A machine learning based dual-energy CT elemental decomposition method and its physical-biological impacts on carbon ion therapy.

Background: Dual-energy computed tomography (DECT) enhances material differentiation by leveraging energy-dependent attenuation properties particularly for carbon ion therapy. Accurate estimation of tissue elemental composition via DECT can improve quantification of physical and biological doses.

Objective: This study proposed a novel machine-learning-based DECT (ML-DECT) method to predict the physical density and mass ratios of H, C, N, O, P, and Ca. The physical and biological impacts on carbon ion therapy was also investigated.

Methods: Taking DECT-derived CT numbers as inputs, a fully connected neural network was employed to predict the physical density or the elemental mass ratio. The training and testing utilized a dataset of 85 biological tissues with data augmentation. The prediction accuracy and noise analysis were compared against the parameterization DECT (PA-DECT) and SECT methods. By applying the proposed method on the DECT images of 10 head-and-neck patients, the physical and biological doses as well as the linear energy transfer (LET) were calculated for a set of carbon ion pencil beams using Monte-Carlo simulations. Patient-based results were compared with the PA-DECT method.

Results: The ML-DECT method yielded for physical density and for the six elemental mass ratios across 85 materials. Compared to the PA-DECT and SECT methods, the accuracy was improved by over 20% and 50%; the noise robustness was improved by over three times and up to 25%, respectively. In the patient dose evaluation, the ML-DECT method yielded comparable physical and biological doses, yet up to ∼1% higher LET, and up to ∼2 mm shallower peak positions than those of the PA-DECT method.

Conclusion: The ML-DECT method provided precise estimation of physical density and elemental mass ratios of human tissues. Compared with the PA-DECT method, the ML-DECT method displayed stronger robustness to image noise.