Harmonization of chest CT scans for different doses and reconstruction methods

Abstract

Purpose: To develop and validate a computed tomography (CT) harmonization technique by combining noise-stabilization and autocalibration methodologies to provide reliable densitometry measurements in heterogeneous acquisition protocols. Methods: We propose to reduce the effects of spatially variant noise such as nonuniform patterns of noise and biases. The method combines the statistical characterization of the signal-to-noise relationship in the CT image intensities, which allows us to estimate both the signal and spatially variant variance of noise, with an autocalibration technique that reduces the nonuniform biases caused by noise and reconstruction techniques. The method is firstly validated with anthropomorphic synthetic images that simulate CT acquisitions with variable scanning parameters: different dosage, nonhomogeneous variance of noise, and various reconstruction methods. We finally evaluate these effects and the ability of our method to provide consistent densitometric measurements in a cohort of clinical chest CT scans from two vendors (Siemens, n = 54 subjects; and GE, n = 50 subjects) acquired with several reconstruction algorithms (filtered back-projection and iterative reconstructions) with highdose and low-dose protocols. Results: The harmonization reduces the effect of nonhomogeneous noise without compromising the resolution of the images (25% RMSE reduction in both clinical datasets). An analysis through hierarchical linear models showed that the average biases induced by differences in dosage and reconstruction methods are also reduced up to 74.20%, enabling comparable results between high-dose and low-dose reconstructions. We also assessed the statistical similarity between acquisitions obtaining increases of up to 30% points and showing that the low-dose vs high-dose comparisons of harmonized data obtain similar and even higher similarity than the observed for high-dose vs high-dose comparisons of nonharmonized data. Conclusion: The proposed harmonization technique allows to compare measures of low-dose with high-dose acquisitions without using a specific reconstruction as a reference. Since the harmonization does not require a precalibration with a phantom, it can be applied to retrospective studies. This approach might be suitable for multicenter trials for which a reference reconstruction is not feasible or hard to define due to differences in vendors, models, and reconstruction techniques.

Harmonization of in‐plane resolution in CT using multiple reconstructions from single acquisitions

“…We employed this methodology because it can be easily extended to clinical scans utilizing the subject's contour. 13,29 Finally, the LS problem was solved using the conjugate gradient method with an inverse-variance weighting as the regularization term. This term ensures that the sharp estimate will have low noise.…”

“…We evaluate the changes in resolution by estimating the ESF and MTF in the subject's external contour by following the method proposed by Sanders et al 29 and adapted to axial planes. 13 We are aware that this MTF estimation method is not as precise as the one used for the phantoms. However, it has been effective in distinguishing resolution differences between reconstruction methods.…”

“…11 For this reason, multicenter studies with different CT scanners may exhibit significant differences in measures that are attributable to vendors. 12,13 The application of iterative reconstruction methods does not alleviate this problem because they exhibit a nonlinear behavior that may introduce biases in densities. 14 To minimize variability across manufacturers, researchers suggest using kernels that affect similarly to resolution, SNR, density measures, and texture.…”

Statistical characterization of the linear attenuation coefficient in polychromatic CT scans

“…This could enable the development of likelihood-based iterative reconstruction methods and the noise characterization in the reconstructed CT scans. 3,11,20,21 Probably, the most straightforward application of these results is by considering the log-likelihood function, L, of the post-log signal PDF (the Gumbel distribution) shown Eq. 7:…”

“…The nc‐Gamma could be postulated as a general distribution that works across the acquisition stages. This could enable the development of likelihood‐based iterative reconstruction methods and the noise characterization in the reconstructed CT scans 3,11,20,21 …”