To correct maternal breathing and fetal bulk motion during fetal 4D flow MRI.

A Doppler-ultrasound fetal cardiac-gated free-running 4D flow acquisition was corrected post hoc for maternal respiratory and fetal bulk motion in separate automated steps, with optional manual intervention to assess and limit fetal motion artifacts. Compressed-sensing reconstruction with a data outlier rejection algorithm was adapted from previous work. Pre- and post-motion correction comparison included qualitative visibility of vasculature on phase-contrast MR angiograms (five-point Likert scale), conservation of mass of the aortic isthmus, ductus arteriosus, and descending aorta, and coefficient of variation of flow along the descending aorta.

Twenty-nine third trimester acquisitions were performed for 15 healthy fetuses and two patients with postnatally confirmed aortic coarctation during a single examination for each participant. Only 15/27 (56%) of all volunteers and 1/2 (50%) of all patient precorrection acquisitions were suitable for flow analysis. Motion correction recovered eight "failed" acquisitions, including one patient, with 24/29 (83%) suitable for flow analysis. In the 15 viable uncorrected volunteer acquisitions, motion correction improved phase-contrast MR angiograms visibility significantly in the ductus arteriosus (from 4.0 to 4.3, p = 0.04) and aortic arch (3.7 to 4.0, p = 0.03). Motion correction improved conservation of mass to a median (interquartile range) percent difference of 5% (9%) from 14% (24%) with improvement shown in 14/15 acquisitions (p = 0.002), whereas coefficient of variation changes were not significantly different (uncorrected: 0.15 (0.09), corrected: 0.11 (0.09), p = 0.3).

Motion correction compensated for maternal and fetal motion in fetal 4D flow MRI data, improving image quality and conservation of mass.

Fluid dynamics of the heart chamber can provide critical biological cues for understanding cardiac health and disease and have the potential for supporting diagnosis and prognosis. However, directly acquiring fluid dynamics information from clinical imaging remains challenging, as they are often noisy and have limited resolution, preventing accurate detailed fluid dynamics analysis. Image-based flow simulations offer high detail but are typically difficult to align with clinical velocity measurements, and as a result, may not accurately depict true fluid dynamics. Inverse-computing velocity fields from images via intra-ventricular flow mapping (VFM) has been reported, but it can become inaccurate when faced with missing or noisy measurement data, which is common with modalities such as ultrasound. Here, we propose a physics-informed neural network (PINN) framework that can accurately reconstruct detailed 3D flow fields of the cardiac left ventricle within a localized time window, using supervision from color Doppler measurements, despite their low resolution and signal-to-noise ratio. This framework couples PINN solvers at consecutive time frames with discrete temporal numerical differentiation and is thus named the "Coupled Sequential Frame PINN" or CSF-PINN. We used image-based flow simulations of fetal and adult hearts to generate synthetic color Doppler velocity data at different spatial and temporal resolution for testing the framework. Results show that CSF-PINN can accurately predict high levels of fluid dynamics details, including flow patterns, intraventricular pressure gradients, vorticity structures, and energy losses. CSF-PINN outperforms vanilla PINN in both accuracy and computational efficiency, however, its accuracy is more limited for velocity-gradient-dependent parameters, such as vorticity and wall shear stress (WSS) magnitude. CSF-PINN's accuracy is maintained even when color Doppler velocity data are spatially and temporally sparse and noisy, and when complex motions of the mitral valve are modelled. These are scenarios in which previous methodologies, including image-based flow simulations and VFM, have struggled. Additionally, we propose a scheme for advancing fluid dynamics predictions to subsequent time windows by using training from the previous time window to initialize networks for the subsequent window, further minimizing errors.

Detailed hemodynamic parameters are essential for managing cardiovascular diseases, as they reveal blood flow dynamics that affect disease progression and treatment. However, even such advanced techniques as 4D Phase Contrast MRI face challenges in providing accurate, high-resolution data due to limitations in spatial and temporal resolution and image artifacts. Computational Fluid Dynamics (CFD) can estimate these parameters theoretically, but patient-specific accuracy may be compromised due to assumptions in boundary conditions and material properties.

Here, we aim to circumvent current limitations in medical imaging and CFD simulations by creating a comprehensive cardiovascular analytics model informed by clinical data. We develop a patient-specific simulation framework by deriving critical geometric parameters, boundary conditions, and aortic wall material properties directly from medical investigation and imaging data. This detailed information is subsequently integrated into Fluid-Structure-Interaction simulations to predict such key hemodynamic indicators as pressure distribution, wall deformation, time-averaged wall shear stress and oscillatory shear index to better assess individual vascular health. This approach effectively links imaging technology with computational modeling, as evidenced from our findings based on the medical imaging data of a representative human subject.

