BACKGROUND:4D flow MRI hemodynamic biomarkers (e.g. peak velocity and wall shear stress [WSS]) have shown promise for improved risk-stratification in patients with thoracic aortic disease (TAD). However, lengthy 4D flow scan times, complex data analysis and need for dedicated expertise limit clinical translation. In this study, we developed a fluid-physics informed deep generative adversarial neural network, CE-MRA-FLOWnet, to predict aortic hemodynamics directly from standard-of-care contrast-enhanced magnetic resonance angiography (CE-MRA) images. METHODS:We retrospectively identified 1392 patients (age: 52±12years, 1011 male, 954 with bicuspid aortic valve, BAV; 438 with tricuspid aortic valve, TAV) who underwent paired clinical 4D flow MRI and CE-MRA between 2011 and 2020 for suspected TAD. The CE-MRA-FLOWnet used CE-MRA data (1127 for network training, 265 for testing) to generate a prediction of aortic hemodynamics. 4D flow-measured aortic systolic 3D blood flow velocity vector fields served as ground truth. Analysis included comparison of AI-derived and ground truth aortic systolic peak velocity (PV) and WSS, the relative area of the AAo (%) exposed to elevated WSS, and aortic valve stenosis severity grading. The predictive value of CE-MRA-FLOWnet hemodynamics metrics was assessed in a subgroup of 133 BAV patients with known multi-year adverse outcomes. RESULTS:CE-MRA-FLOWnet training time was 8100mins; inference time per CE-MRA was 0.88±0.05seconds. AI-derived PV showed strong regional agreement and low biases with ground truth 4D flow (0.00-0.04m/s), and relative differences within 10.6%-12.7%. Aortic WSS also demonstrated minimal bias (-0.01-0.04Pa) and close alignment of the area of elevate WSS between AI and 4D flow (19.6±15.1% vs. 19.3±16.2%, p=0.84). AS severity was accurately classified in 88% of cases, with all grading errors limited to a one-grade difference (Kappa 0.84). ROC analysis showed that CE-MRA-FLOWnet derived hemodynamic metrics outperformed diameter alone for predicting adverse outcomes (PV AUC = 0.73-0.76; WSS = 0.83-0.88; diameter = 0.52-0.62). CONCLUSIONS:CE-MRA-FLOWnet accurately predicts aortic hemodynamics in TAD patients using standard-of-care CE-MRA images. Our results demonstrate potential for clinical integration by providing physicians with near real-time hemodynamic data from widely available clinical CE-MRA images. CLINICAL PERSPECTIVE:Approximately 3% of the population has or is at risk for thoracic aortic disease (TAD) which can result in significant complications including progressive aortic dilation and dissection. Historically, aortic diameter measured on CT angiography or MR angiography (MRA) has been the primary marker of risk and TAD patients undergo frequent surveillance imaging to evaluate aortic size and growth. However, it is well established that aortic diameter is an imperfect risk assessment tool and a large percentage of patients who are outside of guideline-endorsed diameter surgical thresholds can have aortic dissection. Recently, aortic hemodynamics measured with 4D flow MRI have shown promise for detecting patients who have higher aortic growth rates and 4D flow derived wall shear stress (WSS) has been linked to histopathologic damage to the aortic wall. Thus, aortic hemodynamic assessment could supplement aortic diameter to improve TAD risk-stratification. Unfortunately, 4D flow MRI is not widely available and can be difficult acquire, analyze, and interpret which has led to low utilization. In this study, we have developed CE-MRA-FLOWnet, a fluid-physics informed generative neural network that quantifies peak systolic aorta hemodynamics using only standard anatomic contrast-enhanced (CE) MRA as input. We found that CE-MRA-FLOWnet peak systolic velocity and WSS quantification is highly accurate compared to 4D flow MRI. We also found that these outputs can accurately grade aortic stenosis severity and are superior to aortic diameter for predicting adverse aortic outcomes. CE-MRA-FLOWnet could significantly expand access these important risk metrics to many more TAD patients.

