By applying the 2D DL method in three different orthogonal planes and calculating the sparse coefficients accordingly, much of the 3D spatial information can be preserved without incurring the computational penalty of the 3D DL method. For performance evaluation, we use baggage phantoms with different number of projection views. In order to quantitatively compare the performance of different algorithms, we use PSNR, SSIM and region based standard deviation to measure the noise level, and use the edge response to calculate the resolution. Experimental results with full view datasets show that the different DL based algorithms have similar performance and 2.5D DL has the best resolution. Results with sparse view datasets show that 2.5D DL outperforms both 2D and 3D DL in terms of noise reduction. We also compare the computational costs, and 2.5D DL shows strong advantage over 3D DL in both full-view and sparse-view cases.
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Jiajia Luo, Haneda Eri, Ali Can, Sathish Ramani, Lin Fu, Bruno De Man, "2.5D dictionary learning based computed tomography reconstruction," Proc. SPIE 9847, Anomaly Detection and Imaging with X-Rays (ADIX), 98470L (12 May 2016);