Publications by authors named "Ethan M I Johnson"

Background: Pulmonary artery stenosis, neoaortic dilatation, and neoaortic valve insufficiency are among the most frequent complications of the arterial switch operation for repair of dextro-transposition of the great arteries (d-TGA). It remains difficult to predict which patients will require great arterial reintervention.

Objective: We aimed to characterize hemodynamics within the great arteries using 4D flow MRI in patients with d-TGA after the arterial switch operation.

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The diagnosis of aneurysms is informed by empirically tracking their size and growth rate. Here, by analysing the growth of aortic aneurysms from first principles via linear stability analysis of flow through an elastic blood vessel, we show that abnormal aortic dilatation is associated with a transition from stable flow to unstable aortic fluttering. This transition to instability can be described by the critical threshold for a dimensionless number that depends on blood pressure, the size of the aorta, and the shear stress and stiffness of the aortic wall.

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In this paper, we explored the use of deep learning for the prediction of aortic flow metrics obtained using 4-dimensional (4D) flow magnetic resonance imaging (MRI) using wearable seismocardiography (SCG) devices. 4D flow MRI provides a comprehensive assessment of cardiovascular hemodynamics, but it is costly and time-consuming. We hypothesized that deep learning could be used to identify pathological changes in blood flow, such as elevated peak systolic velocity ([Formula: see text]) in patients with heart valve diseases, from SCG signals.

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The non-uniform Discrete Fourier Transform algorithm has shown great utility for reconstructing images from non-uniformly spaced Fourier samples in several imaging modalities. Due to the non-uniform spacing, some correction for the variable density of the samples must be made. Common methods for generating density compensation values are either sub-optimal or only consider a finite set of points in the optimization.

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Background: Aortopathy is common with bicuspid aortic valve (BAV), and underlying intrinsic tissue abnormalities are believed causative. Valve-mediated hemodynamics are altered in BAV and may contribute to aortopathy and its progression. The contribution of intrinsic tissue defects versus altered hemodynamics to aortopathy progression is not known.

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Compressed sensing has empowered quality image reconstruction with fewer data samples than previously thought possible. These techniques rely on a sparsifying linear transformation. The Daubechies wavelet transform is commonly used for this purpose.

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We present a fast method for generating random samples according to a variable density poisson-disc distribution. A minimum parameter value is used to create a background grid array for keeping track of those points that might affect any new candidate point; this reduces the number of conflicts that must be checked before acceptance of a new point, thus reducing the number of computations required. We demonstrate the algorithm's ability to generate variable density poisson-disc sampling patterns according to a parameterized function, including patterns where the variations in density are a function of direction.

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Cardiac MRI (CMR) techniques offer non-invasive visualizations of cardiac morphology and function. However, imaging can be time-consuming and complex. Seismocardiography (SCG) measures physical vibrations transmitted through the chest from the beating heart and pulsatile blood flow.

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Aortic valve replacement (AVR) is a common treatment for severe aortic valve disease, which can adversely affect blood flow in the aorta. Seismocardiography (SCG) measures physical vibrations at the exterior of the chest, which can be sensitive to altered cardiac function and flow dynamics. Magnetic resonance imaging (MRI) can image blood movement, and it can provide depiction and quantification of aortic flow.

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