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Updated: Jun 8, 2026

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images
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Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

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Automatic, three-segment, MR-based attenuation correction for whole-body PET/MR data.

V Schulz1, I Torres-Espallardo, S Renisch

  • 1Department of Molecular Imaging Systems, Philips Research Europe, Weishausstrasse 2, 52066, Aachen, Germany. volkmar.schulz@philips.com

European Journal of Nuclear Medicine and Molecular Imaging
|October 6, 2010
PubMed
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A new automated method uses magnetic resonance (MR) imaging to create attenuation maps for positron emission tomography (PET) scans. This MR-based attenuation correction (AC) offers accuracy comparable to CT-based methods, maintaining diagnostic value in PET imaging.

Area of Science:

  • Medical Imaging
  • Radiology
  • Biophysics

Background:

  • Positron emission tomography/computed tomography (PET/CT) is a standard for molecular imaging.
  • PET/MR offers advantages like soft tissue contrast and no ionizing radiation but lacks direct attenuation quantification.
  • Accurate attenuation correction (AC) is crucial for quantitative PET imaging.

Purpose of the Study:

  • To develop and evaluate a fully automated MR-based method for generating attenuation maps for whole-body PET/MR imaging.
  • To overcome the challenge of deriving attenuation values from MR data for PET reconstruction.
  • To assess the accuracy and diagnostic impact of MR-based AC compared to CT-based AC.

Main Methods:

  • A dedicated T1-weighted MR sequence and customized image processing were used.

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  • Automated body and lung contour segmentation via region-growing and intensity analysis.
  • No user interaction was required for attenuation map generation.
  • PET images were reconstructed using elastically registered MR-based and CT-based attenuation maps.

    • Main Results:

    • The automated segmentation of body and lung contours showed high accuracy (mean >3.8 on a four-point scale).
    • MR-based AC demonstrated similar behavior and accuracy to CT-based AC.
    • Standardized uptake value (SUV) differences for bone lesions were minimal (-3.0% ± 3.9% for MR vs. -6.5% ± 4.1% for segmented CT).
    • Blind comparison revealed identical lesion detectability between MR-based and CT-based AC, with only slight image quality differences.

    Conclusions:

    • The proposed MR-based attenuation correction method achieves accuracy comparable to segmented CT-based AC.
    • The observed differences in image quality do not compromise the diagnostic value of PET images.
    • This automated MR-based AC method is a viable alternative for PET/MR imaging.