Intensity-based iterative reconstruction for helical grating interferometry breast CT with static grating configuration

Towards clinical-dose grating interferometry breast CT with fused intensity-based iterative reconstruction

X-ray grating interferometry CT (GI-CT) is an emerging imaging modality which provides three complementary contrasts that could increase the diagnostic content of clinical breast CT: absorption, phase, and dark-field. Yet, reconstructing the three image channels under clinically compatible conditions is challenging because of severe ill-conditioning of the tomographic reconstruction problem. In this work we propose to solve this problem with a novel reconstruction algorithm that assumes a fixed relation between the absorption and the phase-contrast channel to reconstruct a single image by automatically fusing the absorption and phase channels. The results on both simulations and real data show that, enabled by the proposed algorithm, GI-CT outperforms conventional CT at a clinical dose.

Technical Design Considerations of a Human-Scale Talbot-Lau Interferometer for Dark-Field CT

Computed tomography (CT) as an important clinical diagnostics method can profit from extension with dark-field imaging, as it is currently restricted to X-rays' attenuation contrast only. Dark-field imaging allows access to more tissue properties, such as micro-structural texture or porosity. The up-scaling process to clinical scale is complex because several design constraints must be considered. The two most important ones are that the finest grating is limited by current manufacturing technology to a [Formula: see text] period and that the interferometer should fit into the CT gantry with minimal modifications only. In this work we discuss why an inverse interferometer and a triangular G1 profile are advantageous and make a compact and sensitive interferometer implementation feasible. Our evaluation of the triangular grating profile reveals a deviation in the interference pattern compared to standard grating profiles, which must be considered in the subsequent data processing. An analysis of the grating orientation demonstrates that currently only a vertical layout can be combined with cylindrical bending of the gratings. We also provide an in-depth discussion, including a new simulation approach, of the impact of the extended X-ray source spot which can lead to large performance loss and present supporting experimental results. This analysis reveals a vastly increased sensitivity to geometry and grating period deviations, which must be considered early in the system design process.

Iterative phase contrast CT reconstruction with novel tomographic operator and data-driven prior

Breast cancer remains the most prevalent malignancy in women in many countries around the world, thus calling for better imaging technologies to improve screening and diagnosis. Grating interferometry (GI)-based phase contrast X-ray CT is a promising technique which could make the transition to clinical practice and improve breast cancer diagnosis by combining the high three-dimensional resolution of conventional CT with higher soft-tissue contrast. Unfortunately though, obtaining high-quality images is challenging. Grating fabrication defects and photon starvation lead to high noise amplitudes in the measured data. Moreover, the highly ill-conditioned differential nature of the GI-CT forward operator renders the inversion from corrupted data even more cumbersome. In this paper, we propose a novel regularized iterative reconstruction algorithm with an improved tomographic operator and a powerful data-driven regularizer to tackle this challenging inverse problem. Our algorithm combines the L-BFGS optimization scheme with a data-driven prior parameterized by a deep neural network. Importantly, we propose a novel regularization strategy to ensure that the trained network is non-expansive, which is critical for the convergence and stability analysis we provide. We empirically show that the proposed method achieves high quality images, both on simulated data as well as on real measurements.