3d single and multimodal medical image registration using
TRANSCRIPT
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
Medical image registration (2)
• Similarity measure– standard distances;– principal axes;– correlation;– histogram
• variance;• entropy.
• Image modality– single modal;– multimodal;– modality to model
(atlas).
State of the art
Medical image registration (3)
• 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
Robust voxel similarity measures
Standard similarity measures(2)
• Single modal images (quadratic error)
E I x I T xx
( ) [ ( ) ( ( ))]Θ Θ= −� 1 22
Robust voxel similarity measures
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
Robust voxel similarity measures
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
Robust voxel similarity measures
How realistic is the uniformity assumption?
Robust voxel similarity measures
Uniformity validation?
Ideal histogram Real histogram
Robust voxel similarity measures
Robust similarity measures (1)
• Single modal images (robust quadratic error)
� Θ−=Θx
CxTIxIE ))),(()(()( 21ρ
Robust voxel similarity measures
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
Robust voxel similarity measures
The Geman-McClure robust estimator
ρ function ψ function
Robust voxel similarity measures
Registration algorithm
• Multiresolusion strategy• Stochastic optimization
– Fast simulated annealing• Deterministic optimisation
– Iterated Conditionnal Modes (ICM)
Robust voxel similarity measures
Experimental results
• MRI phantom• simulation (noise)• blind evaluation (comparative protocol)• Clinical applications cliniques (epilepsy,
MS)
Robust voxel similarity measures
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
Robust voxel similarity measures
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
Robust voxel similarity measures
2D MRI/MRI (1)
Reference image Image to beregistered
Robust voxel similarity measures
2D MRI/MRI(2)
LS RLS
Robust voxel similarity measures
3D MRI/MRI (1)
Before registration
Robust voxel similarity measures
3D MRI/MRI (2)
Least squares
Robust voxel similarity measures
3D MRI/MRI(3)
Robust leastsquares
Robust voxel similarity measures
3D MRI/SPECT (1)
Inter-image uniformity
Robust voxel similarity measures
3D MRI/SPECT(2)
Mutual information
Robust voxel similarity measures
3D MRI/SPECT(3)
Robust inter-imageuniformity
Robust voxel similarity measures
Blind evaluation (1)
• Vanderbilt University (Nashville, TN, USA)image database
• CT/MRI et PET/MRI• Registration error computed on VOIs
Robust voxel similarity measures
Blind evaluation (2)
• Several MRI modalities(T1, T2, PD …)
• MRI/PET registrationwithout removing nonbrain structures
Robust voxel similarity measures
Blind evaluation (3)
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
Robust voxel similarity measures
Blind evaluation (4)
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
Robust voxel similarity measures
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
Robust voxel similarity measures
Clinical application
Jan 1998 – May 1999 : 108 cases treatedictal SPECT
Inter-ictal SPECT
superimpoposition
registration
registration
Robust voxel similarity measures
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
Robust voxel similarity measures
Visible / IR Registration
Robust voxel similarity measures
Visible / IR Registration
Robust voxel similarity measures
Image uniformity Robust image uniformity
Perspectives
• connected components approach inRIU
• robust estimator parametersadaptation to image modality
• robust mutual information
Perspectives