The results reveal that such amalgamation of patient-specific parameters enhances the simulation's accuracy, offering a more comprehensive and precise assessment of cardiovascular health than the traditional generic approaches. This comprehensive framework thus has potential to become an invaluable clinical tool, enhancing the accuracy of hemodynamic assessment, moving toward more personalized care and informing effective treatment decision-making.

Intracranial atherosclerotic disease (ICAD) significantly increases the risk of ischemic stroke. It involves the accumulation of plaque within arterial walls and narrowing or blockage of blood vessel lumens. Accurate imaging is crucial for the diagnosis and management of ICAD at both acute and chronic stages. However, imaging the small, tortuous intracranial arterial walls amidst complex structures is challenging. Clinicians have employed diverse approaches to improve imaging quality, with a particular emphasis on optimizing the acquisition of images using new techniques, enhancing spatial and temporal resolution of images, and refining post-processing techniques. ICAD imaging has evolved from depicting lumen stenosis to assessing blood flow reserve and identifying plaque components. Advanced techniques such as fractional flow reserve (FFR), high-resolution vessel wall magnetic resonance (VW-MR), optical coherence tomography (OCT), and radial wall strain (RWS) now allow direct visualization of flow impairment, vulnerable plaques, and blood flow strain to plaque, aiding in the selection of high-risk stroke patients for intervention. This article reviews the progression of imaging modalities from lumen stenosis to vessel wall pathology and compares their diagnostic value for risk stratification in ICAD patients.

Vector Flow Mapping (VFM) is a novel technique for visualizing intracardiac blood flow. A few reports have been made regarding the reproducibility of energy loss (EL) measurements using VFM. This study aims to elucidate the intra-class and inter-class correlation coefficient (intra-ICC and inter-ICC) in the EL measurements.

Seven healthy participants were enrolled, and echocardiographic data were obtained by two cardiac sonographers (14 images). Three independent analysts analyzed all images in three different cardiac cycles three times: 378 data points (14 images × analysts × 3 cardiac cycles × 3 times). The intra-ICC (1, 1) and inter-ICC (2, 1) were calculated. Furthermore, the intra-ICC (1, k) and inter-ICC (2, k) were applied using the averaged EL value in three cardiac cycles. An ICC value greater than 0.75 was defined as acceptable reproducibility.

In diastole, the intra-ICC (1, 1) by the three analysts was 0.890, 0.830, and 0.802; the intra-ICC (1, k), using the average EL value was 0.986, 0.978, and 0.973. In systole, the intra-ICC (1, 1) was 0.729, 0.698, and 0.733; the intra-ICC (1, k) was 0.960, 0.954, and 0.961. In diastole, the inter-ICC (2, 1) was 0.950, and the inter-ICC (2, k) was 0.958. In contrast, in systole, the inter-ICC (2, 1) was 0.772, and the inter-ICC (2, k) was 0.774; these values were lower than those in diastole.

These findings indicate that the intra- and inter-measurer reproducibility of diastolic and systolic EL is favorable for clinical use.

To describe the reliability of quantitative MRI analysis of flows using 4D-PC (Phase Contrast) compared to 2D-PC flow, without contrast agents.

The study was conducted at one centre in a retrospective manner from 2021 to 2023. Eligible patients were adults with repaired Tetralogy of Fallot (rToF) whose follow-up MRI protocol included 4D-PC and 2D-PC, without contrast agent. Assessment involved quantitative measurement of aortic and pulmonary flows on both 4D and 2D. Student's T was used for comparisons of means and Lin's concordance correlation coefficient agreement (CCC). A 2nd observer was hired.

Twenty-six patients were included (50% men, median age of 27 [20; 34]). Scanning mean time was 13 min for 4D and 47 min for non-4D part. Systematic underestimation in pulmonary regurgitation fraction (-10 ± 15%, p = 0.02) and backward volume (-13 ± 18 mL, p = 0.02) was found. Agreement on regurgitant fraction was strong for pulmonary (CCC = 0.75 [0.58-0.86], p < 0.001) and aortic (CCC = 0.91 [0.81-0.95], p < 0.001). Inter-observer showed overall strong results.

Non-enhanced 4D-PC flow MRI seems to be a reliable method of quantitative flow in rToF patients, but it may underestimate pulmonary regurgitation.

Question 4D-MRI stands out as an innovative tool for evaluating flows in adults with repaired Tetralogy of Fallot during follow-up. Findings 4D phase-contrast imaging without the use of contrast agents exhibits robust results and high reproducibility. Clinical relevance 4D-MR is presented as an accurate and versatile tool of cardiac exploration that could improve patient comfort. Minimizing brain gadolinium exposure and reducing scanning time are key objectives of non-enhanced 4D-MR.

