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Neuroimage Clin. 2017 Oct 14;17:169-178. doi: 10.1016/j.nicl.2017.10.015. eCollection 2018.

Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls.

Yoo Y1,2,3, Tang LYW4,3, Brosch T1,2,3, Li DKB4,3, Kolind S4,5,3,6, Vavasour I4, Rauscher A7, MacKay AL4,6, Traboulsee A5,3, Tam RC2,4,3.

Author information

1
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
2
Biomedical Engineering Program, University of British Columbia, Vancouver, BC, Canada.
3
MS/MRI Research Group, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada.
4
Department of Radiology, University of British Columbia, Vancouver, BC, Canada.
5
Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada.
6
Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.
7
Division of Neurology, Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada.

Abstract

Myelin imaging is a form of quantitative magnetic resonance imaging (MRI) that measures myelin content and can potentially allow demyelinating diseases such as multiple sclerosis (MS) to be detected earlier. Although focal lesions are the most visible signs of MS pathology on conventional MRI, it has been shown that even tissues that appear normal may exhibit decreased myelin content as revealed by myelin-specific images (i.e., myelin maps). Current methods for analyzing myelin maps typically use global or regional mean myelin measurements to detect abnormalities, but ignore finer spatial patterns that may be characteristic of MS. In this paper, we present a machine learning method to automatically learn, from multimodal MR images, latent spatial features that can potentially improve the detection of MS pathology at early stage. More specifically, 3D image patches are extracted from myelin maps and the corresponding T1-weighted (T1w) MRIs, and are used to learn a latent joint myelin-T1w feature representation via unsupervised deep learning. Using a data set of images from MS patients and healthy controls, a common set of patches are selected via a voxel-wise t-test performed between the two groups. In each MS image, any patches overlapping with focal lesions are excluded, and a feature imputation method is used to fill in the missing values. A feature selection process (LASSO) is then utilized to construct a sparse representation. The resulting normal-appearing features are used to train a random forest classifier. Using the myelin and T1w images of 55 relapse-remitting MS patients and 44 healthy controls in an 11-fold cross-validation experiment, the proposed method achieved an average classification accuracy of 87.9% (SD = 8.4%), which is higher and more consistent across folds than those attained by regional mean myelin (73.7%, SD = 13.7%) and T1w measurements (66.7%, SD = 10.6%), or deep-learned features in either the myelin (83.8%, SD = 11.0%) or T1w (70.1%, SD = 13.6%) images alone, suggesting that the proposed method has strong potential for identifying image features that are more sensitive and specific to MS pathology in normal-appearing brain tissues.

KEYWORDS:

Deep learning; Machine learning; Magnetic resonance imaging; Multiple sclerosis; Myelin water imaging

PMID:
29071211
PMCID:
PMC5651626
DOI:
10.1016/j.nicl.2017.10.015
[Indexed for MEDLINE]
Free PMC Article

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