This document discusses using computational models and machine learning techniques to analyze cardiac shape and function from medical images. Key points include: 1) Models can regularize and extract diagnostic metrics from clinical data to provide clinical support and patient stratification. 2) Examples of metrics that can be extracted include cardiac shape, diastolic performance, and non-invasive blood pressure measurements. 3) There is an opportunity for these techniques to have a clinical impact but they must also demonstrate robustness.

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)