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Deep-learning-based methods of attenuation correction for SPECT and PET.

Xiongchao Chen1, Chi Liu2,3

  • 1Department of Biomedical Engineering, Yale University, New Haven, CT, USA.

Journal of Nuclear Cardiology : Official Publication of the American Society of Nuclear Cardiology
|June 10, 2022
PubMed
Summary
This summary is machine-generated.

Deep learning significantly improves attenuation correction (AC) for positron emission tomography (PET) and single-photon emission computed tomography (SPECT) imaging. These advanced methods offer superior accuracy and efficiency compared to traditional techniques, enhancing diagnostic capabilities.

Keywords:
Attenuation correctionPETSPECTdeep learning

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Area of Science:

  • Medical Imaging Physics
  • Radiological Sciences
  • Artificial Intelligence in Medicine

Background:

  • Accurate attenuation correction (AC) is critical for quantitative analysis in single-photon emission computed tomography (SPECT) and positron emission tomography (PET).
  • Current CT-based AC methods in hybrid SPECT/CT and PET/CT systems are prone to artifacts from CT inaccuracies and misregistration.
  • MRI-based AC for PET/MRI is complex due to MRI's lack of direct attenuation information, and computational methods suffer from low accuracy and high noise.

Purpose of the Study:

  • To review and compare traditional non-deep-learning attenuation correction (AC) methods for SPECT and PET.
  • To explore the emerging role and potential of deep-learning-based strategies for AC in SPECT and PET.
  • To provide insights into the status and future prospects of deep learning in quantitative SPECT and PET imaging.

Main Methods:

  • Discussion of principles and limitations of conventional AC techniques (CT-based, MRI-based, computational).
  • Categorization of deep-learning AC methods into indirect (synthetic μ-map/CT generation) and direct (direct AC image prediction) strategies.
  • Review of recent advancements and performance evaluations of deep learning models for AC in SPECT and PET.

Main Results:

  • Non-deep-learning AC methods, while essential, face challenges like artifacts and complexity.
  • Deep learning-based AC methods demonstrate promising results, achieving performance comparable or superior to traditional approaches.
  • Both indirect and direct deep learning strategies show significant potential for improving AC accuracy and efficiency.

Conclusions:

  • Deep learning represents a significant advancement in overcoming the limitations of traditional attenuation correction in SPECT and PET.
  • These AI-driven methods offer enhanced accuracy and robustness, paving the way for more reliable quantitative imaging.
  • Further research and development in deep learning hold great promise for the future of nuclear medicine imaging.