rician noise removal in diffusion tensor mri saurav basu, tom fletcher, ross whitaker school of...
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Rician Noise Removal in
Diffusion Tensor MRI
Saurav Basu, Tom Fletcher, Ross WhitakerSchool of ComputingUniversity of Utah
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• DT-MRI plagued by low SNR
‣ Multiple scans needed to increase SNR
‣ Issues: long acquisition time, patient comfort system throughput
‣ Noise in DT-MRI adversely affects tensor measurements used in clinical studies
Why DT-MRI filtering?
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Rician noise in DT-MRI
•DW images are magnitudes of complex valued signals.
•If the real and imaginary components of the signal are assumed to have a Gaussian noise, the resulting magnitude image will have Rician distributed noise.
gaussian
magnitude
where is zero mean, stationary Gaussian noise with standard deviation
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Rician Noise
Unlike the normal distribution the pdf is not symmetric about the true signal value A
A signal is said to be corrupted with Rician noise if the pdf of the noisy signal has a Rice distribution
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p(x)
ARice Distribution
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How does Rician noise affect estimated tensors?
Tensor splitting gradient direction
Tensor aligned with gradient direction
We performed Monte Carlo simulations with two cases:
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Previous filtering approaches2 categorie
s
DWI spaceTensor Space
Anisotropic DiffusionParker(2000)
Constrained Variational approach Wang, Vemuri (2004)
Bayesian regularization using Gaussian markov random fields. Martin (2004)
Riemannian Space filtering Pennec (2004)
Very effective techniques, but do not explicitly handling Rician noise as part of the filtering process.
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• Based on maximum a posteriori (MAP) approach to image reconstruction
• In statistics MAP estimation is used to obtain a point estimate of an unobserved quantity based on empirical data
Rician Bias Correction Filter
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• Given an initial noisy image u0 we want to estimate the clean image u.
• We know that p(u0|u) has a Rician distribution.
• To estimate the clean value we want to maximize p(u|u0)
From Baye’s Rule
constant for a given noisy image u0
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posterior
noise model(likelihood )
prior
prior: what is pdf of the unobserved data (clean image) whichwe are trying to estimate?noise model (likelihood) : what is the conditional probability of the observed data( noisy image) , given a particular value of the unobserved data (clean image)? posterior: The probability of the unobserved data (clean image)given the observed data (noisy image)
maximizewith
gradient ascent
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Rician likelihood term
Using the Rician pdf for the noise model we get
rician likelihood term
Taking derivative w.r.t. u,
Rician attachment Term
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The Rician attachment term can be combined with any image prior.
We use a Gibbs prior with Perona Malik Energy functional.
Combining with the prior:
Gibb’s prior
Perona Malik energy
conductance
weighing factor
edge preserving smoothing prior
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Combining the Rician correction term with prior we get the update equation for the
filtered image
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Preliminary ResultsWe compared 4 different filtering
methods on both synthetic and real datasets
DWI SpaceTensor Space1.Anisotropic Diffusion
without Rician attachment
2. Rician Bias Correction filter
1.Anisotropic Diffusion in Euclidean space
2.Anisotropic Diffusion on the Riemannian manifold
Error Metrics:1.RMS error in tensor components2.Fractional Anisotropy3.Trace
•Parameters optimized for RMS error in tensor components.
•For both synthetic and real data we used 7 images for each slice (6 gradient directions + 1 baseline)
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Synthetic Data Results
• 10x10x4 volume of tensors
• 2 tensor orientations (along gradient and splitting the gradient directions)
• Synthetic rician noise
CleanNoisy
(SNR=15)
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Aniso DWIRician DWI
DWI Space Filters
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Euclidean Riemannian
Tensor Space Filters
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Real Data Results
Issue: No ground truth data available for DT-MRI !
How do we evaluate filtering performance quantitatively?
• we developed a method to estimate a ground truth data from repeated scans of the same object
• if {xi} is a set of intensities for the same voxel in N repeated scans we find the ML estimate of the true value A by maximizing the log likelihood function:
Solution:
p(x|A) is the Rician pdf
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• Generated ground truth from 5 scans• added known Rician noise (SNR=10,15,20)• compared errors as before
Clean Coronal Slice Noisy Coronal Slice(SNR=15)
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Aniso DWI Rician DWI
Both Aniso-DWI and Rician DWI gave very good results with Rician being marginally better
DWI Space
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Euclidean Tensor Riemannian Tensor
Tensor Space
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