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Updated: Dec 18, 2025
Author Spotlight: Noninvasive Cerebral Blood Flow Determination in Human Functional Brain Region for Diagnosis of Neurological Disorders
Published on: May 31, 2024
Combined Denoising and Suppression of Transient Artifacts in Arterial Spin Labeling MRI Using Deep Learning
Patrick W Hales1, Josef Pfeuffer2, Chris A Clark1
1Developmental Imaging & Biophysics Section, UCL Great Ormond Street Institute of Child Health, London, UK.
Background:
Arterial spin labeling (ASL) is a useful tool for measuring cerebral blood flow (CBF). However, due to the low signal-to-noise ratio (SNR) of the technique, multiple repetitions are required, which results in prolonged scan times and increased susceptibility to artifacts.
Purpose:
To develop a deep-learning-based algorithm for simultaneous denoising and suppression of transient artifacts in ASL images.
Study Type:
Retrospective.
Subjects:
131 pediatric neuro-oncology patients for model training and 11 healthy adult subjects for model evaluation.
Field Strength/Sequence:
3T / pseudo-continuous and pulsed ASL with 3D gradient-and-spin-echo readout.
Assessment:
A denoising autoencoder (DAE) model was designed with stacked encoding/decoding convolutional layers. Reference standard images were generated by averaging 10 pairwise ASL subtraction images. The model was trained to produce perfusion images of a similar quality using a single subtraction image. Performance was compared against Gaussian and non-local means (NLM) filters. Evaluation metrics included SNR, peak SNR (PSNR), and structural similarity index (SSIM) of the CBF images, compared to the reference standard.
Statistical Tests:
One-way analysis of variance (ANOVA) tests for group comparisons.
Results:
The DAE model was the only model to produce a significant increase in SNR compared to the raw images (P < 0.05), providing an average SNR gain of 62%. The DAE model was also effective at suppressing transient artifacts, and was the only model to show a significant improvement in accuracy in the generated CBF images, as assessed using PSNR values (P < 0.05). In addition, using data from multiple inflow time acquisitions, the DAE images produced the best fit to the Buxton kinetic model, offering a 75% reduction in the fitting error compared to the raw images.
Data Conclusion:
Deep-learning-based algorithms provide superior accuracy when denoising ASL images, due to their ability to simultaneously increase SNR and suppress artifactual signals in raw ASL images.
Level Of Evidence:
3 TECHNICAL EFFICACY STAGE: 1.
