Quantitative Susceptibility Mapping of Hockey Players After Mild Traumatic Brain Injury
Anna Pukropski1, Alexander Weber1, Michael Jarrett1, Christian Kames2, Shiroy Dadachanji, David K. B. Li3, Jack Taunton4, and Alexander Rauscher5

1Pediatrics, University of British Columbia, Vancouver, BC, Canada, 2University of British Columbia, Vancouver, BC, Canada, 3Radiology, University of British Columbia, Vancouver, BC, Canada, 4Division of Sports Medicine, University of British Columbia, Vancouver, BC, Canada, 5Pediatrics, University of British Columbia, BC, Canada


We followed 45 varsity hockey players during one season of play, and scanned all players at the beginning of the season. 11 players received a concussion, and were scanned within 72hrs post-concussion, and then again after 2 weeks and 2 months. Quantitative Susceptibility Maps were created from the multi-echo 3D gradient-echo data, and susceptibility values were measured in deep grey matter (caudate, pallidum, putamen, and thalamus) and frontal and posterior WM in the corpus callosum (genu and splenium). A linear mixed-effect model analysis of the regions of interest revealed no significant changes over time compared to baseline.


Mild traumatic brain injury (mTBI; concussion) is a very common form of brain damage sustained through sports, traffic accidents, blasts, and/or slips/trips/falls, yet remains poorly understood. There is currently no gold standard for its diagnosis. The symptoms vary from small, such as no symptoms, headaches and/or imbalance, to unconsciousness (under 30mins) and cognitive or behavioural deficits [1]. The pathophysiology of concussions include the impact of the brain with the skull, resulting in cerebral contusions, the stretching of and damage to axons, and often times a 'contrecoup' to the opposite side of the head [2]. Secondary processes further broaden the spectrum of possible symptoms. Animal studies have shown that mTBI can create oxidative stress and axonal damage in the absence of gross focal lesions [3,4]. Iron accumulation in the pathology of TBI has been found in the brains of mice after a controlled cortical impact injury [5]. To get a better understanding of mTBI, we used Quantitative Susceptibility Mapping (QSM) to measure magnetic susceptibility changes in the brains of ice-hockey players after a concussion. Eleven of forty-five varsity hockey players received a concussion during one season of play. These subjects underwent a pre-season baseline scan with follow-up scans at three-days, two-weeks and two-months post-concussion.



Forty-five ice hockey-players underwent a pre-season baseline scan and took part in follow-up scans (72 hours, 2 weeks, 2 months) if they received a concussion during play (assessed using Sport Concussion Assessment Tool 2 by a physician). 11 players received a concussion during play (5 males, 6 females). Their images were acquired with a 3T Philips Achieva scanner.

Data Acquisition:

Acquired MRI images were: 3D-sagittal T1-weighted image (TR=8.1ms, TE=3.7ms, flip angle=6°, voxel size= 1x1x1mm^3, acquisition matrix= 256x256x160, field of view= 256x256x160mm^3) and a multi-echo SWI with a 3D gradient echo (TR=36ms, TE=6,12,18,24,30ms, flip angle=17°, acquisition matrix=440x222x64, FOV=220x166x128mm^3, voxel size= 0.5x0.5x1mm^3).

Image Analysis:

All SWI images were post-processed as QSM images using in-house MATLAB code [6]. FSL (FMRIB Software Library, Oxford, United Kingdom), was used for display, brain extraction, segmentation and registration of the regions of interest. For the DGM structures, FSL FIRST was used to segment ROIs, whereas the ICBM-DTI-81 WM labeled atlas from the Johns Hopkins University [7] was used for the WM ROIs.

Sample segmentation and atlas registration are shown in Fig 1.


Statistical analysis was performed with MATLB and Statistics Toolbox Release 2016a (The MathWorks, Inc., Natick, Massachusetts, United States). Each region was processed separately with the use of a linear mixed-effect model. A multiple comparison corrected p-value lower than 0.05 indicated a significant correlation.


All 11 concussed subjects (21.18 ± 1.66 years old) had successfully undergone a baseline scan, with 8, 10 and 9 scans acquired within 72hours, two weeks, and two months post-concussion, respectively. Statistical analysis showed neither a significant correlation between a mTBI and DGM susceptibility value changes nor in the white matter regions (p-value > 0.05 (uncorrected)). (Fig 2)


We found no significant magnetic susceptibility changes at 3 days, 2 weeks, and 2 months post-concussion in the DGM (caudate, pallidum, putamen, and thalamus) or CC WM (genu and splenium).

Previous studies, using similar MRI methods, have found increased magnetic susceptibility levels in thalamus [5,8,9], globus pallidus [8], caudate [9], lenticular nucleus [9], hippocampus [9], right substantia nigra [9], red nucleus [9], splenium [9], and internal capsule [5]. These findings were often much later (18months [8], 19 months [9], and 1-2 months in mice [5]) than our own time-points, suggesting a possible delay in iron accumulation post-concussion before being detectable using MRI.

This is the first study to measure magnetic susceptibility both before and soon after concussion. Previously published findings from the same cohort showed reduced fractional anisotropy in the genu (but no changes in DGM) [10], and reduced myelin water fraction (MWF) in splenium [11]. Thus, although we can detect changes in microarchitecture and myelin levels after sport-related mTBI, secondary accumulation of iron, at 3 days, 2 weeks, or 2 months, may not have yet occurred or accumulated enough to be detectable through 3T QSM. Alternatively, lack of change in splenium susceptibility may be interpreted as no reduction of myelin content in these areas, which is in agreement with our finding of recovery at 2 months with MWF [11]. This suggests that myelin changes, but is not reduced.


Author contributions: A.R., J.T. and D.L. designed the study. A.R. and D.L. designed the imaging protocol. M.J. and S.D. collected data and helped coordinate the study. A.P. performed data analysis under the supervision of A.M.W. A.P. and A.M.W. wrote the manuscript. C.K. wrote the in-house MATLAB QSM algorithm software. Competing interests: The authors declare that they have no competing interests.

Funding: Canada Research Chairs, CFRI postdocoral fellowship, DAAD RISE program, and London Drugs Award.


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QSM images showing the deep grey matter regions of interest (white) and the white matter regions (red)

Mean and standard deviation of QSM values in the regions of interest for each time point of scan. The right most column displays the uncorrected p-values for the corresponding region.

Proc. Intl. Soc. Mag. Reson. Med. 25 (2017)