Volumetric Computed Tomography Reconstruction with Dictionary Learning

Ti Bai, Hao Yan, Xun Jia, Steve Jiang, Ge Wang, Xi Chen, Xuanqin Mou


Published in:Fully3D 2017 Proceedings


dictionary learning, sparse representation, low-dose CT, cone-beam CT, noise suppression, GPU
Despite the rapid developments of x-ray cone-beam CT (CBCT), image noise still remains a major issue for the low-dose CBCT. Iterative reconstruction algorithms with 2D dictionary learning (DL) were validated for fine structures and suppressed noise in the case of low-dose CT reconstruction. However, an enhanced version for volumetric CBCT is absent. Besides, it is recognized that representation efficiencies of the sparsity-promotion regularizers are of primary importance for the success of the image processing tasks. In this work, a sparse constraint based on the 3D dictionary is incorporated into a statistical iterative reconstruction, defining the 3D-DL reconstruction framework. From a statistical viewpoint, the distributions of the representation coefficients associated with the 2D/3D dictionaries are analyzed to compare their efficiencies in representing volumetric images. The whole program is implemented on graphic processor units (GPU) to boost the computation efficiency. Experiments demonstrated that the 3D dictionary allows a much higher representation efficiency and a better image quality compared to the 2D dictionary case. Regarding the tested radiation therapy datasets, with a volume of 512×512×512 and a projection dataset of 512×384×363, the whole reconstruction process can be finished within 5 minutes.
Ti Bai
Institute of Image Processing and Pattern Recognition, Xi'an Jiaotong University, China
Hao Yan
Cyber Medical Corporation, China
Xun Jia
Department of Radiation Oncology, UT Southwestern Medical Center, USA
Steve Jiang
Department of Radiation Oncology, UT Southwestern Medical Center, USA
Ge Wang
Biomedical Imaging Center, Rensselaer Polytechnic Institute, USA
Xuanqin Mou
Institute of Image Processing and Pattern Recognition, Xi'an Jiaotong University, China
Illustration of 2D (first row) and 3D (second row) dictionary learning based sparse representations.
Transversal views of the HN patient images reconstructed by different methods. From left to right in the first row, the images are regular dose FDK reconstruction, reconstructions from the 3D-DL, 2D-DL and TF methods, respectively. From left to right in the second row, the images are reconstructed by the FDK, 881-DL, 818-DL and 188-DL methods, respectively. The last two rows show the corresponding zoomed-in ROIs of the red box in the first two rows. The display window is [-750 750] HU.
Distributions of the representation coefficients among different dictionaries for the HN patient (a) and prostate patient (b). The x-axis is the values of the coefficients, the y-axis is the logarithmic probabilities. 3D-444 denotes the distributions of the coefficients for the 3D data samples represented by the 3D dictionary of dimension 4*4*4. 2D-881/2D-818/2D-188 denote the distributions of the coefficients with the 2D dictionary for the 2D data samples extracted from the transversal/coronal/sagittal views, respectively.

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