Improving The Stratification Power

Of Cardiac Ventricular Shape

Gonzalez1, Nolte1, Lewandowski2, Leeson2, Smith3, Lamata1

1 Dept. Biomedical Engineering, King’s College Of London2 Dept. Cardiovascular Medicine, University Of Oxford3 Faculty Of Engineering, University Of Auckland

SUMMARY

Computational anatomy to improve shape stratification

INTRODUCTION &

OBJECTIVE

Motivation: measure cardiac shape remodelling

- Much more detail available in images

Hypothesis: Computational anatomy

METHODS

METHODOLOGY

Capture anatomy in a consistent manner

- Mapping, correspondence…

Reduce dimensionality (statistics)

…

IN MORE DETAIL…

1. Mesh personalization [1,2]

2. Atlas construction: mean + anatomical modes (Mi)

[1] Lamata et al. “An automatic service for the personalization of ventricular cardiac meshes.” J R

Soc Interface. 2014

[2] Lamata et al. “An accurate, fast and robust method to generate patient-specific cubic Hermite

meshes.” Med Image Anal. 2011

-2std +2std

ANATOMICAL MODE

SHAPE COEFFICIENTS

The directions of shape change

- Mathematically perfect, capturing biggest variance or differences

- Clinically difficult to interpret

How much of change in each direction

(each anatomical mode)

Shape = mean + Sum (Ci * Mi)

Coefficient

Anatomical Mode

3 CASE

STUDIES

CASE 1: PREDICT

GESTATIONAL AGE (I)

Study of effect of premature birth

- Adults (20s to 30s)

- Subgroups: pre-term (30±2.5 weeks), term birth (40±1 weeks)

Circulation. 2013 Jan 15;127(2):197-206.

CASE 1: PREDICT

GESTATIONAL AGE (II)

Circulation. 2013 Jan 15;127(2):197-206.

CASE 1: PREDICT

GESTATIONAL AGE (III)

5 clinical metrics:

- Length

- Epicardium diameter

- Endocardium diameter

- Cavity volume

- Mass

Computational mesh Modes of variation

Classification

Task

Conventional metrics

Input images

CASE 1: PREDICT

GESTATIONAL AGE (IV)

CASE 2: REVEAL HLHS

REMODELLING (I)

Hypoplastic Left Heart Syndrome (HLHS)

Reveal impact of shunt choice

MBT: Modified Blalock-Taussig

RVPA: Right Ventricle to Pulmonary Artery

CASE 2: REVEAL HLHS

REMODELLING (II)

Ventricle grow differently depending on surgical choice in

HLHS [M12].

[M12] Wong et al. “Using Cardiac Magnetic Resonance and Computational Modelling to Assess

the Systemic Right Ventricle Following Different Norwood Procedures: A Dual Centre Study”

CASE 3: PREDICT AF

RECURRENCE (I)

Problem: atrial fibrillation recurrence after ablation

Shape of the left atrial blood pool to predict recurrence

Antero-Posterior direction

S

I

LR

Average recurrent

Average non-recurrent

CASE 3: PREDICT AF

RECURRENCE (II)

Second mode: better predictive power than previous metrics

(work in progress)

Generate virtual extreme geometries within the range of

physiological variation

Antero-Posterior direction

S

I

LR

Extreme recurrent

Extreme non-recurrent

CONCLUSIONS

CONCLUSIONS

Shape is much more than length or volume

Computational Anatomy tools mature and available

http://amdb.isd.kcl.ac.uk/

Disclaimer: research prototype, easily adaptable to needs,

but be patient if not 100% reliable!

ACKNOWLEDGEMENTS

Q&A

Pablo.Lamata@kcl.ac.uk