bayesian estimation of acoustic aberrations in high intensity ......bayesian estimation of acoustic...
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Bayesian estimation of acoustic aberrations in highintensity focused ultrasound treatment
Bamdad Hosseini 1, Charles Mougenot 2, Samuel Pichardo 3 , ElodieConstanciel 4, James M. Drake 4, John M. Stockie 1
1Simon Fraser University
2Philips Healthcare
3Thunder Bay Regional Research Institute
4Hospital for Sick Children
June 28, 2016
www.sfu.ca/~bhossein
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What is HIFU?
High intensity focused ultrasound (HIFU).
A focused beam of acoustic waves converging in a small volume.
The generated heat ablates diseased tissue.
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What are the challenges?
Clinical success in treatment of prostate cancer, liver tumours, uterinefibroids, etc.
Treatment of brain tumours remains a challenge.
Strong aberrations due to skull bone → defocused beam.
Estimate aberrations → compensate phase shift → refocus.
Phas
e sh
ift
(deg
)
−20
0
20
Att
enuat
ion (
%)
90
100
110
−40
−20
0
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An approximate forward model
The pressure field due to a single piezoelectric element:
p(x) = p0 exp
(i
[ωt+
ω
c0|x|
])× µ µ = ζ exp(iφt).
p0 signal amplitude.
ω frequency.
c0 speed of sound.
µ aberration due to tissue
ζ is attenuation
φ phase shift
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Energy based measurements
Assemble in to a linearforward map:
p = FZa
F free field matrix(Green’s function).
Z input phase andamplitude (experimentaldesign).
a aberration due totissue.
Measure the amplitudeof pressure
d = diag(p)p∗.
Phas
e des
ign (
deg
)
0
10
20
30
Am
pli
tude
des
ign (
%)
0
50
100
Dat
a
0
2
4
0
10
20
30
0
50
100
0
2
4
0
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The inverse problem
Estimate the vector of aberrations a given the data d and matrices Fand Z.
Use Bayes’ rule1:π(a|d) ∝ π(d|a)π0(a).
π(d|a) likelihood.
π0(a) prior.
π(a|d) posterior.
1J. Kaipio and E. Somersalo. Statistical and Computational Inverse Problems.Springer Science and Business Media, New York, 2005.
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The likelihood
π(a|d) ∝ π(d|a)π0(a).
Additive noise model
G(a) := diag(FZa)(FZa)∗ d = G(a) + εεε, εεε ∼ N (0,ΣΣΣ).
Likelihood:
π(d|a) ∝ exp
(−1
2
∥∥∥ΣΣΣ−1/2(d− G(a))∥∥∥22
)
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Choosing the prior π0
π(a|d) ∝ π(d|a)π0(a).
Ph
ase
shif
t (d
eg)
−20
0
20
Att
enu
atio
n (
%)
90
100
110
−1 −0.5 0 0.5 1−1
−0.5
0
0.5
1
x
y
γ =
1
0.2
0.4
−0.4
−0.2
0.2
0.4
−0.6
−0.4
−0.2
γ =
0.1
−0.5
0
0.5
1
1.5
−1.5
−1
−0.5
0
0
0.5
1
1.5
−1.5
−1
−0.5
γ =
0.0
1
−4
−2
0
2
−4
−2
0
2
4
−4
−2
0
2
4
−4
−2
0
2
4
Measured aberrations hint at an underlying continuous field.
Construct π0(a) by pointwise evaluation of a Gaussian random field:
N (0, (I − γ∆)−2)
(I − γ∆)−2 biharmonic operator with Neumann boundary condition.
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Sampling from π0
Pick σ > 0.
Discretize (I −∆)−2.
Sampleu ∼ N (0, (I − γ∆)−2), α1 ∼ N(0, σ)
v ∼ N (0, (I − γ∆)−2), α2 ∼ N(0, σ)
Set a = diag(α21u) exp(iα2
2v).
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Sampling from the posterior π(a|d)
Forward problem is nonlinear → posterior π(a|d) is not Gaussian.
