title: Machine learning based compartment models with permeability for white matter microstructure imaging.
abstract: Some microstructure parameters, such as permeability, remain elusive because mathematical models that express their relationship to the MR signal accurately are intractable. Here, we propose to use computational models learned from simulations to estimate these parameters. We demonstrate the approach in an example which estimates water residence time in brain white matter. The residence time τiof water inside axons is a potentially important biomarker for white matter pathologies of the human central nervous system, as myelin damage is hypothesised to affect axonal permeability, and thus τi. We construct a computational model using Monte Carlo simulations and machine learning (specifically here a random forest regressor) in order to learn a mapping between features derived from diffusion weighted MR signals and ground truth microstructure parameters, including τi. We test our numerical model using simulated and in vivo human brain data. Simulation results show that estimated parameters have strong correlations with the ground truth parameters (R2={0.88,0.95,0.82,0.99}) for volume fraction, residence time, axon radius and diffusivity respectively), and provide a marked improvement over the most widely used Kärger model (R2={0.75,0.60,0.11,0.99}). The trained model also estimates sensible microstructure parameters from in vivo human brain data acquired from healthy controls, matching values found in literature, and provides better reproducibility than the Kärger model on both the voxel and ROI level. Finally, we acquire data from two Multiple Sclerosis (MS) patients and compare to the values in healthy subjects. We find that in the splenium of corpus callosum (CC-S) the estimate of the residence time is 0.57±0.05s for the healthy subjects, while in the MS patient with a lesion in CC-S it is 0.33±0.12s in the normal appearing white matter (NAWM) and 0.19±0.11s in the lesion. In the corticospinal tracts (CST) the estimate of the residence time is 0.52±0.09s for the healthy subjects, while in the MS patient with a lesion in CST it is 0.56±0.05s in the NAWM and 0.13±0.09s in the lesion. These results agree with our expectations that the residence time in lesions would be lower than in NAWM because the loss of myelin should increase permeability. Overall, we find parameter estimates in the two MS patients consistent with expectations from the pathology of MS lesions demonstrating the clinical potential of this new technique.
authors.affiliation: Centre for Medical Image Computing and Dept of Computer Science, University College London, Gower Street, London WC1E 6BT, UK; MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
authors.forename: Gemma L
authors.initials: GL
authors.lastname: Nedjati-Gilani
authors.affiliation: Department of Neuroinflammation, Institute of Neurology, University College London, London, UK.
authors.forename: Torben
authors.initials: T
authors.lastname: Schneider
authors.affiliation: Institute of Child Health, University College London, London, UK.
authors.forename: Matt G
authors.initials: MG
authors.lastname: Hall
authors.affiliation: Department of Neuroinflammation, Institute of Neurology, University College London, London, UK.
authors.forename: Niamh
authors.initials: N
authors.lastname: Cawley
authors.affiliation: Centre for Medical Image Computing and Dept of Computer Science, University College London, Gower Street, London WC1E 6BT, UK.
authors.forename: Ioana
authors.initials: I
authors.lastname: Hill
authors.affiliation: Department of Neuroinflammation, Institute of Neurology, University College London, London, UK.
authors.forename: Olga
authors.initials: O
authors.lastname: Ciccarelli
authors.affiliation: Centre for Medical Image Computing and Dept of Computer Science, University College London, Gower Street, London WC1E 6BT, UK. Electronic address: i.drobnjak@ucl.ac.uk.
authors.forename: Ivana
authors.initials: I
authors.lastname: Drobnjak
authors.affiliation: Department of Neuroinflammation, Institute of Neurology, University College London, London, UK; Brain MRI 3T Mondino Research Center, C. Mondino National Neurological Institute, Pavia, Italy; Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy.
authors.forename: Claudia A M Gandini
authors.initials: CAMG
authors.lastname: Wheeler-Kingshott
authors.affiliation: Centre for Medical Image Computing and Dept of Computer Science, University College London, Gower Street, London WC1E 6BT, UK.
authors.forename: Daniel C
authors.initials: DC
authors.lastname: Alexander
mesh_terms: D000328:Adult; D001921:Brain / Q000000981:diagnostic imaging* / Q000473:pathology; D003198:Computer Simulation; D017352:Echo-Planar Imaging; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D009010:Monte Carlo Method; D009103:Multiple Sclerosis / Q000000981:diagnostic imaging / Q000473:pathology; D010539:Permeability; D066127:White Matter / Q000000981:diagnostic imaging* / Q000473:pathology; D055815:Young Adult