Stenosis of major intracranial arteries is a significant cause of stroke, with assessment of trans-stenotic pressure drops being a key marker of functional stenosis severity. Non-invasive methods for quantifying intracranial pressure changes are hence crucial; however, the narrow and tortuous cerebrovascular network poses challenges to traditional assessment methods such as transcranial Doppler. This study investigates the use of novel deep learning-enhanced super-resolution (SR) four-dimensional (4D) flow magnetic resonance imaging (MRI) in combination with a physics-informed virtual work-energy relative pressure technique to quantify pressure drops across stenotic intracranial arteries. Performance was validated in intracranial-mimicking in vitro experiments using pulsatile flow before being transferred into an in vivo cohort of patients with intracranial atherosclerotic disease. Conversion into sub-millimetre SR imaging significantly improved the accuracy of regional relative pressure estimations in the pulsing brain arteries, mitigating biases observed at >1 mm resolution imaging, and agreeing strongly with reference catheter-based invasive measurements across both moderate and severe stenoses. The in vivo analysis also revealed a significant increase in pressure drops when converting into sub-millimetre SR data, underlining the importance of apparent image resolution in a clinical setting. The results highlight the potential of SR 4D flow MRI for non-invasive quantification of cerebrovascular pressure changes in pulsing intracranial arteries across stenotic vessel segments.

Cardiovascular digital twins and mechanistic models can be used to obtain new biomarkers from patient-specific hemodynamic data. However, such model-derived biomarkers are only clinically relevant if the uncertainty of the biomarkers is smaller than the variation between timepoints/patients. Unfortunately, this uncertainty is challenging to calculate, as the uncertainty of the underlying hemodynamic data is largely unknown and has several sources that are not additive or normally distributed. This violates normality assumptions of current methods; implying that also biomarkers have an unknown uncertainty. To remedy these problems, we herein present a method, with attached code, for uncertainty calculation of model-derived biomarkers using non-normal data. First, we estimated all sources of uncertainty, both normal and non-normal, in hemodynamic data used to personalize an existing model; the errors in 4D flow MRI-derived stroke volumes were 5-20 % and the blood pressure errors were 0 ± 8 mmHg. Second, we estimated the resulting model-derived biomarker uncertainty for 100 simulated datasets, sampled from the data distributions, by: 1) combining data uncertainties 2) parameter estimation, 3) profile-likelihood. The true biomarker values were found within a 95 % confidence interval in 98 % (median) of the cases. This shows both that our estimated data uncertainty is reasonable, and that we can use profile-likelihood despite the non-normality. Finally, we demonstrated that e.g. ventricular relaxation rate has a smaller uncertainty (∼10 %) than the variation across a clinical cohort (∼40 %), meaning that these biomarkers have clinical potential. Our results take us one step closer to the usage of model-derived biomarkers for cardiovascular patient characterization.

Flow quantification using phase-contrast (PC) MRI is based on steady-state gradient echo (GRE) sequences and is hampered by spatially varying background phase offsets. The purpose of this work was to investigate the effect of steady-state disruptions during PC-MRI GRE sequences on these background phases. Based on these findings, a specific sequence and timing is suggested, and caution is expressed when using typical correction algorithms.

Steady-state responses in stationary tissue were investigated in different prospectively triggered through-plane phase-contrast MRI sequence. Different spoiling methods (gradient spoiling/FISP versus gradient+RF spoiling/FLASH) and interleaving of flow encoding gradients (every TR vs. every ECG cycle) were investigated using simulations, in phantoms and in vivo. Additionally, the effect of relaxation times on the phase offsets was simulated and measured. The impact on image- and phantom-based background phase correction was studied.

Good agreement between simulation and phantom measurements were observed. Different sequences lead to different spatiotemporal and tissue dependent background phases. Average flow rates in the popliteal artery were over- and underestimated for ECG-interleaved and TR-interleaved FISP acquisitions compared to FLASH, respectively.

Background phase measurements are influenced by steady-state effects leading to potentially false background phase quantification. Current background phase correction methods cannot correct for the disturbance.

4D flow MRI-derived variables from Marfan patients are highly heterogeneous. Our aim was to identify distinct Marfan patient subgroups based on aortic 4D flow MRI and Z-score for stratification of distinct hemodynamic profiles and clinical features by means of hierarchical cluster analysis.

One hundred Marfan patients underwent baseline aortic 4D flow MRI at 3 T. Z-scores, degree of helical and vortical flow, wall shear stress, flow displacement, and peak velocity were determined in the ascending aorta. Sex, age, BMI, antihypertensive medication, and dural ectasia were recorded. Hierarchical cluster analysis was performed using 4D flow MRI variables and Z-scores as input.

