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Learning Implicit Brain MRI Manifolds with Deep Learning.

Camilo Bermudez1, Andrew J Plassard2, Taylor L Davis3

  • 1Department of Biomedical Engineering, Vanderbilt University, 2201 West End Ave, Nashville, TN, USA 37235.

Proceedings of Spie--The International Society for Optical Engineering
|June 12, 2018
PubMed
Summary
This summary is machine-generated.

Deep learning models can now generate realistic brain MRI images using Generative Adversarial Networks (GANs). This advancement aids in improving image processing and understanding brain structure changes.

Keywords:
Manifold learningbrain MRIdeep neural networksgenerative adversarial networksimage synthesis

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Extracting quantitative image data is crucial for disease detection and developmental studies.
  • Low-dimensional image manifolds facilitate group comparisons and representative synthesis.
  • Prior methods for mapping brain MRI to manifolds were constrained by explicit similarity assumptions.

Purpose of the Study:

  • Investigate implicit manifolds of normal brains using deep learning.
  • Generate high-quality synthetic brain MRI images.
  • Explore image synthesis and denoising as manifold learning tools.

Main Methods:

  • Unsupervised synthesis of T1-weighted brain MRI using a Generative Adversarial Network (GAN) on 528 2D axial slices.
  • Assessed image uniqueness via cross-correlation with the training set.
  • Employed an autoencoder with skip connections for image denoising.

Main Results:

  • Synthesized images demonstrated uniqueness compared to the training set.
  • Expert evaluation showed synthetic image quality comparable to real MRI scans.
  • The autoencoder denoising method achieved higher Peak Signal-to-Noise Ratio (PSNR) than FSL SUSAN.

Conclusions:

  • Artificial neural networks effectively synthesize realistic neuroimaging data.
  • The generated data can enhance image processing techniques.
  • This approach offers a quantitative framework for analyzing structural brain changes.