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Positron Emission Tomography01:29

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Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body...
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Radiosynthesis, Quality Control, and Small Animal Positron Emission Tomography Imaging of 68Ga-Labelled Nano Molecules
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Deep learning-based attenuation map generation with simultaneously reconstructed PET activity and attenuation and

Luyao Shi1, Jiazhen Zhang2, Takuya Toyonaga2

  • 1Department of Biomedical Engineering, Yale University, New Haven, CT, United States of America.

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|December 30, 2022
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Summary

This study optimizes deep learning for CT-less PET attenuation correction, achieving under 1% error in tumor quantification for both full-dose and low-dose scans. This advances accurate PET imaging without CT, especially for low-dose studies.

Keywords:
MLAAPETattenuation correctiondeep learninglow-dose

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Area of Science:

  • Medical Imaging
  • Nuclear Medicine
  • Artificial Intelligence in Healthcare

Background:

  • Computed tomography (CT) is crucial for positron emission tomography (PET) attenuation correction (AC), but artifacts or misalignment cause errors.
  • Simultaneous reconstruction methods (MLAA) using only PET data exist but still have quantification errors compared to CT-based methods.
  • Deep learning (DL) frameworks improved MLAA but relied on image-domain loss (IM-loss), which doesn't directly address PET attenuation physics.

Purpose of the Study:

  • To optimize a deep learning (DL) framework for generating accurate PET attenuation maps, reducing reliance on CT.
  • To incorporate a physics-based loss function to improve the accuracy of CT-less PET attenuation correction (AC).
  • To evaluate the optimized DL framework for both full-dose and low-dose oncological PET/CT studies.

Main Methods:

  • Optimized a DL framework by adjusting preprocessing, patch size, loss term weights, and projection-domain loss parameters.
  • Trained and validated the framework on 100 full-dose 18F-FDG PET/CT scans with minimal misalignment.
  • Evaluated the optimized framework on 85 clinical full-dose and synthetic low-dose (10% data) 18F-FDG cancer PET datasets.

Main Results:

  • The optimized DL framework achieved less than 1% error in tumor standardized uptake value (SUV) measures for both full-dose and low-dose studies.
  • Clinical evaluation and physics-based metrics confirmed the promising performance of the proposed CT-less attenuation map generation method.
  • The framework demonstrated effectiveness in predicting accurate attenuation maps for low-dose oncological PET imaging.

Conclusions:

  • The optimized DL framework successfully generates accurate PET attenuation maps, significantly reducing quantification errors without CT.
  • This CT-less approach is highly effective for both full-dose and low-dose PET imaging, showing <1% SUV error.
  • The method holds significant clinical potential for improving PET reconstruction, particularly in low-dose scenarios and reducing CT-related artifacts.