Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Glucose Absorption Into the Small Intestine

Glucose Absorption Into the Small Intestine

Complex carbohydrates consumed cannot be absorbed into the small intestine in their original form. First, they must be hydrolyzed to a monosaccharide form such as glucose or galactose. These monosaccharides are then transported across the intestinal membrane and into the blood via transcellular transport. The intestinal epithelial cells allow the movement of these monosaccharides with a defined 'entry' through membrane transporter proteins present on their apical membrane and...

Glucose Homeostasis: Regulation of Blood Glucose

Glucose Homeostasis: Regulation of Blood Glucose

Carbohydrates consumed through foods are converted into glucose, a crucial energy source for the body. In the prandial state, high blood glucose levels stimulate the secretion of insulin from the pancreas. Insulin inhibits hepatic glucose production and stimulates glucose uptake and metabolism by muscle and adipose tissue. The excess glucose is converted into glycogen and stored in the liver and muscles.
During fasting, when blood glucose levels are low, the pancreas secretes glucagon. it...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Postmortem brain MRI reveals differential associations of subcortical and limbic volumes with cortical thinning and neurodegenerative pathologies.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026

Same author

Arterial spin labeling MRI denoising via locally adaptive regularization with structure-guided collaborative data selection.

NeuroImage·2026

Same author

Characterizing the MRI signature of hippocampal sclerosis of aging.

Alzheimer's research & therapy·2026

Same author

An open, fully-processed data resource for studying mood and sleep variability in the developing brain.

Aperture neuro·2026

Same author

Clinicoanatomic localization of iron-rich gliosis in aphasic presentations of globular glial tauopathy.

Brain communications·2026

Same author

Consistent Brain Entropy Changes During Rumination Across Three Magnetic Resonance Imaging Scanners.

Neural plasticity·2026

Search research articles

Related Experiment Video

Updated: Dec 23, 2025

Near Infrared Optical Projection Tomography for Assessments of β-cell Mass Distribution in Diabetes Research

Near Infrared Optical Projection Tomography for Assessments of β-cell Mass Distribution in Diabetes Research

Published on: January 12, 2013

Accelerating GluCEST imaging using deep learning for B₀ correction.

Yiran Li¹, Danfeng Xie¹, Abigail Cember²

¹Department of Electrical and Computer Engineering, Temple University, Philadelphia, Pennsylvania, USA.

Magnetic Resonance in Medicine

|April 18, 2020

Summary

A new deep learning algorithm significantly improves glutamate-weighted Chemical Exchange Saturation Transfer (GluCEST) MRI by reducing scan time by 50% and boosting signal-to-noise ratio (SNR). This advanced technique enhances brain glutamate mapping efficiency and accuracy.

Keywords:

GluCEST deep learning deep residual network wide activation

More Related Videos

Measuring Glucose Uptake in Drosophila Models of TDP-43 Proteinopathy

Measuring Glucose Uptake in Drosophila Models of TDP-43 Proteinopathy

Published on: August 3, 2021

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Related Experiment Videos

Last Updated: Dec 23, 2025

Near Infrared Optical Projection Tomography for Assessments of β-cell Mass Distribution in Diabetes Research

Near Infrared Optical Projection Tomography for Assessments of β-cell Mass Distribution in Diabetes Research

Published on: January 12, 2013

Measuring Glucose Uptake in Drosophila Models of TDP-43 Proteinopathy

Measuring Glucose Uptake in Drosophila Models of TDP-43 Proteinopathy

Published on: August 3, 2021

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Neuroimaging
Medical Physics
Artificial Intelligence in Medicine

Background:

Glutamate-weighted Chemical Exchange Saturation Transfer (GluCEST) MRI is a noninvasive brain imaging technique.
Current GluCEST MRI is limited by long acquisition times and low signal-to-noise ratio (SNR) due to field inhomogeneity and small signal differences.

Purpose of the Study:

To address the limitations of conventional GluCEST MRI.
To develop a novel deep learning (DL)-based algorithm for faster and more accurate GluCEST imaging.

Main Methods:

A DL algorithm utilizing wide activation neural network blocks was proposed.
B0 correction involved reduced saturation offset acquisitions.
Deep residual networks were trained to map sparse CEST-weighted images to a target spectrum.

Main Results:

The DL-based methods outperformed traditional approaches visually and quantitatively.
The wide activation blocks method achieved the highest Structural Similarity Index (SSIM) of 0.84 and peak SNR (PSNR) of 25dB.
Significant SNR increases (over 8dB) were observed in regions of interest.

Conclusions:

The novel DL method effectively reduces GluCEST imaging time by approximately 50%.
The algorithm yields higher SNR compared to current state-of-the-art methods.
This approach offers a more practical and sensitive solution for brain glutamate mapping.