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A deep learning-based whole-body solution for PET/MRI attenuation correction.

Sahar Ahangari1, Anders Beck Olin2, Marianne Kinggård Federspiel2

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Summary
This summary is machine-generated.

Deep learning (DL) for whole-body PET attenuation correction (AC) in PET/MRI generates synthetic CT (sCT) from MRI. This DL-based sCT method offers more accurate PETAC than atlas-based approaches.

Keywords:
Attenuation correctionDeep learningMR-ACPET/MRIWhole body

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Radiology

Background:

  • Deep convolutional neural networks show promise for PET attenuation correction (AC) in PET/MRI.
  • Whole-body implementation is challenging due to anatomical variations and limited MRI field of view.

Purpose of the Study:

  • Investigate a deep learning (DL) method to generate voxel-based synthetic CT (sCT) from Dixon MRI.
  • Utilize sCT as a whole-body solution for PET AC in PET/MRI systems.

Main Methods:

  • Trained a DL network using co-registered MRI and CT images from 15 patients with whole-body PET/CT and PET/MRI scans.
  • Assessed AC map accuracy and PET image quantification using DL-based sCT (PET_sCT) versus an atlas-based method (PET_Atlas), with CT-based reconstruction (PET_CT) as reference.
  • Performed voxel-wise and region-specific analyses (brain, lung, liver, spine, bone, aorta).

Main Results:

  • DL-based sCT achieved a lower mean absolute error (62 HU) compared to the atlas-based method (109 HU).
  • PET_sCT showed excellent correlation with PET_CT (R²=0.98) and lower PET quantification error (6.1%) than PET_Atlas (11.2%).
  • PET_sCT demonstrated reduced average errors and variability across all analyzed regions.

Conclusions:

  • A DL approach for whole-body PET AC in PET/MRI is feasible and yields more accurate results than conventional methods.
  • Further validation with larger training datasets is recommended for enhanced robustness and accuracy.