Dynamic SPECT reconstruction with temporal edge correlation
This work addresses the challenge of reconstructing dynamic image sequences with high temporal resolution in medical imaging, offering a method that outperforms existing approaches in simulations.
The paper proposes a variational model for dynamic SPECT reconstruction that uses infimal convolution of Bregman distance with total variation to model temporal edge correlation, enabling high temporal resolution from undersampled projections. Simulations on two dynamic image sets show improved reconstruction quality over previous methods.
In dynamic imaging, a key challenge is to reconstruct image sequences with high temporal resolution from strong undersampling projections due to a relatively slow data acquisition speed. In this paper, we propose a variational model using the infimal convolution of Bregman distance with respect to total variation to model edge dependence of sequential frames. The proposed model is solved via an alternating iterative scheme, for which each subproblem is convex and can be solved by existing algorithms. The proposed model is formulated under both Gaussian and Poisson noise assumption and the simulation on two sets of dynamic images shows the advantage of the proposed method compared to previous methods.