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We propose a direct reconstruction algorithm for Computed Tomography, basedon a local fusion of a few preliminary image estimates by means of a non-linearfusion rule. One such rule is based on a signal denoising technique which isspatially adaptive to the unknown local smoothness. Another, more powerfulfusion rule, is based on a neural network trained off-line with a high-qualitytraining set of images. Two types of linear reconstruction algorithms for thepreliminary images are employed for two different reconstruction tasks. For anentire image reconstruction from full projection data, the proposed scheme usesa sequence of Filtered Back-Projection algorithms with a gradually growingcut-off frequency. To recover a Region Of Interest only from local projections,statistically-trained linear reconstruction algorithms are employed. Numericalexperiments display the improvement in reconstruction quality when compared tolinear reconstruction algorithms.

Spatially-Adaptive Reconstruction in Computed Tomography Based on Statistical Learning