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Whole-body PET/MRI of Pediatric Patients: The Details That Matter
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Whole-body bone segmentation from MRI for PET/MRI attenuation correction using shape-based averaging.

Hossein Arabi1, Habib Zaidi2

  • 1Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, Geneva 4 CH-1211, Switzerland.

Medical Physics
|November 4, 2016
PubMed
Summary

Shape-based averaging (SBA) for MRI bone segmentation in PET/MRI was improved using local atlas methods (L-STAPLE, L-Shp). These techniques enhance accuracy and reduce fragmentation, with L-Shp offering faster computation for better PET attenuation correction.

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Area of Science:

  • Medical Imaging
  • Radiology
  • Image Segmentation

Background:

  • Hybrid PET/MRI systems require accurate MRI-based attenuation correction (MRAC).
  • Whole-body bone segmentation from MRI is crucial for MRAC but challenging.
  • Shape-based averaging (SBA) is a technique for bone segmentation, but its performance can be limited.

Purpose of the Study:

  • To evaluate and enhance the performance of shape-based averaging (SBA) for whole-body bone segmentation in hybrid PET/MRI.
  • To improve MRI-guided attenuation correction (MRAC) accuracy.
  • To investigate the combination of SBA with statistical atlas fusion and develop an efficient atlas selection scheme.

Main Methods:

  • Evaluated SBA performance on 21 patient PET/CT and MR datasets.
  • Assessed SBA combined with simultaneous truth and performance level estimation (STAPLE) and selective and iterative method for performance level estimation (SIMPLE) at global and local levels.
  • Proposed and evaluated a local shape comparison (L-Shp) method and compared it with majority voting (MV) and other atlas fusion techniques.

Main Results:

  • Standard SBA and MV methods showed poor bone identification (Dice ≈ 0.62) and high fragmentation.
  • Global atlas methods improved accuracy (Dice = 0.66), but local methods (L-STAPLE, L-Shp) achieved the best results (Dice = 0.76 and 0.75, respectively).
  • L-Shp significantly reduced computation time compared to L-STAPLE, with both methods yielding low SUV errors (<3% relative, <6% absolute) and good correlation with PET-CT attenuation correction.

Conclusions:

  • Local atlas weighting/regularization schemes (L-STAPLE, L-Shp) significantly enhance SBA for whole-body bone segmentation.
  • These improved methods dramatically increase bone recognition, reduce fragmentation, and enhance quantitative PET uptake recovery.
  • The proposed L-Shp method provides a computationally efficient solution with high accuracy for MRAC in hybrid PET/MRI.