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Improving the stratification power of cardiac ventricular shape

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Improving The Stratification Power
Of Cardiac Ventricular Shape
Gonzalez1, Nolte1, Lewandowski2, Leeson2, Smith3, Lamata1
...

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SUMMARY
Computational anatomy to improve shape stratification

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INTRODUCTION &
OBJECTIVE
Motivation: measure cardiac shape remodelling
- Much more detail available in images
Hypothesis: ...

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Improving the stratification power of cardiac ventricular shape

  1. 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. 2. SUMMARY Computational anatomy to improve shape stratification
  3. 3. INTRODUCTION & OBJECTIVE Motivation: measure cardiac shape remodelling - Much more detail available in images Hypothesis: Computational anatomy
  4. 4. METHODS
  5. 5. METHODOLOGY Capture anatomy in a consistent manner - Mapping, correspondence… Reduce dimensionality (statistics) …
  6. 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
  7. 7. ANATOMICAL MODE
  8. 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
  9. 9. 3 CASE STUDIES
  10. 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. 11. CASE 1: PREDICT GESTATIONAL AGE (II) Circulation. 2013 Jan 15;127(2):197-206.
  12. 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
  13. 13. CASE 1: PREDICT GESTATIONAL AGE (IV)
  14. 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. 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. 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. 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
  18. 18. CONCLUSIONS
  19. 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!
  20. 20. ACKNOWLEDGEMENTS Q&A Pablo.Lamata@kcl.ac.uk

Editor's Notes

  • Good morning, and thanks to the organisers for giving me the opportunity to present this work.
  • 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.

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