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Examining Online Syntactic Processing of Spoken Complex Sentences in Chinese Using Dual-Modal Interference Tasks
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DUAL-GLOW: Conditional Flow-Based Generative Model for Modality Transfer.

Haoliang Sun1,2,3, Ronak Mehta1, Hao H Zhou1

  • 1University of Wisconsin-Madison.

Proceedings. IEEE International Conference on Computer Vision
|February 7, 2022
PubMed
Summary
This summary is machine-generated.

Researchers developed DUAL-GLOW, a new generative model, to create Positron Emission Tomography (PET) images from Magnetic Resonance Imaging (MRI) scans. This method shows promise for neurological disease diagnosis, especially with limited data.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuroscience

Background:

  • Positron Emission Tomography (PET) is crucial for diagnosing neurological diseases but is expensive and invasive.
  • Magnetic Resonance Imaging (MRI) is a more accessible imaging technique.
  • Bridging the gap between MRI and PET could improve diagnostic accessibility.

Purpose of the Study:

  • To generate PET images from MRI data using advanced generative models.
  • To address the challenge of limited data in medical imaging tasks.
  • To develop a model capable of capturing age-related brain changes.

Main Methods:

  • Developed DUAL-GLOW, a novel flow-based generative model utilizing two invertible networks.
  • Employed a relation network to map latent spaces between MRI and PET data.
  • Extended the framework to incorporate conditional information, such as patient age.

Main Results:

  • DUAL-GLOW demonstrated strong performance in synthesizing PET images from MRI data, even with small sample sizes.
  • The model successfully captured age-dependent brain hypometabolism (FDG-PET) when conditioned on age.
  • Quantitative and qualitative results surpassed recent works on the ADNI dataset.

Conclusions:

  • DUAL-GLOW offers a promising approach for generating PET images from MRI, potentially reducing costs and invasiveness.
  • The model's ability to leverage conditional information enhances its utility for studying disease progression.
  • This work advances the application of generative AI in medical image synthesis and neurological disease research.