Image reconstruction (CT)

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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