Image Reconstruction from Sparse Low-Dose CT Data via Score Matching.

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Abstract

Computed tomography (CT) reconstructs sectional images from X-ray projections acquired from multiple angles around an object. By measuring only a fraction of full projection data, CT image reconstruction can reduce both radiation dose and scan time. However, with a classic analytic algorithm, the reconstruction of insufficient data CT always compromises structural details and suffers from severe artifacts. To address this issue, we present a deep learning-based image reconstruction method derived from maximum a posteriori (MAP) estimation. In the Bayesian statistics framework, the gradient of logarithmic probability density distribution of the image, i.e., the score function, plays a crucial role, contributing to the process of image reconstruction. The reconstruction algorithm theoretically guarantees the convergence of the iterative process. Our numerical results also show that this method produces decent sparse-view CT images.