Nonlinear Deformations Atlases Spaces and Advanced Concepts

Nonlinear Deformations, Atlases, Spaces and
Advanced Concepts
LEAD-DBS WORKSHOP BRISBANE 02/2020 | ANDREAS HORN

ahorn1@bidmc.harvard.eduLead-DBS Workshop
HANDS ON NORMALIZATIONS
– Hands on Session –
Start Normalization

Horsley-Clarke
Apparatus
(applied in cats and monkeys in 1906)
STEREOTACTIC COORDINATES
Aubrey Mussen: First in man (1918)
cf Picard 1983 J Neurosurg

STEREOTACTIC SPACES
Talairach Tournoux 1988 Schaltenbrand Wahren 1977
Three important innovations:
1. a coordinate system to identify a particular brain
location relative to anatomical landmarks
2. a spatial transformation to match one brain to another
3. an atlas describing a standard brain, with anatomical
and cytoarchitectonic labels.
(Brett 2002 Nat. Rev. Neurosc.)
…but each based on a single brain

Tal2MNI (Brett 2002)
Tal2ICBM (Lancaster 2007)
MNI 305 (Evans 1994) … the rest is history.
MNI 152 Linear (used by SPM 99)
MNI 152 6th gen Nonlinear (still used by SPM & FSL)
MNI 152 2009a/b/c Nonlinear series
THE MNI SPACE

THE MNI SPACE
152 “6th gen” NLIN
(used by SPM/FSL)
2009b NLIN Asym Series
(used by Lead-DBS)

THE MNI SPACE
curiously, the neurosurgical literature stuck to this..
Schaltenbrand & Wahren 1977

THE MNI SPACE
Other spaces:
• FSaverage
• Bigbrain (coregistered to 2009b SYM)
• HCP templates (very similar to 6th gen NLIN)
• Conte69 (Caret)

Lead-DBS IS OPEN TO OTHER SPACES

…FURTHER READING
http://www.lead-dbs.org/about-the-mni-spaces/

IMAGING DATA WITHIN LEAD-DBS
Pre-OP Post-OP
MRI CT–or–
co-register
co-register

IMAGING DATA WITHIN LEAD-DBS
Fused pre- and post
“Normalize”
MNI space
MNI 152 2009a/b/c Nonlinear series

– A short tutorial on MRI sequences –

THE MRI SEQUENCE FAMILY TREE
http://www.ukrc.org.uk/
also see: mrimaster.com

MR-SEQUENCES
Which MRI sequence is “best”?
• MNI 2009b NLIN Asym Series
available in T1/T2/PD
• STN visible on T2-template
• GPi visible on T1/PD templates

SPECIALIZED “BASAL GANGLIA MRI SEQUENCES”
QSM FGATIR

MRI SEQUENCES
http://mriquestions.com/image-contrast-trte.html

– Subcortical Atlases –

STN VISIBLE ON MNI 2009b NLIN ASYM

patient
template

6.3 mA5.3 mA4.8 mA
THE SITUATION IN 3D

6.3 mA5.3 mA4.8 mA
Ewert, in review
MOST ATLASES NOT CORRECTLY CO-REGISTERED TO ICBM
2009b

Truism Conclusion:
Atlas and Template do need to fit!

6.3 mA5.3 mA4.8 mA
DISTAL Atlas
DISTAL ATLAS

6.3 mA5.3 mA4.8 mA
„2009b safe“

6.3 mA
„2009b safe“
„Made for Lead-DBS“ (i.e. perfectly safe)
• DISTAL Atlas (Ewert 2017)
• Human Motor Thalamus Atlas (Ilinsky 2017)
• MNI PD25 subcortical 2009b version (Xiao 2017)
• Connectomic ET target (Al-Fatly 2019)
• Electrophysiological Atlas of STN Activity (Horn 2017)
• DBS targets (Horn 2017)
Pretty safe to use in our view
• CIT168_Reinf_Learn [imported from CIT168]
• MNI PD25 histology (Xiao 2017) [imported from
PD25_MNI]

– Nonlinear Coregistration (“Normalization”) –

So let’s assume the left eye were the basal ganglia
LINEAR TRANSFORMATIONS USING SMALL MASKS

So let’s assume the left eye were the basal ganglia
-> Root-mean-square error of 1.29 +/- 0.78 mm
(Schönecker 2009)
LINEAR TRANSFORMATIONS USING SMALL MASKS

6.3 mA
Normalization Options within Lead-DBS
• SPM-based linear three-step Normalizations
(Schönecker 2008)

