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Material Quantification in Spectral X-Ray Imaging: Optimization and Validation

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S Nik

S J Nik1*, R S Thing1,2, R Watts1,3, J Meyer1, (1) University of Canterbury, Christchurch, New Zealand,(2) Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Odense DK-5000, Denmark,(3) UVM MRI Center for Biomedical Imaging, University of Vermont College of Medicine, Burlington, VT, USA

SU-D-218-5 Sunday 2:15:00 PM - 3:00:00 PM Room: 218

Purpose: To develop and validate a multivariate statistical method to optimize scanning parameters for material quantification in spectral x-ray imaging.
Methods: An optimization metric was constructed by extensively sampling the thickness space for the expected number of counts for m (two or three) materials. This resulted in an m-dimensional confidence region of material quantities, e.g. thicknesses. Minimization of the ellipsoidal confidence region leads to the optimization of energy bins. For the given spectrum, the minimum counts required for effective material separation can be determined by predicting the signal-to-noise ratio (SNR) of the quantification. A Monte Carlo (MC) simulation framework using BEAM was developed to validate the metric. Projection data of the m-materials was generated and material decomposition was performed for combinations of iodine, calcium and water by minimizing the z-score between the expected spectrum and binned measurements. The mean square error (MSE) and variance were calculated to measure the accuracy and precision of this approach, respectively. The minimum MSE corresponds to the optimal energy bins in the BEAM simulations. In the optimization metric, this is equivalent to the smallest confidence region. The SNR of the simulated images was also compared to the predictions from the metric.
Results: The MSE was dominated by the variance for the given material combinations, which demonstrates accurate material quantifications. The BEAM simulations revealed that the optimization of energy bins was accurate to within 1keV. The SNRs predicted by the optimization metric yielded satisfactory agreement but were expectedly higher for the BEAM simulations due to the inclusion of scattered radiation.
Conclusions: The validation showed that the multivariate statistical method provides accurate material quantification, correct location of optimal energy bins and adequate prediction of image SNR. The BEAM code system is suitable for generating spectral x- ray imaging simulations.

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