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DIASTOLIC BIOMARKERS
THROUGH MODEL
PERSONALIZATION
Dr Pablo Lamata
Lecturer & Sir Henry Dale Fellow
QBIO - Bilbao, 17th February 2015
• Introduction
• The problem and the hypothesis
• Methods & results
• Discussion
INDEX
INTRODUCTION
MEDICAL IMAGING
COMPUTATIONAL
MODELLING
Video courtesy of Dr. Nordsletten, King’s College of London
COMPUTATIONAL
MODELLING
CLINICAL PULL AND
TECHNICAL PUSH
What’s needed?
• Prevention
(screening)
• Diagnosis
• Treatment
Tech. offer?
• Predictions
• Biomarkers
• Treatment tools
WHAT CAN WE
OFFER?
Imaging
• Anatomy and
function
• Objective metrics
• Biased, noisy?
• Reproducibility?
Modelling
• Physiological
understanding
• Predictions
• Assumptions?
• Validity?
THE VISION
Patient
Therapy
Images and observations
THE PROBLEM AND THE
HYPOTHESIS
HF with Normal Ejection Fraction
• Evidence of abnormal filling caused by stiffer
myocardium, delayed relaxation, impaired atrio-
ventricular conduit function.
Diagnostic surrogates [Maeder09]:
• Lab: natruiretic peptides
• Echo: ratio early/late filling
• Catheters: LV pressure
Stratification: on-going challenge [Maeder09]
PROBLEM: MANAGEMENT OF
HEART FAILURE (HF)
[Maeder09] Maeder and Kaye,
“Heart Failure With Normal Left
Ventricular Ejection Fraction,” J.
Am. Coll. Cardiol. 2009
Is this heart relaxing well?
• Catheter, PV loop, exponential fitting
• Echo: filling waves OR tissue mapping, ratio
STRATIFY DIASTOLIC
HEART FAILURE
IS THE HEART
FILLING WELL?
Catheter, PV loop: exponential fitting
Coupling between relaxation and stiffness
P
V
Passive elastic
Active fibre relaxation
Total LV pressure
Assessment of fundamental mechanisms improves
management of HF
HYPOTHESIS
Clinical
data
Diastolic biomarkers
-Compliance
-Relaxation
Make your model to reproduce the observation
• Capture the inherent constitutive and physiological
parameters
PERSONALIZATION IN
A NUTSHELL
METHODS & RESULTS
METHODS OVERVIEW
2. Motion
tracking
1. Clinical measurements
3. Mechanical
simulation
4. Parameter
identification
Need two ingredients
• Deformation
• Pressure
Issues
• Availability
• SNR
• Range
• Synchrony
1. CLINICAL
MEASUREMENTS
Key ingredients:
• A similarity metric
• A solution space (transformation space)
• An optimizer
2. MOTION TRACKING
(IMAGE REGISTRATION)
Frame N
Frame 1
Measure
similarity
Apply
Transformation
Optimise M
over T
Similarity metric (M)
Transformation (T)
Assumptions
• Incompressibility
• Quasi-static
FEM:
• Mass and momentum
conservation
• Principle of virtual work
3. MECHANICAL
SIMULATION
• Match deformations!
• Only in LV free wall
• Boundary conditions
• Apex and base from data
• Optimiser
• Brute force or sequential
4. PARAMETER
IDENTIFICATION
Tissue stiffness: not unique, but clear differences between
health and disease
DIASTOLIC BIOMARKERS:
STIFFNESS (I)
α=C2+C3+C4
Break uniqueness: observe inflation through different times
(in-silico proof)
DIASTOLIC BIOMARKERS:
STIFFNESS (II)
Real data: with
active tension!
If not accounted, as
filling progresses,
fibre stiffness
decreases
DIASTOLIC BIOMARKERS:
STIFFNESS (III)
Estimate decay active tension, but identifiability still not
solved
DIASTOLIC BIOMARKERS:
DECAYING AT
TWO CHALLENGES
How to uncouple the decaying active
tension (AT) and passive stiffness
Reduce invasiveness (catheter
pressure sensor)
METHOD TO UNCOUPLE
STIFFNESS/AT (I)
6 unknowns
4 data points
Additional constraints [7]
• End diastole: null active tension
• Positive, and monotonically decaying active tension
Criterion to choose reference configuration
UNCOUPLE
STIFFNESS/AT (II)
Criterion to choose reference configuration
ROUTE FOR NON-
INVASIVENESS (I)
Stiffness = f(deform., pressure)
• LV filling pressure: only catheter
Two aims [8]:
• Hypothesis: P = f(V)
• Characterise impact of pressure
offset errors
ROUTE FOR NON-
INVASIVENESS (II)
Literature surrogate
• Able to differentiate
stiffness
• Stiffness = f(ejection
fraction)
• Unable to different. active
tension
ROUTE FOR NON-
INVASIVENESS (III)
Able to recover pressure offset errors
• Need temporal resolution!
