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Deep learning-assisted PET imaging achieves fast scan/low-dose examination.

Yan Xing1, Wenli Qiao1, Taisong Wang1

  • 1Department of Nuclear Medicine, Shanghai General Hospital, Shanghai Jiaotong University, No. 100 Haining Road, Shanghai, 200080, People's Republic of China.

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Summary

A novel deep learning (DL) method enhances 18F-FDG PET image quality by improving liver signal-to-noise ratio and lesion detectability, even with reduced scan counts.

Keywords:
Deep learningDenoising techniqueImage qualityPositron emission tomography and computed tomography (PET/CT)

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Nuclear Medicine

Background:

  • 18F-FDG PET/CT is crucial for oncological imaging.
  • Image quality and lesion detection are vital for accurate diagnosis.
  • Deep learning (DL) offers potential for image reconstruction enhancement.

Purpose of the Study:

  • To evaluate a DL-based denoising method for 18F-FDG PET images.
  • To assess the impact of DL on image quality and lesion detectability.
  • To compare DL performance against traditional Gaussian filtering (GS) and reference scans.

Main Methods:

  • Retrospective analysis of 52 oncological patients' 18F-FDG PET/CT data.
  • Data were reduced to 75%, 50%, and 33.3% of total counts.
  • Images reconstructed with OSEM and post-processed using DL or GS filters.
  • Image quality assessed via Likert scale, lesion counts, SUVs, SNR, and TBR.

Main Results:

  • DL method showed superior noise reduction and comparable lesion contrast.
  • Over 96.8% of lesions detected in DL images.
  • DL significantly improved liver SNR with count reduction (p < 0.001).
  • Non-inferior lesion detectability (TBR) for DL vs. reference at 75% counts, especially for larger or higher-uptake lesions.

Conclusions:

  • The DL method enhances 18F-FDG PET image quality and lesion conspicuousness.
  • DL provides non-inferior image quality with 25-50% reduced scan counts.
  • This DL approach shows promise for efficient and effective oncological imaging.