The video recording is available here: 👉 https://youtu.be/3_xJCSqu5xs Neuroimaging MRI biomarkers include volumetric measures, microstructure imaging such as diffusion-weighted imaging and magnetization transfer, and functional MRI. These biomarkers nicely complement clinical indices and provide objective means to monitor disease evolution in patients. While being very popular in the brain, MRI biomarkers have been slow to translate to the spinal cord because of the technical difficulties in imaging this organ. In this talk, I will present state-of-the-art solutions for the acquisition and automatic analysis of MRI biomarkers in the spinal cord. During the first part of the talk, I will talk about a recent initiative to standardize acquisition protocol in the spinal cord: the spine-generic project (https://spine-generic.rtfd.io/​). During the second part of the talk, we will go through some of the main features of the Spinal Cord Toolbox (SCT, http://spinalcordtoolbox.com/​), a popular open-source software package which performs automatic analysis of spinal cord MRI biomarkers. Finally, we will show example applications of these advanced acquisition and processing methods in various multi-center studies and applied to a variety of diseases: multiple sclerosis, amyotrophic lateral sclerosis, degenerative cervical myelopathy, chronic pain and cancer. Dr. Cohen-Adad is an Associate Professor at Polytechnique Montreal, Adjunct Professor in the Department of Neurosciences at University of Montreal, Associate Director of the Neuroimaging Functional Unit at the University of Montreal, and Canada Research Chair in Quantitative Magnetic Resonance Imaging. His research focuses on advancing hardware and software MRI methods to help characterizing pathologies in the central nervous system, with a particular focus in the spinal cord. He has published over 130 articles on that topic (https://scholar.google.ca/​). Dr. Cohen-Adad also dedicates efforts in bringing the community together by developing open source solutions and by organizing yearly workshops via the www.spinalcordmri.org platform, which he initiated. Links to publications and work of Dr. Julien Cohen-Adad: https://pubmed.ncbi.nlm.nih.gov/33039...​ https://pubmed.ncbi.nlm.nih.gov/32572...​ https://scholar.google.ca/citations?u...​ https://spine-generic.rtfd.io/

1. MRI biomarkers for
the spinal cord
Seminar at IMEKA
December 8th, 2020
Julien Cohen-Adad, PhD
Associate Professor, Ecole Polytechnique de Montreal
Associate Director, Functional Neuroimaging Unit, University of Montreal
Canada Research Chair in Quantitative Magnetic Resonance Imaging

2. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
Clinico-radiological paradox
2
Cohen-Adad et al., Pain 2012
No abnormality above and below
lesion with conventional MRI…
is it healthy tissue?
• 30 y.o. patient
• Needle injury during corticosteroid injection
at C6 level (malpractice)
clinico-radiological
paradox

3. What is quantitative MRI?

4. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
What is “Quantitative MRI”?
4
Quantitative
MRI of the
Spinal Cord
Edited by
Julien Cohen-Adad
Claudia A. M. Wheeler-Kingshott
Neuronal activity
fMRI
Metabolism
Spectroscopy
F T1
Microstructure NODDI
DTI_FA
DTI_MD
qMT_kf
MTsat
B1+ MWF T2* MTR
What is qMRI used for?
• Diagnosis/prognosis traumas, neurological diseases, cancers
• Objective biomarkers for testing new drugs
qMRI is popular for brain studies, but not so much in the
spinal cord... why is that?

5. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
Challenge #1: Resolution vs. SNR
5
brain spinal cord
Higher resolution
implies lower signal-
to-noise ratio (SNR)
Resolution
SNR
problem solutions
Better RF
Coils
Larger field
strength
3T 7T

6. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
Challenge #2: Susceptibility artifacts
6
Tractography stops ≠ spinal cord injury!!!
solutions
Better sequences
Saritas, in: Quantitative
MRI of the spinal cord,
Elsevier 2014
Better
shimming
Barry, Neuroimage 2017

7. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
Challenge #3: What protocol to use?
7
If a researcher not familiar with qMRI was to
start a project involving spinal cord imaging,
what sequence and parameters to choose?
qMT
MTR
MT_sat
MWF
CHARMED
NODDI
DTI
DKI
g-ratio
ultra-short TE
T1
T2*
T2
PD
This can become overwhelming!

8. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
"Spine Generic" protocol
8
Cohen-Adad et al., Nature Protocols & Scientific Data (under revision)
• Prospectively harmonized acquisition protocol for
quantitative MRI of the spinal cord
• 42 international sites involved in the optimization
• Protocol available for GE, Philips and Siemens
• Open source license (MIT)
www.spinalcordmri.org/protocols

9. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
Spine Generic: Guideline
9
Cohen-Adad et al., Nature Protocols & Scientific Data (under revision)
• Prospectively harmonized acquisition protocol for
quantitative MRI of the spinal cord
• 42 international sites involved in the optimization
• Protocol available for GE, Philips and Siemens
• Open source license (MIT)
• Standard Operating Procedure (SOP): includes step-by-
step procedure, with pictures and troubleshooting.
www.spinalcordmri.org/protocols

10. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
Spine Generic: Open Datasets
10
Cohen-Adad et al., Nature Protocols & Scientific Data (under revision)
• Prospectively harmonized acquisition protocol for
quantitative MRI of the spinal cord
• 42 international sites involved in the optimization
• Protocol available for GE, Philips and Siemens
• Open source license (MIT)
• Standard Operating Procedure (SOP): includes step-by-
step procedure, with pictures and troubleshooting.
• Publicly-available dataset (MIT license):
• Multi-site (n=19), single subject
• Multi-site (n=42), multi-subject (n=260)
• Organized with Brain Imaging Data Structure (BIDS)
• Downloadable with Datalad / git-annex

11. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
Spine Generic: Analysis pipeline
11
Cohen-Adad et al., Nature Protocols & Scientific Data (under revision)
https://spine-generic.rtfd.io/ Includes intuitive documentation & video tutorials

12. (*)
Cord CSA
Cord CSA
GM CSA
MTR
MTsat
T1
(*) Powered by multiple open-source libraries,
incl. ANTs, dipy, nibabel, numpy, scipy, etc.
FA
MD
RD
…
T1w Seg Vert. labeling
Warping
field
Coregistration
ME-GRE
MTon
MToff
T1w Seg WM
T2w
WM
Seg
DWI
Motion
correction
DWI
GM Seg
Seg
T8
T1w
T2w
Cord & CSF
WM & GM
WM atlas
PAM50
Template

13. Results of the multi-subject spine generic protocol

14. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
T1w: Image quality
14

15. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
Results: Cross-sectional area
15
T1w 1 mm isotropic
CSA averaged
between C2-C3

16. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
× ×
Results: Magnetization transfer ratio
16
Magnetization Transfer
Ratio (MTR) averaged
in WM between C2-C5
A
P
R L
+100
-100
WM mask

17. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
Results: Diffusion Tensor Imaging
17
Fractional Anisotropy
(FA) averaged in WM
between C2-C5
A
P
R L
WM mask
1
0

19. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
Challenges of spinal cord qMRI: Quick Summary
• Challenge #1: SNR and better hardware
• Challenge #2: Susceptibility artifact
• Challenge #3: Standardization of acquisition protocol
19

20. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
Challenge #4: How to analyse data?
20
problem: no standard processing tool for spinal cord
brain
data
?
spinal cord
data
solution: SCT
http://spinalcordtoolbox.com/
“SCT (Spinal Cord Toolbox) is a comprehensive and open-
source library of analysis tools for multi-parametric MRI of
the spinal cord.”

21. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
Overview of SCT
21
De Leener, Neuroimage 2016
Atlas-based analysis
• 2D slice-by-slice
• Regularized across
slices & time
• Robust for DWI
(group-wise)
Motion correction
and many more
features…
Template and atlas
C1
C3
C5
T1
Registration framework
Segmentation
T2w
T1w
SCT

22. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
PAM50: template of the spinal cord
22
De Leener, Neuroimage 2017 —> PAM50
Fonov, NeuroImage 2014 —> Methods for template creation
Taso, MAGMA 2014 —> white matter probabilistic template
Taso, NeuroImage 2015 —> white matter probabilistic template (new version)
Lévy, NeuroImage 2015 —> white matter atlas
T2-weighted template
spinothalamic
spinocerebellar
corticospinal
cuneatus
gracilis
C1
C5
T1
C3
gray matter
white matter
cerebrospinal fluid
0 1
0 1
Probabilistic structure White matter atlas
T2-weighted template
spinothalamic
spinocerebellar
corticospinal
cuneatus
gracilis
C1
C5
T1
C3
gray matter
white matter
cerebrospinal fluid
0 1
0 1
Probabilistic structure White matter atlas
Spinal cord & brainstem templates in ICBM space
Benjamin
De Leener
Simon
Lévy

23. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
Segmentation with deep learning s
23
Spinal cord segmentation
• Models trained on ~3000 subjects from ~30 centers
• Robust towards various pathologies
• Works for T1w, T2w, T2*w, DWI, MT, etc.
Charley
Gros
Patient with cord
compression
Gros et al., NeuroImage 2019
Christian
Perone
in
vivo
ex
vivo
Perone et al. Sci Rep 2018
Gray matter segmentation
4676 axial slices @ 100 μm isotropic
Powered by
Tumor segmentation
T2w
T1w+Gd tumor
cavity
edema
Lemay et al. OHBM 2020
Andréanne
Lemay
MS lesion segmentation
T2*w Manual DeepSeg
Gros et al., NeuroImage 2019

25. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
Graphical User Interface (GUI)
25
Powered by FSLeyes
Terminal
GUI 😊

26. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
Applications (190+ citations)
Quantitative structural MRI (diffusion, MT, etc.)
• Taso et al. A reliable spatially normalized template of the human spinal cord — Applications to automated white matter/gray matter segmentation…. Neuroimage 2015
• Weber et al. Lateralization of cervical spinal cord activity during an isometric upper extremity motor task with functional magnetic resonance imaging. Neuroimage 2016
• Samson et al., ZOOM or non-ZOOM? Assessing Spinal Cord Diffusion Tensor Imaging protocols for multi-centre studies. PLOS One 2016 (in press)
• Taso et al.Tract-specific and age-related variations of the spinal cord microstructure: a multi-parametric MRI study using diffusion tensor imaging (DTI) and ihMT. NMR Biomed 2016
• Massire A. et al. High-resolution multi-parametric quantitative magnetic resonance imaging of the human cervical spinal cord at 7T. Neuroimage 2016
• Duval et al. g-Ratio weighted imaging of the human spinal cord in vivo. Neuroimage 2016
• ...
Functional MRI
• Kong et al. Intrinsically organized resting state networks in the human spinal cord. PNAS 2014
• Vahdat et al. Simultaneous Brain–Cervical Cord fMRI Reveals Intrinsic Spinal Cord Plasticity during Motor Sequence Learning. PLOS Biology 2015
• Eippert F. et al. Investigating resting-state functional connectivity in the cervical spinal cord at 3T. BioRxiv 2016
• Weber K.A. et al. Functional Magnetic Resonance Imaging of the Cervical Spinal Cord During Thermal Stimulation Across Consecutive Runs. Neuroimage 2016
• Eippert et al. Denoising spinal cord fMRI data: Approaches to acquisition and analysis. Neuroimage 2016
• ...
Application in patients
• Yiannakas et al. Fully automated segmentation of the cervical cord from T1-weighted MRI using PropSeg: Application to multiple sclerosis. NeuroImage: Clinical 2015
• Castellano et al., Quantitative MRI of the spinal cord and brain in adrenomyeloneuropathy: in vivo assessment of structural changes. Brain 2016
• Grabher et al., Voxel-based analysis of grey and white matter degeneration in cervical spondylotic myelopathy. Sci Rep 2016;6:24636.
• Talbott JF, Narvid J, Chazen JL, Chin CT, Shah V. An Imaging Based Approach to Spinal Cord Infection. Seminars in Ultrasound, CT and MRI. 2016
• Martin et al. A Novel MRI Biomarker of Spinal Cord White Matter Injury: T2*-Weighted White Matter to Gray Matter Signal Intensity Ratio. AJNR 2017
• David et al. The efficiency of retrospective artifact correction methods in improving the statistical power of between-group differences in spinal cord DTI. Neuroimage 2017
• Peterson et al. Test-Retest and Interreader Reproducibility of Semiautomated Atlas-Based Analysis of Diffusion Tensor Imaging Data in Acute Cervical Spine Trauma in Adult Patients
• Grabher et al. Neurodegeneration in the Spinal Ventral Horn Prior to Motor Impairment in Cervical Spondylotic Myelopathy. Journal of Neurotrauma 2017
• Smith et al. Lateral corticospinal tract damage correlates with motor output in incomplete spinal cord injury. Archives of Physical Medicine and Rehabilitation 2017
• McCoy et al. MRI Atlas-Based Measurement of Spinal Cord Injury Predicts Outcome in Acute Flaccid Myelitis. AJNR 2016
• Grabher et al., Voxel-based analysis of grey and white matter degeneration in cervical spondylotic myelopathy. Sci Rep 2016
• Hori et al., Application of Quantitative Microstructural MR Imaging with Atlas-based Analysis for the Spinal Cord in Cervical Spondylotic Myelopathy. Scientific Reports. 2018
• ...
26
https://spinalcordtoolbox.com/en/stable/overview/references.html#applications

