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Peiyuan Guo

Publications and source records attributed to Peiyuan Guo.

2 recordsLinked to original sources

Coupling Spectrum Estimation and Single Energy Material Decomposition via X-ray Grating Interferometry

Dark-field imaging based on grating interferometry is an emerging X-ray modality in medical imaging, which is particularly capable of providing complementary diagnostic information by visualizing the microstructural properties of lung tissue. However, quantitative dark-field imaging remains fundamentally challenged by beam hardening, which arises from the energy-dependent fringe visibility under polychromatic illumination. The resulting artifacts substantially degrade the quantitative accuracy of dark-field images. In this work, motivated by our key observation of an intrinsic similarity between the X-ray energy spectrum and the system-related coupling spectrum, we propose a unified framework to simultaneously and independently estimate both spectra. By measuring the transmission associated with the zeroth- and first-order components of the phase-stepping curve using solid step-wedge phantoms, the two spectra are robustly estimated via an expectation-maximization algorithm. The recovered spectra are subsequently incorporated into a physics-based correction model to mitigate beam-hardening-induced artifacts in dark-field imaging effectively. Furthermore, leveraging the inherent availability of two independent spectra within X-ray grating interferometry, we introduce a single-energy material decomposition method that achieves basis material imaging without dual-energy scans. Wave-optical simulations and experiments demonstrate accurate spectrum estimation, effective dark-field signal correction, and reliable material decomposition. Consequently, the proposed framework extends the diagnostic potential of X-ray grating interferometry beyond pulmonary imaging, facilitating broader applications in medical imaging.

physics.med-ph

Optimization of Dark-Field CT for Lung Imaging

Background: X-ray grating-based dark-field imaging can sense the small angle scattering caused by an object's micro-structure. This technique is sensitive to lung's porous alveoli and is able to detect lung disease at an early stage. Up to now, a human-scale dark-field CT has been built for lung imaging. Purpose: This study aimed to develop a more thorough optimization method for dark-field lung CT and summarize principles for system design. Methods: We proposed a metric in the form of contrast-to-noise ratio (CNR) for system parameter optimization, and designed a phantom with concentric circle shape to fit the task of lung disease detection. Finally, we developed the calculation method of the CNR metric, and analyzed the relation between CNR and system parameters. Results: We showed that with other parameters held constant, the CNR first increases and then decreases with the system auto-correlation length (ACL). The optimal ACL is nearly not influenced by system's visibility, and is only related to phantom's property, i.e., scattering material's size and phantom's absorption. For our phantom, the optimal ACL is about 0.21 μm. As for system geometry, larger source-detector and isocenter-detector distance can increase the system's maximal ACL, helping the system meet the optimal ACL more easily. Conclusions: This study proposed a more reasonable metric and a task-based process for optimization, and demonstrated that the system optimal ACL is only related to the phantom's property.

physics.med-ph