Accurate segmentation of 4D flow MRI is essential for assessing hemodynamic biomarkers such as flow patterns and pressure gradients. We analyzed magnitude images in 5,440 4D flow MR images from 34 patients diagnosed with aortic stenosis using the Segment Anything Model 2 (SAM 2) with point and rectangle box prompting. SAM 2 consistently delivered strong performance. Compared to manual segmentations, SAM 2 achieved a Dice Similarity Coefficient (DSC) of 0.85 and an Intersection over Union (IoU) of 0.82, indicating the model's robustness in segmenting magnitude images of 4D flow MR scans. SAM 2's segmentation performance was particularly enhanced in regions with clearer vessel background contrast, highlighting the importance of contrast in accurate segmentation. The model's ability to handle various cardiac phases reinforces its adaptability to different clinical conditions, demonstrating the potential utility of SAM 2 in 4D flow MRI segmentation within clinical settings.

BACKGROUND:Primary aortic thrombus (PAT) is a rare, life-threatening condition that can cause myocardial infarction, stroke, and acute limb or visceral ischemia. CASE SUMMARY:A 48-year-old woman was diagnosed with right-hand ischemia and non-ST-segment elevation myocardial infarction from coronary embolism attributable to a floating PAT in the ascending aorta. She underwent successful surgical resection of the PAT and was discharged on warfarin. DISCUSSION:More cases of PAT are being diagnosed because of improved cardiovascular imaging techniques. Coronary computed tomographic angiography can provide a detailed evaluation of the coronary arteries and the aorta in such patients. Management by a multidisciplinary team is needed to carefully select patients for initial surgical management vs medical therapy with anticoagulation alone. TAKE-HOME MESSAGES:Aortic thrombi are rare in the absence of underlying risk factors such as atherosclerosis, iatrogenic factors, trauma, dissection, aneurysms, aortitis, and hypercoagulable disorders. Surgical treatment should be considered early for proximal, large, free-floating PAT because of the embolic potential to multiple vascular beds, including the coronary circulation.

•Rapidly growing cardiac tumors can be benign or malignant.•Rarely, cardiac myxomas may grow rapidly, causing heart failure or obstructive symptoms.•Echocardiography remains the first-line imaging for cardiac tumors.•Multimodality imaging with CCT, CMR, and PET may aid in diagnosis.

Arterial stenosis is one of the most prevalent diseases with significant morbidity and mortality requiring accurate quantification of hemodynamic parameters for diagnosis and prognosis. In particular, variations in velocity derivatives have been correlated with variations in pressure gradient, an important marker of hemodynamic significance of stenosis. 4D flow MRI provides time-resolved 3D velocity mapping, however, image denoising and super-resolution techniques are required for precise velocity fluctuation quantification. To address this issue, we propose TKE-Net, a novel network that uses a ResNet convolutional neural network to estimate Turbulent Kinetic Energy (TKE). We trained and tested the network with high-resolution simulated CFD data in a phantom model of arterial stenosis. Our proposed network was further tested on in-vitro 4D flow MRI data in identical geometry, demonstrating good accuracy in estimating TKE.

Purpose: To estimate relative transvalvular pressure gradient (TVPG) noninvasively from 4D flow MRI. Methods: A novel deep learning-based approach is proposed to estimate pressure gradient across stenosis from four-dimensional flow MRI (4D flow MRI) velocities. A deep neural network 4D flow Velocity-to-Presure Network (4Dflow-VP-Net) was trained to learn the spatiotemporal relationship between velocities and pressure in stenotic vessels. Training data were simulated by computational fluid dynamics (CFD) for different pulsatile flow conditions under an aortic flow waveform. The network was tested to predict pressure from CFD- simulated velocity data, in vitro 4DflowMRI data, and in vivo 4D flow MRI data of patients with both moderate and severe aortic stenosis. TVPG derived from 4Dflow-VP-Net was compared to catheter-based pressure measurements for available flow rates, in vitro and Doppler echocardiography-based pressure measurement, in vivo. Results: Relative pressures calculated by 4Dflow-VP-Net and in vitro pressure catheterization revealed strong correlation (r(2) = 0.91). Correlations analysis of TVPG from reference CFD and 4Dflow-VP-Net for 450 simulated flow conditions showed strong correlation (r(2) = 0.99). TVPG from in vitro MRI had a correlation coefficient of r(2) = 0.98 with reference CFD. 4Dflow-VP-Net, applied to 4D flow MRI in 16 patients, showed comparable TVPG measurement with Doppler echocardiography (r2 = 0.85). Bland-Altman analysis of TVPG measurements showed mean bias and limits of agreement of -0.20 +/- 2.07 mmHg and 0.19 +/- 0.45mmHg for CFD-simulated velocities and in vitro 4D flow velocities. In patients, overestimation of Doppler echocardiography relative to TVPG from 4Dflow-VP-Net (10.99 +/- 6.77 mmHg) was observed. Conclusion: The proposed approach can predict relative pressure in both in vitro and in vivo 4D flow MRI of aortic stenotic patients with high fidelity.

