The future of CT: deep learning reconstruction.

Journal: Clinical radiology
Published Date:

Abstract

There have been substantial advances in computed tomography (CT) technology since its introduction in the 1970s. More recently, these advances have focused on image reconstruction. Deep learning reconstruction (DLR) is the latest complex reconstruction algorithm to be introduced, which harnesses advances in artificial intelligence (AI) and affordable supercomputer technology to achieve the previously elusive triad of high image quality, low radiation dose, and fast reconstruction speeds. The dose reductions achieved with DLR are redefining ultra-low-dose into the realm of plain radiographs whilst maintaining image quality. This review aims to demonstrate the advantages of DLR over other reconstruction methods in terms of dose reduction and image quality in addition to being able to tailor protocols to specific clinical situations. DLR is the future of CT technology and should be considered when procuring new scanners.

Authors

  • C M McLeavy
    Department of Radiology, Leighton Hospital, Middlewich Road, Crewe, CW1 4QJ, UK.
  • M H Chunara
    Department of Radiology, Leighton Hospital, Middlewich Road, Crewe, CW1 4QJ, UK.
  • R J Gravell
    Department of Radiology, Leighton Hospital, Middlewich Road, Crewe, CW1 4QJ, UK.
  • A Rauf
    Department of Urology, Leighton Hospital, Middlewich Road, Crewe, CW1 4QJ, UK.
  • A Cushnie
    Department of Radiology, Leighton Hospital, Middlewich Road, Crewe, CW1 4QJ, UK.
  • C Staley Talbot
    Department of Radiology, Leighton Hospital, Middlewich Road, Crewe, CW1 4QJ, UK.
  • R M Hawkins
    Department of Radiology, Leighton Hospital, Middlewich Road, Crewe, CW1 4QJ, UK. Electronic address: richard.hawkins@mcht.nhs.uk.