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Data Management and Network Architecture Effect on Performance Variability in Direct Attenuation Correction via Deep

Mahsa Torkaman1, Jaewon Yang1, Luyao Shi2

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Summary

Deep learning (DL) improves attenuation correction (AC) in SPECT myocardial perfusion imaging (MPI) by reducing patient variability. Advanced networks and data management strategies enhance AC performance, aiding clinical translation.

Keywords:
Attenuation correctionDeep learningHierarchical clusteringMyocardial perfusion imaging (MPI)Performance variabilitySPECTWasserstein cycle GANt-SNE

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Area of Science:

  • Nuclear Medicine
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Accurate attenuation correction (AC) is crucial for interpreting SPECT myocardial perfusion imaging (MPI).
  • Dedicated cardiac systems often lack transmission imaging capabilities, complicating AC.
  • Previous deep learning (DL) methods showed feasibility but suffered from patient-to-patient performance variability.

Purpose of the Study:

  • To investigate methods for overcoming performance variability in direct AC for SPECT MPI.
  • To develop and evaluate advanced DL networks and data management strategies for improved AC accuracy.

Main Methods:

  • Compared U-Net and Wasserstein cycle GAN (WCycleGAN) for DL-based AC (SPECT_DL).
  • Implemented a data management strategy using clustering in a lower-dimensional space to select training data.
  • Quantitatively analyzed global and regional AC performance.

Main Results:

  • Advanced DL networks improved global AC performance with limited data.
  • Regional AC accuracy did not show significant improvement.
  • Clustered training demonstrated potential benefits for effective DL model training.

Conclusions:

  • Advanced DL networks enhance the global performance of direct AC in SPECT MPI.
  • The proposed data management strategy shows promise for effective training and reducing variability.
  • Further research is needed to improve regional AC accuracy and facilitate clinical translation.