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Eur Radiol. 2019 May;29(5):2185-2195. doi: 10.1007/s00330-018-5810-7. Epub 2018 Oct 30.

The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence.

Author information

1
Department of Radiology, Stanford University School of Medicine, 300 Pasteur Drive, Room M-039, Stanford, CA, 94305-5105, USA. m.j.willemink@gmail.com.
2
Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands. m.j.willemink@gmail.com.
3
Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
4
Department of Diagnostic and Interventional Radiology, Technische Universität München, Munich, Germany.

Abstract

The first CT scanners in the early 1970s already used iterative reconstruction algorithms; however, lack of computational power prevented their clinical use. In fact, it took until 2009 for the first iterative reconstruction algorithms to come commercially available and replace conventional filtered back projection. Since then, this technique has caused a true hype in the field of radiology. Within a few years, all major CT vendors introduced iterative reconstruction algorithms for clinical routine, which evolved rapidly into increasingly advanced reconstruction algorithms. The complexity of algorithms ranges from hybrid-, model-based to fully iterative algorithms. As a result, the number of scientific publications on this topic has skyrocketed over the last decade. But what exactly has this technology brought us so far? And what can we expect from future hardware as well as software developments, such as photon-counting CT and artificial intelligence? This paper will try answer those questions by taking a concise look at the overall evolution of CT image reconstruction and its clinical implementations. Subsequently, we will give a prospect towards future developments in this domain. KEY POINTS: • Advanced CT reconstruction methods are indispensable in the current clinical setting. • IR is essential for photon-counting CT, phase-contrast CT, and dark-field CT. • Artificial intelligence will potentially further increase the performance of reconstruction methods.

KEYWORDS:

Artificial intelligence; Image reconstruction; Tomography, x-ray

PMID:
30377791
PMCID:
PMC6443602
DOI:
10.1007/s00330-018-5810-7
[Indexed for MEDLINE]
Free PMC Article

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