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

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.

Fig. 1

Filtered back projection (FBP), hybrid iterative reconstruction (IR), and model-based IR. With FBP, images are reconstructed from projection data (sinograms) by applying a high-pass filter followed by a backward projection step (left column). In hybrid IR, the projection data is iteratively filtered to reduce artifacts, and after the backward projection step, the image data are iteratively filtered to reduce image noise (middle column). In model-based IR, the projection data are backward projected into the cross-sectional image space. Subsequently, image space data are forward projected to calculate artificial projection data. The artificial projection data are compared to the true projection data to thereupon update the cross-sectional image. In parallel, image noise is removed via a regularization step

Fig. 2

Number of publications on iterative reconstruction for computed tomography. Results based on Pubmed search (“iterative reconstruction” AND (“computed tomography” OR “CT”))

Fig. 3

One ex vivo human heart, scanned at 4 mGy and 1 mGy (75% dose-reduction) with high-end CT scanners from four vendors. Images are reconstructed with filtered back projection (FBP), hybrid iterative reconstruction, and model-based iterative reconstruction. Numbers represent noise levels (standard deviations) in air. Images derived from a study published before by Willemink et al [39]

Fig. 4

Reconstructions in dual-energy and photon-counting computed tomography. Differentiation of energy levels of x-ray photons allows for the reconstruction of energy-selective images. Material-selective images are reconstructed based on interaction of materials at varying energy levels. Finally, a combined image with different colors per material is reconstructed