Impact of right ventricular endocardial trabeculae on volumes and function assessed by CMR in patients with tetralogy of Fallot

Abstract

The objective of this study was to assess the impact of right ventricular (RV) trabeculae and papillary muscles on measured volumes and function assessed by cardiovascular magnetic resonance imaging in patients with repaired tetralogy of Fallot. Sixty-five patients with repaired tetralogy of Fallot underwent routine cardiovascular magnetic resonance imaging. Endocardial and epicardial contours were drawn manually and included trabeculae and papillary muscles in the blood volume. Semi-automatic threshold-based segmentation software excluded these structures. Both methods were compared in terms of end-diastolic, end-systolic and stroke volume, ejection fraction and mass. Observer agreement was determined for all measures. Exclusion of trabeculae and papillary muscle in the RV blood volume decreased measured RV end-diastolic volume by 15 % (from 140 ± 35 to 120 ± 32 ml/m²) compared to inclusion, end-systolic volume by 21 % (from 74 ± 23 to 59 ± 20 ml/m²), stroke volume by 9 % (from 66 ± 16 to 60 ± 16 ml/m²) and relatively increased ejection fraction by 7 % (from 48 ± 7 to 51 ± 8 %) and end-diastolic mass by 79 % (from 28 ± 7 to 51 ± 10 g/m²), p < .01. Excluding trabeculae and papillary muscle resulted in an improved interobserver agreement of RV mass compared to including these structures (coefficient of agreement of 87 versus 78 %, p < .01). Trabeculae and papillary muscle significantly affect measured RV volumes, function and mass. Semi-automatic threshold-based segmentation software can reliably exclude trabeculae and papillary muscles from the RV blood volume.

Appendix 1

The technique of normalized convolution is used to estimate the spatially varying blood and muscle intensities within a user-provided epicardial contour. The voxel intensity is defined by a first order model with six variables:

$$ I(x,y) = (1 - w(x,y)) \times I_{m} (x,y) + w(x,y) \times I_{b} (x,y) $$

(3)

with:

$$ \begin{gathered} Muscle :\,I_{m} (x,y) = a_{0} + a_{1} x + a_{2} y \hfill \\Blood :\,I_{b} (x,y) = b_{0} + b_{1} x + b_{2} y \hfill \\ \end{gathered} $$

(4)

where a₀, a₁, a₂, b₀, b₁ and b₂ are constants that vary among scans according to the grey value distribution of the image within the epicardial contour. I _m(x,y) and I _b(x,y) represent the approximation of the intensity of muscle and blood, respectively, at the position (x,y) The constants are obtained with an iterative optimization procedure, during which the weight w(x,y) is initialized to either 1 or 0 using the Otsu threshold method.

The procedure is stopped when the classification w > 0.5 is unaltered between iterations or when the number of iterations exceeds ten.

Assuming a linear relationship between the fraction of blood and the intensity of the voxel (I(x,y)), the weight w(x,y) represents the fraction of blood in the voxel.

$$ {w(x,y) = \frac{{I(x,y) - I_{m} (x,y)}}{{I_{b} (x,y) - I_{m} (x,y)}}} $$

(5)

A binary classification is obtained by thresholding w(x,y). If w(x,y) is higher than the threshold value, the voxel is considered pure blood, otherwise it is defined as pure muscle. In this experiment the threshold value was set to 70 %. Blood volume measures are obtained by multiplying the number of voxels classified as blood with the voxel volume.