Fimh revealing differences

1. REVEALING DIFFERENCES IN
ANATOMICAL REMODELLING OF
THE SYSTEMIC RIGHT VENTRICLE
E.ZACUR1, J.WONG2, R.RAZAVI2, T.GEVA3, G.GREIL2, P.LAMATA1
1 DEPT. BIOMEDICAL ENGINEERING, KINGS COLLEGE LONDON, UK
2 DIV. IMAGING SCIENCES, KINGS COLLEGE LONDON, LONDON, UK
3 BOSTON CHILDREN’S HOSPITAL, HARVARD MEDICAL SCHOOL, USA

2. TAKE HOME
MESSAGES
Models to reveal biomarkers
Shape biomarker
•Model: smooth ellipsoid
•Unveil differences in development

3. OVERVIEW
HLHS: the clinical problem and hypothesis
Coping with data
Revealing growth differences
How good is the picture?

4. HLHS…

5. HYPOPLASTIC LEFT
HEART SYNDROME (I)

6. HYPOPLASTIC LEFT
HEART SYNDROME (II)
Challenges:
Complex surgery - different
“schools”
Small numbers – many
factors
Opportunity: huge
impact for life

7. SHUNTS: TWO
“SCHOOLS”
modified Blalock-Taussig (MBT) right ventricle-to-pulmonary artery (RVPA)

8. IMPACT OF SHUNT
CHOICE?
Outcome (transplantation free) [1]:
Early outcome: RVPA better
Mid outcome (32 months): equal
Hypothesis: scar in RVPA introduces an
adverse remodelling (growth) and impaired RV
function as compared to MBT.
[1] Frommelt et al (2014). "Impact of initial shunt type on cardiac size and function in
children with single right ventricle anomalies before the Fontan procedure: the Single
Ventricle Reconstruction Extension Trial", JACC 64(19):2026-2035.

9. Computational Anatomy is able to reveal
differences in RV growth caused by a
choice of shunt technique.

10. CLINICAL
DATA…
MODEL FITTING
PRINCIPAL COMPONENT ANALYSIS

16. MODEL: ELLIPSOID
Minimise acquisition / segmentation artefacts

17. MESH
PERSONALIZATION
[2] Lamata P et al (2011), “An accurate, fast and robust method to generate patient-
specific cubic Hermite meshes.” Med Image Anal. 15(6):801-13”
[3] Lamata P et al (2014), “An automatic service for the personalization of ventricular
cardiac meshes.” J R Soc Interface. 11(91):20131023.

22. COMMON SHAPE
CHANGES?
Shape variation due to:
• Errors, artefacts…
• Physiological causes
Decompose shape in orthogonal
components
• Reduce dimensionality
• Get the salient modes

24. M

25. 1

26. 2

27. 3

28. 4

29. 5

30. 113

31. 128

32. REVEALING
GROWTH
DIFFERENCES

34. MEAN

35. MODE 1

36. MODES 2, 3, 4

37. RVPA
shunt
MBT shunt
Mode 1

38. STAGE I (ALL) VS MBT

39. STAGE I (ALL) VS
RVPA

40. HOW GOOD IS
THE PICTURE?

41. BETTER THAN
“TRADITIONAL”
Differences never revealed before
• Risk: “black box”
• Mitigation plan: map of regional changes
Function correlates with shape
(remodelling)
• Impaired contraction in RVPA [4]
[4] Wong et al (2015). "Using cardiac magnetic resonance and computational modelling
to assess the systemic right ventricle following different Norwood procedures: a dual
centre study", J Card Mag Res, 17(Suppl 1):M12.

42. STILL A BLURRY
PICTURE
Data issues: short axis stack, etc
Processing issues.
• Fitting error / LV length = 1.43/78 mm
• Adult LV: 1.28/95 mm

43. TAKE HOME
MESSAGES
Models to reveal biomarkers
Shape biomarker
• Model: smooth ellipsoid
• Unveil differences in development

44. ACKNOWLEDGEMENTS