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Improved low-dose positron emission tomography image reconstruction using deep learned prior.

Xinhui Wang1,2, Long Zhou1,2, Yaofa Wang1,3

  • 1MinFound Medical Systems Co., Ltd., Hangzhou, People's Republic of China.

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|April 21, 2021
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This study introduces a novel deep learning algorithm for low-dose Positron Emission Tomography (PET) imaging. The proposed method enhances image quality by integrating convolutional neural networks (CNNs) into the reconstruction process, improving the noise-bias tradeoff.

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Area of Science:

  • Medical Imaging
  • Computational Imaging
  • Radiophysics

Background:

  • Positron Emission Tomography (PET) is a vital non-invasive imaging technique for quantitative biochemical analysis.
  • PET image reconstruction is inherently challenging due to ill-posed inverse problems, leading to significant noise and quality degradation in low-dose imaging.
  • Deep Neural Networks (DNNs) show promise in enhancing medical image analysis and reconstruction.

Purpose of the Study:

  • To develop and evaluate a novel Maximum A Posteriori (MAP) image reconstruction algorithm for low-dose PET.
  • To integrate a Convolutional Neural Network (CNN) within the reconstruction framework, rather than as a post-processing step.
  • To assess the performance of the proposed CNN-MAP method against conventional techniques in terms of noise-bias and noise-contrast tradeoffs.

Main Methods:

  • A novel Maximum A Posteriori (MAP) reconstruction algorithm was developed, incorporating a Convolutional Neural Network (CNN) for image prior representation.
  • The CNN was embedded directly within the reconstruction framework, not used for post-processing.
  • The method was evaluated using simulated PET data and subsequently validated on acquired patient brain and body PET data.

Main Results:

  • The proposed CNN-MAP method demonstrated an improved noise-bias tradeoff compared to filtered Maximum Likelihood (ML), conventional MAP, and CNN post-processing methods in simulation studies.
  • Quantitative validation on patient data showed that the CNN-MAP method achieved a superior noise-contrast tradeoff over the other evaluated methods.
  • These results highlight the effectiveness of integrating CNNs directly into the PET reconstruction process.

Conclusions:

  • The developed CNN-MAP algorithm offers significant improvements in image quality for low-dose PET imaging.
  • Embedding CNNs within the reconstruction framework provides a more effective approach than post-processing for enhancing noise and contrast tradeoffs.
  • The proposed method holds considerable potential for advancing low-dose PET imaging applications.