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Related Experiment Video

Updated: Jul 3, 2025

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Generating PET Attenuation Maps via Sim2Real Deep Learning-Based Tissue Composition Estimation Combined with MLACF.

Tetsuya Kobayashi1, Yui Shigeki2, Yoshiyuki Yamakawa3

  • 1Technology Research Laboratory, Shimadzu Corporation, 3-9-4, Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0237, Japan. t_kobaya@shimadzu.co.jp.

Journal of Imaging Informatics in Medicine
|February 12, 2024
PubMed
Summary

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This summary is machine-generated.

This study introduces a deep learning (DL) method for CT-less attenuation correction (AC) in positron emission tomography (PET) imaging. The DL model estimates tissue composition to generate attenuation maps, showing comparable accuracy to CT-based methods.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiophysics

Background:

  • Positron Emission Tomography (PET) imaging requires accurate attenuation correction (AC) for quantitative analysis.
  • Computed Tomography (CT) is typically used for AC, but this adds radiation dose and complexity.
  • Developing CT-less AC methods is a significant goal in PET research.

Purpose of the Study:

  • To present the first Sim2Real deep learning (DL) based approach for generating human head attenuation maps using only simulated PET data.
  • To evaluate the feasibility of DL-based tissue composition estimation for CT-less attenuation correction in PET.

Main Methods:

  • A DL model was trained on simulated PET data to estimate a four-channel tissue composition map (soft tissue, bone, cavity, background) from a 2D non-AC PET image.
Keywords:
Attenuation correctionDeep learningPositron emission tomographySemantic soft segmentationTissue composition

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  • Attenuation maps were generated from the DL-derived tissue composition maps.
  • The DL-based attenuation maps were used for scatter+random estimation and as initial estimates for Maximum Likelihood Attenuation Correction Factor (MLACF) refinement.

    • Main Results:

    • The DL model demonstrated the ability to estimate overall anatomical structures in clinical brain PET data.
    • While some inaccuracies in anatomical detail were noted, particularly in neck-side slices, the DL-based AC achieved quantitative accuracy comparable to CT-based AC.
    • The combined DL and MLACF approach showed promise as a CT-less AC solution.

    Conclusions:

    • The proposed DL-based method offers a viable approach for CT-less attenuation correction in PET.
    • Combining DL-based tissue estimation with MLACF refinement presents a promising strategy for improving PET imaging without CT.
    • Further development is warranted to address limitations in anatomical detail estimation for broader clinical application.