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Related Experiment Video

Updated: Jul 2, 2026

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Automatic Phase and Sequence Identification in Gd-EOB-DTPA-Enhanced Liver MRI Using Deep Convolutional and Sequential

Tomomi Takenaga1, Shouhei Hanaoka2, Yukihiro Nomura3,4

  • 1Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, Japan. takenaga-tky@umin.ac.jp.

Journal of Imaging Informatics in Medicine
|July 1, 2026
PubMed
Summary

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This summary is machine-generated.

A new deep learning model accurately identifies MRI sequences in gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced liver scans. This automated approach aids in organizing liver MRI data for multicenter studies.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Accurate identification of acquisition sequences in liver MRI is crucial for data curation.
  • Gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI is widely used for liver imaging.
  • Manual data organization is time-consuming and prone to errors.

Purpose of the Study:

  • To develop and validate a deep learning model for automatic identification of Gd-EOB-DTPA-enhanced liver MRI sequences.
  • To enable automated examination-level data curation for multicenter studies.
  • To improve the efficiency and accuracy of liver MRI data organization.

Main Methods:

  • A deep learning pipeline using ConvNeXt for feature extraction and gated recurrent unit (GRU)-based or transformer-based sequential models was developed.
Keywords:
Data curationDeep learningImage processingLiverMultiparametric magnetic resonance imaging

Related Experiment Videos

Last Updated: Jul 2, 2026

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

  • Models were trained on series-level 3D image volumes, excluding textual metadata.
  • Performance was evaluated on internal and external test sets using examination-level and category-level accuracy metrics.

    • Main Results:

    • The ConvNeXt + GRU model achieved the highest examination-level accuracy.
    • Dynamic contrast-enhanced phases were identified with high accuracy across datasets.
    • Performance on auxiliary sequences varied, with lower accuracy on external datasets, particularly for T2-weighted imaging.

    Conclusions:

    • The proposed framework accurately identifies dynamic phases and auxiliary sequences in Gd-EOB-DTPA-enhanced liver MRI.
    • Automated identification supports robust examination-level data organization in multicenter settings.
    • Further refinement may be needed for auxiliary sequences with high inter-institutional variability.