Blood-wall fluttering instability as a physiomarker of the progression of thoracic aortic aneurysms.

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. By analysing data from four-dimensional flow magnetic resonance imaging for 117 patients who had undergone cardiothoracic imaging and for 100 healthy volunteers, we show that the dimensionless number is a physiomarker for the growth of thoracic ascending aortic aneurysms and that it can be used to accurately discriminate abnormal versus natural growth. Further characterization of the transition to blood-wall fluttering instability may aid the understanding of the mechanisms underlying aneurysm progression in patients.

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 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. 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 [Formula: see text] values obtained using deep learning and SCGs were in good agreement with those obtained by 4D flow MRI. Additionally, subjects with non-AS TAV, non-AS BAV, non-AS MAV, and AS could be classified with ROC-AUC (area under the receiver operating characteristic curves) 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.

Optimization in the space domain for density compensation with the nonuniform FFT.

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. This manuscript presents an algorithm for generating density compensation values from a set of Fourier samples that takes into account the point spread function over an entire rectangular region in the image domain. We show that the reconstructed images using the density compensation values of this method are of superior quality when compared to other standard methods. Results are shown with a numerical phantom and with magnetic resonance images of the abdomen and the knee.

Global Aortic Pulse Wave Velocity is Unchanged in Bicuspid Aortopathy With Normal Valve Function but Elevated in Patients With Aortic Valve Stenosis: Insights From a 4D Flow MRI Study of 597 Subjects.

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. PURPOSE: To investigate relative contributions of tissue-innate versus hemodynamics in progression of BAV aortopathy. STUDY TYPE: Retrospective. SUBJECTS: Four hundred seventy-three patients with aortic dilatation (diameter ≥40 mm; comprised of 281 BAV with varied AS severity, 192 tricuspid aortic valve [TAV] without AS) and 124 healthy controls. Subjects were 19-91 years (141/24% female). FIELD STRENGTH/SEQUENCE: 1.5T, 3T; time-resolved gradient-echo 3D phase-contrast (4D flow) MRI. ASSESSMENT: A surrogate measure for global aortic wall stiffness, pulse wave velocity (PWV), was quantified from MRI by standardized, automated technique based on through-plane flow cross-correlation maximization. Comparisons were made between BAV patients with aortic dilatation and varying aortic valve stenosis (AS) severity and healthy subjects and aortopathy patients with normal TAV. STATISTICAL TESTS: Multivariable regression, analysis of covariance (ANCOVA), Tukey's, student's (t), Mann-Whitney (U) tests, were used with significance levels P < 0.05 or P < 0.01 for post-hoc Bonferroni-corrected t/U tests. Bland-Altman and ICC calculations were performed. RESULTS: Multivariable regression showed age with the most significant association for increased PWV in all groups (increase 0.073-0.156 m/sec/year, R2  = 0.30-48). No significant differences in aortic PWV were observed between groups without AS (P = 0.20-0.99), nor were associations between PWV and regurgitation or Sievers type observed (P = 0.60, 0.31 respectively). In contrast, BAV AS patients demonstrated elevated PWV and a significant relationship for AS severity with increased PWV (covariate: age, R2  = 0.48). BAV and TAV patients showed no association between aortic diameter and PWV (P = 0.73). DATA CONCLUSION: No significant PWV differences were observed between BAV patients with normal valve function and control groups. However, AS severity and age in BAV patients were directly associated with PWV increases. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Utilizing the Wavelet Transform's Structure in Compressed Sensing.

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. 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 using both magnetic resonance and optical images.

Fast variable density Poisson-disc sample generation with directional variation for compressed sensing in MRI.

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. 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.

Detecting Aortic Valve-Induced Abnormal Flow with Seismocardiography and Cardiac MRI.

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. SCG signals can be acquired quickly and easily, with inexpensive electronics. This study investigates relationships between CMR metrics of function and SCG signal features. Same-day CMR and SCG data were collected from 28 healthy adults and 6 subjects with aortic valve disease history. Correlation testing and statistical median/decile calculations were performed with data from the healthy cohort. MR-quantified flow and function parameters in the healthy cohort correlated with particular SCG energy levels, such as peak aortic velocity with low-frequency SCG (coefficient 0.43, significance 0.02) and peak flow with high-frequency SCG (coefficient 0.40, significance 0.03). Valve disease-induced flow abnormalities in patients were visualized with MRI, and corresponding abnormalities in SCG signals were identified. This investigation found significant cross-modality correlations in cardiac function metrics and SCG signals features from healthy subjects. Additionally, through comparison to normative ranges from healthy subjects, it observed correspondences between pathological flow and abnormal SCG. This may support development of an easy clinical test used to identify potential aortic flow abnormalities.

Seismocardiography and 4D flow MRI reveal impact of aortic valve replacement on chest acceleration and aortic hemodynamics.

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. Here we present SCG and MRI measurements from before and after AVR and ascending aorta replacement, in the case of a woman with bicuspid aortic valve disease and a dilated ascending aorta. SCG measurements show elevated energy during systole indicating stenotic flow before surgery and lowered systolic energy levels after replacement with a prosthetic valve. MRI shows jetting, helical flow before surgery, and cohesive flow after.