  Regularization methods are commonly used in X-ray CT image reconstruction.
Different regularization methods reflect the characterization of different
prior knowledge of images. In a recent work, a new regularization method called
a low-dimensional manifold model (LDMM) is investigated to characterize the
low-dimensional patch manifold structure of natural images, where the manifold
dimensionality characterizes structural information of an image. In this paper,
we propose a CT image reconstruction method based on the prior knowledge of the
low-dimensional manifold of CT image. Using the clinical raw projection data
from GE clinic, we conduct comparisons for the CT image reconstruction among
the proposed method, the simultaneous algebraic reconstruction technique (SART)
with the total variation (TV) regularization, and the filtered back projection
(FBP) method. Results show that the proposed method can successfully recover
structural details of an imaging object, and achieve higher spatial and
contrast resolution of the reconstructed image than counterparts of FBP and
SART with TV.
