Se ha denunciado esta presentación.
Utilizamos tu perfil de LinkedIn y tus datos de actividad para personalizar los anuncios y mostrarte publicidad más relevante. Puedes cambiar tus preferencias de publicidad en cualquier momento.

Imaging and modeling biomarkers

271 visualizaciones

Publicado el

Imaging and modeling biomarkers

Publicado en: Ingeniería
  • Sé el primero en comentar

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)

×