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Related Experiment Video

Updated: Feb 2, 2026

Visualization and Quantification of Brown and Beige Adipose Tissues in Mice using [18F]FDG Micro-PET/MR Imaging
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A deep learning approach for 18F-FDG PET attenuation correction.

Fang Liu1,2, Hyungseok Jang3, Richard Kijowski3

  • 1Departments of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue, Madison, WI, 53705-2275, USA. leoliuf@gmail.com.

EJNMMI Physics
|November 13, 2018
PubMed
Summary
This summary is machine-generated.

A novel deep learning method, deepAC, generates pseudo-CT images from PET scans for accurate attenuation correction. This deepAC approach offers a feasible alternative to traditional CT-based methods in PET/CT brain imaging.

Keywords:
Attenuation correctionCTDeep learningMRIPETPET/CTPET/MR

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Positron-emission tomography (PET) imaging requires accurate attenuation correction for quantitative analysis.
  • Traditional attenuation correction relies on computed tomography (CT) scans, increasing radiation dose and scan time.
  • Developing alternative methods for PET attenuation correction is crucial for improving imaging efficiency and patient safety.

Purpose of the Study:

  • To develop and assess the feasibility of a deep learning-based approach (deepAC) for PET image attenuation correction without anatomical imaging.
  • To generate pseudo-CT images from uncorrected 18F-fluorodeoxyglucose (18F-FDG) PET images using deep learning.
  • To evaluate the performance of deepAC in comparison to CT-based attenuation correction in PET/CT brain imaging.

Main Methods:

  • A deep convolutional encoder-decoder network was trained on 100 retrospective 3D FDG PET head images co-registered to CT data.
  • The model learned to identify tissue contrast and generate continuously valued pseudo-CT images from uncorrected PET data.
  • Model performance was evaluated in 28 patients by comparing pseudo-CTs to acquired CTs (Dice coefficient, MAE) and by assessing PET reconstruction accuracy.

Main Results:

  • deepAC generated pseudo-CTs with high accuracy, achieving Dice coefficients of 0.80 for air, 0.94 for soft tissue, and 0.75 for bone.
  • Mean Absolute Error (MAE) for pseudo-CTs was 111±16 HU, demonstrating quantitative accuracy relative to the PET/CT dataset.
  • 18F-FDG PET results reconstructed using deepAC showed average errors of less than 1% in most brain regions, comparable to CT-based correction.

Conclusions:

  • An automated deep learning approach, deepAC, was successfully developed for generating pseudo-CTs from non-attenuation-corrected PET images.
  • deepAC demonstrated feasibility and accuracy in PET/CT brain imaging, offering a potential alternative to conventional CT-based attenuation correction.
  • This method has the potential to reduce scan time and radiation exposure in PET imaging.