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Improving The Stratification Power
Of Cardiac Ventricular Shape
Gonzalez1, Nolte1, Lewandowski2, Leeson2, Smith3, Lamata1
1 Dept. Biomedical Engineering, King’s College Of London
2 Dept. Cardiovascular Medicine, University Of Oxford
3 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

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Improving the stratification power of cardiac ventricular shape

  • 1. Improving The Stratification Power Of Cardiac Ventricular Shape Gonzalez1, Nolte1, Lewandowski2, Leeson2, Smith3, Lamata1 1 Dept. Biomedical Engineering, King’s College Of London 2 Dept. Cardiovascular Medicine, University Of Oxford 3 Faculty Of Engineering, University Of Auckland
  • 2. SUMMARY Computational anatomy to improve shape stratification
  • 3. INTRODUCTION & OBJECTIVE Motivation: measure cardiac shape remodelling - Much more detail available in images Hypothesis: Computational anatomy
  • 5. METHODOLOGY Capture anatomy in a consistent manner - Mapping, correspondence… Reduce dimensionality (statistics) …
  • 6. 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
  • 8. 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
  • 10. 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.
  • 11. CASE 1: PREDICT GESTATIONAL AGE (II) Circulation. 2013 Jan 15;127(2):197-206.
  • 12. 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
  • 14. 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
  • 15. 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”
  • 16. 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
  • 17. 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
  • 19. 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!

Editor's Notes

  1. Good morning, and thanks to the organisers for giving me the opportunity to present this work.
  2. My objective today is to transmit you the message of this picture. I want to illustrate the potential diagnostic gain through the application of the concepts of a quite mature field of research from the technological perspective, called “computational anatomy”, also known as statistical atlases.