Material Decomposition Using Ensemble Learning for Spectral X-ray Imaging

Material decomposition allows the reconstruction of material-specific images in spectral X-ray imaging, which requires efficient decomposition models. Due to the presence of nonideal effects in X-ray imaging systems, it is difficult to explicitly estimate the imaging systems for material decomposition tasks. As an alternative to previous empirical material decomposition methods, we investigated material decomposition using ensemble learning methods in this paper. Three ensemble methods with two decision trees as the base learning algorithms were investigated to perform material decomposition in both simulation and experiment. The results were quantitatively evaluated for comparison studies. In general, the results demonstrate that the proposed ensemble learning methods often outperform their base learning algorithms, and rarely reduce performance. Compared to the reference methods and its base learning algorithm, the performance of the Boosting method using REPTree with regularization is improved by over 42% and 13%, respectively, in the noiseless simulated scenario of the XCAT phantom with cardiac and respiratory motion, and over 36% and 17%, respectively, in the noisy scenario. Simultaneously, the performance is improved by over 9% and 8%, respectively, in the original torso phantom scenario, and over 13% and 12%, respectively, in the denoising scenario. The results indicate that ensemble learning with gradient descent optimization algorithms is more appropriate for material decomposition tasks. Further studies are encouraged to facilitate future efforts toward clinical applications.