No pressure offset With pressure offset
DISCUSSION: OVERLOAD
OR BENEFIT?
Data driven:
• “Clean” pressure
• Exponential fit
Model driven
• (as explained)
• Higher
significance,
reproducibility
BUT:
• Assumptions
• Tedious
DATA VS. MODEL
DRIVEN APPROACH
Right choice of complexity for each
research question!
In general, models bring
• In-silico experimentation
• Data enhancement and unveil biomarkers
• Predictions of clinical outcome
ADDED VALUE
KEY POINTS
Clinical motivation:
myocardial stiffness and
relaxation are important
Methods: FEM to
reproduce the
observation (pressure
and deformation)
Results: biomarkers for
diastolic heart failure
Clinical data
Diastolic biomarkers
-Stiffness
-Relaxation
Key references
• [Xi11] “Myocardial transversely isotropic material parameter
estimation from in-silico measurements based on a reduced-
order unscented Kalman filter” J Mech. Behav. Biomed. Mat.
• [Xi13] “Diastolic functions from clinical measurements.” Med.
image Anal.
• [Xi14] “Understanding the need of LV pressure” Biomechs & Mod
Mechanobiology
Acknowledgements
• Dr. Jihae Xi
• Prof Nic Smith
• Dr. Steven Niederer
• Dr. David Nordsletten
• Dr. Sander Land
REFERENCES AND
ACKNOWLEDGEMENTS

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Diastolic biomarkers

  • 1. DIASTOLIC BIOMARKERS THROUGH MODEL PERSONALIZATION Dr Pablo Lamata Lecturer & Sir Henry Dale Fellow QBIO - Bilbao, 17th February 2015
  • 2. • Introduction • The problem and the hypothesis • Methods & results • Discussion INDEX
  • 5. COMPUTATIONAL MODELLING Video courtesy of Dr. Nordsletten, King’s College of London
  • 7. CLINICAL PULL AND TECHNICAL PUSH What’s needed? • Prevention (screening) • Diagnosis • Treatment Tech. offer? • Predictions • Biomarkers • Treatment tools
  • 8. WHAT CAN WE OFFER? Imaging • Anatomy and function • Objective metrics • Biased, noisy? • Reproducibility? Modelling • Physiological understanding • Predictions • Assumptions? • Validity?
  • 10. THE PROBLEM AND THE HYPOTHESIS
  • 11. HF with Normal Ejection Fraction • Evidence of abnormal filling caused by stiffer myocardium, delayed relaxation, impaired atrio- ventricular conduit function. Diagnostic surrogates [Maeder09]: • Lab: natruiretic peptides • Echo: ratio early/late filling • Catheters: LV pressure Stratification: on-going challenge [Maeder09] PROBLEM: MANAGEMENT OF HEART FAILURE (HF) [Maeder09] Maeder and Kaye, “Heart Failure With Normal Left Ventricular Ejection Fraction,” J. Am. Coll. Cardiol. 2009
  • 12. Is this heart relaxing well? • Catheter, PV loop, exponential fitting • Echo: filling waves OR tissue mapping, ratio STRATIFY DIASTOLIC HEART FAILURE
  • 13. IS THE HEART FILLING WELL? Catheter, PV loop: exponential fitting Coupling between relaxation and stiffness P V Passive elastic Active fibre relaxation Total LV pressure
  • 14. Assessment of fundamental mechanisms improves management of HF HYPOTHESIS Clinical data Diastolic biomarkers -Compliance -Relaxation
  • 15. Make your model to reproduce the observation • Capture the inherent constitutive and physiological parameters PERSONALIZATION IN A NUTSHELL
  • 17. METHODS OVERVIEW 2. Motion tracking 1. Clinical measurements 3. Mechanical simulation 4. Parameter identification
  • 18. Need two ingredients • Deformation • Pressure Issues • Availability • SNR • Range • Synchrony 1. CLINICAL MEASUREMENTS
  • 19. Key ingredients: • A similarity metric • A solution space (transformation space) • An optimizer 2. MOTION TRACKING (IMAGE REGISTRATION) Frame N Frame 1 Measure similarity Apply Transformation Optimise M over T Similarity metric (M) Transformation (T)
  • 20. Assumptions • Incompressibility • Quasi-static FEM: • Mass and momentum conservation • Principle of virtual work 3. MECHANICAL SIMULATION
  • 21. • Match deformations! • Only in LV free wall • Boundary conditions • Apex and base from data • Optimiser • Brute force or sequential 4. PARAMETER IDENTIFICATION
  • 22. Tissue stiffness: not unique, but clear differences between health and disease DIASTOLIC BIOMARKERS: STIFFNESS (I) α=C2+C3+C4
  • 23. Break uniqueness: observe inflation through different times (in-silico proof) DIASTOLIC BIOMARKERS: STIFFNESS (II)
  • 24. Real data: with active tension! If not accounted, as filling progresses, fibre stiffness decreases DIASTOLIC BIOMARKERS: STIFFNESS (III)
