3D single and multimodal medical image registration using ...

3D single and multimodal medical imageregistration using robust voxel similarity measuresand statistically constrained deformable models

Christoforos NIKOU

Laboratoire des Sciences de l’Image de l’Informatiqueet de la Télédétection (LSIIT)

Institut de Physique Biologique (IPB) – Faculté de Médecine

Centre National de la Recherche Scientifique (CNRS)Université Louis Pasteur-Strasbourg I (ULP)

Research framework

• General framework– brain imaging– intra-subject– rigid registration– images

• Single modal (MR/MR,SPECT/SPECT)

• multimodal(MRI/SPECT)

Introduction

MRI SPECT

Registration shortcomings

• Robustness to:– non gaussian noise;– lesion evolution;– non overlaping informations or structures;– incomplete acquisitions.

Introduction

Example 1 : lesion evolution

Reference image Image to beregistered

Introduction

Example 2 : non overlaping structures

Introduction

Example 3 : incomplete acquisition

Introduction

Methods

• Robust similarity functions– reject outliers

• Deformable modeling– Statistical constraints through training

Introduction

Presentation outline

• State of the art• Robust similarity metric-based registration• Statistically constrained deformable model-

based registration• Conclusion

Medical image registration (1)

• Transformation– rigid– affine– projective– deformable

• Image primitives– non image-based methods

• stereostatic frame• markers

– image-based methods• deformable models• contours• surfaces• anatomical landmarks• voxels

State of the art


• Similarity measure– standard distances;– principal axes;– correlation;– histogram

• variance;• entropy.

• Image modality– single modal;– multimodal;– modality to model

(atlas).

State of the art


• Validation– precision

• blind evaluation• simulations

– robustness– CPU time– clinical routine application

State of the art

Robustness to outliers

• Single modal images– Median least squares [Alexander-96]– Sign changes [Herbin-89]

• Multimodal images– Mutual information [Collignon-94, Wells-96]

State of the art

Presentation outline

• State of the art• Robust similarity metric-based registration• Statistically constrained deformable model-

based registration• Conclusion

Rigid registration

I x I T x

E F I x I T x

t t t x y z s s sx y z x y zT

1 2

1 2

( ) ( ( ))

( ) ( ( ), ( ( )))

[ , , , � , � , �, , , ]

⇔

=

=

Θ

ΘΘ

Θ

Robust voxel similarity measures

Standard similarity measures (1)

• Quadratic error [Hajnal-95, Alpert-96]• Correlation [Van den Elsen-95]• Sign changes [Venot-84]

• Ratio uniformity [Woods-92]• Inter-image uniformity [Woods-93]• Mutual information [Wells-96]

Single modal images

Multiomodalimages


Standard similarity measures(2)

• Single modal images (quadratic error)

E I x I T xx

( ) [ ( ) ( ( ))]Θ Θ= −� 1 22


Inter-image uniformity (1)

• Fundamental hypothesis:– correspondance between

uniform regions

• Application:– partitioning of the reference

image to its grey levels– Projection of the

partitioning to the floatingimage

– variance minimization in theprojected regions


Inter-image uniformity(2)

• Multimodal images (inter-image uniformity)

E N

I T x

NI T x

g gg

G

gx I x g

g

gg x I x g

( ) ( )

( ) ( ( )) ( )

( ) ( ( ))

( )

( )

Θ Θ

Θ Θ

Θ

Θ

Θ

= ×

= −

=

=

=

=

�

�

�

σ

σ µ

µ

1

2

2

1

1

1


How realistic is the uniformity assumption?


Uniformity validation?

Ideal histogram Real histogram


Robust similarity measures (1)

• Single modal images (robust quadratic error)

� Θ−=Θx

CxTIxIE ))),(()(()( 21ρ


Robust similarity measures(2)

• Multimodal images (robust inter-image uniformity)

E N

I T x C

NI T x C

g gg

G

g gx I x g

gg x I x g

gg

( ) ~ ( )

~ ( ) ( ( ( )) ~ ( ), )

~ ( ) argmin ( ( ( )) , )

( )

( )

Θ Θ

Θ Θ

Θ

Θ

Θ

= ×

= −

= −

=

=

=

�

�

�

σ

σ ρ µ

µ ρ µµ

1

2

2

1

1

1


The Geman-McClure robust estimator

ρ function ψ function


Registration algorithm

• Multiresolusion strategy• Stochastic optimization

– Fast simulated annealing• Deterministic optimisation

– Iterated Conditionnal Modes (ICM)


Experimental results

• MRI phantom• simulation (noise)• blind evaluation (comparative protocol)• Clinical applications cliniques (epilepsy,

MS)


Approach Translation (vox) Rotation (deg)

LS 2,30 ± 1,75 4,71 ± 2,88

RLS 0,03 ± 0,07 0,41 ± 0,21

MRI/MRI : simulation

LS : robust least squaresRLS : robust least squares


MRI/SPECT : simulation

Approach Translation (vox) Rotation (deg)

IU 3,85 ± 5,59 8,33 ± 4,51

MI 1,41 ± 0,74 0,94 ± 1,58

RIU 0,82 ± 0,53 0,21 ± 0,48

IU : inter-image uniformityMI : mutual informationRIU : robust inter-image uniformity


2D MRI/MRI (1)

Reference image Image to beregistered


2D MRI/MRI(2)

LS RLS


3D MRI/MRI (1)

Before registration


3D MRI/MRI (2)

Least squares


3D MRI/MRI(3)

Robust leastsquares


3D MRI/SPECT (1)

Inter-image uniformity


3D MRI/SPECT(2)

Mutual information


3D MRI/SPECT(3)

Robust inter-imageuniformity


Blind evaluation (1)

• Vanderbilt University (Nashville, TN, USA)image database

• CT/MRI et PET/MRI• Registration error computed on VOIs



• Several MRI modalities(T1, T2, PD …)

• MRI/PET registrationwithout removing nonbrain structures



Erreur médiane (mm) Erreur maximale (mm)

Type RIU Others RIU Others MRI/CT 1,6 - 2,9 3,3 - 4,0 4,3 - 6,2 10,7 - 12,2

MRI/PET 1,9 - 4,3 3,3 - 3,6 5,2 - 9,0 7,4 - 9,7



Rang de classement MRI/CT

(15 groups) MRI/PET

(13 groups)

MRI modality Méd Max Méd Max

T1 6 3 12 4 DP 7 4 5 1 T2 3 4 4 1

T1 rect. 7 5 13 11 DP rect. 7 5 1 1 T2 rect. 6 4 5 2


CPU time (3D)CPU time (3D)

HP C360 (image 1283)

LS RLS IU MI RIU

5 mn 7 mn 10 mn 10 mn 20 mn

IU : inter-image uniformityMI : mutual informationRIU : robust inter-image uniformity

LS : least squaresRLS : robust least squares


Clinical application

Jan 1998 – May 1999 : 108 cases treatedictal SPECT

Inter-ictal SPECT

superimpoposition

registration

registration


Partial conclusion

• fully automatic method• redundant information is considered• local minima is not a problem• brain extraction is overcome• blind evaluation• clinical routine• other applications


Visible / IR Registration


Visible / IR Registration


Image uniformity Robust image uniformity

Perspectives

• connected components approach inRIU

• robust estimator parametersadaptation to image modality

• robust mutual information

Perspectives

Date post:	17-Apr-2022
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3D single and multimodal medical image registration using ...

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