Why the study?
Does a 4D ventricular wall motion analysis framework using C-arm CT data allow for the detection of ventricular dyssynchrony directly in the catheter lab?
Does a 4D ventricular wall motion analysis framework using C-arm CT data allow for the detection of ventricular dyssynchrony directly in the catheter lab?
A novel 4D wall motion analysis framework using C-arm CT systems shows feasibility for quantitative assessment of left ventricular dyssynchrony directly in the catheterization laboratory.
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May enable cath-lab LV dyssynchrony assessment via C-arm CT; leaves open larger prospective validation before clinical adoption.
Today, quantitative analysis of three-dimensional (3D) dynamics of the left ventricle (LV) cannot be performed directly in the catheter lab using a current angiographic C-arm system, which is the workhorse imaging modality for cardiac interventions. Therefore, myocardial wall analysis is completely based on the 2D angiographic images or pre-interventional 3D/4D imaging. In this paper, we present a complete framework to study the ventricular wall motion in 4D (3D+t) directly in the catheter lab. From the acquired 2D projection images, a dynamic 3D surface model of the LV is generated, which is then used to detect ventricular dyssynchrony. Different quantitative features to evaluate LV dynamics known from other modalities (ultrasound, magnetic resonance imaging) are transferred to the C-arm CT data. We use the ejection fraction, the systolic dyssynchrony index a 3D fractional shortening and the phase to maximal contraction (ϕi, max) to determine an indicator of LV dyssynchrony and to discriminate regionally pathological from normal myocardium. The proposed analysis tool was evaluated on simulated phantom LV data with and without pathological wall dysfunctions. The LV data used is publicly available online at https://conrad.stanford.edu/data/heart. In addition, the presented framework was tested on eight clinical patient data sets. The first clinical results demonstrate promising performance of the proposed analysis tool and encourage the application of the presented framework to a larger study in clinical practice.
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Müller et al. (2014) studied this question.
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