Optimization of x-ray dark-field CT for human-scale lung imaging.

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X-ray grating-based dark-field imaging can sense the small angle scattering caused by object's micro-structures. This technique is sensitive to the porous microstructure of lung alveoli and has the potential to detect lung diseases at an early stage. Up to now, a human-scale dark-field CT (DF-CT) prototype has been built for lung imaging. This study aimed to develop a thorough optimization method for human-scale dark-field lung CT and guide the system design. We introduced a task-based metric formulated as the contrast-to-noise ratio (CNR) between normal and lesioned alveoli for system parameter optimization and designed a digital human-thorax phantom to fit the task of lung disease detection. Furthermore, a computational framework was developed to model the signal propagation in DF-CT and established the link between system parameters and the CNR metric. We showed that for a DF-CT system, its CNR first increases and then decreases with the system auto-correlation length (ACL). The optimal ACL is mostly independent of system's visibility, and is only related to the phantom's properties, that is, its size and absorption. For our phantom, the optimal ACL is about 0.35 µm at the design energy of 60keV. As for system geometry, increasing source-detector and isocenter-detector distance can extend the system's maximal ACL, making it easier for the system to meet the optimal ACL and relaxing the grating pitches. We proposed a set of parameters for a projective fringe system that can satisfy the simulated optimal ACL. This study introduced a task-based metric and a process for DF-CT optimization. We demonstrated that for a given phantom, the detection performance of the system is optimized at a specific ACL. The optimization method and design principles are independent from the underlying dark-field imaging method and can be applied to DF-CT system design using different grating-based implementations such as Talbot-Lau interferometer (TLI) or projective fringe method.