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Traditional machine learning for limited angle tomography

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

The application of traditional machine learning techniques, in the form of regression models based on conventional, “hand-crafted” features, to artifact reduction in limited angle tomography is investigated.

Methods

Mean-variation-median (MVM), Laplacian, Hessian, and shift-variant data loss (SVDL) features are extracted from the images reconstructed from limited angle data. The regression models linear regression (LR), multilayer perceptron (MLP), and reduced-error pruning tree (REPTree) are applied to predict artifact images.

Results

REPTree learns artifacts best and reaches the smallest root-mean-square error (RMSE) of 29 HU for the Shepp–Logan phantom in a parallel-beam study. Further experiments demonstrate that the MVM and Hessian features complement each other, whereas the Laplacian feature is redundant in the presence of MVM. In fan-beam, the SVDL features are also beneficial. A preliminary experiment on clinical data in a fan-beam study demonstrates that REPTree can reduce some artifacts for clinical data. However, it is not sufficient as a lot of incorrect pixel intensities still remain in the estimated reconstruction images.

Conclusion

REPTree has the best performance on learning artifacts in limited angle tomography compared with LR and MLP. The features of MVM, Hessian, and SVDL are beneficial for artifact prediction in limited angle tomography. Preliminary experiments on clinical data suggest that the investigation on more features is necessary for clinical applications of REPTree.

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Authors and Affiliations

  1. Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstr. 3, 91058, Erlangen, Germany

    Yixing Huang, Yanye Lu, Oliver Taubmann & Andreas Maier

  2. Erlangen Graduate School in Advanced Optical Technologies (SAOT), Friedrich-Alexander-Universität Erlangen-Nürnberg, Paul-Gordan-Str. 6, 91052, Erlangen, Germany

    Oliver Taubmann & Andreas Maier

  3. Siemens Healthcare GmbH, Siemensstr. 1, 91301, Forchheim, Germany

    Oliver Taubmann & Guenter Lauritsch

Authors
  1. Yixing Huang
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  2. Yanye Lu
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  3. Oliver Taubmann
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  4. Guenter Lauritsch
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  5. Andreas Maier
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Corresponding authors

Correspondence to Yixing Huang or Yanye Lu.

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Conflict of interest

Oliver Taubmann and Guenter Lauritsch are with Siemens Healthcare GmbH, Forchheim, Germany. Yixing Huang is supported by Siemens Healthcare GmbH, Forchheim, Germany.

Ethical approval

All data shared in the challenge were fully anonymized. This article does not contain any studies with animals performed by any of the authors.

Informed consent

The clinical data in this paper are from the library of the Low Dose CT Grand Challenge [31]. The library was HIPAAcompliant and built with waiver of informed consent.

Disclaimer

The concepts and information presented in this paper are based on research and are not commercially available.

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Cite this article

Huang, Y., Lu, Y., Taubmann, O. et al. Traditional machine learning for limited angle tomography. Int J CARS 14, 11–19 (2019). https://doi.org/10.1007/s11548-018-1851-2

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  • DOI: https://doi.org/10.1007/s11548-018-1851-2

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