Image mapping, registration and
atlases
Derek Hill
Imaging Sciences
School of Medicine
King’s College London
Overview
• Definitions
• Applications
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–
–
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Medical Research
Diagnosis
Therapy planning and guidance
Drug discovery
• E-science issues
• Breakout group sub-headings
Definition of registration
• Determining the transformation,or mapping
T that relates positions in image A, to
positions in a second image (or physical
space) B.
• When registered, a position x in image A
and position T(x) in image B are the same
position in the object.
Correspondence
• Registration is a technique for aligning
images so that corresponding features can
be related.
• For image-to-physical space registration, we
determine correspondence between an
image and physical positions identified with
a 3D localiser
Atlases
• Can mean several things
– Single reference subject used to assist in analysis of
others
– Combination of multiple reference subjects
• intensity average
• Representation of variability
– Pre-labelled dataset used for image segmentation
• Registration required to:
– Create atlas if it is from multiple subjects
– Map atlas to patients or research subjects
Registration examples
•
•
•
•
Multimodality
Image guided surgery/therapy
Detecting change over time
Identifying differences between groups
PET-CT
registration
MRC cyclotron unit
MR-CT registration
CT
MR
CT bone overlaid on MR
After affine transformation
Multi-modal volume rendering
(Ruff 1994)
Hill et al Radiology 191:447-454 1994
Registration for image guided surgery
MRI tumour surface overlaid in
microscope
Edwards et al IEEE TMI 19:1082-1093 2000
2D + 3D registration for therapy
guidance
MRI + x-ray
Combining MRI and x-ray
 Case 2. Electrophysiology study and RF ablation.
 3D multiphase SSFP MR sequence
3 phases, 256x256x128, 1.13x1.13x1.0mm3,
TR=3.1ms, TE=1.6ms, =45
 Tracked biplane x-ray
LAO
AP
Registered MRI and catheters
Non-rigid registration
• The previous examples have all assumed
that the mapping has the degrees of freedom
of a rigid body
• Tissue deformation, image distortion and
intersubject variability mean more degrees
of freedom are needed to establish
corresondence
Pre-contrast
Post-contrast (rigid registration)
Subtraction (rigid registration)
Post-contrast
Subtraction
Post-contrast (affine registration) Post-contrast (non-rigid registration)
Subtraction (affine registration) Subtraction (non-rigid registration)
MIP rendering
No registration
Rigid registration
Non-rigid registration
Rueckert et al IEEE TMI 18: 712-721 1999
Intersubject comparisons
8 subject average
Rigid registration
Affine registration
Rueckert, from “Medical Image Registration” Hajnal, Hill, Hawkes (eds) CRC Press 2001
Intersubject comparison
8 subject average, non-rigid registration using 10mm grid
Rueckert, from “Medical Image Registration” Hajnal, Hill, Hawkes (eds) CRC Press 2001
Using deformation fields in
neuroscience research
Nature, 9 March 2000
contracting
6 month interval, baseline 2 years prior to symptoms
expanding
Fox et al Lancet 358:201-205 2001
contracting
29 month interval, symptoms appearing
expanding
Fox et al Lancet 358:201-205 2001
contracting
5 year interval, symptomatic for 2+ years
expanding
Fox et al Lancet 358:201-205 2001
Intersubject comparison by
Voxel Based Morphometry
(provided by Colin Studolme, UCSF)
PD MRI
Tissue
Segmentation
(from
PD+T2+T1)
Regional Tissue Label
Density Filter
Regional
Gray matter
Density
Group Comparison of
Local Gray Matter Density
Age Matched Normal Group
Subj 1
Test Group: FTD or AD
Estimate Warp to Map Each Individual
Anatomy to Common Coordinates
Subj 2
Subj 1
Subj 2
Warp Tissue Density
Maps to Common Coordinates
Subj N
Compare Tissue Density
In Common Coordinates
Subj M
E-science issues
• Algorithms run slowly: excellent candidates for
grid services
• Aggregation of data needed to answer medical
research and drug discovery questions
• Variety of ancillary metadata formats
• Rich and large intrinsic metadata.
• Collaborative working desirable for healthcare and
research
• Curation currently poor
Possible Breakout group subheadings
• How do we make image registration grid services
intraoperable?
– Do we need to devise an abstract model for these
services?
• How should we represent mappings?
– Do we need an ontology?
• Should we use grid-services for a major cross
validation of algorithms?
• How can, or should,atlases be shared?
• How could these services be used commercially
(eg: for drug discovery)
Results with model
Method – Optical Tracking
 Registration by optical tracking
 X-ray table & c-arm are tracked by Optotrak
 Sliding patient table is tracked by MR system
Method – Registration Matrix Calculation
 Overall registration transform is composed of a series
of stages
 Calibration + tracking during intervention
M1
Scanner Space
3D Image Space
X-ray Table Space
T
M2
R*P
X-ray C-arm
Space
M3
2D Image Space
Kilner et al Nature 404:759-761 2000
http://spl.bwh.harvard.edu:8000/pages/ppl/westin/papers/smr97/node4.html
Other data to register: vectors and
tensors
Cerebral atrophy: a macroscopic
concomitant of neurodegeneration
Alzheimer’s disease: plaques and tangles, dendritic,
neuronal, synaptic loss... and atrophy
– Advanced disease = widespread severe atrophy
– Early disease: overlap with normal aging
FLUID REGISTRATION
Non-linear, high-dimensional voxel-by-voxel
registration.
Viscous fluid model preserves topology
 . v(x)  .  v(x)  b(x)  0
T
T
Regional volume atrophy can be quantified from the
match.
RIGID
FLUID