TY - JOUR
T1 - 4D-CT reconstruction with unified spatial-temporal patch-based regularization
AU - Kazantsev, Daniil
AU - Thompson, William M.
AU - Lionheart, William R.B.
AU - Van Eyndhoven, Geert
AU - Kaestner, Anders P.
AU - Dobson, Katherine J.
AU - Withers, Philip J.
AU - Lee, Peter D.
N1 - This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Inverse Problems and Imaging following peer review. The definitive publisher-authenticated version is available online at: https://doi.org/10.3934/ipi.2015.9.447.
PY - 2015/5/1
Y1 - 2015/5/1
N2 - In this paper, we consider a limited data reconstruction problem for temporarily evolving computed tomography (CT), where some regions are static during the whole scan and some are dynamic (intensely or slowly changing). When motion occurs during a tomographic experiment one would like to minimize the number of projections used and reconstruct the image it-eratively. To ensure stability of the iterative method spatial and temporal constraints are highly desirable. Here, we present a novel spatial-temporal regularization approach where all time frames are reconstructed collectively as a unified function of space and time. Our method has two main differences from the state-of-the-art spatial-temporal regularization methods. Firstly, all available temporal information is used to improve the spatial resolution of each time frame. Secondly, our method does not treat spatial and temporal penalty terms separately but rather unifies them in one regularization term. Addition- ally we optimize the temporal smoothing part of the method by considering the non-local patches which are most likely to belong to one intensity class. This modification significantly improves the signal-to-noise ratio of the reconstructed images and reduces computational time. The proposed approach is used in combination with golden ratio sampling of the projection data which allows one to find a better trade-off between temporal and spatial resolution scenarios.
AB - In this paper, we consider a limited data reconstruction problem for temporarily evolving computed tomography (CT), where some regions are static during the whole scan and some are dynamic (intensely or slowly changing). When motion occurs during a tomographic experiment one would like to minimize the number of projections used and reconstruct the image it-eratively. To ensure stability of the iterative method spatial and temporal constraints are highly desirable. Here, we present a novel spatial-temporal regularization approach where all time frames are reconstructed collectively as a unified function of space and time. Our method has two main differences from the state-of-the-art spatial-temporal regularization methods. Firstly, all available temporal information is used to improve the spatial resolution of each time frame. Secondly, our method does not treat spatial and temporal penalty terms separately but rather unifies them in one regularization term. Addition- ally we optimize the temporal smoothing part of the method by considering the non-local patches which are most likely to belong to one intensity class. This modification significantly improves the signal-to-noise ratio of the reconstructed images and reduces computational time. The proposed approach is used in combination with golden ratio sampling of the projection data which allows one to find a better trade-off between temporal and spatial resolution scenarios.
KW - GPU acceleration
KW - neutron tomography
KW - non local means
KW - spatial-temporal penalties
KW - time lapse tomography
U2 - 10.3934/ipi.2015.9.447
DO - 10.3934/ipi.2015.9.447
M3 - Article
AN - SCOPUS:84925012334
SN - 1930-8337
VL - 9
SP - 447
EP - 467
JO - Inverse Problems and Imaging
JF - Inverse Problems and Imaging
IS - 2
ER -