Phase Noise and Noise Measurement Pengcheng Jia Nov 21, 2002.
Linwei Wang, Ken C.L. Wong, Pengcheng Shi Computational System Biomedicine Laboratory
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Transcript of Linwei Wang, Ken C.L. Wong, Pengcheng Shi Computational System Biomedicine Laboratory
![Page 1: Linwei Wang, Ken C.L. Wong, Pengcheng Shi Computational System Biomedicine Laboratory](https://reader037.fdocuments.us/reader037/viewer/2022110101/56812b77550346895d8f96aa/html5/thumbnails/1.jpg)
CPP, INI, Cambridge, July 2009
Integrative System Framework for Noninvasive Understanding of
Myocardial Tissue Electrophysiology
Linwei Wang, Ken C.L. Wong, Pengcheng Shi
Computational System Biomedicine LaboratoryB. Thomas Golisano College of Computing and Information Sciences
Rochester Institute of Technology
![Page 2: Linwei Wang, Ken C.L. Wong, Pengcheng Shi Computational System Biomedicine Laboratory](https://reader037.fdocuments.us/reader037/viewer/2022110101/56812b77550346895d8f96aa/html5/thumbnails/2.jpg)
• Noninvasive observations on specific patient:– Structural: tomographic image– Functional: projective ECG/BSPM
• In clinical settings, it really has been a sophisticated pattern recognition process to decipher the information.
• How can models offer help?– Better models lead to more appropriate constraints in
data analysis.• Physiological plausibility vs. algorithmic/computational
feasibility• Volumetric?
– Ultimately, models have to be personalized to be truly meaningful.
From patient observations to personalized electrophysiology
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Integrative system perspective
Patient Observations
• Subject-specific information
• Noisy, sparse, incomplete
Prior Knowledge: Models
• Built up over many years
• General population
Data Acquisition
• BSP sequence• Tomographic
images
System Modeling
• System dynamics• System
observations
Personalized Information Recovery
• Electrical function• Tissue property• Latent substrate
Individual Subject
Wang et al. IEEE-TBME, 2009 (in press).
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Physiological model constrained statistical framework
• System perspective to recover personalized cardiac electrophysiology – (phenomenal)
Model constrained data analysis: prior knowledge guides a physiologically-meaningful understanding of personal data
– Data driven model personalization: patient data helps to identifies parameter changes in specific system models
Information Recovery
Data Acquisitio
n
System Modeling
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Volumetric myocardial representation
• Ventricular wall: point cloud
• Fiber structure: mapped from Auckland model
Slice segmentation
Surface mesh
Volume representation
Surface registration
Surface fiber structure
3D fiber structure
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Surface body torso representation
• (Isotropic and homogeneous volume conductor)
Patient’s images
Surface model
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Cardiac electrophysiological system
Personalized 3D-BEM mixed
heart-torso model
Volumetric TMP
dynamics model
TMP-to-BSP mapping
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System dynamics: volumetric TMP activity
• Diffusion-reaction system: 2-variable ordinary differential equation
• Meshfree representation and computation
u
t (Du) f1(u,v)
v
t f2(u,v)
U
t M 1KU f1(U,V)
V
t f2(U,V)
- u: excitation variable: TMP- v: recovery variable: current- D: diffusion tensor
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System observation: TMP-to-BSP mapping
• Quasi-static electromagnetism Poisson’s equation
• Mixed meshfree and boundary element methodsGoverning equation
Direct solution method
Meshfree+BEM
2(r) (D(r)u(r))
c()() (r)q*(t ,r)dt
(r)
r n
*(t ,r)dt
1
4(
Di(r)
| r |
u(r)
r n t
dt 1
| r | (Di(r)u(r))
t dt )
HU
Surface integral: BEM
Volume integral: meshfree
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State space system representation
Ut
M 1KU f1(U,V,)
V
t f2(U,V,)
HU
X UT VT T
Y Uncertainty:
,
Nonlinear dynamic model Local linearization Temporal discretization
Large-scale & high-dimensional system U,V is of dimension 2000-3000
Monte Carlo
integration
✗Predictio
nCorrectio
n
TMP activity:
