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Diastolic biomarkers


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Diastolic Biomarkers

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Diastolic biomarkers

  1. 1. DIASTOLIC BIOMARKERS THROUGH MODEL PERSONALIZATION Dr Pablo Lamata Lecturer & Sir Henry Dale Fellow QBIO - Bilbao, 17th February 2015
  2. 2. • Introduction • The problem and the hypothesis • Methods & results • Discussion INDEX
  5. 5. COMPUTATIONAL MODELLING Video courtesy of Dr. Nordsletten, King’s College of London
  7. 7. CLINICAL PULL AND TECHNICAL PUSH What’s needed? • Prevention (screening) • Diagnosis • Treatment Tech. offer? • Predictions • Biomarkers • Treatment tools
  8. 8. WHAT CAN WE OFFER? Imaging • Anatomy and function • Objective metrics • Biased, noisy? • Reproducibility? Modelling • Physiological understanding • Predictions • Assumptions? • Validity?
  9. 9. THE VISION Patient Therapy Images and observations
  11. 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. 12. Is this heart relaxing well? • Catheter, PV loop, exponential fitting • Echo: filling waves OR tissue mapping, ratio STRATIFY DIASTOLIC HEART FAILURE
  13. 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. 14. Assessment of fundamental mechanisms improves management of HF HYPOTHESIS Clinical data Diastolic biomarkers -Compliance -Relaxation
  15. 15. Make your model to reproduce the observation • Capture the inherent constitutive and physiological parameters PERSONALIZATION IN A NUTSHELL
  17. 17. METHODS OVERVIEW 2. Motion tracking 1. Clinical measurements 3. Mechanical simulation 4. Parameter identification
  18. 18. Need two ingredients • Deformation • Pressure Issues • Availability • SNR • Range • Synchrony 1. CLINICAL MEASUREMENTS
  19. 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. 20. Assumptions • Incompressibility • Quasi-static FEM: • Mass and momentum conservation • Principle of virtual work 3. MECHANICAL SIMULATION
  21. 21. • Match deformations! • Only in LV free wall • Boundary conditions • Apex and base from data • Optimiser • Brute force or sequential 4. PARAMETER IDENTIFICATION
  22. 22. Tissue stiffness: not unique, but clear differences between health and disease DIASTOLIC BIOMARKERS: STIFFNESS (I) α=C2+C3+C4
  23. 23. Break uniqueness: observe inflation through different times (in-silico proof) DIASTOLIC BIOMARKERS: STIFFNESS (II)
  24. 24. Real data: with active tension! If not accounted, as filling progresses, fibre stiffness decreases DIASTOLIC BIOMARKERS: STIFFNESS (III)
  25. 25. Estimate decay active tension, but identifiability still not solved DIASTOLIC BIOMARKERS: DECAYING AT
  26. 26. TWO CHALLENGES How to uncouple the decaying active tension (AT) and passive stiffness Reduce invasiveness (catheter pressure sensor)
  27. 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. 28. UNCOUPLE STIFFNESS/AT (II) Criterion to choose reference configuration
  29. 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. 30. ROUTE FOR NON- INVASIVENESS (II) Literature surrogate • Able to differentiate stiffness • Stiffness = f(ejection fraction) • Unable to different. active tension
  31. 31. ROUTE FOR NON- INVASIVENESS (III) Able to recover pressure offset errors • Need temporal resolution! No pressure offset With pressure offset
  33. 33. Data driven: • “Clean” pressure • Exponential fit Model driven • (as explained) • Higher significance, reproducibility BUT: • Assumptions • Tedious DATA VS. MODEL DRIVEN APPROACH
  34. 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. 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. 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