From EMI to AI: a brief history of commercial CT reconstruction algorithms

Abstract

Computed tomography was one of the first imaging modalities to require a computerized solution of an inverse problem to produce a useful image from the data acquired by the sensor hardware. The computerized solutions, which are known as image reconstruction algorithms, have thus been a critical component of every CT scanner ever sold. We review the history of commercially deployed CT reconstruction algorithms and consider the forces that led, at various points, both to innovation and to convergence around certain broadly useful algorithms. The forces include the emergence of new hardware capabilities, competitive pressures, the availability of computational power, and regulatory considerations. We consider four major historical periods and turning points. The original EMI scanner was developed with an iterative reconstruction algorithm, but an explosion of innovation coupled with rediscovery of an older literature led to the development of alternative algorithms throughout the early 1970s. Most CT vendors quickly converged on the use of the filtered back-projection (FBP) algorithm, albeit layered with a variety of proprietary corrections in both projection data and image domains to improve image quality. Innovations such as helical scanning and multi-row detectors were both enabled by and drove the development of additional applications of FBP in the 1990s and 2000s. Finally, the last two decades have seen a return of iterative reconstruction and the introduction of artificial intelligence approaches that benefit from increased computational power to reduce radiation dose and improve image quality.

Keywords: algorithms; computed tomography; history; image reconstruction.

Fig. 1

First EMI image of first patient scan demonstrating a cystic astrocytoma.

Fig. 2

Block diagram of a reconstruction algorithm before the advent of helical and multi-slice CT. The top row shows the key elements of the scanner and the patient that need to be addressed in the reconstruction algorithm. The middle row shows the reconstruction algorithm. The step labeled inverse Radon transform is the mathematical step of reconstructing an image from its projections. The additional steps in the middle row are used to correct for imperfections in the x-ray measurements. The bottom row indicates that the scanner has to be calibrated to correct the x-ray measurements as shown in the middle row.

Fig. 3

An example of early progress in ring artifact suppression. The display window is −120 to 149 HU and the arrow points to the center of rotation.

Fig. 4

The clinical benefit of increasing the speed of CT scanners through increasing the number of rows. With this change, CT made the transition to a truly volumetric modality, able to render the body with isotropic resolution. (a) The 16-slice coronal image shows motion inconsistencies in the heart. These are greatly reduced in (b) the coronal image acquired with a 64-slice scanner.

Fig. 5

An example of the dose-reduction and resolution-enhancement capabilities of iterative, model-based image reconstruction (MBIR) algorithms. Reconstructed by (a) FBP and (b) MBIR algorithm.