Highlights The proposed SIRT-FISTA-TV algorithm is adapted to tomography reconstruction from tilt series of images such as recorded in Transmission Electron Microscopy. It produces reconstructed volumes with clear and sharp edges, and thus, the segmentation step was quite easy to perform after 3D reconstruction. SIRT-FISTA-TV can suppress noise artifacts without affecting spatial resolution. SIRT-FISTA-TV is robust to blurred data and gives better results than conventional reconstruction algorithms. SIRT-FISTA-TV is very well adapted to 3D reconstruction of experimental data (i.e. tilt series of projections) collected during very rapid tilt series acquisitions (down to 5 seconds) in situ or operando environmental TEM for studying dynamics of nanomaterials such as nanocatalysts. Fast tomography in Environmental Transmission Electron Microscopy (ETEM) is of a great interest for in situ experiments where it allows to observe 3D real-time evolution of nanomaterials under operating conditions. In this context, we are working on speeding up the acquisition step to a few seconds mainly with applications on nanocatalysts. In order to accomplish such rapid acquisitions of the required tilt series of projections, a modern 4K high-speed camera is used, that can capture up to 100 images per second in a 2K binning mode. However, due to the fast rotation of the sample during the tilt procedure, noise and blur effects may occur in many projections which in turn would lead to poor quality reconstructions. Blurred projections make classical reconstruction algorithms inappropriate and require the use of prior information. In this work, a regularized algebraic reconstruction algorithm named SIRT-FISTA-TV is proposed. The performance of this algorithm using blurred data is studied by means of a numerical blur introduced into simulated images series to mimic possible mechanical instabilities/drifts during fast acquisitions. We also present reconstruction results from noisy data to show the robustness of the algorithm to noise. Finally, we show reconstructions with experimental datasets and we demonstrate the interest of fast tomography with an ultra-fast acquisition performed under environmental conditions, i.e. gas and temperature, in the ETEM. Compared to classically used SIRT and SART approaches, our proposed SIRT-FISTA-TV reconstruction algorithm provides higher quality tomograms allowing easier segmentation of the reconstructed volume for a better final processing and analysis.