Volume 15 Supplement 1

Abstracts of the 16th Annual SCMR Scientific Sessions

Open Access

Automated 3D morphometric difference biomarker for abnormal ventricular morphology

  • Prahlad G Menon1 and
  • Robert W Biederman2, 1
Journal of Cardiovascular Magnetic Resonance201315(Suppl 1):E35

DOI: 10.1186/1532-429X-15-S1-E35

Published: 30 January 2013

Background

Left-ventricular (LV) shape remodeling has known association with cardiac pathophysiology and dysfunction. While a prolate spheroid shape is vital for optimal cardiac function, LV dilatation and high sphericity is characteristic of cardiomyopathy. Early quantification of abnormal LV morphology may potentially guide clinical decisions to check disease progression. However a quantitative biomarker of 3D LV morphometry is found wanting in routine clinical practice as an upgrade to the rudimentary sphericity index.

Methods

Spherical harmonics (SPHARM) shape descriptors were sought to quantify patient-specific LV shape. Morphometric variation from a normal LV shape was studied with application to diagnostic identification of cardiomyopathy. Cardiac MR (CMR) was employed for anatomical examination and end-diastolic LV endocardial surface models were segmented from short-axis SSFP cine scans of 11 pediatric subjects between the ages 2 and 17. Surface models were individually registered by rigid transformation and scaling to match the LV base diameter of a reference 17 year old normal LV that approximated well to a prolate spheroid. Two characteristic morphometric difference percentages (DP) were computed as a percentage of normal LV length for each case with respect to the reference normal model i.e. maximum DP (MDP) and average DP (ADP). A 16 patient cohort was used for training, comprising 10 simulated normals (ADP < 3.5 % & MDP < 13%) and 6 abnormal cases including 2 with LV hypertrophy (LVH), 3 with arrythmogenic right ventricular dysplasia (ADVD), 1 post myocardial infarction (MI). Rule-based decision trees were prepared to predict a response function defining normal vs. pathological cases using the new MDP and ADP predictors simultaneously with the conventional LV sphericity index as a third predictor, through a 3D generative clustering approach. Classification accuracy was evaluated with a 6 patient test cohort comprising 3 ARVDs, 2 LVH and 2 normal subjects.

Results

The underlying principle of this LV-base normalized diffeomorphic analysis is that LV remodeling is accompanied by heightened sphericity. This assumption was verified by the existence of positive Spearman rank correlations for sphericity with ADP (0.41) and MDP (0.28). 100% test classification accuracy was obtained using a simultaneous thresholds for MDP (>13.9%), ADP (>3.5%) and sphericity (>0.7). ARVD patients were all identified as pathologies based upon LV shape alone. Further, strong correlation (0.94) was observed between LV sphericity and age for ARVD patients.

Conclusions

This pilot study suggests DP is a valuable biomarker in identifying cardiomyopathy. Strong correlation between sphericity and ARVD patient age combined with the excellent classification of ARVD as a cardiomyopathy by LV shape alone supports possible left-sided involvement in ARVD. Analysis with a larger cohort comprising more normal controls is warranted to validate this hypothesis.

Funding

No funding sources to disclose.

Table 1

Sl no

Diagnosis

Sphericity

Age

MDP % of normal LV length

ADP % of normal LV length

Rank, Sphericity (low to high)

Rank, MDP (low to high)

Rank, ADP (low to high)

1

ARVD

0.600

2

14.408

4.307

2

5

8

2

ARVD

0.625

6

14.826

4.083

5

6

7

3

ARVD

0.643

7

22.460

5.648

6

12

12

4

LVH

0.672

8

17.546

4.789

8

10

9

5

ARVD

0.703

9

11.82

5.464

10

2

11

6

ARVD

0.726

11

11.941

3.731

11

3

6

7

NORMAL

0.616

13

13.860

3.496

3

4

5

8

ARVD

0.703

15

15.282

2.879

9

8

2

9

LVH

0.625

16

15.930

2.953

4

9

3

10

MI

0.726

16

18.913

5.099

12

11

10

11

NORMAL *

0.584

17

0.000

0.000

1

1

1

12

LVH

0.662

17

15.253

3.356

7

7

4

  

Spearman Correlation with Sphericity

0.943

0.280

0.406

   

* reference normal

https://static-content.springer.com/image/art%3A10.1186%2F1532-429X-15-S1-E35/MediaObjects/12968_2013_Article_3169_Fig1_HTML.jpg
Figure 1

Top left: Generative classification result in a 3D space defined by sphericity, MDP and ADP (Red: pathology, Blue: normal). Each patient is identified by a label constituted of "Age, Diagnosis" . Top right: Decision tree produced from the training data set, using either MDP or ADP. Bottom: 3D morphometric difference colormap mapped onto the surface of a pathological LV model. 40th degree SPHARM representations of a normal and a representative pathological LV endocardium surfaces are shown, as well.

Authors’ Affiliations

(1)
Biomedical Engineering, Carnegie Mellon University
(2)
Cardiac MRI, Allegheny General Hospital

Copyright

© Menon and Biederman; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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