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August 19, 2026SensorsOpen Access

Enhancing Cerebral Blood Flow Quantification: A Comprehensive Review of Denoising, Artifact Correction, and Simulation in Arterial Spin Labeling MRI

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Authors

SAShyna AJRJini RajuAJAnsamma John

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Overview

Comprehensive review reveals advances in denoising, artifact correction, and simulation in arterial spin labeling MRI, highlighting strategies to improve cerebral blood flow quantification.

Key Points

  • To review conventional and artificial intelligence-driven methods for denoising, artifact correction, and dataset simulation to improve cerebral blood flow quantification in arterial spin labeling MRI.
  • Synthesized literature on traditional image-processing techniques alongside machine learning and deep learning algorithms designed for noise reduction and image enhancement in arterial spin labeling (ASL).
  • Evaluated primary acquisition artifacts and simulation frameworks used to generate synthetic data for algorithm development and validation.
  • Advanced machine learning and classical filtering methods effectively overcome low signal-to-noise ratios and motion artifacts without requiring exogenous contrast agents.
  • Simulation pipelines provide critical synthetic validation benchmarks to train and evaluate algorithms when large-scale clinical ASL datasets are limited.

Cite This Study

A et al. (2026) studied this question.

synapsesocial.com/papers/6a8563ae03308d306e2d70dbhttps://doi.org/10.3390/s26165202
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Also Consider

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  1. 1Adaptive Joint Data Selection for Sparsity Based Arterial Spin Labeling MRI Denoising2024
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  3. 3Spatiotemporal redundancy based denoising method in arteprial spin labeling MRI: A ticket to free sensitivity improvement2024
  4. 4Recent advances in arterial spin labeling MRI for imaging brain tumors2026
  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