TU‐A‐213CD‐10: Accelerated Barrier Optimization Compressed Sensing (ABOCS) Reconstruction for Cone‐Beam CT

Purpose: Compressed sensing (CS) enables accurate CTimage reconstruction from low‐dose measurements, due to the sparsifiable feature of most CTimages using total variation (TV). The CS‐based reconstruction is formulated as either a constrained problem to minimize the TV objective within a small and fixed 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. Methods: The data fidelity tolerance of CS reconstruction is well estimated using the measured data, as most of the projection errors are from Poissonnoise. We therefore adopt the TV optimization framework with data fidelity as constraints. To accelerate the convergence, we first convert such a constrained optimization using a logarithmic 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 Log‐barrier Optimization for CS (ABOCS). Results: As demonstrated on Shepp‐Logan phantom, ABOCS achieves consistent reconstruction 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 ASD‐POCS in Matlab by >10 times. ABOCS is further accelerated on GPU to reconstruct a head patient volume of 512×512×200 voxels in <10 minutes using 25% projections, and the image quality is comparable to that of the full‐view FDK reconstruction.Conclusions: We propose ABOCS for CBCTreconstruction. As compared to other published CS‐based algorithms, our method has attractive features of fast convergence and consistent parameter settings for different datasets. NIH under the grant number 1R21EB012700‐01A1