Multivariate signal-to-noise ratio as a metric for characterizing spectral computed tomography.

Conventional image quality metrics assume independence between images which is not preserved within spectral CT datasets, limiting their utility for characterizing energy selective images. In this work, we developed a metrology to characterize energy selective images by incorporating the shared information between images within a spectral CT dataset.
Approach: Signal-to-noise ratio was extended into a multivariate space where each image was treated as a separate information channel. The general definition was applied to contrast to define a multivariate contrast-to-noise ratio (CNR). The matrix contained two types of terms: a conventional CNR term, characterizing image quality within each image, and covariance weighted CNR (Covar-CNR), characterizing contrast relative to covariance between images. The metrology was demonstrated using experimental data from an investigational photon-counting CT scanner. A cylindrical water phantom containing vials of iodine and gadolinium (2, 4, 8 mg/mL) was imaged with variable tube current, tube voltage, and energy threshold. Two image series (threshold and bin images) containing two images each were defined based upon the contribution of photons to reconstructed images. Analysis of variance was calculated between CNR terms and image acquisition variables. A multivariate regression was fit to experimental data.
Main Results: Bin images had a slightly higher mean and wider standard deviation (Covar-CNRlo: 3.38 ±17.25, Covar-CNRhi: 5.77±30.64) than threshold images (Covar-CNRlo: 2.08 ±1.89, Covar-CNRhi: 3.45±2.49) across all conditions. Analysis of variance found each acquisition variable had a significant relationship with both Covar-CNR terms. The multivariate regression model suggested that material concentration had the largest impact on all CNR terms.
Significance: In this work, we described a theoretical framework to extend the signal-to-noise ratio to a multivariate form to characterize images independently and provide insight regarding the relationship between images. Experimental data was used to demonstrate the insight that this metrology provides about image formation factors in spectral CT.&#xD.