Markov Chain Monte Carlo.
Requires sampling from the prior π0 and evaluating likelihood π(d|a).
π(d|a) ∝ exp
(−1
2
∥∥∥ΣΣΣ−1/2(d− G(a))∥∥∥22
).
Differentiable in real arguments, not differentiable in the complexarguments.
MALA + Random walk block sampler2 (at every step):
(1) Fix v and α2 and sample u and α1 using Metropolis adjusted Langevinalgorithm (uses gradient information).
(2) Fix u and α1 and sample v and α2 using preconditionedCrank-Nicolson random walk algorithm.
2S. L. Cotter et al. “MCMC methods for functions: modifying old algorithms tomake them faster”. In: Statistical Science 28.3 (2013), pp. 424–446.
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Synthetic experiment
Philips Sonalleve device with 256 piezoelectric elements.512 unknowns.Discretize the continuous field on a 8× 8 mesh (64 dimensionalparameter space).16 sonication tests.19× 19 voxel MRI window for each sonication test.SNR = 5.3× 105 burn-in + 5× 105 steps of MCMC.
Phas
e des
ign (
deg
)
0
10
20
30
Am
pli
tude
des
ign (
%)
0
50
100
Dat
a
0
2
4
0
10
20
30
0
50
100
0
2
4
0
10
20
30
0
50
100
0
2
4
0
10
20
30
0
50
100
0
2
4
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Synthetic experiment
Phase Target (deg)
−60
−40
−20
0
Attenuation Target (%)
70
80
90
100
Phase PM (deg)
−60
−40
−20
0
Attenuation PM (%)
60
80
100
Phase error (deg)
5
10
15
20
Attenuation error (%)
10
20
30
40
Phase std (deg)
2
4
6
8
10
Attenuation std (%)
6
8
10
12
14
3 4 5 6 7 8
x 105
8.63
8.632
8.634
8.636
8.638
iteration
Φ Φ := −12
∥∥ΣΣΣ−1/2(d− G(a))∥∥22
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Synthetic experiment
Phase Target (deg)
−60
−40
−20
0
Attenuation Target (%)
70
80
90
100
Phase PM (deg)
−60
−40
−20
0
Attenuation PM (%)
60
80
100
Phase error (deg)
5
10
15
20
Attenuation error (%)
10
20
30
40
Phase std (deg)
2
4
6
8
10
Attenuation std (%)
6
8
10
12
14
0 5 10 15 20−0.5
−0.45
−0.4
−0.35
−0.3
−0.25
α1
PM = −0.33542std = 0.023736
0 5 10 15 200.55
0.6
0.65
0.7
0.75
α2
Density
PM = 0.62485std = 0.022769
PM
pre
dic
tio
n
Dat
a
2
4
6
0
2
4
6
0.5
1
1.5
0
0.5
1
1.5
0.2
0.4
0.6
0.8
1
1.2
1.4
0
0.5
1
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Synthetic experiment
Phase Target (deg)
−60
−40
−20
0
Attenuation Target (%)
70
80
90
100
Phase PM (deg)
−60
−40
−20
0
Attenuation PM (%)
60
80
100
Phase error (deg)
5
10
15
20
Attenuation error (%)
10
20
30
40
Phase std (deg)
2
4
6
8
10
Attenuation std (%)
6
8
10
12
14
Ph
ase
(deg
)
−40
−20
0
Att
enu
atio
n (
%)
80
90
100
−40
−20
0
60
80
100
−40
−20
0
70
80
90
100
−60
−40
−20
0
70
80
90
100
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Physical experiment
The same Philips Sonalleve device.
Artificial aberrator.
Discretize the Gaussian field on a 8× 8 grid.
32 sonication tests.
7× 7 voxel MRI window for each test.
3× 105 burn-in + 5× 105 steps of MCMC.