Introduction: Patients with aortic valve disease, such as bicuspid aortic valve (BAV), require regular echocardiography or cardiovascular (CV) MRI to monitor for complications such as valve stenosis (AS) and aortic dilation. However, repeated imaging can be burdensome and incur substantial cost. Seismocardiogram (SCG) chest acceleration measurements recorded by inexpensive wearable devices can give indicators of valve-mediated hemodynamic changes, and as such may have supplemental value for such patients. This study investigated using SCG recordings coupled with a novel machine-learned (ML) classifier for SCG signals to identify patient valve type and presence/absence of aortic valve stenosis (AS). Hypothesis: We hypothesize that accurate classification of aortic valve type and AS can be made from SCG recordings with ML analysis compared to those from standard-of-care imaging (ground truth: cardiac MRI or echo). Methods: Healthy controls (no known CV disease) and aortic valve disease patients with tricuspid (TAV), BAV, or post-repair mechanical valve who received echo or MRI (clinical CV protocol) were enrolled for same-day 2-minute wearable SCG measurement (fig. A). Standard clinical assessment of valve/flow function was used (fig. B). Informed consent was given with IRB oversight. Clinical imaging used 4D flow MRI (1.5T,1-3mm3/30-40ms) or 2D Doppler echo (1.7-3.3MHz,12-40FPS). From clinical read of valve type/function, subjects were grouped in four classes: AS (any degree), BAV no-AS, TAV no-AS, mechanical. A hybrid network with convolutional neural network and multi-layer perceptron was trained (80/20 train/test) to classify patient valve status from SCG wavelet coefficients and demographics (age/sex/height/weight). Performance was evaluated by 20-fold cross-validation. Results: Enrolment was 129 subjects (97 MRI/32 echo): 46 controls (45.9±17.4y/20F) and 83 patients (22.4±15.8y/20F; 67 BAV/6 TAV/10 mech.). Classification area-under-curve (AUC) was high for all classes (AUC≥0.79). Across all ML validations, correct classification was achieved for ≥75% of subjects. Conclusion: This evaluation of a machine-learned classifier for SCG indicate potential utility in screening for valve-mediated hemodynamic changes, which reverberate through the chest and cause altered vibrations. The low cost and ease of acquisition for SCG would make it an appealing complement to imaging as the current standard for aortic valve abnormality screening and management.

Background: In patients who have undergone Fontan palliation, suboptimal geometry of the reconstructed aorta may contribute to abnormal aortic blood flow patterns, ventriculo-vascular decoupling, and worse clinical outcomes. 4D Flow magnetic resonance imaging (MRI) enables detailed hemodynamic assessment, but large-scale studies are hindered by labor-intensive image analysis. Deep learning models have successfully automated aortic segmentation in single-center adult cohorts with bicuspid aortic valve or conventional anatomy but perform poorly in patients with complex congenital heart disease. Training on 4D Flow data from Fontan patients may improve performance in this heterogeneous population. Methods: We compiled 215 4D Flow CMR studies (200 unique patients from 17 centers) from the Fontan Outcomes Registry Using Cardiac Magnetic Resonance Examination (FORCE). Manual aortic segmentation was performed on a subset of 78 studies (n=63 training, n=15 testing). Studies with metallic artifact obscuring the aorta, aortic cropping, or non-sagittal image orientation were excluded. A convolutional neural network with 3D U-Net architecture incorporating dense blocks was used to generate 3D aortic segmentations from the 4D Flow data. Segmentation accuracy was evaluated using dice similarity coefficients (DSCs). Results: The mean age at MRI was 17.7 ± 8.8 years. The most common diagnoses were hypoplastic left heart syndrome (33%), double outlet right ventricle (16.6%), and tricuspid atresia (12%). Segmentation performance was variable, with DSCs ranging from 0.001 to 0.47 (mean=0.20). Modeling was likely challenged by the heterogeneity of Fontan anatomy as well as variation in imaging protocols across the 17 contributing centers. Conclusions: Anatomical and imaging variability across centers likely contributed to poor model performance compared to more uniform, single-center adult studies. To mitigate this, we plan to standardize images and segmentations to a uniform voxel size and field of view. We will also apply data augmentation techniques, including spatial transformations and intensity perturbations, to synthetically increase dataset size and expose the model to a wider range of anatomical and imaging variability. This approach may improve generalizability and robustness in the setting of a small, non-uniform training dataset. Future efforts will also include k-fold cross-validation and expansion of the training cohort to further enhance model performance.

