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Image mapping, registration and atlases. Derek Hill Imaging Sciences School of Medicine King’s College London . Overview. Definitions Applications Medical Research Diagnosis Therapy planning and guidance Drug discovery E-science issues Breakout group sub-headings.

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image mapping registration and atlases

Image mapping, registration and atlases

Derek Hill

Imaging Sciences

School of Medicine

King’s College London

overview
Overview
  • Definitions
  • Applications
    • Medical Research
    • Diagnosis
    • Therapy planning and guidance
    • Drug discovery
  • E-science issues
  • Breakout group sub-headings
definition of registration
Definition of registration
  • Determining the transformation,or mappingT 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
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
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
Registration examples
  • Multimodality
  • Image guided surgery/therapy
  • Detecting change over time
  • Identifying differences between groups
pet ct registration
PET-CT registration

MRC cyclotron unit

mr ct registration
MR-CT registration

CT

MR

CT bone overlaid on MR

After affine transformation

multi modal volume rendering ruff 1994
Multi-modal volume rendering (Ruff 1994)

Hill et al Radiology 191:447-454 1994

mri tumour surface overlaid in microscope
MRI tumour surface overlaid in microscope

Edwards et al IEEE TMI 19:1082-1093 2000

combining mri and 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

non rigid registration
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
slide16

Pre-contrast

Post-contrast

Subtraction

Post-contrast (rigid registration)

Post-contrast (affine registration)

Post-contrast (non-rigid registration)

Subtraction (rigid registration)

Subtraction (affine registration)

Subtraction (non-rigid registration)

mip rendering
MIP rendering

Non-rigid registration

Rigid registration

No registration

Rueckert et al IEEE TMI 18: 712-721 1999

intersubject comparisons
Intersubject comparisons

8 subject average

Rigid registration

Affine registration

Rueckert, from “Medical Image Registration” Hajnal, Hill, Hawkes (eds) CRC Press 2001

intersubject comparison
Intersubject comparison

8 subject average, non-rigid registration using 10mm grid

Rueckert, from “Medical Image Registration” Hajnal, Hill, Hawkes (eds) CRC Press 2001

slide21

contracting

expanding

6 month interval, baseline 2 years prior to symptoms

Fox et al Lancet 358:201-205 2001

slide22

contracting

expanding

29 month interval, symptoms appearing

Fox et al Lancet 358:201-205 2001

slide23

contracting

expanding

5 year interval, symptomatic for 2+ years

Fox et al Lancet 358:201-205 2001

intersubject comparison by voxel based morphometry provided by colin studolme ucsf
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

Subj 1

Subj 1

Subj 2

Subj 2

Subj N

Subj M

Group Comparison of Local Gray Matter Density

Age Matched Normal Group

Test Group: FTD or AD

Estimate Warp to Map Each Individual

Anatomy to Common Coordinates

Warp Tissue Density

Maps to Common Coordinates

Compare Tissue Density

In Common Coordinates

e science issues
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 sub headings
Possible Breakout group sub-headings
  • 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)
method optical tracking
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
Method – Registration Matrix Calculation
  • Overall registration transform is composed of a series of stages
  • Calibration + tracking during intervention
other data to register vectors and tensors
Other data to register: vectors and tensors

http://spl.bwh.harvard.edu:8000/pages/ppl/westin/papers/smr97/node4.html

Kilner et al Nature 404:759-761 2000

slide33

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
slide34

FLUID REGISTRATION

Non-linear, high-dimensional voxel-by-voxel registration.

Viscous fluid model preserves topology

Regional volume atrophy can be quantified from the match.

RIGID

FLUID