Registration motivation

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Registration motivation

Mads Nielsen

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270312 Mads Nielsen

Statistical Network

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Registration in Medical Imaging

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Find correspondences – intrapatient:

inhale phase to exhale phase

Castillo, R., Castillo, E., Guerra, R., Johnson, V.E., McPhail, T., Garg, A.K., Guerrero, T. 2009 “A framework for

evaluation of deformable image registration spatial accuracy using large landmark point sets” Phys Med Biol 54 1849-1870.

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270312 Mads Nielsen Statistical Network

Monitoring subtle changes Local is better!

Problem: which pixel goes where?

Baseline Follow-up

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270312 Mads Nielsen Statistical Network

Optimizatio Transfor n

m

F ix e d Im a g e

M o v in g Im a g e

Cost Functio

n

Transform Coefficients

Incorporate model of density change

Image registration

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270312 Mads Nielsen Statistical Network

Disease monitoring using image registration

Possible to indicate which locations change Consistent in time

Local changes predict decline better

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270312 Mads Nielsen

Statistical Network

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Registration in Medical Imaging

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Find correspondences – intrapatient:

Marcus, DS, Fotenos, AF, Csernansky, JG, Morris, JC, Buckner, RL, 2009. Open Access Series of Imaging Studies

(OASIS): Longitudinal MRI Data in Nondemented and Demented Older Adults. Journal of Cognitive Neuroscience, in press.

disease progression

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270312 Mads Nielsen Statistical Network

Readings: Atrophy Assessment

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Step 1: Whole Brain Segmentation on baseline

Step 2: Un-biased Multi-scale Nonlinear Deformation from Baseline to Follow-up

Step 3: Estimate the volume change per voxel from the deformation field

Step 4: Estimate the structure change by

summing up values in Step 3

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270312 Mads Nielsen Statistical Network

Readings: Atrophy Accuracy Test

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Group Hippocampus baseline volume FreeSurfer (mm³)

Cross- Sectional FreeSurfer (%)

Longitudinal

FreeSurfer (%) Hippocampus baseline volume

Synarc (mm³)

Cross- Sectional Synarc (%)

Registration Based(%)

Normal

(N = 27) 3410 ± 329 (L)

3430 ± 416 (R) -0.9 ± 2.6

0.9 ± 6.6 -0.8 ± 1.4

-0.6 ± 2.4 2333 ± 330 (L)

2268 ± 299 (R) -1.9 ± 2.0 -1.4 ± 2.9

-0.8 ± 2.0 -0.6 ± 1.9

MCI

(N = 37) 2992 ± 281(L)

3070 ± 420(R) -0.9 ± 2.9

-0.5 ± 7.0 -1.2 ± 1.5

-1.4 ± 3.0 2081 ± 312 (L)

2075 ± 363 (R) -2.5 ± 3.1 -3.1 ± 3.3

-1.1 ± 1.2 -2.0 ± 2.0

AD (N = 57)

2542 ± 349(L)

2728 ± 621(R) -4.3 ± 3.1

-4.0 ± 7.1 -4.2 ± 2.4

-4.4 ± 4.1 1787 ± 482 (L)

1773 ± 484 (R) -4.0 ± 4.4 -5.2 ± 4.3

-4.2 ± 1.6 -4.4 ± 2.4

101 Subjects From ADNI. Changes from BLM12

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270312 Mads Nielsen

Statistical Network

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Registration at Large and Small Scale

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Inter-patient variation at large scale Atrophy at

smaller scale

Data from: Marcus, DS, Fotenos, AF, Csernansky, JG, Morris, JC, Buckner, RL, 2009.

Open Access Series of Imaging Studies (OASIS):

“Longitudinal MRI Data in Nondemented and Demented Older Adults.“

Journal of Cognitive Neuroscience, in press.

disease progression

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270312 Mads Nielsen

Statistical Network

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LDDMM/LDDKBM registration

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Large Deformation Diffeomorphic Metric Mapping

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Domain , deformations

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Find minimizing

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regularization/smoothness term

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matching term Images

Ω ⊆ℝ ^d ϕ∈ G _V , ϕ : Ω → Ω ϕ

E (ϕ)= E ₁ ( ϕ)+ U (ϕ)

E ₁ (ϕ)

U (ϕ)= U (ϕ . I _m , I _f )

I _m ^I _f

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270312 Mads Nielsen Statistical Network

in LDDMM, regularization is the length of minimal paths

If Sobolev-norm <Lv,v>

on v, then diffeomorphism L is the momentum

Operator: Lv = a

V _t = ∫K(:,x)a _t (x)dx

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Registration motivation