplified spectral model). In this work, we present a model-based material decomposition algorithm which performs the reconstruction and decomposition simultaneously using a multienergy forward model. In a kV-switching simulation study, the presented method is capable of reconstructing iodine at 0.5 mg ml−1 with a contrast-to-noise ratio greater than two, as compared to 3.0 mg ml−1 for image domain decomposition. The presented method also enables novel acquisition methods, which was demonstrated in this work with a combined kV-switching/split-filter acquisition explored in simulation and physical test bench studies. This novel design used four spectral channels to decompose three materials: water, iodine, and gadolinium. In simulation, the presented method accurately reconstructed concentration value estimates with RMSE values of 4.86 mg ml−1 for water, 0.108 mg ml−1 for iodine and 0.170 mg ml−1 for gadolinium. In test-bench data, the RMSE values were 134 mg ml−1, 5.26 mg ml−1 and 1.85 mg ml−1, respectively. These studies demonstrate the ability of model-based material decomposition to produce accurate concentration estimates in challenging spatial/spectral sampling acquisitions.