4D Flow Magnetic Resonance Imaging (MRI) allows non-invasive assessment of cardiovascular hemodynamics through the acquisition of three-dimensional pulsatile velocities in a single scan. However, this technique is often plagued by issues of noise and low resolution. In this paper, we employed a deep learning-based super-resolution method utilizing an SR residual network (ResNet) to enhance the measurement of hemodynamic indices at a higher resolution. Our approach enables the derivation of hemodynamic parameters dependent on spatiotemporal velocity derivatives such as vorticity, circulation, and turbulent kinetic energy, which were validated using a phantom model of arterial stenosis. We also compared the deep learning approach with linear, nearest neighbor, and natural interpolation methods with a 2x upsampling factor. The results were evaluated against Computational Fluid Dynamics simulations as a reference and showed that the deep learning approach improved the accuracy of turbulent kinetic energy (TKE) and viscous energy loss at peak systole by 7% and 9%, respectively, indicating a significant enhancement over traditional interpolation methods. Additionally, herein we introduce a novel hemodynamic parameter, enstrophy, as a potential diagnostic biomarker for assessing stenosis severity. Overall, our findings suggest that deep learning is a reliable and efficient approach for predicting hemodynamic parameters from 4Dflow MRI.

Severe arterial stenosis encompasses complex flow structures especially when the blood flow rate exceeds the critical Reynolds number (Re ≥ 2000), resulting in ow instability and turbulence. Uncovering reduced-order ow characteristics in blood flow data facilitate understanding flow physics and efficient data-driven modeling. In this paper, we used Computational Fluid Dynamics (CFD) and 4D flow MRI data in a phantom model of arterial stenosis with 87% degree of narrowing for performing Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD) on the velocity and pressure data. We found the required modes to reconstruct the CFD and 4D flow MRI velocity and pressure data in the phantom model and identified the most energetic modes with temporal dynamics of coherent structures. In addition, we evaluated the compromise between the simplicity and accuracy of the reconstructed data. These data-driven modeling techniques have the potential to reduce the complexity of 4D flow MRI data. We envisage that it can ultimately be applied to enhancing the resolution, denoising 4D flow MRI data, and impacting data collection requirements.

•Midventricular TTC is a rare variant comprising 14.6% of stress cardiomyopathies.•TTC includes midventricular dyskinesis with normal basal and apical contractility.•Etiologies for all Takotsubo variants include physical/emotion stressors.•Pathophysiology remains unknown, although some theories exist.

BACKGROUND:This study investigates the hemodynamics of a dual-orifice mitral valve after mitral valve clip closure (MVCC) in patients with functional and nonfunctional mitral regurgitation (MR). If inflow velocity-time integral (VTi) of both orifices is equal, then the standard continuity equation can be applied to calculate the total mitral valve area (MVA).METHODS AND RESULTS:Adults undergoing MVCC placement were prospectively enrolled. With transesophageal echocardiography (TEE), the vena contracta (VC) of the medial and lateral mitral valve (MV) orifices were determined using color-flow Doppler and dual MV orifice areas were calculated. Valve orifices were classified as large vs small based on VC diameters. Continuous-wave Doppler measurements from both orifices were obtained. Forty-nine patients with severe MR (functional, n = 18) were enrolled. The VTi, mean gradient, peak gradient, and mean velocity of the larger vs smaller orifice were not significantly different, irrespective of MR etiology (P=nonsignificant). There was no difference in these parameters between large and small orifice regardless of MR mechanism (P=nonsignificant). There were no differences in the means of MVA as derived from either large or small VTi-derived and VC-derived areas (P=nonsignificant).CONCLUSIONS:Mitral valve inflow hemodynamics were the same regardless of the size differences between the large and small orifices. Therefore, total MVA can be calculated using the continuity equation in patients irrespective of MR mechanism. This allows for a derivation of total MVA at the time of MVCC placement to evaluate for mitral stenosis.

