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  1. Home
  3. Model-based Deep Learning Pet Image Reconstruction Using Forward-backward Splitting Expectation-maximization.
  1. Home
  3. Model-based Deep Learning Pet Image Reconstruction Using Forward-backward Splitting Expectation-maximization.

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Model-Based Deep Learning PET Image Reconstruction Using Forward-Backward Splitting Expectation-Maximization.

Abolfazl Mehranian1, Andrew J Reader1

  • 1School of Biomedical Engineering and Imaging Sciences, Department of Biomedical Engineering, King's College London, London SE1 7EH, U.K.

IEEE Transactions on Radiation and Plasma Medical Sciences
|May 31, 2021

View abstract on PubMed

Summary
This summary is machine-generated.

Deep learning enhances Positron Emission Tomography (PET) image reconstruction. A novel algorithm, FBSEM, integrates deep learning with Expectation-Maximization for improved image quality, comparable to U-Net denoising.

Keywords:
Deep learning (DL)MRIimage reconstructionpositron emission tomography (PET)

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Science

Background:

  • Maximum-a-posteriori (MAP) image reconstruction is crucial for Positron Emission Tomography (PET).
  • Integrating deep learning offers potential for improved PET image quality, especially in low-dose scenarios.
  • Existing methods like OSEM and Bowsher MAPEM have limitations in noise reduction and detail preservation.

Purpose of the Study:

  • To propose and evaluate a novel deep learning-based algorithm, FBSEM, for PET image reconstruction.
  • To compare the performance of FBSEM against traditional methods and U-Net denoising.
  • To assess the algorithm's effectiveness in both simulated and in-vivo low-dose PET data.

Main Methods:

  • Developed a forward-backward splitting EM (FBSEM) algorithm, unrolled into a recurrent neural network.
  • Network parameters, including regularization strength, were learned during PET reconstruction.
  • Evaluated FBSEM-p (PET-only) and FBSEM-pm (PET-MR) on simulated and in-vivo brain imaging datasets.
  • Compared against OSEM, Bowsher MAPEM, and post-reconstruction U-Net denoising (Unet-p, Unet-pm).

Main Results:

  • FBSEM-p(m) and Unet-p(m) achieved comparable performance in simulations (14.4% and 13.4% NRMSE vs. 20.7% and 17.7% for OSEM/MAPEM).
  • For in-vivo data, FBSEM-p(m) showed the lowest error (3.9%) compared to Unet-p(m) (5.7%), MAPEM (5.9%), and OSEM (7.8%).
  • The U-Net denoising method demonstrated comparable performance to the FBSEM net.

Conclusions:

  • The proposed FBSEM algorithm effectively integrates deep learning into PET image reconstruction.
  • FBSEM and U-Net denoising offer significant improvements over traditional methods for low-dose PET imaging.
  • Both deep learning approaches show promise for enhancing the quality of PET scans.