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An Extended Bayesian-FBP Algorithm.

Gengsheng L Zeng1, Zeljko Divkovic2

  • 1Department of Radiology, University of Utah, Salt Lake City, UT 84108, USA and Department of Engineering, Weber State University, Ogden, UT 84408, USA, (801) 581-3918.

IEEE Transactions on Nuclear Science
|April 5, 2016
PubMed
Summary
This summary is machine-generated.

We extended the Bayesian-Filtered Backprojection (FBP) algorithm for faster CT image reconstruction. Proper parameter selection in this enhanced Bayesian-FBP method can improve image quality compared to the original algorithm.

Keywords:
Analytical reconstructionDynamic imagingFiltered backprojectionMAP objective functionMRIReal time imaging

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Area of Science:

  • Medical Imaging
  • Computational Imaging
  • Bayesian Inference

Background:

  • Developed a Bayesian-Filtered Backprojection (FBP) algorithm, also known as FBP Maximum a Posteriori (MAP).
  • Applied this non-iterative Bayesian algorithm to real-time MRI with undersampled k-space data.

Purpose of the Study:

  • Investigate extending the original Bayesian-FBP algorithm by incorporating additional control parameters.
  • Evaluate the performance of the extended Bayesian-FBP algorithm using cardiac patient data.

Main Methods:

  • Introduced new controlling parameters to the original Bayesian-FBP algorithm, making it a special case of the extended version.
  • Utilized a cardiac patient dataset for evaluation.
  • Compared results against a well-established iterative algorithm employing L1-norms as a gold standard.

Main Results:

  • The extended Bayesian-FBP algorithm, with carefully selected parameters, demonstrated potential to outperform the original Bayesian-FBP algorithm.
  • Performance was evaluated against a gold standard iterative reconstruction method.

Conclusions:

  • The extended Bayesian-FBP algorithm offers improved performance over the original method when parameters are optimized.
  • This enhancement provides a more flexible and potentially superior approach for CT image reconstruction, especially in undersampled scenarios.