Virtual cardiac histology: Towards a radiodensitometric characterization of left ventricular cardiac muscle in healthy and pathological conditions.

BACKGROUND AND

OBJECTIVE

Cardiovascular imaging plays a crucial role in disease understanding and case severity. Despite good results in morphological assessment due to an elevated spatial resolution, functional evaluation about cardiac tissue status is still lacking. The aim of the work was to perform a virtual cardiac histology, meaning to characterize cardiac tissue of the left ventricle with Computed Tomography images and use densitometric distribution to detect the presence of cardiac diseases such as acute myocardial infarction and hypertrophic cardiomyopathy.

METHODS

The study retrospectively analyzed volumetric data from sixty subjects, equally distributed among classes, developing a pipeline of image processing for the semi-automatic extraction of 3D virtual samples from different levels and segments. From each sample's densitometric profile, a set of statistical descriptor were extracted.

RESULTS

The densitometric characterization detected heterogeneity in the left ventricular tissue, differentiating the more conductive myocytes of the septum with the more contractive myocytes of the other segments. In addition, a gradient of radiodensity was found as moving from the valvular plane (basal) to the apex of the heart. The intraventricular septum was also found as an eloquent structure in pathological changes due to myocardial infarction since a geometrical modification and shift of the profile was observed (Amplitude = 0.02, Muscle HU = 57). The hypertrophic cardiomyopathy caused significative changes in the contractile segments intensity (Muscle 5-7 HU increase) and shape of the profile (Amplitude = 0.21 inferior wall) reporting the absence of physiological fat and connective tissue in those segments (fat volume = 0.2 %).

CONCLUSION

This study introduces a novel methodology leveraging CT densitometric properties to characterize left ventricular myocardium and distinguish healthy from pathological tissue. Significant patterns associated with hypertrophic cardiomyopathy and acute myocardial infarction highlight the potential of this approach for cardiac risk stratification.