Autor: |
van Gogh S, Mukherjee S, Rawlik M, Pereira A, Spindler S, Zdora MC, Stauber M, Varga Z, Stampanoni M |
Jazyk: |
angličtina |
Zdroj: |
IEEE transactions on medical imaging [IEEE Trans Med Imaging] 2024 Mar; Vol. 43 (3), pp. 1033-1044. Date of Electronic Publication: 2024 Mar 05. |
DOI: |
10.1109/TMI.2023.3325442 |
Abstrakt: |
Grating interferometry CT (GI-CT) is a promising technology that could play an important role in future breast cancer imaging. Thanks to its sensitivity to refraction and small-angle scattering, GI-CT could augment the diagnostic content of conventional absorption-based CT. However, reconstructing GI-CT tomographies is a complex task because of ill problem conditioning and high noise amplitudes. It has previously been shown that combining data-driven regularization with iterative reconstruction is promising for tackling challenging inverse problems in medical imaging. In this work, we present an algorithm that allows seamless combination of data-driven regularization with quasi-Newton solvers, which can better deal with ill-conditioned problems compared to gradient descent-based optimization algorithms. Contrary to most available algorithms, our method applies regularization in the gradient domain rather than in the image domain. This comes with a crucial advantage when applied in conjunction with quasi-Newton solvers: the Hessian is approximated solely based on denoised data. We apply the proposed method, which we call GradReg, to both conventional breast CT and GI-CT and show that both significantly benefit from our approach in terms of dose efficiency. Moreover, our results suggest that thanks to its sharper gradients that carry more high spatial-frequency content, GI-CT can benefit more from GradReg compared to conventional breast CT. Crucially, GradReg can be applied to any image reconstruction task which relies on gradient-based updates. |
Databáze: |
MEDLINE |
Externí odkaz: |
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