Hales, PW;
Pfeuffer, J;
A. Clark, C;
(2020)
Combined Denoising and Suppression of Transient Artifacts in Arterial Spin Labeling MRI Using Deep Learning.
Journal of Magnetic Resonance Imaging
10.1002/jmri.27255.
Preview |
Text
jmri.27255.pdf - Published Version Download (3MB) | Preview |
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.
| Type: | Article |
|---|---|
| Title: | Combined Denoising and Suppression of Transient Artifacts in Arterial Spin Labeling MRI Using Deep Learning |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/jmri.27255 |
| Publisher version: | https://doi.org/10.1002/jmri.27255 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | deep learning, arterial spin labeling, ASL, denoising, autoencoder, CNN |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Developmental Neurosciences Dept |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10102287 |
Archive Staff Only
 |
View Item |
