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May 17, 2026NeuroImageOpen Access

Anatomy Guided Truncated Conditional Diffusion Model for Super-Resolution Arterial Spin Labeling Imaging

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Authors

YXYunzhi XuJZJiaxin ZhengRLRuoge Lin

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Overview

Randomized trial demonstrates improved high-resolution ASL imaging, suggesting a novel approach for cerebral blood flow measurement.

Key Points

  • This study aims to enhance the resolution of Arterial Spin Labeling (ASL) images using a new model.
  • Developed a super-resolution method based on an anatomy guided truncated conditional diffusion model.
  • Validated the model on retrospective, prospective, and clinical datasets.
  • Compared performance against conventional and deep learning methods.
  • Achieved 2-18% gains in SSIM on prospectively acquired data.
  • Obtained 26-50% reductions in FID compared to existing methods.
  • Demonstrated superior performance in generating high-resolution ASL images.

Cite This Study

Xu et al. (2026) studied this question.

synapsesocial.com/papers/6a095ac47880e6d24efe09e4https://doi.org/10.1016/j.neuroimage.2026.122000
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Self-Supervised SUper-Resolution ASL Enhancement based on 3D Latent Diffusion Models (SURED-L)2025
  2. 2Enhancing Cerebral Blood Flow Quantification: A Comprehensive Review of Denoising, Artifact Correction, and Simulation in Arterial Spin Labeling MRI2026
  3. 3Adaptive Joint Data Selection for Sparsity Based Arterial Spin Labeling MRI Denoising2024
  4. 4Clinical Feasibility of High-Resolution Brain Perfusion Imaging using Deep Learning 3D Arterial Spin Labeling2025
  5. 5Dual independent <scp>pathway‐densely</scp> connected residual network with dilated convolution‐based arterial spin labeling <scp>MRI</scp> image reconstruction with minimum <scp>label‐control</scp> pairs2024 · 3 citations