TMP-to-BSP
mapping:
Xk Fd (Xk 1,k 1) kState equation:
Yk HXk kMeasurement equation:
k k 1 kParameter:
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Sequential data assimilation
• Combination of unscented transform (UT) and Kalman filter: unscented Kalman filter
Correction: KF update
Computational feasibility
Prediction: UT(MC integration + deterministic sampling)
Preserve intact model nonlinearity
Black-box discretization
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Ensemble generation (unscented transform)
Prediction (MC integration)
Filter Gain
Correction (KF update)
k 1,i 0
n ˆ U k 1
ˆ U k 1 (n
2 ) ˆ P uk 1
( k|k 1,i, k|k 1,i) ˜ F d ( k 1,i,V k 1)
U k W i
m k|k 1,ii0
n ,V k W i
m k|k 1,ii0
n
Puk
W ic ( k|k 1,i U k
)( k|k 1,i U k )T Q
ku
i0
n
K ku Puk
HT (HPuk
HT Rvk) 1
ˆ U k U k K k
u(Yk HU k )
ˆ P uk(I K k
uH)Puk
Initialization
ˆ U 0 ˆ P u0 k = k+1
TMP estimator: reconstructing TMP from BSP
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Ensemble generation (unscented transform)
Prediction (MC integration)
Filter Gain
Correction (KF update)
k 1,i 0
2n ˆ k 1
ˆ k 1 (n ) ˆ P k 1
k |k 1,i
˜ F d ( ˆ U k 1,k 1,i)
k|k 1,i ˜ H
k|k 1,i
P k
ˆ P k 1 Qk
Y k W i
mk|k 1,ii0
2n
Pyk
W ic (k|k 1,i
Y k )(k|k 1,i
Y k )T R ki0
2n
P k yk W i
c (k 1,i ˆ k 1)(k|k 1,i Y k
)T
i0
2n
K k P k yk
Pyk
1
ˆ k ˆ k 1 K k(Yk Y k
)
ˆ P kP k
K kPyk
K kT
Initialization
ˆ 0 ˆ P 0
k = k+1
Nonlinear measurement
model
Parameter estimator: reconstructing model parameters
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BSP
Gd-enhanced MRI
MRI personalized heart-torso structures
gold standard
Cardiac: 1.33×1.33×8mmWhole-body: 1.56×1.56×5mm
123 electrodes, QRST @ 2KHz sampling
Experiments (PhysioNet.org): electrocardiographic imaging of myocardial infarction
• Four post-MI patients
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Goals and procedures
• Quantitative reconstruction of tissue property and electrical functioning
Tissue excitability TMP dynamics
Procedures Initialization – TMP estimation with general normal model Simultaneous estimation of TMP and excitability
Identify arrhythmogenic substrates (imaging + quantitative evaluation)
Localize abnormality in TMP and excitability
Investigate the correlation of local abnormality between TMP and excitability
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Result: case II
• Infarct location: septal-inferior basal-middle LV
Simulated normal TMP dynamics
Estimated TMP dynamics
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Result: case II
• Location, extent, and 3D complex shape of infarct tissues• Correlation of abnormality between electrical functions
and tissue property– Abnormal electrical functioning occurs within infarct zone– Border zone exhibits normal electrical functioning
-Black contour: abnormal TMP dynamics-Color: recovered tissue excitability
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Delay enhanced MRI registered with epicardial electrical signals
Delayed activation: 4-9
Infarct: 3-14
H. Ashikaga, Am J Physiol Heart Circ Physiol, 2005
Result: case II
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Result: case I
• Infarct: septal-anterior basal LV, septal middle LV
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Result: case III
• Infarct: inferior basal-middle LV, lateral middle-apical LV
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Result: case V
• Infarct: anterior basal LV, septal middle-apical LV
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Quantitative validation
- EP: Percentage of infarct in ventricular mass- CE: Center of infarct, labeled by segment- Segments: A set of segments which contain infarct- SO: Percentage of correct identification compared to gold standard
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Comparison with existent results
- Dawoud et al: epicardial potential imaging- Farina et al: optimization of infarct model- Mneimneh et al: pure ECG analysis
- EPD: difference of EP from gold standard- CED: difference of CE from gold standard
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Conclusion
• Personalized noninvasive imaging of volumetric cardiac electrophysiology
Noninvasive observations
Personalized volumetric
cardiac electrophysiolo
gy
Latent substrates