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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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PET scatter estimation using deep learning U-Net architecture.

Baptiste Laurent1, Alexandre Bousse1, Thibaut Merlin1

  • 1LaTIM, INSERM, UMR 1101, UBO, Brest, France.

Physics in Medicine and Biology
|October 14, 2022
PubMed
Summary
This summary is machine-generated.

Deep learning accurately corrects positron emission tomography (PET) scatter, improving image accuracy over traditional methods. This novel approach enhances quantitative accuracy in PET imaging.

Keywords:
PETdeep learningreconstructionscatter correctionscatter estimation

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Area of Science:

  • Medical Imaging
  • Nuclear Medicine
  • Artificial Intelligence

Background:

  • Positron emission tomography (PET) image reconstruction requires scatter correction for quantitative accuracy.
  • Current scatter correction methods, like single scatter simulation (SSS), have limitations in modeling multiple scatter and external scatter.
  • Monte Carlo (MC) methods offer high precision but are computationally intensive.

Purpose of the Study:

  • To explore deep learning (DL) for accurate PET scatter correction, encompassing all scatter coincidences.
  • To develop and evaluate a DL-based scatter estimation (DLSE) method for PET imaging.

Main Methods:

  • A U-Net convolutional neural network architecture was employed for scatter estimation.
  • The network input included emission and CT-derived attenuation factor (AF) sinograms.
  • Training utilized MC simulated PET datasets from anthropomorphic phantoms and clinical patient data.

Main Results:

  • DLSE demonstrated accuracy independent of anatomical region (lung/pelvis).
  • DLSE-corrected images showed higher accuracy compared to SSS-corrected images.
  • DLSE-corrected images were comparable to those reconstructed from scatter-free data.

Conclusions:

  • The proposed DLSE method effectively estimates scatter sinograms from emission and attenuation data.
  • DLSE offers superior accuracy to SSS and improved speed over MC methods for PET scatter correction.