Computer-implemented method of iteratively reconstructing CT images or a CT volume using deep learning, comprising the steps: d) automatically tuning a spatial projection filter parameter σ s,sin and/or an intensity projection filter parameter σ i,sin of a projection bilateral filter, BF, or projection joint bilateral filter, JBF, F sin by a first trained neural network NN sin based on data of projections of an imaging procedure in case the first iteration starts at step d), or on data of forward projected projections, otherwise; e) filtering the projections with the projection BF or projection JBF F sin using the respective automatically tuned projection filter parameter (s) σ s,sin /σ i,sin ; f) backward-projecting a volume from the projections of the imaging procedure, in case the first iteration starts at step f), or from the filtered projections, otherwise, using a filtered back-projection or weighted filtered back-projection ; g) automatically tuning a spatial volume filter parameter σ s,vol and/or an intensity volume filter parameter σ i,vol of a volume BF or volume JBF F vol by a second trained neural network NN vol based on data of a volume of the imaging procedure, in case the first iteration starts at step g), or on the backward-projected volume, otherwise; h) filtering the volume with the volume BF or volume JBF F vol using the respective automatically tuned volume filter parameter(s) σ s,vol /σ i,vol ; and i) forward-projecting projections from the volume of the imaging procedure, in case the first iteration starts at step i), or from the filtered volume and returning to step d), in case the filtered volume does not meet a predefined quality criterion, otherwise; or j) outputting the filtered volume as the reconstructed medical images or volume, in case the filtered volume meets the predefined quality criterion; wherein the first iteration starts at one of the group comprising step d); step f); step g) and step i). d) automatically tuning a spatial projection filter parameter σ s,sin and/or an intensity projection filter parameter σ i,sin of a projection bilateral filter, BF, or projection joint bilateral filter, JBF, F sin by a first trained neural network NN sin based on data of projections of an imaging procedure in case the first iteration starts at step d), or on data of forward projected projections, otherwise; e) filtering the projections with the projection BF or projection JBF F sin using the respective automatically tuned projection filter parameter (s) σ s,sin /σ i,sin ; f) backward-projecting a volume from the projections of the imaging procedure, in case the first iteration starts at step f), or from the filtered projections, otherwise, using a filtered back-projection or weighted filtered back-projection ; g) automatically tuning a spatial volume filter parameter σ s,vol and/or an intensity volume filter parameter σ i,vol of a volume BF or volume JBF F vol by a second trained neural network NN vol based on data of a volume of the imaging procedure, in case the first iteration starts at step g), or on the backward-projected volume, otherwise; h) filtering the volume with the volume BF or volume JBF F vol using the respective automatically tuned volume filter parameter(s) σ s,vol /σ i,vol ; and i) forward-projecting projections from the volume of the imaging procedure, in case the first iteration starts at step i), or from the filtered volume and returning to step d), in case the filtered volume does not meet a predefined quality criterion, otherwise; or j) outputting the filtered volume as the reconstructed medical images or volume, in case the filtered volume meets the predefined quality criterion; wherein the first iteration starts at one of the group comprising step d); step f); step g) and step i). <EOS>