A very fast iterative algorithm for TV-regularized image reconstruction with applications to low-dose and few-view CT
This addresses faster image reconstruction for medical CT applications, though it appears incremental as it builds on existing primal-dual and FBP methods.
The paper tackles the problem of slow iterative reconstruction for low-dose and few-view CT by proposing a very fast algorithm that minimizes a TV-regularized data-fidelity term, achieving rapid convergence to the exact minimizer.
This paper concerns iterative reconstruction for low-dose and few-view CT by minimizing a data-fidelity term regularized with the Total Variation (TV) penalty. We propose a very fast iterative algorithm to solve this problem. The algorithm derivation is outlined as follows. First, the original minimization problem is reformulated into the saddle point (primal-dual) problem by using the Lagrangian duality, to which we apply the first-order primal-dual iterative methods. Second, we precondition the iteration formula using the ramp flter of Filtered Backprojection (FBP) reconstruction algorithm in such a way that the problem solution is not altered. The resulting algorithm resembles the structure of so-called iterative FBP algorithm, and it converges to the exact minimizer of cost function very fast.