Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 12, 2026

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Denoising of ASL Data Using Deep Learning Priors Generated From Distribution Remapping.

Ziyang Xu1,2, Rong Guo1,3, Ziwen Ke1,4

  • 1Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.

Magnetic Resonance in Medicine
|June 11, 2026
PubMed
Summary

Related Concept Videos

Deconvolution01:20

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...

You might also read

Related Articles

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

Sort by
Same author

Age-Specific Cerebral Vessel Templates Across the Lifespan of Healthy Adults.

Scientific data·2026
Same author

Physics-encoded convolutional neural operators for parametric PDEs: A convergence-guaranteed framework via pre-computed kernel fields.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Multiplexed magnetic resonance imaging.

Nature·2026
Same author

Temporal-Spatial Fusion Vision Hardware Enables Streamlined In-Sensor Computing for Dynamic Scenes.

Nature communications·2026
Same author

Motion Correction in High-Resolution 3D Brain MRSI Without Water and Lipid Suppression.

Magnetic resonance in medicine·2025
Same author

Unsupervised Brain Lesion Segmentation Using Posterior Distributions Learned by Subspace-Based Generative Model.

IEEE transactions on medical imaging·2025

This study introduces a novel deep learning method for denoising arterial spin labeling (ASL) data, significantly improving signal-to-noise ratio (SNR) even with limited training data. The technique enhances image quality and accelerates ASL acquisition for better clinical use.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Arterial Spin Labeling (ASL) is crucial for non-invasive brain perfusion imaging.
  • Conventional deep learning (DL) methods for ASL denoising struggle with limited data, leading to overfitting and poor generalization.
  • Improving ASL data quality is essential for accurate cerebral blood flow (CBF) quantification.

Purpose of the Study:

  • To develop an effective deep learning (DL)-based method for denoising arterial spin labeling (ASL) data.
  • To address the limitations of existing DL methods in scenarios with insufficient training data.
  • To enhance the signal-to-noise ratio (SNR) and generalizability of ASL imaging.

Main Methods:

  • Employed data augmentation via Image-to-Image Schrödinger Bridge (I²SB)-based distribution remapping to create larger, diverse training datasets.
Keywords:
arterial spin labelingdeep learningdenoisingdistribution remapping

Related Experiment Videos

Last Updated: Jun 12, 2026

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

  • Separately denoised in-distribution and out-of-distribution components of the ASL data using U-Net-based DL denoisers and Bayesian reconstruction with sparsity constraints.
  • Trained DL denoisers on remapped public ASL datasets to capture in-distribution features and reconstructed out-of-distribution features for improved image quality.

    • Main Results:

    • Simulation studies confirmed the effectiveness of distribution remapping for data augmentation in limited-data scenarios.
    • The proposed method achieved an average SNR improvement of approximately 7 dB, outperforming state-of-the-art approaches in both simulation and in vivo experiments.
    • Demonstrated robust and generalizable performance across various ASL sequences and protocols, with comparable CBF maps to conventional methods even with an 83% reduction in scan time for stroke patient data.

    Conclusions:

    • The developed DL method effectively denoises ASL data, even with limited training datasets.
    • The technique shows significant potential for accelerating ASL acquisition and enhancing overall image quality.
    • This advancement promises to improve the clinical utility of ASL imaging for various neurological applications.