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Physical experiment
Phase Target (deg)
−60
−40
−20
0
Attenuation Target (%)
70
80
90
100
Phase PM (deg)
−100
−50
0
Attenuation PM (%)
99.992
99.994
99.996
99.998
100
Phase error (deg)
10
20
30
40
Attenuation error (%)
10
20
30
Phase std (deg)
2
4
6
8
10
12
Attenuation std (%)
0.999
0.9995
1
0 200 400 600 800−2
−1
0
1
2x 10
−3
α1 std = 0.0005178
PM = −1.2685e−06
std = 0.0005178
0 2 4 6 80.7
0.8
0.9
1
1.1
α2
Density
PM = 0.89489
std = 0.047994
PM
pre
dic
tio
n
20
40
60
80
Dat
a
0
20
40
60
5
10
15
0
5
10
5
10
15
20
0
10
20
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Physical experiment
Phase Target (deg)
−60
−40
−20
0
Attenuation Target (%)
70
80
90
100
Phase PM (deg)
−100
−50
0
Attenuation PM (%)
99.992
99.994
99.996
99.998
100
Phase error (deg)
10
20
30
40
Attenuation error (%)
10
20
30
Phase std (deg)
2
4
6
8
10
12
Attenuation std (%)
0.999
0.9995
1
Phas
e (d
eg)
−80
−60
−40
−20
Att
enuat
ion (
%)
99.99
99.995
100
−80
−60
−40
−20
99.99
99.995
100
−80
−60
−40
−20
99.99
100
−80
−60
−40
−20
99.9995
100
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Selling points
For the practitioner:
The “state of the art”3 uses 256 sonication tests as compared to 32tests used here.
Patient spends less time in the MRI machine.
Save energy.
flexible design of experiments (choice of matrix Z).
For the mathematician:
Well-posed inverse problem.
Estimate uncertainty.
Compute in 2-4 hours on a laptop.
Experimental design.
3E. Herbert et al. “Energy-based adaptive focusing of waves: application tononinvasive aberration correction of ultrasonic wavefields”. In: IEEE Transactions onUltrasonics, Ferroelectrics and Frequency Control 56.11 (2009), pp. 2388–2399.
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Challenges and future directions
Challenges:
Large discrepancy between the model and physical process
Calibration of F.
Better data.
Regularizing the likelihood.
Better sampling algorithm.
Future direction:
Phase retrieval techniques4.
Matrix completion to estimate the free-field F.
Design of experiments.
4E. J. Candes et al. “Phase retrieval via matrix completion”. In: SIAM Review 57.2(2015), pp. 225–251.
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Acknowledgement
Fields institute and the organizers of Fields-Mprime IndustrialProblem Solving Workshop, August 2014.
NSERC
Brain Canada Multi-Investigator Research Initiative
Focused Ultrasound Foundation
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References
Candes, E. J. et al. “Phase retrieval via matrix completion”. In: SIAM Review57.2 (2015), pp. 225–251.
Cotter, S. L. et al. “MCMC methods for functions: modifying old algorithms tomake them faster”. In: Statistical Science 28.3 (2013), pp. 424–446.
Herbert, E. et al. “Energy-based adaptive focusing of waves: application tononinvasive aberration correction of ultrasonic wavefields”. In: IEEETransactions on Ultrasonics, Ferroelectrics and Frequency Control 56.11(2009), pp. 2388–2399.
Hosseini, B. et al. “A Bayesian approach for energy-based estimation of acousticaberrations in high intensity focused ultrasound treatment”. In: arXiv preprintarXiv:1602.08080 (2016).
Kaipio, J. and E. Somersalo. Statistical and Computational Inverse Problems.Springer Science and Business Media, New York, 2005.
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MCMC performance
3 4 5 6 7 8
x 105
8.63
8.632
8.634
8.636
8.638
iteration
Φ
0 5 10
x 104
0
0.5
1
lag
Φ a
uto
corr
elat
ion
3 4 5 6 7 8
x 105
10.3765
10.377
10.3775
iteration
Φ
0 5 10
x 104
0
0.5
1
lag
Φ a
uto
corr
elat
ion
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