Imaging and modeling biomarkers

1. Pablo Lamata
Lecturer. Sir Henry Dale Fellow

3.  “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!!

4.  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!!

6.  Cardiac remodelling
 Development
 Disease
 State of art: coarse metrics
 Length, diameter, volume…
 Opportunities
 Myriad of shape patterns
 Tons of data

7.  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)

8. [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/

9.  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)

10.  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)

11. [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)

13.  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

14.  State of art (catheter): exponential fitting
 Coupling between relaxation and stiffness
P
V
Passive elastic
Active fibre relaxation
Total LV pressure

15.  Myocardial properties (relaxation/stiffness)
 Input: deformation and pressure
 Method: Model personalization
 Output: Decouple relaxation / stiffness

16. [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

17. [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

18.  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)

19.  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)

20.  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)

22.  Pressure:
important
biomarker
 What if…
 Central
pressure?
 Time + space

23.  Coarctation  Obstruction LVOT
∆P=P(B)-P(A)?
B
A
∆P=P(B)-P(A)?

24. P (mmHg) = 4 𝑽 𝒎𝒂𝒙
𝟐
(m/sec)

25.  PC-MRI
 Navier Stokes Eq.
x 4

26.  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)

27.  Mass and momentum conservation:
Viscous forces
Convective acceleration
(in-space)
Transient acceleration
(in-time)
Inertial forces
t=1
t=0

28. [9] P. Lamata, A. Pitcher et al. “Aortic relative pressure components derived from 4D flow
cardiovascular magnetic resonance”. MRM (2014)

29.  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)

31.  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)

32.  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)

33.  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)

34.  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!!

35.  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)