PURPOSE:This study aimed to investigate the changes in aortic pulse wave velocity (PWV) and wall shear stress (WSS) in COVID-19 using 4D Flow MRI. METHODS:Thirty-seven COVID-19 patients and 37 healthy controls underwent thoracic cardiovascular MRI. The PWV and WSS comparisons were performed using independent t-test. Peak velocity (PV)-peak WSS correlations in patients; aortic dimension-regional WSS correlations; PWV-age correlations were reported using Pearson correlation coefficient (r) analysis. RESULTS:The global aortic PWV was higher in the patient group (p = 0.007). There was a positive correlation between patient age and PWV values (r = 0.650, p = 0.000). The patient ascending aorta (AAo) WSS levels were lower in the entire cohort, in the subgroup of ages between 50 and 70, and in the age/gender matched subgroup (p < 0.05 for all). Voxelwise 5 % PV was lower in the patient group (p = 0.005) and showed strong correlation with the 5 % peak WSS (r = 0.957). In the patient group there was a negative correlation between the maximal aortic dimension and AAo WSS (r = -0.398, p = 0.014) and aortic arch WSS (r = -0.388, p = 0.017). CONCLUSION:The alterations to aortic stiffness in COVID-19 might be a late effect of the disease and should be confirmed in larger studies with longer follow-ups. The reasons behind the low AAo WSS levels in the COVID-19 group appears to be multifactorial and further work in larger cohorts eliminating the baseline aortic diameter and preexisting atherosclerotic risk factor differences is needed to validate our results and to establish reproducibility of the technique.

Introduction: Severe aortic regurgitation (AR) is characterized by significant retrograde blood flow in the aorta and remains difficult to quantitively evaluate by echocardiography. By providing comprehensive insights into hemodynamic changes and quantifying regurgitant fraction (RF) across various locations of the aorta, this study investigated the potential of 4D flow MRI to enhance diagnostic accuracy and inform clinical decision-making. Methods: An institutional database was queried for patients with chronic AR on echocardiography and paired cardiac MRIs with aortic 4D flow MRI. Patients with LVEF < 50%, concomitant mitral regurgitation and aortic stenosis were excluded. A fully automated 4D flow MRI processing tool, performing standard preprocessing corrections and aortic 3D segmentation using separately trained machine learning models (Dense U-net convolutional neural network architecture) was used. Through-plane flow was quantified at 7 AHA-standardized locations: aortic annulus, sinotubular junction, mid ascending aorta, distal ascending aorta, aortic arch, proximal descending aorta and mid descending aorta. 4D flow MRI-based quantifications of RF were assessed for differentiating severe AR, using echo gradings as reference classification. Adjudicated clinical outcome data included cardiac-related hospitalizations such as heart failure, arrhythmias, and inpatient management of valve intervention. Results: Of 59 patients with chronic AR, the mean age was 49 ± 14.5 years, LVEF 56.5 ± 8.3%, LV end diastolic volume 251 ± 74 mL, 90% male and 73% had bicuspid aortic valves. Receiver operator characteristic (ROC) analysis of 4D flow MRI RFs revealed the optimal anatomic location to differentiate severe AR, as graded by echo was the mid descending aorta (AUC = 0.79). In patients with moderate, moderate-severe, and severe AR on echo, Kaplan-Meyer analysis reveals significant differences in cardiac-related hospitalization rates and time to valve intervention when patients were median split by optimal mid-descending aorta ROC RF (35%) but not at other locations of the aorta nor RFs calculated by traditional 2D Phase Contrast MRI (Figure 1). Conclusion: The optimal location in discerning severe aortic regurgitation as per RF by 4D flow analysis is the mid-descending aorta. 4D flow quantified RF of 35% at the mid-descending aorta was associated with cardiac related hospitalizations.

