Myelin water imaging data analysis in less than one minute

Hanwen Liu; Qing-San Xiang; Roger Tam; Adam V Dvorak; Alex L MacKay; Shannon H Kolind; Anthony Traboulsee; Irene M Vavasour; David K B Li; John K Kramer; Cornelia Laule

doi:10.1016/j.neuroimage.2020.116551

Myelin water imaging data analysis in less than one minute

Neuroimage. 2020 Apr 15:210:116551. doi: 10.1016/j.neuroimage.2020.116551. Epub 2020 Jan 21.

Authors

Affiliations

¹ Physics & Astronomy, University of British Columbia, Canada; International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Canada.
² Physics & Astronomy, University of British Columbia, Canada; Radiology, University of British Columbia, Canada.
³ Radiology, University of British Columbia, Canada; Biomedical Engineering, University of British Columbia, Canada.
⁴ Physics & Astronomy, University of British Columbia, Canada; International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Canada; Radiology, University of British Columbia, Canada; Medicine, University of British Columbia, Canada.
⁵ Medicine, University of British Columbia, Canada.
⁶ International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Canada; Radiology, University of British Columbia, Canada.
⁷ Radiology, University of British Columbia, Canada; Medicine, University of British Columbia, Canada.
⁸ International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Canada; Kinesiology, University of British Columbia, Canada.
⁹ Physics & Astronomy, University of British Columbia, Canada; International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Canada; Radiology, University of British Columbia, Canada; Pathology & Laboratory Medicine, University of British Columbia, Canada. Electronic address: claule@physics.ubc.ca.

PMID: 31978542
DOI: 10.1016/j.neuroimage.2020.116551

Abstract

Purpose: Based on a deep learning neural network (NN) algorithm, a super fast and easy to implement data analysis method was proposed for myelin water imaging (MWI) to calculate the myelin water fraction (MWF).

Methods: A NN was constructed and trained on MWI data acquired by a 32-echo 3D gradient and spin echo (GRASE) sequence. Ground truth labels were created by regularized non-negative least squares (NNLS) with stimulated echo corrections. Voxel-wise GRASE data from 5 brains (4 healthy, 1 multiple sclerosis (MS)) were used for NN training. The trained NN was tested on 2 healthy brains, 1 MS brain with segmented lesions, 1 healthy spinal cord, and 1 healthy brain acquired from a different scanner.

Results: Production of whole brain MWF maps in approximately 33 s can be achieved by a trained NN without graphics card acceleration. For all testing regions, no visual differences between NN and NNLS MWF maps were observed, and no obvious regional biases were found. Quantitatively, all voxels exhibited excellent agreement between NN and NNLS (all R²>0.98, p < 0.001, mean absolute error <0.01).

Conclusion: The time for accurate MWF calculation can be dramatically reduced to less than 1 min by the proposed NN, addressing one of the barriers facing future clinical feasibility of MWI.

Keywords: Deep learning; Multiple sclerosis; Myelin water fraction; Myelin water imaging; Neural network; Quantitative MRI.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adult
Body Water / diagnostic imaging*
Brain / diagnostic imaging*
Deep Learning*
Feasibility Studies
Female
Humans
Magnetic Resonance Imaging / methods*
Male
Middle Aged
Multiple Sclerosis / diagnostic imaging*
Myelin Sheath*
Neuroimaging / methods*