Fig 1.
Translation based CT.
Fig 2.
Different translational modes.
The X-ray source is translated on the line where the red points located, and the corresponding flat panel detector is opposite translated on the green line.
Fig 3.
Limited-angle TCT scanning mode.
Fig 4.
Reconstruction results for the scanning range [0°, 120°].
The first row is the images reconstructed by the FBP method, and the second row is the images reconstructed by the SART method.
Fig 5.
Architecture of SARTConvNet.
Table 1.
Geometrical scanning parameters of simulated TCT system.
Fig 6.
The reconstructed results of the abdomen image.
The image on the first column is the reference image. The subsequent columns are the results reconstructed using L0 method, ATV method, FBPConvNet method and our algorithm. The images from top to bottom in each row are the results reconstructed for the scan ranges [0°, 90°], [0°, 120°] and [0°, 150°], respectively. The location of red arrows present some obvious artifacts, and the display window is [800 1200] HU.
Fig 7.
The reconstructed results of the chest image.
The image on the first column is the reference image. The subsequent columns are the results reconstructed using L0 method, ATV method, FBPConvNet method and our algorithm. The images from top to bottom in each row are the results reconstructed for the scan ranges [0°, 90°], [0°, 120°] and [0°, 150°], respectively. The location of red arrows present some obvious artifacts, and the display window is [800 1200] HU.
Fig 8.
The zoom-in view of the ROIs for the Fig 6.
The image on the first column is the ROI of the reference image. The subsequent columns are the ROIs of the reconstructed image for L0 method, ATV method, FBPConvNet method and our algorithm.
Fig 9.
The zoom-in view of the ROIs for the Fig 7.
The images on the first column are the ROIs of the reference image. The subsequent columns are the ROIs of the reconstructed image for L0 method, ATV method, FBPConvNet method and our algorithm.
Table 2.
Quantitative results associated with different algorithms for the abdomen image from different angle projection data.
Table 3.
Quantitative results associated with different algorithms for the chest image from different angle projection data.
Fig 10.
The reconstructed results of the abdomen image from the noise-add experiment.
The image on the first column is the reference image. The subsequent columns are the results reconstructed using L0 method, ATV method, FBPConvNet method and our algorithm. The images from top to bottom in each row are the results reconstructed for the scan ranges [0°, 90°], [0°, 120°] and [0°, 150°], respectively. The location of red arrows present some obvious artifacts, and the display window is [800 1200] HU.
Fig 11.
The reconstructed results of the chest image from the noise-add experiment.
The image on the first column is the reference image. The subsequent columns are the results reconstructed using L0 method, ATV method, FBPConvNet method and our algorithm. The images from top to bottom in each row are the results reconstructed for the scan ranges [0°, 90°], [0°, 120°] and [0°, 150°], respectively. The location of red arrows present some obvious artifacts, and the display window is [800 1200] HU.
Fig 12.
The zoom-in view of the ROIs for the Fig 10.
The image on the first column is the ROI of the reference image. The subsequent columns are the ROIs of the reconstructed image for L0 method, ATV method, FBPConvNet method and our algorithm.
Fig 13.
The zoom-in view of the ROIs for the Fig 11.
The image on the first column is ROI of the reference image. The subsequent columns are the ROIs of the reconstructed image for L0 method, ATV method, FBPConvNet method and our algorithm.
Table 4.
Quantitative results associated with different algorithms for the abdomen image from different angle noise-add projection data.
Table 5.
Quantitative results associated with different algorithms for the chest image from different angle noise-add projection data.
Fig 14.
Loss function value changes in CNN training changes with epochs for both training dataset and testing dataset.
Table 6.
Execution time with different algorithms.