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. We aimed to characterize hemodynamics within the great arteries using 4D flow MRI in patients with d-TGA after the arterial switch operation. Patients with d-TGA after the arterial switch operation and controls with normal cardiac anatomy who underwent 4D flow MRI between 2012 and 2024 were included in this IRB-approved retrospective cohort study. Controls included patients undergoing MRI for other indications who consented or assented to the addition of a 4D flow sequence, as well as patients who underwent clinically indicated 4D flow MRI and were found to have normal cardiac anatomy and function. Velocity, stasis, kinetic energy, energy loss, wall shear stress, and pulse wave velocity were quantified in the aorta and pulmonary arteries. To compare each parameter between d-TGA patients and controls, unpaired t-tests were used for normally distributed data and Mann–Whitney tests for non-normal data. P < 0.05 was significant. Patients with d-TGA after the arterial switch operation (15.7 years ± 2.4, 2 females) demonstrated significantly higher maximum and mean velocity, maximum and mean kinetic energy, energy loss, and maximum and mean wall shear stress within the pulmonary arteries (P < 0.0001 for all parameters) compared with age-matched controls (15.5 years ± 2.4, 14 females). Aortic maximum (P = 0.001) and mean (P = 0.048) velocity, maximum (P = 0.0008) and mean (P = 0.003) kinetic energy, energy loss (P < 0.0001), maximum wall shear stress in five of six regions (range P < 0.0001 to P = 0.002), and mean wall shear stress in three regions (range P = 0.005 to P = 0.03) were significantly higher in patients with d-TGA after the arterial switch operation patients compared with age-matched controls. Patients with d-TGA after the arterial switch operation demonstrate hemodynamic abnormalities within the great arteries, which may provide insight into the mechanisms underlying postoperative consequences of the arterial switch operation.

Purpose: To evaluate the reproducibility of important biomarkers like wall shear stress (WSS), pulse wave velocity (PWV), and net flow across two 4D flow MRI imaging protocols with different coverages: aorta-targeted 4D flow MRI (AT4D) and whole-heart 4D flow (WH4D) protocols. Methods: Thirty-eight control subjects (43.2 ± 10.1 years old; 22 males) and ten patients (45.7 ± 8.9 years old; 7 males) with bicuspid aortic valve (BAV) were included. Each subject underwent AT4D and WH4D scans. Absolute WSS, PWV, and net flow were assessed for each patient across both protocols and compared using Bland-Altman analysis. Areas of elevated WSS were assessed for BAV patients across different WSS thresholds that define WSS to be elevated compared to a normal population average. A sensitivity analysis was conducted to determine the best WSS threshold at which WH4D-derived areas most closely resemble AT4D-derived areas. Inter-rater reproducibility was evaluated in twenty-four subjects. Results: AT4D and WH4D PWV and WSS estimates demonstrated good agreement (PWV: -0.12 ± 1.84 m/s, p = 0.4; Median WSS: 0.06 ± 0.13 Pa, p < 0.01; Maximum WSS: 0.04 ± 0.27 Pa, p = 0.07). Good agreement was also found for AAo net flow (8.14 ± 24.86 mL/cycle, p < 0.01). PWV correlated with age across protocols (AT4D: r = 0.68, p < 0.01; WH4D: r = 0.72, p < 0.01). Sensitivity analysis identified a WSS threshold where WH4D-derived areas of elevated WSS most closely resembled AT4D-derived areas. Inter-rater assessment of the tested parameters resulted in a small mean difference percentage of < 3

Background:In the past decade, the Ross procedure has reemerged as a promising approach for young adult patients with aortic valve disease. We evaluated its efficacy in restoration of selected aortic hemodynamic parameters in patients with unicuspid aortic valve (UAV) using 4-dimensional (4D) flow magnetic resonance imaging (MRI). This study examined whether the Ross procedure would normalize ascending aorta (AAo) hemodynamics in patients with UAV. Methods:Thirteen patients with UAV disease who underwent the Ross procedure and received preoperative and postoperative 4D flow MRI were matched to healthy controls (n = 52). Systolic peak velocity (PV), wall shear stress, and viscous energy loss (EL) of each MRI were assessed and compared. Results:There was a significant decrease in systolic PV in the AAo postoperatively (P < .0001), suggesting that the Ross procedure normalizes high flow velocities and gradients across the aortic valve and aorta. This study also found a significant global reduction in systolic viscous EL in the thoracic aorta postoperatively (P < .01), suggesting that the Ross procedure reduces the turbulence of blood flow through the aorta. Additionally, no differences in these variables were seen between postoperative patients and healthy controls. Conclusions:This study demonstrates the value of using 4D flow MRI to understand aortic hemodynamics after the Ross procedure. Additionally, this study shows that in patients with UAV disease, the Ross procedure can normalize aortic hemodynamics similar to healthy controls.

