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Updated: Jan 11, 2026

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ASL 4D MRA Intracranial Vessel Segmentation With Deep Learning U-Nets.

Sang Hun Chung1, Zihan Wang2, Tianrui Zhao1,3

  • 1Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.

Magnetic Resonance in Medicine
|November 9, 2025
PubMed
Summary

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The novel 4DST network improves 4D MR angiography (4D MRA) vessel segmentation by effectively using spatial and temporal data. This method enhances accuracy and reduces processing needs for medical imaging analysis.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Accurate segmentation of 4-dimensional (4D) MR angiography (MRA) is crucial for diagnosing vascular diseases.
  • Pulsed ASL-based 4D MRA offers non-contrast enhanced imaging but presents segmentation challenges.
  • Existing methods often struggle with computational demands or suboptimal performance.

Purpose of the Study:

  • To introduce a novel spatio-temporal U-Net based network, termed 4DST.
  • To enable efficient 4D MRA vessel segmentation by leveraging spatial and dynamic information.
  • To avoid memory-intensive 4D convolutional layers in the segmentation process.

Main Methods:

  • Developed and tested the 4DST network architecture for pulsed ASL-based 4D MRA.
Keywords:
4D MRAASLU‐netarteriovenous malformationdeep learningvessel segmentation

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  • Compared 4DST against spatial-only (2D, 3D) U-Net variations and other state-of-the-art methods (isolation forest, BRAVE-Net).
  • Evaluated performance using metrics like Dice-Sørensen coefficient (DSC), center-line Dice (clDice), Hausdorff distance (HD), and sensitivity relative to SNR and arterial transit time (ATT).

    • Main Results:

    • 4DST achieved superior segmentation performance, evidenced by the best DSC (0.876 ± 0.03), clDice (0.865 ± 0.02), and HD (6.241 ± 0.95).
    • The model demonstrated high sensitivity across a wide range of SNR (1-10) and ATT (500-800 ms).
    • 4DST segmentations more accurately reflected ground truth for total vessel length and branch splits compared to other methods.

    Conclusions:

    • The 4DST network provides a significant advancement in 4D MRA vessel segmentation.
    • It offers an end-to-end trainable framework suitable for spatio-temporal datasets.
    • 4DST presents an attractive, low-preprocessing solution for pulsed ASL-based 4D MRA vessel segmentation.