Generation of a cardiac shape model from CT Data

Lorenz, Cristian; Berg, Jens von

doi:10.1109/iembs.2006.4397710

Cited by 2 publications

(2 citation statements)

References 15 publications

(17 reference statements)

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“…We would contend, however, that this is a minor disadvantage in view of today's ubiquitous availability of affordable computer resources. Finally, more detailed models of ventricular shape than the simple ellipsoid have been developed for various applications . However, the constrained ellipsoid may represent the ideal level of abstraction for global twist quantification because it appears as one of the simplest parametric shapes with the required symmetries that can match ventricular shape data reasonably well and can be estimated from sparse data.…”

Section: Discussionmentioning

confidence: 99%

Robust Model‐Based Quantification of Global Ventricular Torsion from Spatially Sparse Three‐Dimensional Time Series Data by Orthogonal Distance Regression: Evaluation in a Canine Animal Model Under Different Pacing Regimes

Zenker

Kim

Clermont

et al. 2012

Pacing Clinical Electrophis

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Quantification of global ventricular rotational deformation, expressed as twist or torsion, and its dynamic changes is important in understanding the pathophysiology of heart disease and its therapy. Various techniques, such as sonomicrometry, allow tracking of specific sites within the myocardium. Quantification of twist from such data requires a longitudinal reference axis of rotation. Current methods require specific positioning and numbers of myocardial markers and assumptions about temporal positional evolution that may be violated during dyssynchronous contraction. We present a new method to assess myocardial twist that makes minimal fully explicit assumptions while removing extraneous assumptions, by performing a least squares orthogonal distance regression of all position data on an ellipsoidal ventricular model. Rotational deformation is quantified in terms of the ellipsoid’s internal coordinate system, allowing intuitive visualization. We tested this method on a set of sparse, noisy sonomicrometric crystal data in dogs under different pacing regimes to model dyssynchrony and cardiac resynchronization. We found that this method yielded robust and plausible data. This technique is also fully automated while identifying when data may be insufficient for reliable quantification of rotational deformation. This approach may allow future analysis of myocardial contraction with less tracking sites and relaxed positioning requirements while identifying situations where data are insufficient for reliable quantification of rotational deformation.

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Section: Discussionmentioning

confidence: 99%

Robust Model‐Based Quantification of Global Ventricular Torsion from Spatially Sparse Three‐Dimensional Time Series Data by Orthogonal Distance Regression: Evaluation in a Canine Animal Model Under Different Pacing Regimes

Zenker

Kim

Clermont

et al. 2012

Pacing Clinical Electrophis

View full text Add to dashboard Cite

show abstract

“…Cardiac computed tomography (CCT) provides acceptable estimates of the right ventricle volume as well [6]. The techniques which based on active shape model [7,8], multi-atlas approach [9], triangulation [10], motion model [11], medial axis [12], etc. are used to segment ventricle region and estimate ventricle volume in CMR and CCT images.…”

Section: Introductionmentioning

confidence: 99%