computerized craniofacial reconstruction using ct-derived implicit surface representations d....
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Computerized Craniofacial Reconstruction using CT-derived Implicit Surface Representations
D. Vandermeulen, P. Claes, D. Loeckx, P. SuetensMedical Image Computing (ESAT-Radiology)
S. De Greef, G. WillemsCentre of Forensic Odontology
K.U.Leuven, Faculties of Medicine and Engineering
“Louvre” Seminar March 29 2006
Cranio-facial reconstruction: What?
Manual Craniofacial Reconstruction
• subjective/ artistic talentLots of expertise, both explicit
(documented) and implicit
• Errors by inconsistencies in application
Misalignment of LM on the skull
• time consuming• Only few reconstructions
possible
TAYLOR, K. T. 2001. Forensic Art and Illustration. CRC Press LLC.
Introduction
•Manual
•Computer
•Bias
•Dense LM
Data
Method
Results
Discussion
Conclusion
Computer-based reconstructions
• Small number of tissue thickness measurement landmarks (LMs)
• Independent soft tissue thickness and facial surfaces
• Template-bias: face interpolation with a single facial template (either generic or gender/ancestry/age matched)
http://www.cs.ubc.ca/nest/imager/contributions/katrinaa/recon.html
Introduction
•Manual
•Computer
•Bias
•Dense LM
Data
Method
Results
Discussion
Conclusion
Removing template bias
• Using statistical facial templates to remove bias (Claes et al.)
• Using combined statistical model of facial template and soft tissue thicknesses (Claes et al.)
Fitting Algorithm
. . .
Database
Introduction
•Manual
•Computer
•Bias
•Dense LM
Data
Method
Results
Discussion
Conclusion
Surface and sparse Landmark-based Craniofacial Reconstruction
Dense Landmark model
Warp W
Warp W
target
Reference skull
Reference skin Warped skin
Warped skull
Introduction
•Manual
•Computer
•Bias
•Dense LM
Data
Method
Results
Discussion
Conclusion
Statistical dense LM model
…..
…..
Template database
target
Introduction
•Manual
•Computer
•Bias
•Dense LM
Data
Method
Results
Discussion
Conclusion
CT scan+ Simultaneous visualisation hard & soft tissues- Using ionising radiation
• PM-CT can be used as golden standard?dehydration!
• in-vivo CT on control population? only by lowering radiation dose!
Volumetric Template Data : CTIntroduction
Data
•MR
•CT
•LD-CT
Method
Results
Discussion
Conclusion
Volumetric Template Data : MRI
MRI scan+ Excellent visualisation of soft tissues
- Bone details lost
Introduction
Data
•MR
•CT
•LD-CT
Method
Results
Discussion
Conclusion
Low-Dose CT
• Decrease radiation dose to acceptable (?) level while retaining sufficient quality for diagnosis, therapy or image-based measurements
• Starting from clinical multi-slice spiral CT protocol (Siemens Sensation 16 (Erlangen, Germany)) by lowering the X-ray source current and voltage and increasing the pitch.
• Measured effective radiation dose: 0.18 mSv i.o. 1.5 mSv
• Measuring image quality: thickness differences smaller than a voxel (<0.5 mm).
Introduction
Data
•MR
•CT
•LD-CT
Method
Results
Discussion
Conclusion
CT image preprocessing
• Acquisition and conversion from DICOM to Analyze
• Noise reduction using edge preserving filtering
• Metal artifact removal
• Segmentation of skin and bone surfaces by (hysteresis) thresholding and mathematical morphology
• Implicit Surface representation by signed Distance Transformation
Introduction
Data
Method
Results
Discussion
Conclusion
Implicit Functions for Object Representations and
Transformations:a tentative tutorial
Dirk Vandermeulen
Medical Image Computing
Seminar January 17, 2003
3-D example
Copyright FarField Technology Ltd.
… Shape Morphing
Alternative: interpolate the smooth implicit functions!Example: f(x) = signed distanceG. Turk and J. F. O’Brien, Shape Transformation using Variational Implicit Functions, Siggraph 99
f1(x)>0 f2(x)>0t.f1(x)+(1-t).f2(x)>0
0t1
Shape Transformation Using Variational Implicit Functions, Greg Turk James F. O’Brien, ACM Siggraph99
Implicit Surface Representation
Signed Distance Transform (sDT)Introduction
Data
Method
•MAR
•sDT
•Warping
•Reconstruction
Results
Discussion
Conclusion
Craniofacial reconstruction: method
Warp W
target
Reference skull Warped skull
Warp W
Reference skin Warped skin
Introduction
Data
Method
•MAR
•sDT
•Warping
•Reconstruction
Results
Discussion
Conclusion
Warping method (D.Loeckx et al.)
