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

Abstract

Grating interferometry breast computed tomography (GI-BCT) has the potential to provide enhanced soft tissue contrast and to improve visualization of cancerous lesions for breast imaging. However, with a conventional scanning protocol, a GI-BCT scan requires longer scanning time and higher operation complexity compared to conventional attenuation-based CT. This is mainly due to multiple grating movements at every projection angle, so-called phase stepping, which is used to retrieve attenuation, phase, and scattering (dark-field) signals. To reduce the measurement time and complexity and extend the field of view, we have adopted a helical GI-CT setup and present here the corresponding tomographic reconstruction algorithm. This method allows simultaneous reconstruction of attenuation, phase contrast, and scattering images while avoiding grating movements. Experiments on simulated phantom and real initial intensity, visibility and phase maps are provided to validate our method.

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

“…To benchmark the performance of the proposed method, we compared it to three alternative methods: 1) a classical iterative phase contrast reconstruction algorithm which uses a finite difference-based numerical differentiation strategy for the forward and backward operators along with the well-known TV-based regularization [ 20 ]; 2) a filtered backprojection; and 3) a filtered backprojection followed by a deep learning-based post-processing step. For the comparison to be fair, we used the same network for the post-processing as we used within the iterative reconstruction.…”

“…To benchmark the performance of the proposed method, we compared it to three alternative methods: 1) a classical iterative phase contrast reconstruction algorithm which uses a finite difference-based numerical differentiation strategy for the forward and backward operators along with the well-known TV-based regularization [20]; 2) a filtered backprojection; and 3) a…”

“…as a consequence of (17). Therefore, under reasonable assumptions on the image manifold M, (20) indicates that PnP-BFGS can achieve super-linear convergence, leading to significantly faster convergence that GD. Nevertheless, it remains to be shown rigorously that (17) indeed holds for L-BFGS-based updates, and, therefore, the analysis presented here only serves as a motivation behind choosing PnP-LBFGS for reconstruction.…”

“…Nonetheless, the implementation of the forward-backward operator pair has an impact on the stability of the reconstruction process. This article proposes new operators which are less ill-conditioned and thus less prone to instabilities as compared to the mainstream implementation [ 20 ].…”

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

“…The second processing approach is the intensity based statistical iterative reconstruction (IBSIR), which directly reconstructs the different modalities from the acquired data without any intermediate signal extraction [54], [55]. This algorithm is more complex than sliding window and requires a slightly de-tuned fringe pattern of the interferometer for better sampling in Radon space in order to circumvent artifacts.…”