Uniform Spatial Resolution for Statistical Image Reconstruction for 3D Cone-Beam CT

Statistical image reconstruction methods for X-ray computed tomography (CT) provide improved spatial resolution and noise properties over conventional filtered back-projection (FBP) reconstruction along with other potential advantages such as reduced patient dose and artifacts. Regularized image reconstruction leads to spatially-variant spatial resolution because of the interaction between the system models and the regularization. Previous regularization design methods aiming to solve such issue mostly rely on the circulant approximation of the Fisher information matrix, which fails for the undersampled image locations. This paper extends the spatially-invariant regularization method of Fessler et al. [1] to 3D cone-beam CT by introducing a hypothetical scanning geometry that deals with the undersampling problem. The proposed regularization design were compared with the original method in [1] with both phantom simulation and clinical reconstructions in 3D axial X-ray CT. The proposed regularization method yields improved uniform resolution characteristics in statistical image reconstruction for axial cone-beam CT.

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