• Represent warping by tensor-product B-Spline Free Form Deformation (FFD)Introduction
Data
Method
•MAR
•sDT
•Warping
•Reconstruction
Results
Discussion
Conclusion
Warping method (D.Loeckx et al.)
Regularization of FFD by Volume-preserving penalty
Example: template skull to target skull warping
Example: extrapolation to template skin warping
=?
=?
Example: extrapolation to template skin warping
Skin Surface Reconstruction
• Construct (weighted) average of warped skin sDT’s
Introduction
Data
Method
•MAR
•sDT
•Warping
•Reconstruction
Results
Discussion
Conclusion
Example
Validation
• Given only small-sized database (N=20), how to separate into test and validation subsets?
• N-fold Cross-Validation or Leave-one-out CV:– For i=1:NrSubjects
• Reconstruct Subject i from all other subjects in Database• Compare Result to ground truth of i• Evaluate Error
}0/))(ˆ(|{ˆ N
iH
it NxDxS
Qualitative Validation
• Qualitative: 3D reconstructions vs subset of database (face pool comparisons)
Introduction
Data
Method
Results
Discussion
Conclusion
Face pool comparisons
Quantitative Validation I
• Calculate distances between reconstructed and ground truth surface
Introduction
Data
Method
Results
Discussion
Conclusion
|d| = 1.6 ± 1.2 mm
Quantitative Validation I
• Gather error statistics over all subjects• Define M ( 500) test points on a reference head surface
• Find corresponding points on all surfaces by non-rigid surface-based warping (Claes et al.)
• Evaluate error (distance from reconstructed surface to real surface) at test points: mean, std, etc…
Quantitative Validation I: results
Average (1.9mm) Std (1.7mm)
Quantitative Validation II
• Not reconstruction accuracy but recognition accuracy• Based on similarity measure between surfaces or, in this case,
M reference points pi (same as before) on the surfaces S
• Use coordinate-system free representation (invariant to translation/rotation) of surface SEuclidean Distance Matrix ES: ES (i,j) = ||pi-pj||
Quantitative Validation II
• How to measure similarity between two surfaces S1 and S2?
• Compare ES1 to ES2: e.g. – Sum of Squared Differences:
||ES1-ES2|| = i,j>i ( ES1 (i,j) – ES2 (i,j) )2/L >= 0
– (normalized) Cross-Correlation
• Invariance to scaling/size by normalizing EDM with size factor, e.g. geometric mean
NS (i,j) = ES (i,j) / (ES), with (ES) = (ij ES (i,j) )1/L
Quantitative Validation II• Generate Classification Rank Matrix
1 7 20 11 5 4 16 13 3 12 6 15 19 17 8 9 2 18 14 102 6 15 12 19 16 3 5 17 7 11 4 8 20 1 13 18 10 14 93 5 4 16 11 12 7 6 13 15 17 19 2 20 8 1 14 9 10 1813 4 3 14 9 8 16 12 17 5 10 1 11 7 2 15 6 20 19 187 3 11 5 20 1 16 4 6 19 2 12 15 13 17 18 8 9 14 106 15 19 11 2 7 16 12 3 20 5 1 17 13 4 18 8 14 10 95 1 13 6 9 3 14 7 4 16 2 8 11 17 12 20 10 15 19 188 10 14 4 13 17 12 3 9 7 16 15 6 2 1 5 11 20 19 189 7 4 14 8 13 10 3 1 17 5 12 6 16 11 20 15 2 19 1810 8 14 4 13 17 12 7 9 3 16 15 6 1 2 11 5 20 19 1811 5 6 3 20 1 16 19 12 4 15 13 2 7 17 18 8 14 10 912 15 3 16 19 6 2 17 4 20 11 8 5 13 1 7 10 18 14 94 13 14 7 17 8 3 16 10 1 11 15 12 6 9 5 20 2 19 1814 13 8 4 10 9 7 17 3 16 12 1 6 15 11 5 2 20 19 186 12 2 3 15 20 11 16 13 5 17 4 1 8 19 7 10 18 14 916 3 19 12 6 11 4 5 13 17 2 1 15 7 20 8 10 14 18 917 13 12 3 8 4 16 10 2 14 15 6 19 7 11 5 1 20 9 1818 6 19 5 12 11 16 1 2 20 15 3 17 4 13 7 8 10 14 96 16 12 2 19 3 5 11 17 1 15 20 18 4 13 7 8 10 14 920 1 11 5 6 12 3 15 16 7 17 4 19 13 2 18 8 14 10 9
• Correct Rank 1 Classification: 14/20 (13/20)• Correct Rank <= 2 Classification: 16/20 (14/20)
Algorithmic improvements
• Metal Artifact Reduction: using morphology (opening) only in slices ~ artifacts
