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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. 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.

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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. 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.

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Dynamic magnetic resonance imaging (MRI) is a popular medical imaging technique that generates image sequences of the flow of a contrast material inside tissues and organs. However, its application to imaging bolus movement through the esophagus has only been demonstrated in few feasibility studies and is relatively unexplored. In this work, we present a computational framework called mechanics-informed MRI (MRI-MECH) that enhances that capability, thereby increasing the applicability of dynamic MRI for diagnosing esophageal disorders. Pineapple juice was used as the swallowed contrast material for the dynamic MRI, and the MRI image sequence was used as input to the MRI-MECH. The MRI-MECH modeled the esophagus as a flexible one-dimensional tube, and the elastic tube walls followed a linear tube law. Flow through the esophagus was governed by one-dimensional mass and momentum conservation equations. These equations were solved using a physics-informed neural network. The physics-informed neural network minimized the difference between the measurements from the MRI and model predictions and ensured that the physics of the fluid flow problem was always followed. MRI-MECH calculated the fluid velocity and pressure during esophageal transit and estimated the mechanical health of the esophagus by calculating wall stiffness and active relaxation. Additionally, MRI-MECH predicted missing information about the lower esophageal sphincter during the emptying process, demonstrating its applicability to scenarios with missing data or poor image resolution. In addition to potentially improving clinical decisions based on quantitative estimates of the mechanical health of the esophagus, MRI-MECH can also be adapted for application to other medical imaging modalities to enhance their functionality.

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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. 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.

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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. 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.

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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. 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.

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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. 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.

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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. 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.

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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. 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.

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PURPOSE: To develop a radial, double-echo steady-state (DESS) sequence with ultra-short echo-time (UTE) capabilities for T2 measurement of short-T2 tissues along with simultaneous rapid, signal-to-noise ratio (SNR)-efficient, and high-isotropic-resolution morphological knee imaging. METHODS: THe 3D radial UTE readouts were incorporated into DESS, termed UTEDESS. Multiple-echo-time UTEDESS was used for performing T2 relaxometry for short-T2 tendons, ligaments, and menisci; and for Dixon water-fat imaging. In vivo T2 estimate repeatability and SNR efficiency for UTEDESS and Cartesian DESS were compared. The impact of coil combination methods on short-T2 measurements was evaluated by means of simulations. UTEDESS T2 measurements were compared with T2 measurements from Cartesian DESS, multi-echo spin-echo (MESE), and fast spin-echo (FSE). RESULTS: UTEDESS produced isotropic resolution images with high SNR efficiency in all short-T2 tissues. Simulations and experiments demonstrated that sum-of-squares coil combinations overestimated short-T2 measurements. UTEDESS measurements of meniscal T2 were comparable to DESS, MESE, and FSE measurements while the tendon and ligament measurements were less biased than those from Cartesian DESS. Average UTEDESS T2 repeatability variation was under 10% in all tissues. CONCLUSION: The T2 measurements of short-T2 tissues and high-resolution morphological imaging provided by UTEDESS makes it promising for studying the whole knee, both in routine clinical examinations and longitudinal studies. Magn Reson Med 78:2136-2148, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

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PURPOSE: Methods for direct visualization of compact bone using MRI have application in several "MR-informed" technologies, such as MR-guided focused ultrasound, MR-PET reconstruction and MR-guided radiation therapy. The specificity of bone imaging can be improved by manipulating image sensitivity to Bloch relaxation phenomena, facilitating distinction of bone from other tissues detected by MRI. METHODS: From Bloch equation dynamics, excitation pulses suitable for creating specific sensitivity to short-T2 magnetization from cortical bone are identified. These pulses are used with UTE subtraction demonstrate feasibility of MR imaging of compact bone with positive contrast. RESULTS: MR images of bone structures are acquired with contrast similar to that observed in x-ray CT images. Through comparison of MR signal intensities with CT Hounsfield units of the skull, the similarity of contrast is quantified. The MR technique is also demonstrated in other regions of the body that are relevant for interventional procedures, such as the shoulder, pelvis and leg. CONCLUSION: Matching RF excitation pulses to relaxation rates improves the specificity to bone of short-T2 contrast. It is demonstrated with a UTE sequence to acquire images of cortical bone with positive contrast, and the contrast is verified by comparison with x-ray CT. Magn Reson Med 77:684-695, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

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PURPOSE: To develop an acceleration method for MR temperature estimation using model-based proton resonance frequency (PRF) shift method. MATERIALS AND METHODS: Images of 16 different echo times (TE) were acquired in one RF excitation using a multi-echo gradient-recalled echo (GRE) sequence. Fully sampled k-space data were retrospectively under-sampled at a net reduction factor between two and three using the proposed under-sampling strategy. K-spaces of three different TEs were combined together to perform the proposed reconstruction method called Echo-based GRAPPA. Ex vivo goose liver cooling experiment and in vivo breast imaging experiment were performed to investigate the accuracy of Echo-based GRAPPA. Conventional GRAPPA reconstruction was implemented for comparison using the same sampling pattern. RESULTS: The goose liver imaging experiment shows that the reconstruction-induced temperature RMSE of a selected region of interest (ROI) is less than 1.4 °C for Echo-based GRAPPA at a net reduction factor of 2.3. The breast imaging experiment shows that the mean temperature error of water-fat mixed ROIs is 2.3 °C at a net reduction factor of 2.7. Conventional GRAPPA shows larger temperature RMSE than Echo-based GRAPPA. CONCLUSION: The proposed method can accelerate the MR temperature estimation using model-based PRF at a net reduction factor between two and three with a reconstruction-induced temperature error less than 3°C in water-fat mixed ROIs.

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