Improving analytical tomographic reconstructions through consistency conditions
This work addresses accuracy issues in tomographic imaging for applications like medical or industrial scanning, but it is incremental as it builds on existing consistency conditions and analytical methods.
The authors tackled the problem of improving analytical tomographic reconstructions for undersampled datasets by introducing a fast parameterless filter based on Helgason-Ludwig consistency conditions, which extrapolates intermediate projections to double the views and improves peak-signal-to-noise ratio by up to 5-6 dB in simulated data.
This work introduces and characterizes a fast parameterless filter based on the Helgason-Ludwig consistency conditions, used to improve the accuracy of analytical reconstructions of tomographic undersampled datasets. The filter, acting in the Radon domain, extrapolates intermediate projections between those existing. The resulting sinogram, doubled in views, is then reconstructed by a standard analytical method. Experiments with simulated data prove that the peak-signal-to-noise ratio of the results computed by filtered backprojection is improved up to 5-6 dB, if the filter is used prior to reconstruction.