Abstract
Myocardial tracking and strain estimation can non-invasively assess cardiac functioning using subject-specific MRI. As the left-ventricle does not have a uniform shape and functioning from base to apex, the development of 3D MRI has provided opportunities for simultaneous 3D tracking, and 3D strain estimation. We have extended a Local Weighted Mean (LWM) transformation function for 3D, and incorporated in a Hierarchical Template Matching model to solve 3D myocardial tracking and strain estimation problem. The LWM does not need to solve a large system of equations, provides smooth displacement of myocardial points, and adapt local geometric differences in images. Hence, 3D myocardial tracking can be performed with 1.49 mm median error, and without large error outliers. The maximum error of tracking is up to 24% reduced compared to benchmark methods. Moreover, the estimated strain can be insightful to improve 3D imaging protocols, and the computer code of LWM could also be useful for geo-spatial and manufacturing image analysis researchers.
Highlights
Heart muscles shrink, expand, rotate and create torsion simultaneously during a cardiac cycle and a common complication that arises in left ventricular (LV) myocardial tracking is the consideration of longitudinal heart movement
We propose an extension of the standard 2D Local Weighted Mean (LWM) function to 3D and use it to perform 3D myocardial tracking
We propose a three steps method with the following stages: (a) Generating control points in moving image that will be interpreted as landmarks, (b) matching corresponding control points in the reference image, and (c) calculating a dense transformation function from the sparsely matched landmarks
Summary
Expand, rotate and create torsion simultaneously during a cardiac cycle and a common complication that arises in left ventricular (LV) myocardial tracking is the consideration of longitudinal heart movement. 3D tagging is emerged as a promising method to allow reconstruction of 3D myocardial strain from single image volume rather than multiple different 2D images[10,11,12]. The accuracy of such a method is highly dependent on the size of the kernel, as too large or too small kernels could lead to incorrect tracking[17] Such technical limitation could be overcome by using a hierarchy of matchings, which can provide a promising correlation up to the smallest level of the matching[25,27]. The method is validated using four different strategies which include tracking of known LV points, calculating displacement, strain values and eigenvalues analysis in a cardiac cycle
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