Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction?

By Xiaochuan Pan, Emil Y. Sidky, Michael W. Vannier. Inverse Problems, 2009

641 citations recorded by the source corpus.

Despite major advances in x-ray sources, detector arrays, gantry mechanical design and especially computer performance, one component of computed tomography (CT) scanners has remained virtually constant for the past 25 years—the reconstruction algorithm. Fundamental advances have been made in the solution of inverse problems, especially tomographic reconstruction, but these works have not been translated into clinical and related practice. The reasons are not obvious and seldom discussed. This review seeks to examine the reasons for this discrepancy and provides recommendations on how it can be resolved. We take the example of field of compressive sensing (CS), summarizing this new area of research from the eyes of practical medical physicists and explaining the disconnection between theoretical and application-oriented research. Using a few issues specific to CT, which engineers have addressed in very specific ways, we try to distill the mathematical problem underlying each of these issues with the hope of demonstrating that there are interesting mathematical problems of general importance that can result from in depth analysis of specific issues. We then sketch some unconventional CT-imaging designs that have the potential to impact on CT applications, if the link between applied mathematicians and engineers/physicists were stronger. Finally, we close with some observations on how the link could be strengthened. There is, we believe, an important opportunity to rapidly improve the performance of CT and related tomographic imaging techniques by addressing these issues.

Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/a59142172671b3a8c370e0514a6dd3396fb01a92

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In Biomedical Engineering

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Successes and challenges in non-destructive testing of aircraft composite structures (2020, methodology): “Most CT systems implement a form of the Filtered Back Projection (FBP) reconstruction algorithm due to the predictable nature of FBP algorithms in terms of computational cost and reconstruction times.(181) To achieve an approximation of the scanned object by FBP (no exact solution exists away from t”
Laser absorption spectroscopy for combustion diagnosis in reactive flows: A review (2019, methodology): “T he ﬁ ltered back-projection (FBP) algorithm that is commonly used based on the analytical solution of the Radon transform (138).”
Artificial intelligence in radiology (2018, neutral): “For instance, CT reconstruction algorithms have seen little to no change in the past 25 years 73 .”
Evaluation of Sparse-view Reconstruction from Flat-panel-detector Cone-beam CT (2010, methodology): “As discussed in detail previously (Sidky et al 2006, Sidky and Pan 2008, Pan et al 2009), image reconstruction based upon equation (1) can also be formulated as a constrained optimization problem: f∗ = argmin‖f‖TV s.t.”
Medical image processing on the GPU - Past, present and future (2013, methodology): “A great deal of effort has clearly been expended on the acceleration of iterative reconstruction algorithms, but FBP is still used with most commercial CT scanners (Pan et al., 2009).”