title: Spatially-Adaptive Reconstruction in Computed Tomography Using Neural Networks.
abstract: 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 feed-forward 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.
authors: David
authors: D
authors: Boublil
authors: Michael
authors: M
authors: Elad
authors: Joseph
authors: J
authors: Shtok
authors: Michael
authors: M
authors: Zibulevsky