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Related Concept Videos

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
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The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
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Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) is an advanced Nuclear Magnetic Resonance (NMR) technique specifically designed to detect and enhance the signals of low-abundance nuclei, such as carbon-13 and nitrogen-15, in small molecules. The fundamental principle behind INEPT is the transfer of polarization from a more abundant and highly polarizable nucleus, typically hydrogen-1, to the low-abundance nucleus of interest. This process effectively boosts the NMR signal of the...
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Related Experiment Video

Updated: Jun 30, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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Accelerated EPR imaging using deep learning denoising.

Irene Canavesi1, Navin Viswakarma1, Boris Epel1,2

  • 1Oxygen Measurement Core, O2M Technologies, LLC, Chicago, Illinois, USA.

Magnetic Resonance in Medicine
|March 17, 2025
PubMed
Summary
This summary is machine-generated.

Deep learning denoising enhances electron paramagnetic resonance imaging (EPRI) for faster oxygen mapping. This technique improves signal-to-noise ratio (SNR), bringing EPRI closer to clinical use.

Keywords:
deep learning denoisingelectron paramagnetic resonance imaginghypoxia imagingoxygen imaging

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Area of Science:

  • Medical imaging
  • Biophysics
  • Artificial intelligence in medicine

Background:

  • Pulse electron paramagnetic resonance imaging (EPRI) using Trityl OXO71 is effective for mapping tissue oxygen partial pressure (pO2).
  • Acquiring high-quality 3D EPRI maps, especially for pO2, can be time-consuming due to the need for numerous signal averages to achieve sufficient signal-to-noise ratio (SNR).
  • Deep learning offers potential for accelerating image acquisition and improving image quality in various medical imaging modalities.

Purpose of the Study:

  • To apply deep learning techniques for denoising 3D EPRI amplitude and pO2 maps.
  • To evaluate the effectiveness of different neural network architectures, specifically UNet with joint bilateral filters (JBF), for enhancing EPRI image quality.
  • To determine if deep learning can accelerate EPRI acquisition without compromising image quality.

Main Methods:

  • Four neural networks (autoencoder, Attention UNet, UNETR, UNet) were implemented using MONAI and tested on a dataset of 227 3D EPRI images (in vivo and in vitro).
  • The best-performing model, UNet, was enhanced with joint bilateral filters (JBF) and trained to improve image SNR while preserving structural similarity and edge details.
  • The optimized UNet+JBF model was validated using in vitro phantom and in vivo mouse tumor data acquired with varying numbers of averages (15, 30, 150).

Main Results:

  • The UNet model with 2 JBF layers (UNet+JBF2) demonstrated the best performance in denoising and enhancing EPRI images.
  • The UNet+JBF2 model achieved higher SNR in 15-shot amplitude maps compared to 150-shot pre-filter maps, enabling a 10-fold acceleration in imaging.
  • The deep learning algorithm significantly improved the SNR of both amplitude and pO2 maps in phantoms and in vivo tumor data.

Conclusions:

  • Deep learning techniques, particularly the UNet+JBF2 model, are effective for denoising 3D EPRI data.
  • The developed method significantly improves image SNR and allows for accelerated data acquisition.
  • This advancement brings the EPRI technique closer to routine clinical application for oxygen mapping.