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
Affiliations
Hanwen Liu
Physics & Astronomy, University of British Columbia, Canada; International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Canada
Qing-San Xiang
Physics & Astronomy, University of British Columbia, Canada; Radiology, University of British Columbia, Canada
Roger Tam
Radiology, University of British Columbia, Canada; Biomedical Engineering, University of British Columbia, Canada
Adam V. Dvorak
Physics & Astronomy, University of British Columbia, Canada; International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Canada
Alex L. MacKay
Physics & Astronomy, University of British Columbia, Canada; Radiology, University of British Columbia, Canada
Shannon H. Kolind
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
Anthony Traboulsee
Medicine, University of British Columbia, Canada
Irene M. Vavasour
International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Canada; Radiology, University of British Columbia, Canada
David K.B. Li
Radiology, University of British Columbia, Canada; Medicine, University of British Columbia, Canada
John K. Kramer
International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Canada; Kinesiology, University of British Columbia, Canada
Cornelia Laule
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; Corresponding author. 818 West 10th Avenue, Vancouver, BC, V5Z 1M9, Canada.
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.