Introduction: Bicuspid aortic valve (BAV) is associated with progressive ascending aorta (AAo) dilation, often leading to aneurysms, dissections, and ruptures. Thus, current guidelines recommend preventive surgery for AAo dilation. Recent 4D flow MRI studies show that BAV morphology causes abnormal transvalvular flow patterns, increasing wall shear stress (WSS), a trigger of aortic growth. Further studies have delineated areas of abnormally high WSS by comparing to estimates of matched controls, and show promise in detecting risk for aortic growth. However, since the long-term prognostic significance of this marker is unclear, we aimed to quantify WSS in BAV patients to assess its value in predicting the need for aortic surgery up to 10 years post-4D flow MRI acquisition. Methods: BAV patients without prior surgical intervention scanned before April 1, 2014 were identified. Using medical records, patients were categorized as ‘operated’ if they underwent aortic surgery post-scan and ‘non-operated’ if they were surgery-free for at least 10 years post-scan. 4D flow MRIs were processed with an AI pipeline, including 3D segmentation of the aorta, followed by peak velocity (PV) and WSS quantification in the AAo (Fig. 1A-C). Patient-specific WSS heatmaps were computed relative to a map based on the WSS of 10 or more sex and age-matched controls. Relative areas of elevated WSS in the AAo were then calculated (Fig. 1D-F). Results: 115 patients were included, with 73 non-operated (age: 42.5±11.5y, 49M) and 42 operated patients (age: 53.5±12.1y, 34M). The mean baseline mid-AAo diameters for non-operated and operated patients were 3.8±0.6 cm and 4.1±0.5 cm, respectively. Among operated patients, the mean scan to surgery time was 5.7±3.3y. All three 4D flow metrics were significantly higher in operated compared to non-operated patients: PV: 2.6±0.6 vs. 1.7±0.4 m/s (p<0.01); WSS: 2.2±0.5 vs. 1.6±0.4 Pa (p<0.01); relative area of high WSS on heatmaps: 31±12 vs. 15±13% (p<0.01). Conclusions: 4D flow MRI parameters provide long-term predictive value in BAV patients, as elevated peak velocity, wall shear stress, and relative area of high wall shear stress on heatmaps are associated with subsequent aortic surgery within 10 years post-scan.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia in clinical practice and has a well-established association with coronary artery bypass graft (CABG) surgery. Being able to predict post-operative AF (POAF) may improve surgical outcomes. This study retrospectively assembled a large cohort of 3,807 first-time CABG patients with no prior AF to study factors that contribute to occurrence of POAF, in addition to testing models that may predict its incidence. Several clinical features with established relevance to POAF were extracted from the EHR, along with a record of medications administered intra-operatively. Tests of performance with logistic regression, decision tree, and neural network predictive models showed slight improvements when incorporating medication information. Analysis of the clinical and medications data indicate that there may be effects contributing to POAF incidence captured in the medication administration records. Our results show that improved predictive performance is achievable by incorporating a record of medications administered intra-operatively.