CT images have been reconstructed from raw data using filtered back projection (FBP) since the inception of the modality. The standard FBP algorithm operates on several fundamental assumptions about scanner geometry but is basically a compromise between reconstruction speed and image noise. One might make different assumptions about scanner geometry and noise statistics which are computationally more complex and combine these with multiple iterations of reconstruction — termed statistical iterative reconstruction. Such iterative reconstruction may result in longer reconstruction time but also in substantially less image noise from the same raw data through more complex modeling of detector response and of the statistical behavior of measurements. An adaptive shortcut which starts iterative reconstruction after a first-pass FBP reconstruction, adaptive statistical iterative reconstruction (ASIR), can help shorten the longer reconstruction time of pure iterative reconstruction while maintaining much lower image noise than if the same raw data were reconstructed with FBP alone. ASIR substantially reduces image quantum noise with no impact on spatial or contrast resolution