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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

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This summary is machine-generated.

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.