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Employing Multiple Low-Dose PET Images (at Different Dose Levels) as Prior Knowledge to Predict Standard-Dose PET Images

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Abstract

The existing deep learning-based denoising methods predicting standard-dose PET images (S-PET) from the low-dose versions (L-PET) solely rely on a single-dose level of PET images as the input of deep learning network. In this work, we exploited the prior knowledge in the form of multiple low-dose levels of PET images to estimate the S-PET images. To this end, a high-resolution ResNet architecture was utilized to predict S-PET images from 6 to 4% L-PET images. For the 6% L-PET imaging, two models were developed; the first and second models were trained using a single input of 6% L-PET and three inputs of 6%, 4%, and 2% L-PET as input to predict S-PET images, respectively. Similarly, for 4% L-PET imaging, a model was trained using a single input of 4% low-dose data, and a three-channel model was developed getting 4%, 3%, and 2% L-PET images. The performance of the four models was evaluated using structural similarity index (SSI), peak signal-to-noise ratio (PSNR), and root mean square error (RMSE) within the entire head regions and malignant lesions. The 4% multi-input model led to improved SSI and PSNR and a significant decrease in RMSE by 22.22% and 25.42% within the entire head region and malignant lesions, respectively. Furthermore, the 4% multi-input network remarkably decreased the lesions’ SUVmean bias and SUVmax bias by 64.58% and 37.12% comparing to single-input network. In addition, the 6% multi-input network decreased the RMSE within the entire head region, within the lesions, lesions’ SUVmean bias, and SUVmax bias by 37.5%, 39.58%, 86.99%, and 45.60%, respectively. This study demonstrated the significant benefits of using prior knowledge in the form of multiple L-PET images to predict S-PET images.

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Data Availability

Data supporting the findings of this study are available from the corresponding author upon journal request.

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Authors and Affiliations

  1. Nuclear Engineering Department, Shiraz University, Shiraz, Iran

    Behnoush Sanaei & Reza Faghihi

  2. Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland

    Hossein Arabi

Authors
  1. Behnoush Sanaei
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  2. Reza Faghihi
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  3. Hossein Arabi
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Contributions

All authors contributed to the study conception and design. All authors read and approved the final manuscript.

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Correspondence to Reza Faghihi.

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The patients involved in this study gave their informed consent for data collection.

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Sanaei, B., Faghihi, R. & Arabi, H. Employing Multiple Low-Dose PET Images (at Different Dose Levels) as Prior Knowledge to Predict Standard-Dose PET Images. J Digit Imaging 36, 1588–1596 (2023). https://doi.org/10.1007/s10278-023-00815-y

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  • DOI: https://doi.org/10.1007/s10278-023-00815-y

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