arXiv subjects

Ethan M. I. Johnson

Publications and source records attributed to Ethan M. I. Johnson.

7 recordsLinked to original sources

arXiv2023

Fluid-structure instability forecasts thoracic aortic aneurysm progression

The basic mechanism driving aneurysm growth is unknown. Currently, clinical diagnosis of an aneurysm is mainly informed by retrospective tracking of its size and growth rate. However, aneurysms can rupture before reactive criteria are met or remain stable when they are exceeded. Here, we identify a fluid-structure instability that is associated with abnormal aortic dilatation. Our analysis yields a measurable dimensionless number and its analytically derived critical threshold. This threshold pinpoints the transition from stable flow to unstable aortic fluttering as a function of the physiological properties composing the dimensionless number, like blood pressure and aortic compliance. A retrospective study was then conducted with 4D-flow MRI data from 117 patients indicated for cardiac imaging and 100 healthy volunteers recruited prospectively. The difference between the dimensionless number and its critical threshold was calculated for every subject from their earliest MRI data and used as an aneurysm physiomarker to forecast future growth. As a binary predictor for abnormal growth and subsequent surgical intervention reported from follow-up imaging, the aneurysm physiomarker yielded an AUC of 0.997 in a receiving operator characteristic analysis. Though validated here for thoracic ascending aortic aneurysms, this instability mechanism may be used to understand, predict and inform patient-specific treatment of aneurysms in any location without fundamental differences.

physics.med-ph↗

arXiv2023

Multi-coil Magnetic Resonance Imaging with Compressed Sensing Using Physically Motivated Regularization

With the advent of multi-coil imaging and compressed sensing, a number of model based reconstruction algorithms have been created. They incorporate a multitude of different regularization functions based on physics, observed phenomenology, and heuristics. Moreover, several iterative methods exist that attempt to simultaneously estimate the sensitivity maps and the image. In this manuscript, we present a generalization of several existing iterative model based algorithms. We devise a calibrationless instance of this generalization that only incorporates regularization terms based on physics and the accepted compressed sensing phenomenology of sparsity in the wavelet domain. We compare the results of the new amalgamated optimization problem with existing methods on both simulated and real datasets. We show that the images reconstructed using the new method, entitled Multi-coil Compressed Sensing (MCCS), are of higher quality than existing methods in all cases studied.

eess.IV↗

arXiv2023

A deep learning approach to using wearable seismocardiography (SCG) for diagnosing aortic valve stenosis and predicting aortic hemodynamics obtained by 4D flow MRI

In this paper, we explored the use of deep learning for the prediction of aortic flow metrics obtained using 4D flow 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 Vmax in patients with heart valve diseases, from SCG signals. We also investigated the ability of this deep learning technique to differentiate between patients diagnosed with aortic valve stenosis (AS), non-AS patients with a bicuspid aortic valve (BAV), non-AS patients with a mechanical aortic valve (MAV), and healthy subjects with a normal tricuspid aortic valve (TAV). In a study of 77 subjects who underwent same-day 4D flow MRI and SCG, we found that the Vmax values obtained using deep learning and SCGs were in good agreement with those obtained by 4D flow MRI. Additionally, subjects with TAV, BAV, MAV, and AS could be classified with ROC-AUC values of 92%, 95%, 81%, and 83%, respectively. This suggests that SCG obtained using low-cost wearable electronics may be used as a supplement to 4D flow MRI exams or as a screening tool for aortic valve disease.

cs.LG↗

arXiv2021

Least Squares Optimal Density Compensation for the Gridding Non-uniform Discrete Fourier Transform

The Gridding algorithm has shown great utility for reconstructing images from non-uniformly spaced samples in the Fourier domain in several imaging modalities. Due to the non-uniform spacing, some correction for the variable density of the samples must be made. Existing methods for generating density compensation values are either sub-optimal or only consider a finite set of points (a set of measure 0) in the optimization. This manuscript presents the first density compensation algorithm for a general trajectory that takes into account the point spread function over a set of non-zero measure. We show that the images reconstructed with Gridding using the density compensation values of this method are of superior quality when compared to density compensation weights determined in other ways. Results are shown with a numerical phantom and with magnetic resonance images of the abdomen and the knee.

eess.IV↗

arXiv2021

Utilizing the Wavelet Transform's Structure in Compressed Sensing

Compressed sensing has empowered quality image reconstruction with fewer data samples than previously though possible. These techniques rely on a sparsifying linear transformation. The Daubechies wavelet transform is a common sparsifying transformation used for this purpose. In this work, we take advantage of the structure of this wavelet transform and identify an affine transformation that increases the sparsity of the result. After inclusion of this affine transformation, we modify the resulting optimization problem to comply with the form of the Basis Pursuit Denoising problem. Finally, we show theoretically that this yields a lower bound on the error of the reconstruction and present results where solving this modified problem yields images of higher quality for the same sampling patterns.

eess.IV↗

arXiv2021

Fast Variable Density Poisson-Disc Sample Generation with Directional Variation

We present a fast method for generating random samples according to a variable density Poisson-disc distribution. A minimum threshold distance 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. We further show that these sampling patterns are appropriate for compressed sensing applications. Finally, we present a method to generate patterns with a specific acceleration rate.

eess.IV↗

arXiv2020

multiMap: A Gradient Spoiled Sequence for Simultaneously Measuring B1+, B0, T1/M0, T2, T2*, and Fat Fraction of a Slice

We propose multiMap, a single scan that can generate several quantitative maps simultaneously. The sequence acquires multiple images in a time-efficient manner, which can be modeled for T_2, T2*, main- and transmit-field inhomogeneity, T_1:equilibrium magnetization, and water and fat content. The sequence is constructed so that cross-dependencies between parameters are isolated. Thus, each parameter can be estimated independently. Estimates of all parameters are shown on bottle phantoms, the brain, and the knee. The results are compared to estimates from established techniques.

eess.IV↗