  • 25. Estimate decay active tension, but identifiability still not solved DIASTOLIC BIOMARKERS: DECAYING AT
  • 26. TWO CHALLENGES How to uncouple the decaying active tension (AT) and passive stiffness Reduce invasiveness (catheter pressure sensor)
  • 27. METHOD TO UNCOUPLE STIFFNESS/AT (I) 6 unknowns 4 data points Additional constraints [7] • End diastole: null active tension • Positive, and monotonically decaying active tension Criterion to choose reference configuration
  • 28. UNCOUPLE STIFFNESS/AT (II) Criterion to choose reference configuration
  • 29. ROUTE FOR NON- INVASIVENESS (I) Stiffness = f(deform., pressure) • LV filling pressure: only catheter Two aims [8]: • Hypothesis: P = f(V) • Characterise impact of pressure offset errors
  • 30. ROUTE FOR NON- INVASIVENESS (II) Literature surrogate • Able to differentiate stiffness • Stiffness = f(ejection fraction) • Unable to different. active tension
  • 31. ROUTE FOR NON- INVASIVENESS (III) Able to recover pressure offset errors • Need temporal resolution! No pressure offset With pressure offset
  • 33. Data driven: • “Clean” pressure • Exponential fit Model driven • (as explained) • Higher significance, reproducibility BUT: • Assumptions • Tedious DATA VS. MODEL DRIVEN APPROACH
  • 34. Right choice of complexity for each research question! In general, models bring • In-silico experimentation • Data enhancement and unveil biomarkers • Predictions of clinical outcome ADDED VALUE
  • 35. KEY POINTS Clinical motivation: myocardial stiffness and relaxation are important Methods: FEM to reproduce the observation (pressure and deformation) Results: biomarkers for diastolic heart failure Clinical data Diastolic biomarkers -Stiffness -Relaxation
  • 36. Key references • [Xi11] “Myocardial transversely isotropic material parameter estimation from in-silico measurements based on a reduced- order unscented Kalman filter” J Mech. Behav. Biomed. Mat. • [Xi13] “Diastolic functions from clinical measurements.” Med. image Anal. • [Xi14] “Understanding the need of LV pressure” Biomechs & Mod Mechanobiology Acknowledgements • Dr. Jihae Xi • Prof Nic Smith • Dr. Steven Niederer • Dr. David Nordsletten • Dr. Sander Land REFERENCES AND ACKNOWLEDGEMENTS

Editor's Notes

  1. General hypothesis: wealth of clinical information, use of computational models to unveil more robust and accurate biomarkers
  2. Need of multidisciplinary language Do not try to “over-engineer” the solutions Importance of building confidence, bounce your ideas off your colleagues!
  3. Try to get the best of these two worlds, Observations and models Models: get metrics, patient selection, intervention planning, unveil mechanisms
  4. The huge potential of the combination of models and images (observations)
  5. The huge potential of the combination of models and images (observations)
  6. Thank you for the introduction, and for the opportunity to present my ideas and goals. The problem that I will address is the management of HF, a major health issue in the UK, which brings annual costs of £0.75 billion to our health system. The lifetime risk of developing HF is one in five, it is expected that 3 of us will develop some form of this disease. HF is the clinical condition in which the heart is not able to pump enough blood to meet the body demands. Two actions govern the mechanical pump function of the heart, ejection and filling. The scope of my work focuses on the second, which relates to the condition of HFNEF, that affects half of the population with HF (the other half have systolic HF). HFNEF patients have abnormal filling, caused by a stiffer myocardium, a delayed relaxation, or an impaired atrio-ventricular counduit function. Current diagnostic clinical guidelines for HFNEG use these surrogates to characterise an impaired filling of the ventricles: 1,2,3 The problem is that the characterization and stratification of patients is an on-going challenge. One of the fundamental reasons for it is that current diagnostic metrics are only surrogates of the mechanisms that impair ventricular filling. In my work, I plan to bridges this gap, estimating the fundamental mechanical properties that govern diastolic filling.