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.