NONLINEAR IMAGE TRANSFORMATION
Mme/Mrs. Obamerkel

andreas.horn@charite.deLead-DBS Workshop
NONLINEAR DEFORMATIONS

6.3 mA
• SPM-based linear three-step Normalizations
(Schönecker 2008)
• SPM Unified Segmentation (Ashburner 2005)
• SPM DARTEL (Ashburner 2007)
• cf. Ashburner NeuroImage 2012: “SPM: a history”
• SPM SHOOT (Ashburner 2011)
• FSL FNIRT (Andersson 2010)
• ANTs SyN (Avants 2008)
• (“MAGeT Brain Like” Generalized approach)

6.3 mA

6.3 mA
Ashburner 2007 Ashburner 2011

Example SHOOT normalization

6.3 mA
Ashburner 2007 Ashburner 2011
Ashburner 2005
Both focus on cortical folds and are based on a pre-segmentation
using SPM “Unified Segmentation” approach.

SPM Segment
-> Aims at
segmenting into
grey / white / csf,
etc. using
Tissue probability
priors
not much grey to see here

Enhanced TPMs
Lorio 2016

Enhanced TPMs
Lorio 2016
good but not perfect fit of STN with ICBM 2009b

Current
implementation
in Lead-DBS
(following the
approach of Lorio
2016)
perfect alignment with STN/GPi on ICBM 2009b (using DISTAL atlas)
enhanced gyral features

accurate subcortical
segmentation
(even with SPM
Segment)
added enhanced
cortical registration
(using
DARTEL/SHOOT)

• Fornix, Toronto data (Lozano)
andreas.horn@charite.deOptimal targets in DBS
HIGH PRECISION OF AUTOMATED SEGMENTATIONS
Todd Herrington
(Harvard)
Siobhan Ewert
(Berlin)
Ewert et al., 2018 NeuroImage

Normalization
73 HCP subjects
- T1
- T2
30 IXI subjects
- T1
- T2
- PD (Proton Density)
Segmented GPi and STN of >100 brains

• ANTs with subcortical refine
• SPM New Segment

andreas.horn@charite.deOptimal targets in DBS
…COMPARABLE TO MANUAL PRECISION
Todd Herrington
(Harvard)
Siobhan Ewert
(Berlin)
Ewert et al., 2018 NeuroImage

6.3 mA5.3 mA4.8 mA
Multimodal Normalizations using ANTs
(x,y,z)
(x’,y’,z’)MNI space
Native space
T2
T2

6.3 mA4.8 mA
5.3 mA
(x,y,z)
(x’,y’,z’)
T2
T2
5.3 mA
(x,y,z)
(x’,y’,z’)
PD
PD
5.3 mA
(x,y,z)
(x’,y’,z’)
T1
T1

WHAT ABOUT WHITE MATTER?
http://dti-tk.sourceforge.net/

WHAT ABOUT WHITE MATTER?
FMRIB58 FA Template

5.3 mA
T2
T2
5.3 mA
PD
PD
T1
T1
5.3 mA
FA
FA

T2
T2
PD
PD
T1
T1
FA
FA PCA
Sequence X

andreas.horn@charite.de
A “NORMALIZATION TOOLBOX”?
/@andreashorn_ /@leaddbs

andreas.horn@charite.de
UPCOMING TOOL
/@andreashorn_ /@leaddbs

HANDS ON NORMALIZATIONS
– Hands on Session –
Multimodal
Normalization in Lead-
DBS

– Advanced concepts –

6.3 mA5.3 mA4.8 mA
The MAGeT-Brain approach
(x,y,z)
Native space

6.3 mA5.3 mA
(x,y,z)
Native space
Peer subject
(space)

6.3 mA5.3 mA
(x,y,z)
Native space
Peer subject 1 Peer subject 2

6.3 mA5.3 mA
(x,y,z)
robust averaging
Native space
Chakravarty, M. M., Steadman, P., van Eede, M. C., Calcott, R. D., Gu, V., Shaw, P., et al. (2012). Performing label-fusion-based segmentation using multiple automatically generated templates. Human Brain Mapping, 34(10), 2635–2654. http://doi.org/10.1002/hbm.22092

“MAGeT trades accuracy with computational time
by introducing redundancy”
– M. Chakravarty

6.3 mA5.3 mA
Validated for subcortical segmentations
Majority voting
STN
STNMNI space
Native space
Chakravarty, M. M., Steadman, P., van Eede, M. C., Calcott, R. D., Gu, V., Shaw, P., et al. (2012). Performing label-fusion-based segmentation using multiple automatically generated templates. Human Brain Mapping, 34(10), 2635–2654. http://doi.org/10.1002/hbm.22092

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