Impact of Norm Selections on the Performance of Four-dimensional Cone-beam Computed Tomography (4DCBCT) using PICCS

摘要

Iterative image reconstruction methods have been proposed in computed tomography to address two major challenges: one is to reduce radiation dose while maintaining image quality and the other is to reconstruct diagnostic quality images from angularly sparse projection datasets. A variety of regularization models have been introduced in these iterative image reconstruction methods to incorporate the desired image features. To address the sparse view angle image reconstruction problem in four-dimensional cone-beam CT (4DCBCT), Prior Image Constrained Compressed Sensing (PICCS) was proposed. In the past in 4DCBCT, as well as other applications of the PICCS algorithm, the PICCS regularization was formulated using the ℓ_1 norm as the means to promote image sparsity. The ℓ_1 norm in the objective function is not differentiable and thus may pose challenges in numerical implementations. When the norm deviates from 1.0, the differentiability of the objective function improves, however, the imaging performance may degrade in image reconstruction from sparse datasets. In this paper, we study how the performance of PICCS-4DCBCT changes with norm selection and whether the introduction of a reweighted scheme in relaxed norm PICCS reconstruction helps improve the imaging performance.