  This paper presents a dictionary learning-based method with region-specific
image patches to maximize the utility of the powerful sparse data processing
technique for CT image reconstruction. Considering heterogeneous distributions
of image features and noise in CT, region-specific customization of
dictionaries is utilized in iterative reconstruction. Thoracic CT images are
partitioned into several regions according to their structural and noise
characteristics. Dictionaries specific to each region are then learned from the
segmented thoracic CT images and applied to subsequent image reconstruction of
the region. Parameters for dictionary learning and sparse representation are
determined according to the structural and noise properties of each region. The
proposed method results in better performance than the conventional
reconstruction based on a single dictionary in recovering structures and
suppressing noise in both simulation and human CT imaging. Quantitatively, the
simulation study shows maximum improvement of image quality for the whole
thorax can achieve 4.88% and 11.1% in terms of the Structure-SIMilarity (SSIM)
and Root-Mean-Square Error (RMSE) indices, respectively. For human imaging
data, it is found that the structures in the lungs and heart can be better
recovered, while simultaneously decreasing noise around the vertebra
effectively. The proposed strategy takes into account inherent regional
differences inside of the reconstructed object and leads to improved images.
The method can be readily extended to CT imaging of other anatomical regions
and other applications.
