Accelerated barrier optimization compressed sensing (ABOCS) reconstruction: Performance evaluation for cone-beam CT

Compressed sensing (CS) enables accurate CT image reconstruction from low-dose measurements, due to the sparsifiable feature of most CT images using total variation (TV). The CS reconstruction is formulated as either a constrained problem to minimize the TV objective within a small data fidelity error, or an unconstrained problem to minimize the data fidelity error with TV regularization. However, the conventional solutions to the above two formulations are either computationally inefficient or involved with inconsistent regularization parameter tuning. In this work, we propose an optimization algorithm for cone-beam CT (CBCT) CS reconstruction which overcomes the above two drawbacks. The data tolerance is well estimated using the measured data, as most of the projection errors are from Poisson noise. We adopt the TV optimization framework with data fidelity as constraints. To accelerate the convergence, we first convert such a constrained optimization using a barrier method into a similar form to the conventional TV-regularization reconstruction but with an automatically adjusted penalty weight. The problem is then solved efficiently by gradient projection. The proposed algorithm is referred to as Accelerated Barrier Optimization for CS (ABOCS). As demonstrated on Shepp-Logan and head phantoms, ABOCS achieves consistent performances using the same parameters on scans with different datasets, while the TV-regularization method needs a large-scale tuning on the penalty weight. ABOCS also requires less computation time than ASDPOCS in Matlab by more than 10 times. ABOCS is further accelerated on GPU to reconstruct a 3D Shepp-Logan volume of 256×256×256 voxels in less than 20 mins using 10% projections, and the image quality is comparable to that of the full-view FDK reconstruction. We propose ABOCS for CBCT reconstruction. As compared to other published CS-based algorithms, our method has attractive features of fast convergence and consistent parameter setting for different datasets.