Combined Denoising and Suppression of Transient Artifacts in Arterial Spin Labeling MRI Using Deep Learning

J Magn Reson Imaging. 2020 Nov;52(5):1413-1426. doi: 10.1002/jmri.27255. Epub 2020 Jun 15.


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.

Keywords: ASL; CNN; arterial spin labeling; autoencoder; deep learning; denoising.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Artifacts*
  • Brain / diagnostic imaging
  • Cerebrovascular Circulation
  • Child
  • Deep Learning*
  • Humans
  • Magnetic Resonance Imaging
  • Retrospective Studies
  • Spin Labels


  • Spin Labels