• Non-rigid registration– fine tuning of regularization parameters to improve skull-
skull matching and extrapolation quality– Alternative deformation models
• Statistical Deformation Models (based on sDT or original CT of database)
– Combination with local models (e.g. nose (De Greef))– Combination with point/surface model (Claes)
• Weighted averaging of sDT– Weights ~ skull overlap– Weights ~ class similarity (gender, age, BMI)– Spatially varying weights
• Statistical Modes of Variation
Introduction
Data
Method
Results
Discussion
Conclusion
Metal ArtifactsIntroduction
Data
Method
Results
Discussion
Conclusion
Metal Artifact RemovalIntroduction
Data
Method
•MAR
•sDT
•Warping
•Reconstruction
Results
Discussion
Conclusion
Algorithmic improvements
• Metal Artifact Reduction: using morphology (opening) only in slices ~ artifacts
• Non-rigid registration– fine tuning of regularization parameters to improve skull-skull
matching and extrapolation quality, again using leave-one-out cross-validation
– Alternative deformation models• Statistical Deformation Models (based on sDT or original CT of
database)
– Combination with local models (e.g. nose (De Greef))– Combination with point/surface model (Claes)
• Weighted averaging of sDT– Weights ~ skull overlap– Weights ~ class similarity (gender, age, BMI)– Spatially varying weights
• Statistical Modes of Variation
Introduction
Data
Method
Results
Discussion
Conclusion
Algorithmic improvements
• Metal Artifact Reduction: using morphology (opening) only in slices ~ artifacts
• Non-rigid registration– fine tuning of regularization parameters to improve skull-
skull matching and extrapolation quality– Alternative deformation models
• Statistical Deformation Models (based on sDT or original CT of database)
– Combination with local models (e.g. nose (De Greef))– Combination with point/surface model (Claes)
• Weighted averaging of sDT– Weights ~ skull overlap– Weights ~ class similarity (gender, age, BMI)– Spatially varying weights
• Statistical Modes of Variation
Introduction
Data
Method
Results
Discussion
Conclusion
Attribute-modulated reconstruction
• All reconstructions so far made with all data in the database, irrespective of gender, age and BMI!
sDT = i wi sDTi , wi = 1/N
Attribute-weighted interpolation• How to bias reconstruction to specific attribute values? (k-)Nearest Neighbour?• Problem: small database, hence weak statistical model (PCA, PLS, …)• Solution(?): “Shape by Example”
– Given attribute values (gender, age, BMI) pi and q of template subjects i and target subject, resp.– Find weight wi(q) to apply to sDTi in weighted average
sDT = I wi(q) sDTi , wi(pj) ij
– Determined using RBF smoothest approximation
Example
Example
All Females only Males only
AWI Females+BMI Ground truth
Algorithmic improvements
• Metal Artifact Reduction: using morphology (opening) only in slices ~ artifacts
• Non-rigid registration– fine tuning of regularization parameters to improve skull-
skull matching and extrapolation quality– Alternative deformation models
• Statistical Deformation Models (based on sDT or original CT of database)
• Weighted averaging of sDT– Weights ~ skull overlap– Weights ~ class similarity (gender, age, BMI)– Spatially varying weights
• Statistical Modes of Variation
Introduction
Data
Method
Results
Discussion
Conclusion
Conclusion
• “Proof of concept” of volumetric cranio-facial reconstruction
• Validation procedure required on a representative database
• Metal Artifact Reduction is required • Missing Data problem using deformation model• Comments?
Introduction
Data
Method
Results
Discussion
Conclusion