Introduction: Primary pericardial malignant tumors are rare clinical entities with an incidence of 0.001%. The symptoms depend on the location and size of the mass, typically causing chest pain, dyspnea, or syncope. We present the case of a young man with atypical chest pain, rapidly progressing to cardiac arrest from a primary pericardial myxoid chondrosarcoma. Case Presentation: A 31-year-old man presented with atypical chest pain. Subsequently, he had a cardiac arrest with pulseless electrical activity. CT chest revealed a 9.5 x 10 cm epicardial mass (Figure 1a, 1b) found to be a myxoid sarcoma on CT-guided biopsy. The morphology was suggestive of a high-grade extra-skeletal myxoid chondrosarcoma. Cardiac MRI had normal function and no myocardial invasion. The staging MRI did not show any other primary source. Due to the large size of the mass, a decision was made to perform surgical resection. Most of the tumor was excised but had positive margins from the part adherent to the inferior vena cava. In the absence of clear guidelines, multiple chemotherapy regimens including doxorubicin, ifosfamide, and etoposide were tried. The initial response was complicated by eventual recurrence and metastasis to the chest wall and mediastinum (Figure 1c, 1d). Further, genetic testing with next-generation sequencing demonstrated MAP3K1 gene mutation. As MAP3K1 mutations have shown sensitivity to MEK inhibition, targeted chemotherapy with trametinib was initiated in combination with pazopanib, a multikinase inhibitor. With the progression of metastatic disease despite treatment, the patient decided to pursue palliative care. Discussion: Malignant pericardial myxoid chondrosarcoma is a rare tumor that may lead to fatal outcomes in young adults. Despite numerous surgical and chemotherapeutic interventions, they have a guarded prognosis. Increased clinician awareness and a wide differential while evaluating cardiac masses are key for early detection and timely treatment.

Objective Arterial stenosis is a significant cardiovascular disease requiring accurate estimation of the pressure gradients for determining hemodynamic significance. In this paper, we propose Generalized Bernoulli Equation (GBE) utilizing interpolated-based method to estimate relative pressures using streamlines and pathlines from 4D Flow MRI. Methods 4D Flow MRI data in a stenotic phantom model and computational fluid dynamics simulated velocities generated under identical flow conditions were processed by Generalized Bernoulli Equation (GBE), Reduced Bernoulli Equations (RBE), as well as the Simple Bernoulli Equation (SBE) which is clinically prevalent. Pressures derived from 4D flow MRI and noise corrupted CFD velocities were compared with pressures generated directly with CFD as well as pressures obtained using Millar catheters under identical flow conditions. Results It was found that SBE and RBE methods underestimated the relative pressure for lower flow rates while overestimating the relative pressure at higher flow rates. Specifically, compared to the reference pressure, SBE underestimated the maximum relative pressure by 22 % for a pulsatile flow data with peak flow rate Q_max=80 ml/s and overestimated by around 40 % when Q_max=130 ml/s . In contrast, for GBE method the relative pressure values were overestimated by 15 % with Q_max=80 ml/s and around 10 % with Q_max= 130 ml/s . Conclusion GBE methods showed robust performance to additive image noise compared to other methods. Our findings indicate that GBE pressure estimation over pathlines attains the highest level of accuracy compared to GBE over streamlines, and the SBE and RBE methods.

Background: Heart failure with preserved ejection fraction continues to pose multiple challenges in terms of accurate diagnosis, treatment, and associated morbidity. Accurate left ventricular (LV) mass calculation yields essential prognostic information relating to structural heart disease. Two-dimensional (2D) echocardiography-based calculations are solely limited to LV geometric assumptions of symmetry, whereas three-dimensional (3D) echocardiography could overcome these limitations. This study aims to compare the performance of 2D and 3D LV mass calculations.Methods: A prospective review of echocardiography findings at the University of Louisville, Kentucky, was conducted and assessed. Normal ejection fraction (EF) was defined as >=52% in males and >=54% in females. The following calculations were performed: relative wall thickness (RWT) = 2x posterior wall thickness/LV internal diastolic dimension (LVIDd) and 2D LV mass = 0.8{1.04([LVIDd + IVSd +PWd]3 - LVIDd3)} + 0.6. Concentric hypertrophy was RWT > 0.42 and LV mass >95 kg/m2 in females or > 115 kg/m2 in males. The same cut-offs were used for 2D and 3D echocardiography.Results: Echocardiographic findings for a total number of 154 patients in the study were investigated. There was a weak positive correlation between 2D and 3D LV mass indices (R= 0.534, r2= 0.286, p= 0.001). Seventy patients had 3D EF >=45% with clinical heart failure (HFpEF). Among HFpEF patients, LV hypertrophy (LVH) was present in 74% of patients by 2D echocardiography and 30% by 3D echocardiography (McNemar test p= 0.001). Using 3D echocardiography as the reference, 68% of normal patients were misdiagnosed with LV hypertrophy by 2D echocardiography. Two-thirds of the patients with concentric remodeling by 3D echocardiography were misclassified as having concentric hypertrophy by 2D echocardiography (p=0.001).Conclusion: Adapting necropsy-proven LV mass index cutoffs, 2D over-diagnosed LV hypertrophy through overestimation of the mass, compared to 3D echocardiography. In turn, the majority of HFpEF patients showed no structural hypertrophy of the LV on 3D imaging. This suggests that the majority of patients with HFpEF may qualify for pharmacological prevention to prevent further progression to LV remodeling or LVH.

