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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Biomarker Discovery

Background:

  • Cross-modality translation between MRI and PET imaging is difficult due to differing mechanisms.
  • Blood-based biomarkers (BBBMs) show promise for Alzheimer's disease (AD) detection and amyloid quantification.
  • The utility of BBBMs in enhancing PET image synthesis is currently unexplored.

Purpose of the Study:

  • To investigate the impact of incorporating BBBMs into deep generative models for MRI-to-PET cross-modality translation.
  • To evaluate the effectiveness of BBBMs in improving the quality of synthesized PET images.
  • To develop a novel generative model for PET image synthesis using BBBMs.

Main Methods:

  • Evaluated three established cross-modality translation models with and without BBBM integration.
  • Assessed generative quality enhancement through visual inspection and quantitative metrics.
  • Proposed Plasma-CycleGAN, a novel conditional generative model based on CycleGAN.

Main Results:

  • BBBM integration consistently improved generative quality across all evaluated models.
  • CycleGAN demonstrated the highest visual fidelity in generating PET images.
  • Plasma-CycleGAN successfully synthesized PET images from MRI using BBBMs as conditions.

Conclusions:

  • Integrating BBBMs is a viable strategy to enhance MRI-to-PET image synthesis.
  • Plasma-CycleGAN represents a novel and effective approach for conditional cross-modality translation in medical imaging.
  • This work opens new avenues for leveraging BBBMs in AI-driven medical image analysis for AD.