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Imaging and modeling biomarkers

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Pablo Lamata
Lecturer. Sir Henry Dale Fellow

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 “All models are wrong”Denis Noble.
 “Nobody believes on the results of an
experimentalist, but him, and everybody
belie...

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 Goal: clinical support. Stratifiation.
 Hypotheses:
 Models regularise / clean clinical data.
 Models unveil diagnost...

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Imaging and modeling biomarkers

  1. 1. Pablo Lamata Lecturer. Sir Henry Dale Fellow
  2. 2.  “All models are wrong”Denis Noble.  “Nobody believes on the results of an experimentalist, but him, and everybody believes on the results of a modeller, but himself”  Take home message: … need to define the problem and hypothesis as clear as possible!!
  3. 3.  Goal: clinical support. Stratifiation.  Hypotheses:  Models regularise / clean clinical data.  Models unveil diagnostic metrics  Examples:  Shape  Diastolic performance  Non-invasive pressure  Discussion:  Opportunity to make a clinical impact  Robustness!!
  4. 4.  Cardiac remodelling  Development  Disease  State of art: coarse metrics  Length, diameter, volume…  Opportunities  Myriad of shape patterns  Tons of data
  5. 5.  Computational statistical atlas of anatomy [1]  Clinicians will adopt novel shape coordinates in this parametric space [1] A. Young, A. Frangi. “Computational cardiac atlases: from patient to population and back.” Exp. Physiol. (2009)
  6. 6. [2] P. Lamata, S. Niederer, et al., “An accurate, fast and robust method to generate patient-specific cubic Hermite meshes,” Med. image Anal. (2011). [3] P. Lamata, M. Sinclair, et al. “An automatic service for the personalization of ventricular cardiac meshes.” J R Soc Interface (2014)  Model: ellipsoid  Meshing [2,3]  Reduce noise and artifacts  Smooth C1 representation  Statistics: PCA  Web-service http://amdb.isd.kcl.ac.uk/
  7. 7.  Give me your short axis stack, and I’ll tell you if you had a premature birth [4]. [4] A. Lewandovski, D. Augustine et al. “Preterm heart in adult life: cardiovascular magnetic resonance reveals distinct differences in left ventricular mass, geometry, and function.” Circulation (2013)
  8. 8.  Ventricle grow differently depending on surgical choice in HLHS [5]. [5] J. Wong, P. Lamata et al. “Right ventricular morphology and function following stage I palliation with a modified Blalock-Taussig shunt versus a right ventricle-to- pulmonary artery conduit” Circ. Imaging (in review)
  9. 9. [5] J. Wong, P. Lamata et al. “Right ventricular morphology and function following stage I palliation with a modified Blalock-Taussig shunt versus a right ventricle-to- pulmonary artery conduit” Circ. Imaging (in review)
  10. 10.  HF with Normal Ejection Fraction  Evidence of abnormal filling caused by stiffer myocardium, delayed relaxation, impaired atrio-ventricular conduit function.  Diagnostic surrogates [6]: • Lab: natruiretic peptides • Echo: ratio early/late filling • Catheters: LV pressure  Stratification: on-going challenge [6] [6] Maeder and Kaye, “Heart Failure With Normal Left Ventricular Ejection Fraction,” J. Am. Coll. Cardiol. 2009
  11. 11.  State of art (catheter): exponential fitting  Coupling between relaxation and stiffness P V Passive elastic Active fibre relaxation Total LV pressure
  12. 12.  Myocardial properties (relaxation/stiffness)  Input: deformation and pressure  Method: Model personalization  Output: Decouple relaxation / stiffness
  13. 13. [7] J. Xi, P. Lamata, et. al, “The estimation of patient-specific cardiac diastolic functions from clinical measurements,” Med. image Anal., 17:133-146 (2013). 6 unknowns 4 data points  Additional constraints [7]  End diastole: null active tension  Positive, and monotonically decaying active tension  Criterion to choose reference configuration
  14. 14. [7] J. Xi, P. Lamata, et. al, “The estimation of patient-specific cardiac diastolic functions from clinical measurements,” Med. image Anal., 17:133-146 (2013).  Criterion to choose reference configuration
  15. 15.  Stiffness = f(deform., pressure)  LV filling pressure: only catheter  Two aims [8]:  Hypothesis: P = f(V)  Characterise impact of pressure offset errors [8] J. Xi, W. Shi, et. al, “Understanding the need of ventricular pressure for the estimation of diastolic biomarkers,” Biomech. Model. Mechanobiol. (2013)
  16. 16.  Literature surrogate  Able to differentiate stiffness  Stiffness = f(ejection fraction)  Unable to different. active tension [8] J. Xi, W. Shi, et. al, “Understanding the need of ventricular pressure for the estimation of diastolic biomarkers,” Biomech. Model. Mechanobiol. (2013)
  17. 17.  Able to recover from pressure offset errors  Need temporal resolution! No pressure offset With pressure offset [8] J. Xi, W. Shi, et. al, “Understanding the need of ventricular pressure for the estimation of diastolic biomarkers,” Biomech. Model. Mechanobiol. (2013)
  18. 18.  Pressure: important biomarker  What if…  Central pressure?  Time + space
  19. 19.  Coarctation  Obstruction LVOT ∆P=P(B)-P(A)? B A ∆P=P(B)-P(A)?
  20. 20. P (mmHg) = 4 𝑽 𝒎𝒂𝒙 𝟐 (m/sec)
  21. 21.  PC-MRI  Navier Stokes Eq. x 4
  22. 22.  No need of boundary conditions  Arbitrary domains  Includes viscous effects [8] S. Krittian, P. Lamata et al. “A FEM approach to the direct computation of relative cardiovascular pressure from time-resolved MR velocity data.” Med. Im. Analysis (2012)
  23. 23.  Mass and momentum conservation: Viscous forces Convective acceleration (in-space) Transient acceleration (in-time) Inertial forces t=1 t=0
  24. 24. [9] P. Lamata, A. Pitcher et al. “Aortic relative pressure components derived from 4D flow cardiovascular magnetic resonance”. MRM (2014)
  25. 25.  Transient: pump action and compliance  Convective: vessel geometry  Viscous: inefficiencies due to friction [9] P. Lamata, A. Pitcher et al. “Aortic relative pressure components derived from 4D flow cardiovascular magnetic resonance”. MRM (2014)
  26. 26.  Images are drivers of modelling progress [10]  Complexity vs. clinical adoption  Robustness!! [10] P. Lamata, R. Casero et al, “Images as drivers of progress in cardiac computational modelling”, Prog Biophys Mol Biol (2014)
  27. 27.  Meshes of high quality [11] [11] P. Lamata, I. Roy et al. “Quality metrics for high order meshes: analysis of the mechanical simulation of the heart beat.” IEEE Trans Med Imag (2013)
  28. 28.  More stable simulations: guide the optimizer to enforce non-compressibility [12] S. Land, S. Niederer et al. “Improving the stability of cardiac mechanical simulations” IEEE Trans Biom Eng (accepted)
  29. 29.  Goal: clinical support. Stratifiation.  Hypotheses:  Models regularise / clean clinical data.  Models unveil diagnostic metrics  Examples:  Shape  Diastolic performance  Non-invasive pressure  Discussion:  Opportunity to make a clinical impact  Robustness!!
  30. 30.  Oxford / KCL  Nic Smith  Steve Niederer  David Nordsletten  Sander Land  [Jiahe Xi]  [Sebastian Krittian]  [Ishani Roi]  Imperial  Daniel Rueckert  Wenzhe Shi  Clinicians  Reza Razavi (KCL)  Aldo Rinaldi (KCL)  Paul Leeson (OXF)  Adam Lewandovski (OXF)  Stefan Neubauer (OXF)  Alex Pitcher (OXF)