27. Example applications of SCT

28. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
Cross-sectional area in MS
28
Yiannakas et al., Neuroimage Clinical 2015
Group n
CSA (PropSeg5)
(Mean ± SD, mm2)
Control 26 70.2 ± 7.5
CIS 21 75.9 ± 7.9
RRMS 23 68.7 ± 7.9
PPMS 22 60.4 ± 10
SPMS 20 56.1 ± 10.5
• Results are consistent with previous literature
• Fully automatic
‣ No user-bias
‣ Reproducible
‣ Applicable to very-large databases

29. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
Mapping MS lesions in the cord
29
Kearney et al., Nat Rev Neuroscience 2015
brain spinal cord
There is a need to systematically include the
spinal cord to have a more comprehensive
assessment of MS pathology

30. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
Mapping MS lesions in the cord
30
Eden et al. Brain 2019
R. Bakshi (USA)
C. Mainero (USA)
T. Shepherd (USA)
S. Smith (USA)
J. Talbott (USA)
D. Reich (USA)
O. Ciccarelli (UK)
E. Bannier (France)
V. Callot (France)
OFSEP (France)
M. Filippi (Italy)
T. Granberg (Sweden)
M. Hori (Japan)
K. Kamiya (Japan)
Y. Tachibana (Japan)
J. Talbott (USA)
T. Granberg (Sweden)
A. Badji (Canada)
J. Maranzano (Canada)
R. Zhuoquiong (China)
SCT
Spinal Cord Toolbox
Charley Gros
Dominique Eden
• 12 MS clinical centers
—> 600 patients
• 8 MD/neuroradiologists who
validated lesion segmentation
• Automatic analysis with SCT

31. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
Mapping MS lesions in the cord
31
Eden et al. Brain 2019
Charley Gros
Dominique Eden

32. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
Mapping MS lesions in the cord
32
Eden et al. Brain 2019
Charley Gros
Dominique Eden
PPMS
RRMS SPMS
CIS
(n=31)
C1
C2
C3
C4
C5
C6
C7
A
P
R L
(n=416) (n=84) (n=73)
8%
6%
4%
2%
0%
10%
R
A
P
L
GM
WM
Normalised
Lesion
Volume
RRMS
CIS SPMS PPMS
0.04
0.02
0.01
0.00
0.05
0.08
0.03
0.06
0.07
EDSS score
8
2 4 6 10
0 1 3 5 7 9
8
2 4 6 10
0 1 3 5 7 9
0
5
10
15
20
25
30
Motor
Tracts
0
5
10
15
20
25
30
Sensory
Tracts
EDSS score
pearsonr = 0.21; p = 4.7e-05
pearsonr = 0.18; p = 0.00044
Lesion Probability Map (N=600)

33. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
Connecting the dots… the brain-spine axis f
33
Kerbrat et al. Brain 2020
Charley Gros
Anne Kerbrat
Lesion frequency in the motor corticospinal tract

34. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
Gray matter atrophy in ALS
34
Paquin et al., AJNR 2017 Collaboration: PF Pradat (Pitié-Salpêtrière, Paris)
p-value:
p=0.0041
p-value:
p=0.0203
A.
B.
0.0065 0.0116 0.0024 0.0707
0.0045 0.0530 0.0337 0.0412
GM atrophy is a good biomarker
for ALS diagnosis
ALSFRS-R
1year
Prediction
Error
Clinical
predictors
Clinical
predictors
+ SCCSA
Clinical predictors
+ GMCSA
+ WM/GMCSA
Mean error
(Best value: 0.0)
2.05 ± 12.97 1.80 ± 8.67 1.63 ± 8.42
Prediction at 1 year
Charley
Gros
Marie-Eve
Paquin
Spinal cord Gray Matter
segmentation
We need better biomarkers of motoneuron degeneration for
earlier diagnosis and treatment monitoring
Perone et al., Sci. Reports 2018

35. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
Shape analysis of cord compression
35
Martin et al. BMJ Open 2018 Collaboration: Drs. Allan Martin & Michael Fehlings (U Toronto)
• SCT provides tools to automatically analyze the shape of the
spinal cord in the axial plane
• Relevant metrics include antero-posterior and right-left
dimensions.
• Particularly interesting for studying traumatic and non-traumatic
cord compression
Patient with degenerative cervical myelopathy
A-P
diameter
(mm)
4
5
6
7
8
9
Area
(mm2)
30
40
50
60
70
Eccentricity
0.7
0.75
0.8
0.85
0.9
0.95
Symmetry
0.9
0.92
0.94
0.96
0.98
1
Orientation
-10
-5
0
5
10
Superior-inferior direction
(Slice #)
R-L
diameter
(mm)
7
9
11
13
Metrics of spinal cord shape sensitive for cord compression

36. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
Detecting anomalies in stroke patients
36
Karbasforoushan et al., Nature Communication 2019

37. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
PET-MRI to study pain
37
Albrecht et al. Pain 2018
Patients suffering from lumbar radiculopathy exhibit elevated
levels of the neuroinflammation marker 18 kDa translocator
protein in the dorsal root ganglion and spinal cord. We can
use PET-MRI for diagnosis and drug development.

38. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
Conclusion
🎉 “Spinal Cord Generic Protocol”: Promotes replicability, dissemination of knowledge
• Facilitates the addition of spinal cord qMRI protocols at non-expert centers
• Minimizes variability in multi-site, multi-vendor studies
• Already implemented in ~50 clinical sites, multicenter initiatives: INSPIRED (Wheeler-Kingshott), CanProCo
(Kolind), NAIMS white paper
👨💻 Analysis tools for spinal cord MRI: Promotes reproducibility
• Open source analysis tools: Fully transparent, promotes cross-validation of published studies.
• Automatic pipelines: Prevent user bias (e.g., manual delineation of ROIs), leverage large multi-center studies
38
This recent paradigm shift in acquisition & analysis of spinal cord MRI will hopefully
pave the way towards a more systematic inclusion of the spinal cord in studies
📣 Communication
• Discussions about unmet needs between physicists, clinicians and MRI vendors is key!

39. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
Spinal Cord MRI Workshops
39
Would you like to be on the mailing list or sponsor the event? 👉 Visit: www.spinalcordmri.org
Toronto’15
45 attendees
Spinal cord MR software
Singapore’16
65 attendees
Gray matter segmentation
Spinal Cord MR Hack
Organizers:
Paul
Summers
UniMoRE,
Modena,
Italy
Carlo
Porro
UniMoRE,
Modena,
Italy
Julien
Cohen-Adad
PolyMTL,
Montreal,
Canada
Registration
via
email:
paul.summers@unimore.it
Event
sponsored
by
the
International
Spinal
Research
Trust.
Friday 16 May, 2014
12:00 – 18:00
Enterprise Hotel
Corso Sempione 91
Milan, Italy
Milan’14
35 attendees
Standardize protocols across vendors
Hawaii’17
70 attendees
Paris’18
70 attendees
sponsored
London’20
70 registered
Montreal’19
65 registered
London’19
60 attendees

40. Cohen-Adad: Standardizing acquisition and processing of spinal cord quantitative MRI data
Acknowledgements
40
Nikola Stikov
Agah Karakuzu
Alexandre D’Astous
Alexandru Foias
Alexandru Jora
Andréanne Lemay
Anne Kerbrat
Anthime Buquet
Atef Badji
Charley Gros
Christian Perone
Eva Alonso-Ortiz
Gabriel Mangeat
Gaspard Cereza
Jan Valošek
Johanan Idicula
Joshua Newton
Lucas Rouhier
Marie-Hélène Bourget
Mathieu Boudreau
Nibardo Lopez-Rios
Nick Guenther
Olivier Vincent
Ryan Topfer
Tommy Boshkovski
Vicente Enguix
Gerald Moran
Bart Schraa
Guillaume Gilbert
Suchandrima Banerjee
Naoyuki Takei
*** Open Positions 🎉 Send me an email ***