Combined Denoising and Suppression of Transient Artifacts in Arterial Spin Labeling MRI Using Deep Learning. Issue 5 (15th June 2020)

• Record Type: Journal Article
• Authors: Hales, Patrick W.; Pfeuffer, Josef; A. Clark, Chris
• Is Part Of: Journal of magnetic resonance imaging. Volume 52:Issue 5(2020)
• Page Start: 1413
• Page End: 1426
• Publication Date: 2020-06-15
• Subjects: deep learning -- arterial spin labeling -- ASL -- denoising -- autoencoder -- CNN; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.27255
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : 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