Highly brilliant X-rays delivered by third generation synchrotron facilities coupled with modern detector technology permit routinely acquisition of high resolution tomograms in few minutes, making high throughput experiments a reality and bringing real-time tomography closer. New solutions for fast post-processing of such large amount of data are mandatory to fully exploit advantages provided by the high acquisition speed enabling new experiments until recently even unimaginable. The TOMCAT beamline1 is well equipped for fast and high throughput experiments2, 3. Here, we will focus on our solutions regarding the reconstruction process and discuss a fast reconstruction algorithm4, based on the Fourier Transform method as opposed to slower standard Filtered Back-Projection routines. We perform the critical step of such method, the polar-to-Cartesian mapping in the Fourier space, by convolution with the Fourier transform of functions with particular characteristics. This convolution approach combines speed with accuracy, making real-time data postprocessing closer to reality. This fast reconstruction algorithm implemented at TOMCAT also features several plug-ins, aimed at taming reconstruction artifacts. Here, we will discuss a new approach for removing rings from reconstructed datasets arising from defective detector pixels and/or damaged scintillator screens. This new method is based on a combined wavelet- FFT decomposition5. Another important feature of the presented reconstruction algorithm deals with local tomographic datasets, characterized by incomplete data. We show here that ad-hoc padding of the sinograms prior to reconstruction significantly reduces typical artifacts related to data incompleteness, making local tomography a valuable acquisition mode when small volumes in relatively large samples are of interest.© (2010) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.