an automatic method for change detection in serial dti-derived...
TRANSCRIPT
An automatic method for change detection in
serial DTI-derived scalar images
H. Boisgontier1,2, V. Noblet1, F. Heitz1,L. Rumbach2,3, J.-P. Armspach2
1LSIIT, UMR CNRS-ULP 7005, Strasbourg, France, 2LINC UMR CNRS ULP 7191 St b F
Work supported by2LINC, UMR CNRS-ULP 7191, Strasbourg, France,
3Service de Neurologie, CHU Minjoz, Besançon, France
MIAMS’08: MICCAI workshop on “Medical Image Analysis on Multiple Sclerosis”
ObjectiveObjective
1st scan 2nd scan
=> Detect significant changes between diffusion-weighted scalar images
MIAMS’08: MICCAI workshop on “Medical Image Analysis on Multiple Sclerosis”
General schemeGeneral scheme
Examination 1Detection map
... CorrectEddy- Compute
...
Compute
Examination 1
... Eddycurrent
distortion
ComputeTensor
...Compare :StatisticalChange
Computeindex
Change Detection
E i ti 2
... ...
Examination 2
Correct
... Computeindex
ComputeTensor
... RegisterEddy-current
distortion
MIAMS’08: MICCAI workshop on “Medical Image Analysis on Multiple Sclerosis”
Statistical change detectionStatistical change detection
We consider the Generalized Likelihood Ratio Test (GLRT):We consider the Generalized Likelihood Ratio Test (GLRT):Likelihood ratio under 2 hypothesis:
H0: there is no significant change between images I1 and I2.0 g g g 1 2
H1: there is a significant change between images I1 and I2.
I1 and I2 are considered as realizations of random variables drawn according to parametric probability density functions:parametric probability density functions:
• with the same parameters θ0 under assumption H0
• with two sets of different parameters θ1≠ θ2 under assumption H1
MIAMS’08: MICCAI workshop on “Medical Image Analysis on Multiple Sclerosis”
with two sets of different parameters θ1≠ θ2 under assumption H1
Bosc’s methodBosc s method
Bosc et al (Neuroimage vol 20 (2) pp 643 656 2003) use a similarBosc et al (Neuroimage, vol 20 (2), pp 643-656, 2003) use a similarframework for detecting MS lesion evolution in conventional multimodal MRIsequencesq
Hypothesis: • Intensities are modeled as a constant value µ in a window Ws of 3×3×3 voxelsµ s
• additive stationary Gaussian noise
MIAMS’08: MICCAI workshop on “Medical Image Analysis on Multiple Sclerosis”
Bosc’s methodBosc s method
However Bosc’s approach fails when coping with DTI derived scalar imagesHowever, Bosc s approach fails when coping with DTI-derived scalar imagesbecause of the non-stationarity of noise.
MDMD FAMD FA
MIAMS’08: MICCAI workshop on “Medical Image Analysis on Multiple Sclerosis”
Proposed approachProposed approach
We propose to account for the non stationarity of the noise by estimating aWe propose to account for the non stationarity of the noise by estimating avariance at each voxel:
Estimation of the variance is done using the method described inChang et al, Magnetic Resonance in Medicine, vol 57(1), pp 141 - 149, 2007
MIAMS’08: MICCAI workshop on “Medical Image Analysis on Multiple Sclerosis”
Variance estimation (1/2)Variance estimation (1/2)
Variance on the tensor elements is obtained as a by product of least squaresVariance on the tensor elements is obtained as a by-product of least squaresestimation:
with
MIAMS’08: MICCAI workshop on “Medical Image Analysis on Multiple Sclerosis”
Variance estimation (2/2)Variance estimation (2/2)
The covariance matrix is propagated to the eigenvalues of XThe covariance matrix is propagated to the eigenvalues of X
and then to FA or MDand then to FA or MD
MIAMS’08: MICCAI workshop on “Medical Image Analysis on Multiple Sclerosis”
GLRT: H1 hypothesis1 yp
1st1st scan
Tensor, index and varianceand variance estimation
2nd scan
MIAMS’08: MICCAI workshop on “Medical Image Analysis on Multiple Sclerosis”
GLRT: H0 hypothesis0 yp
ConcatenationConcatenation of the two scans
Tensor, index and variance estimation
MIAMS’08: MICCAI workshop on “Medical Image Analysis on Multiple Sclerosis”
Spatial informationSpatial information
ThresholdingMedian filtering
GLRT RGLRT Detection
MIAMS’08: MICCAI workshop on “Medical Image Analysis on Multiple Sclerosis”
ValidationValidation
Validation of such method is difficult due to the lack of ground truthtruth
Even for experts, it is difficult to detect changes in DT-images
We resort to simulation of MS lesion appearance
MIAMS’08: MICCAI workshop on “Medical Image Analysis on Multiple Sclerosis”
MS lesion simulationMS lesion simulation
Experimental studies have shown that demyelination leads to an increase of theExperimental studies have shown that demyelination leads to an increase of the radial diffusivity (D ) in DW-images.
[Harsan et al, Journal of neuroscience research, vol 83(3), pp 392-402, 2006]⊥
• Two scans of a healthy subject are considered• The two last eigenvalues (corresponding to the radial diffusion) of the second examination are increased in several regions of Interest to simulate lesion gapparitions
MIAMS’08: MICCAI workshop on “Medical Image Analysis on Multiple Sclerosis”
MS lesion simulationMS lesion simulation
MIAMS’08: MICCAI workshop on “Medical Image Analysis on Multiple Sclerosis”
Follow-up of MS patientsFollow up of MS patients
1st scan 2nd scan Proposedmethod
Standard method
MIAMS’08: MICCAI workshop on “Medical Image Analysis on Multiple Sclerosis”
Conclusion and perspectivesConclusion and perspectives
Further validation should be done on real cases
Automatic threshold selection: p-value estimation, multiple comparison problem
Extension for multimodal detection
Extension to tensor images and to other model of diffusiong
MIAMS’08: MICCAI workshop on “Medical Image Analysis on Multiple Sclerosis”