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Brain Imaging01:14

Brain Imaging

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Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
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Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Related Experiment Video

Updated: Oct 12, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Conditional Invertible Neural Networks for Medical Imaging.

Alexander Denker1, Maximilian Schmidt1, Johannes Leuschner1

  • 1Center for Industrial Mathematics, University of Bremen, Bibliothekstr. 5, 28359 Bremen, Germany.

Journal of Imaging
|November 25, 2021
PubMed
Summary
This summary is machine-generated.

Generative flow-based models using invertible neural networks improve medical imaging reconstructions. Using a radial distribution instead of a Gaussian enhances reconstruction quality for tasks like low-dose CT and accelerated MRI.

Keywords:
image reconstructioninvertible neural networksnormalizing flows

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Area of Science:

  • Medical Imaging
  • Machine Learning
  • Computational Science

Background:

  • Deep learning is increasingly used for inverse problems, but often provides only point estimates.
  • Quantifying uncertainty is crucial for ill-posed inverse problems in medical imaging.
  • Existing deep learning methods for medical imaging often lack uncertainty estimation.

Purpose of the Study:

  • To apply generative flow-based models with invertible neural networks to medical imaging inverse problems.
  • To evaluate different invertible neural network architectures.
  • To investigate the impact of base distribution choice on reconstruction quality.

Main Methods:

  • Generative flow-based models utilizing invertible neural networks.
  • Application to low-dose computed tomography (CT) and accelerated medical resonance imaging (MRI).
  • Testing various invertible neural network architectures and conducting ablation studies.

Main Results:

  • Invertible neural networks show promise for medical imaging reconstruction.
  • The choice of base distribution significantly impacts reconstruction quality.
  • Radial distributions outperform standard Gaussian distributions for these tasks.

Conclusions:

  • Generative flow-based models are effective for uncertainty-aware medical image reconstruction.
  • Optimizing the base distribution is key to improving reconstruction performance.
  • This approach offers a pathway to more robust and reliable medical imaging analysis.