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PET Images Enhancement Using Deep Training of Reconstructed Images with Bayesian Penalized Likelihood Algorithm

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Abstract

Purpose

To adopt the merits of the Bayesian Penalized Likelihood (BPL) reconstruction algorithm (incl. improved contrast recovery), a deep learning ResNet model was trained to estimate BPL-like images using the non-attenuation, non-scatter corrected PET images (PET-nonAC) as inputs.

Methods

Images of 112 patients were used for model training (79 patients), validation (13 patients) and testing (20 patients). The ResNet model used PET-nonAC images as input and predicted corresponding BPL-like images. The model performance regarding image quality was evaluated using metrics such as contrast-to-noise ratio (CNR).

Results

The CNR of the reference BPL images was 2.40, while estimated BPL-like images using the deep learning model have a CNR value of 2.42 indicative of comparable performance.

Conclusion

The estimated BPL-like images of the deep learning model offer comparable quality to the reference BPL images especially regarding the CNR metric. This deep learning model can be used to improve the image quality PET-nonAC by adopting the characteristics of the BPL images.

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Data Availability

Data will be made available upon reasonable request.

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Funding

This research work was supported under grant number 50618, Tehran University of Medical Sciences, Tehran, Iran.

Author information

Author notes

  1. Mahsa Shahrbabaki Mofrad and Ali Ghafari have contributed equally as co-first authors.

Authors and Affiliations

  1. Medical Physics and Biomedical Engineering Department, Medical Faculty, Tehran University of Medical Sciences, Tehran, Iran

    Mahsa Shahrbabaki Mofrad, Mohammad Reza Ay & Peyman Sheikhzadeh

  2. Research Center for Evidence-Based Medicine, Iranian EBM Centre: A JBI Centre of Excellence, Tabriz University of Medical Sciences, Tabriz, Iran

    Ali Ghafari

  3. Department of Radiology, UT Southwestern Medical Center, Dallas, TX, 75390-9071, USA

    Nima Kasraie

  4. Research Center for Molecular and Cellular Imaging (RCMCI), Tehran University of Medical Sciences, Tehran, Iran

    Mohammad Reza Ay

  5. Nursing and Midwifery Care Research Center, Health Management Research Institute, University of Medical Sciences, Tehran, Iran

    Negisa Seyyedi

  6. Department of Nuclear Medicine, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran

    Peyman Sheikhzadeh

Authors
  1. Ali Ghafari
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Contributions

All authors have contributed to the research and manuscript writing.

Corresponding author

Correspondence to Peyman Sheikhzadeh.

Ethics declarations

Ethics Approval

The anonymized patient’s data were used in this study which was approved by the Research Ethics Committees of Imam Khomeini Hospital Complex- Tehran University of Medical Sciences (Approval code: IR.TUMS.IKHC.REC.1399.486).

Informed Consent

No informed consent was required due to the anonymized patient data were used.

Competing Interests

The authors have no relevant financial or nonfinancial interests to disclose.

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Ghafari, A., Mofrad, M.S., Kasraie, N. et al. PET Images Enhancement Using Deep Training of Reconstructed Images with Bayesian Penalized Likelihood Algorithm. J. Med. Biol. Eng. 44, 514–521 (2024). https://doi.org/10.1007/s40846-024-00882-8

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  • DOI: https://doi.org/10.1007/s40846-024-00882-8

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