Editor's Notes

  • 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.
  • The huge potential of the combination of models and images (observations)
  • Therefore, my central hypothesis is that these new mechanical parameters will improve the characterization and stratification of patients, and therefore the management of HF.

    And therefore my objective is to develop a robust and clinically applicable methodology to characterise these mechanical parameters.

    I will address this ambitious goal combining three of my most recent contributions in the field of research overlapping medical imaging and mathematical modelling.
    First, the technology to personalise mechanical computational meshes to the anatomy of the patient, captured from medical images. A proof of the accuracy of this process, and also a significant contribution since the submission of the proposal, is the publication of a computational anatomical atlas of the left ventricle in Circulation, the leading journal in Cardiology, where I have clearly characterised the shape of the LV.
    Once the anatomy is captured, a methodology is used to automatically uncouple the active relaxation and passive inflation of the ventricle, the two interrelated mechanisms that rule diastolic filling.
    DIAGRAM: an overview of the model personalization technique is represented in this diagram.
    MOVIE: clinically available data of deformation, captured through dynamic MRI, and pressure, measured with catheters, is assimilated into the model: the fundamental physical parameters of the model are optimised by minimising the differences between the predicted deformation by the model, and the observed deformation in images. The outcome of this process is the myocardial stiffness and relaxation profile that best explain the data. I have already provided the proof of concept of this methodology with the comparison of two diseased and one healthy subject, as published in the leading journal in the field of Medical Image Analysis.

    2. On the other hand, the characterization of the atria-ventricular conduit will be tackled using one of my recent contributions, a method for the computation of blood pressure differences from velocity data captured by PC-MRI.
    MOVIE 1: this imaging modality enables us to capture the velocity at each instant and voxel of the sequence. In this example we can see the streamlines of velocity colour coded by the magnitude of velocity.
    MOVIE 2: solving the fundamental physical Navier-Stokes equations, the pressure that explains the acceleration and viscous friction of that velocity is computed. Now we have the same streamlines colour coded by pressure.
    The idea is to characterise the presence of any impaired conduit function of the mitral valve through the existence of pressure drops.

    I’d like to finish this presentation with the envisioned solution combining these two technologies, where the pressure that is required to estimate myocardial parameteres, currently only available through invasive catheterised procedures, is estimated with the non-invasive methods that have been described before. This will then lead to a simple clinical workflow for patients, in which they will only require an MRI acquisition for about 20 minutes, and where a computational modelling post-processing step unveils these novel biomarkers. The majority of patients could then benefit from this diagnostic tool minimising any associated risks, and therefore maximising the impact of this envisioned solution
  • 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.

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