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Related Concept Videos

Positron Emission Tomography01:29

Positron Emission Tomography

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 being...

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Using multimodal PET+MR data as conditional generative adversarial network inputs improves pseudo-CT and attenuation

Jonathan Fisher1,2, Emily Anaya1,2, Garry Chinn2

  • 1Department of Electrical Engineering, Stanford University Stanford, CA, USA.

American Journal of Nuclear Medicine and Molecular Imaging
|March 23, 2026
PubMed
Summary

This study demonstrates that combining positron emission tomography (PET) and magnetic resonance (MR) imaging data using a multi-modality conditional generative adversarial network (cGAN) significantly improves brain PET attenuation correction accuracy. The developed method outperforms single-modality approaches and existing clinical techniques.

Keywords:
MR-based attenuation correction (MRAC)PETPET/MRattenuation correction (AC)deep learning (DL)emission-based attenuation correction (EBAC)generative adversarial network (GAN)

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Radiological Physics

Background:

  • Accurate positron emission tomography (PET) imaging requires correction for photon attenuation, typically achieved using X-ray computed tomography (CT) data.
  • Estimating attenuation correction (AC) factors from magnetic resonance (MR) data in integrated PET/MR scanners is challenging due to the lack of direct attenuation information in MR images.
  • Conditional generative adversarial networks (cGANs) have shown potential for both emission-based and MR-based AC.

Purpose of the Study:

  • To investigate the efficacy of combining PET and MR data using a multi-modality cGAN for improved brain PET attenuation correction accuracy.
  • To compare the performance of the multi-modality cGAN against single-modality cGANs and a clinical atlas-based method.

Main Methods:

  • Thirty-five patients underwent same-day whole-body PET/MR and PET/CT scans.
  • Four cGANs were trained to generate pseudo-CT images from non-attenuation-corrected and non-scatter-corrected (NASC) PET and MR data, with one network utilizing multi-modality input.
  • Performance was evaluated using structural similarity index (SSIM) and dice similarity coefficients for pseudo-CTs and SSIM and peak signal-to-noise ratio (PSNR) for AC PET images.

Main Results:

  • The multi-modality cGAN generated significantly superior pseudo-CT images compared to single-modality cGANs, achieving higher SSIM and dice similarity coefficients.
  • All cGAN-based methods outperformed the clinical atlas-based method for AC PET image reconstruction.
  • The multi-modal cGAN achieved the highest quality AC PET images, with average SSIM of 0.9987±0.0001 and PSNR of 50.0±0.4.

Conclusions:

  • Combining PET and MR data with a multi-modality cGAN offers a promising approach for enhancing brain PET attenuation correction.
  • This advanced method yields more accurate AC PET images than single-modality cGANs and current clinical atlas-based techniques.
  • The findings suggest a potential for improved diagnostic accuracy in PET/MR imaging through advanced AI-driven AC methods.