Distribution of left ventricular trabeculation across age and gender in 140 healthy Caucasian subjects on MR imaging

PURPOSE

The purpose of this study was to quantify the distribution of trabeculated (T) and compact (C) left ventricular (LV) myocardium masses in a healthy Caucasian population against age, gender and LV parameters, and to provide normal values for T, C and T/C.

MATERIALS AND METHODS

One hundred and forty healthy subjects were prospectively recruited and underwent cardiac MRI at 1.5T with a stack of short-axis cine sequences covering the entire LV. End-diastolic volume (EDV), C and T masses were quantified using a semi-automatic method. Ejection fraction (EF) and T/C ratio were computed.

RESULTS

We included 70 men and 70 women with a mean age of 44±14 (SD) years (range: 20-69 years). The mean EF was 63.7±6.3 (SD) % (range: 50.7-82.0%), the mean EDV was 75.9±16.2 (SD) mL/m² (range: 36.4-112.2mL/m²), the mean C mass was 53.9±11.2 (SD) g/m² (range: 26.5-93.4g/m²) and the mean T mass was 4.9±2.4 (SD) g/m² (range: 1.1-11.4g/m²). The T/C ratio was 9.2±4.5% (range: 2.0-29.4%). Multivariate ANOVA test showed that the compact mass was influenced by EDV (P<0.0001), EF (P=0.001) and gender (P<0.0001), and the trabeculated mass depended on EDV (P<0.0001), gender (P=0.002) and age (P<0.0001), while the T/C ratio was only influenced by age (P=0.0003). Spearman test showed a correlation between EDV and C (r=0.60; P<0.0001),T (r=0.46; P<0.0001) and T/C ratio (r=0.26; P=0.0023).T and T/C ratio correlated with EF (r=-0.18, P=0.0373; r=-0.18, P=0.0321, respectively).

CONCLUSION

While the compact and trabeculated myocardium masses appear to relate separately to the cardiac function, age and gender, their ratio T/C appears to only decrease with age. Furthermore, we propose here normal values for T, C and T/C in a cohort of healthy Caucasians subjects.

Unifying framework for multimodal brain MRI segmentation based on Hidden Markov Chains

In the frame of 3D medical imaging, accurate segmentation of multimodal brain MR images is of interest for many brain disorders. However, due to several factors such as noise, imaging artifacts, intrinsic tissue variation and partial volume effects, tissue classification remains a challenging task. In this paper, we present a unifying framework for unsupervised segmentation of multimodal brain MR images including partial volume effect, bias field correction, and information given by a probabilistic atlas. Here-proposed method takes into account neighborhood information using a Hidden Markov Chain (HMC) model. Due to the limited resolution of imaging devices, voxels may be composed of a mixture of different tissue types, this partial volume effect is included to achieve an accurate segmentation of brain tissues. Instead of assigning each voxel to a single tissue class (i.e., hard classification), we compute the relative amount of each pure tissue class in each voxel (mixture estimation). Further, a bias field estimation step is added to the proposed algorithm to correct intensity inhomogeneities. Furthermore, atlas priors were incorporated using probabilistic brain atlas containing prior expectations about the spatial localization of different tissue classes. This atlas is considered as a complementary sensor and the proposed method is extended to multimodal brain MRI without any user-tunable parameter (unsupervised algorithm). To validate this new unifying framework, we present experimental results on both synthetic and real brain images, for which the ground truth is available. Comparison with other often used techniques demonstrates the accuracy and the robustness of this new Markovian segmentation scheme.