Altered Aortic Hemodynamics and Relative Pressure in Patients with Dilated Cardiomyopathy

Figures

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  Fig. 1. Overview of assessed study metrics. Overview of the assessed metrics of the study, with cardiovascular (CV) characteristics given by cardiac MRI, aortic hemodynamics from 4D flow MRI, and central blood pressure from sphygmomanometer measures. The isolated influence of listed cardiovascular properties on aortic relative pressure is determined using a computational virtual cohort. LV, left ventricle. RV, right ventricle. EDV, end-diastolic volume. ESV, end-systolic volume. SV, stroke volume. EF, ejection fraction. CO, cardiac output. CI, cardiac index. PWV, pulse wave velocity. TTP, time-to-peak relative pressure

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  Fig. 2. Overview of the virtual work-energy principle. Overview of the virtual work-energy principle to derive aortic relative pressure from 4D flow MRI (left). Aortic segmentations (S, upper mid-portion) are shown together with corresponding virtual fields (lower mid-portion), used to isolate aortic relative pressure. Maximum relative pressure, minimum relative pressure, and time-to-peak relative pressure (TTP) are derived (right). Each case is processed individually, with the colour coding of the segmentations corresponding to the ones of the relative pressure traces

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  Fig. 3. Key results from aortic relative pressure analysis. a Maximum relative pressure, b TTP, c PWV, and d aortic stiffness for DCMred, DCMpres, and the control groups, respectively. p-values are reported throughout with significance indicated by * (p < 0.05) or ** (p < 0.01)

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  Fig. 4. Aortic relative pressure traces from clinical and virtual analysis. Aortic relative pressure traces from the clinical (top row) and virtual cohort (remaining rows), presented as a function of isolated variations of arterial resistance, stroke volume, cardiac period, aortic volumetric size, and aortic stiffness, respectively. Relative pressure is shown for DCMred (left), DCMpres (middle), and controls (right). For the clinical data, each individual subject is given in black. For the virtual data, isolated variations around baseline (black) are given as positive variations (red) and negative variations (blue), respectively, superimposed on the data range (grey)