Image reconstruction (CT)

Last revised by Andrew Murphy on 23 Mar 2023

The rapid evolution of mathematical methods of image reconstruction in computed tomography (CT) reflects the race to produce an efficient yet accurate image reconstruction method while keeping radiation dose to a minimum and has defined improvements in CT over the past decade.

The mathematical problem that CT image reconstruction is trying to solve is to compute the attenuation coefficients of different x-ray absorption paths (ray sum) that are obtained as a set of data (projection).

Reconstruction algorithms

There are various algorithms used in CT image reconstruction, the following are some of the more common algorithms utilized in commercially available CT today. 

  • iterative algorithm without statistical modelling
    • used originally by Godfrey Hounsfield, however not commercially used due to the inherent limitations of microprocessors at that time
    • will use an assumption and will compare to the assumption with its measured data. Then will continue to make iterations until the two data sets are in agreement.
  • iterative algorithm with statistical modelling
    • iterative reconstruction with statistical modelling that takes into account
      • optics (x-ray source, image voxels and detector)
      • noise (photon statistics)
      • physics (data acquisition)
      • object (radiation attenuation)
  • back projection
    • not used in the clinical setting, as it is unable to produce sharp images
    • known for its distinctive artifact that resembles a star
  • filtered back projection (convolution method)
    • still widely used in CT today
    • utilizes a convolution filter to alleviate the blurring associated with back projection
    • fast, however, has several limitations including noise and artifact creation

See also

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