In this work, we propose a novel deep learning reconstruction framework for rapid and accurate reconstruction of 4D flow MRI data. Reconstruction is performed on a slice-by-slice basis by reducing artifacts in zero-filled reconstructed complex images obtained from undersampled k-space. A deep residual attention network FlowRAU-Net is proposed, trained separately for each encoding direction with 2D complex image slices extracted from complex 4D images at each temporal frame and slice position. The network was trained and tested on 4D flow MRI data of aortic valvular flow in 18 human subjects. Performance of the reconstructions was measured in terms of image quality, 3-D velocity vector accuracy, and accuracy in hemodynamic parameters. Reconstruction performance was measured for three different k-space undersamplings and compared with one state of the art compressed sensing reconstruction method and three deep learning-based reconstruction methods. The proposed method outperforms state of the art methods in all performance measures for all three different k-space undersamplings. Hemodynamic parameters such as blood flow rate and peak velocity from the proposed technique show good agreement with reference flow parameters. Visualization of the reconstructed image and velocity magnitude also shows excellent agreement with the fully sampled reference dataset. Moreover, the proposed method is computationally fast. Total 4D flow data (including all slices in space and time) for a subject can be reconstructed in 69 seconds on a single GPU. Although the proposed method has been applied to 4D flow MRI of aortic valvular flows, given a sufficient number of training samples, it should be applicable to other arterial flows.

In this work, we compared steady and pulsatile CFD simulations with 4D Flow MRI in a phantom model of arterial stenosis and investigated the hemodynamic features distal to the occlusion. With CFD, we characterized the flow structure to estimate both the magnitude and degree of anisotropy of the turbulence-related indices including vorticity, wall shear stress, helicity, and swirling strength using Large-Eddy Simulation (LES) and Reynolds-Averaged Navier Stocks (RANS) method. The results revealed that the LES approach captures well both the overall and the detailed flow features in comparison to RANS predictions with the standard k - epsilon model when compared with 4D Flow MRI. Furthermore, the Q-criterion was employed to reveal the temporal evolution of the vortex ring which was observed distal to the stenotic narrowing. It was found that both RANS and LES simulations were highly accurate for wall shear stress when validated against 4D Flow MR Imaging. The initial in-vitro results presented here provide insights for investigating vortex dynamics in-vivo.

Background and Purpose: It is unknown when to start anticoagulation after acute ischemic stroke (AIS) from atrial fibrillation (AF). Early anticoagulation may prevent recurrent infarctions but may provoke hemorrhagic transformation as AF strokes are typically larger and hemorrhagic transformation-prone. Later anticoagulation may prevent hemorrhagic transformation but increases risk of secondary stroke in this time frame. Our aim was to compare early anticoagulation with apixaban in AF patients with stroke or transient ischemic attack (TIA) versus warfarin administration at later intervals. Methods: AREST (Apixaban for Early Prevention of Recurrent Embolic Stroke and Hemorrhagic Transformation) was an open-label, randomized controlled trial comparing the safety of early use of apixaban at day 0 to 3 for TIA, day 3 to 5 for small-sized AIS (<1.5 cm), and day 7 to 9 for medium-sized AIS (≥1.5 cm, excluding full cortical territory), to warfarin, in a 1:1 ratio at 1 week post-TIA, or 2 weeks post-AIS. Results: Although AREST ended prematurely after a national guideline focused update recommended direct oral anticoagulants over warfarin for AF, it revealed that apixaban had statistically similar yet generally numerically lower rates of recurrent strokes/TIA (14.6% versus 19.2%, P =0.78), death (4.9% versus 8.5%, P =0.68), fatal strokes (2.4% versus 8.5%, P =0.37), symptomatic hemorrhages (0% versus 2.1%), and the primary composite outcome of fatal stroke, recurrent ischemic stroke, or TIA (17.1% versus 25.5%, P =0.44). One symptomatic intracerebral hemorrhage occurred on warfarin, none on apixaban. Five asymptomatic hemorrhagic transformation occurred in each arm. Conclusions: Early initiation of anticoagulation after TIA, small-, or medium-sized AIS from AF does not appear to compromise patient safety. Potential efficacy of early initiation of anticoagulation remains to be determined from larger pivotal trials. Registration: URL: https://www.clinicaltrials.gov/ ; Unique identifier: NCT02283294.