Spatially-Adaptive Reconstruction in Computed Tomography Using Neural Networks

David Boublil, Michael Elad, Joseph Shtok, Michael Zibulevsky
2015 IEEE Transactions on Medical Imaging  
We propose a supervised machine learning approach for boosting existing signal and image recovery methods and demonstrate its efficacy on example of image reconstruction in computed tomography. Our technique is based on a local nonlinear fusion of several image estimates, all obtained by applying a chosen reconstruction algorithm with different values of its control parameters. Usually such output images have different bias/variance trade-off. The fusion of the images is performed by
more » ... d neural network trained on a set of known examples. Numerical experiments show an improvement in reconstruction quality relatively to existing direct and iterative reconstruction methods.
doi:10.1109/tmi.2015.2401131 pmid:25675453 fatcat:h6cpdkwqd5g4jb6swj5ye6ochq