Improving the convergence of iterative filtered backprojection algorithms

David S. Lalush, Benjamin M.W. Tsui

Research output: Contribution to journalArticlepeer-review


Several authors have proposed variations of the iterative filtered backprojection (IFBP) reconstruction algorithms claiming fast initial convergence rates. We have found that these algorithms are trying to minimize an unusual squared-error criterion in a suboptimal way. As a result, existing IFBP algorithms are inefficient in the minimization of the criterion, and may become unstable at higher iteration numbers. We show that existing IFBP algorithms can be modified to use the steepest descent technique by simply optimizing the step size at each iteration. Further gains in convergence rates can be achieved with conjugate gradient IFBP algorithms derived from the same criterion. The steepest descent and conjugate gradient IFBP algorithms are guaranteed to converge, unlike some IFBP algorithms, and will do so in fewer iterations than existing IFBP algorithms.

Original languageEnglish (US)
Pages (from-to)1283-1286
Number of pages4
JournalMedical physics
Issue number8
StatePublished - Aug 1994
Externally publishedYes


  • attenuation compensation
  • iterative optimization
  • reconstruction algorithms

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging

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