Cluster analysis resulted in three distinct clusters characterized by different Z-scores (mean ± SD); cluster 1: 0.4 ± 1.1 vs. cluster 2: 3.1 ± 1.1 vs. cluster 3: 3.6 ± 1.9. The three clusters delivered differences in helical and vortical flow patterns (global p = 0.003 and p < 0.001, respectively), wall shear stress (0.49 ± 0.11 vs. 0.44 ± 0.12 vs. 0.37 ± 0.09 N/m2, global p < 0.001), flow displacement (0.11 ± 0.05 vs. 0.16 ± 0.08 vs. 0.15 ± 0.07, global p = 0.006), and peak velocity (76.3 ± 9.0 vs. 60.1 ± 7.3 vs. 56.0 ± 7.8 cm/s, global p < 0.001). Patients in cluster 1 and 2 were relevantly younger than in cluster 3 (32.3 ± 13.8 vs. 32.8 ± 12.6 vs. 40.2 ± 15.0 years, all pairwise ∆p < 0.0297).

Hierarchical cluster analysis based on aortic 4D flow MRI and Z-score revealed three distinct subgroups of Marfan patients, each characterized by specific hemodynamic profiles and clinical features. Follow-up of our patients is warranted to assess if 4D flow MRI- and Z-score-based stratification can predict future aortic diameter growth and ultimately improve outcomes.

A combination of Z-score and 4D flow MRI-derived parameters may help identify subgroups of Marfan patients representing different stages or phenotypes of aortic disease, which require specific management strategies.

Four-dimensional (4D) flow MRI-derived variables of Marfan patients are highly heterogeneous across varying Z-scores. Cluster analysis based on 4D flow MRI and Z-score revealed three distinct subgroups of Marfan patients. A combination of Z-score and 4D flow MRI-derived parameters may identify different stages of aortic disease in Marfan patients.

Type B aortic dissection (TBAD) is associated with high mortality. Multiple in vitro models and computational fluid dynamics (CFD) simulations have been used to mimic the hemodynamic characteristics of TBAD to inform more effective therapeutic strategies. However, the results of these experiments are rarely used in clinical practice due to concerns about their accuracy and reliability. The development of 4-dimensional magnetic resonance imaging (4D-MRI) allows to verify the accuracy of the results of in vitro models and CFD simulations. This review provides an overview of the strengths, limitations, and accuracy of in vitro models, CFD simulations, and in vivo 4D flow MRI for the study of TBAD hemodynamics.Clinical Impact1. Hemodynamic of TBAD is important to improve the long-term outcome of TEVAR.2. This review provides an overview of the in-vitro for the hemodynamic study of TBAD.3. The accuracy and validity of in-vitro TBAD experiments should be further studied.

Cerebrovascular imaging assessments are particularly challenging in adolescent cohorts, where not all modalities are appropriate, and rapid brain maturation alters hemodynamics at both macro- and microvascular scales. In a preliminary sample of healthy adolescents (n = 12, 8-25 years), we investigated relationships between 4D flow MRI-derived blood velocity and blood flow in bilateral anterior, middle, and posterior cerebral arteries and BOLD cerebrovascular reactivity (CVR) in associated vascular territories. As hypothesized, higher velocities in large arteries are associated with an earlier response to a vasodilatory stimulus (cerebrovascular reactivity delay) in the downstream territory. Higher blood flow through these arteries is associated with a larger BOLD response to a vasodilatory stimulus (cerebrovascular reactivity amplitude) in the associated territory. These trends are consistent in a case study of adult moyamoya disease. In our small adolescent cohort, macrovascular-microvascular relationships for velocity/delay and flow/CVR change with age, though underlying mechanisms are unclear. Our work emphasizes the need to better characterize this key stage of human brain development, when cerebrovascular hemodynamics are changing, and standard imaging methods offer limited insight into these processes. We provide important normative data for future comparisons in pathology, where combining macro- and microvascular assessments may better help us prevent, stratify, and treat cerebrovascular disease.

To implement four-dimensional-flow MRI using phase-contrast balanced steady-state free precession (bSSFP) at 0.6 T using a free-running three-dimensional (3D) radial trajectory and referenceless background phase correction.

A free-running, wobbling Archimedean spiral approach including bipolar velocity-encoding gradients (3D PC-bSSFP) was implemented on a 0.6T prototype scanner. Bipolar rewinder gradients were added to ensure first-moment nulling per repetition time. Velocity encoding was performed using a three-point encoding scheme (i.e., omitting a reference measurement). Advanced computer simulations were carried out to validate the approach. Image reconstruction was performed using a locally low-rank approach. Results for anatomical visualization and flow quantification were reconstructed separately with different regularization factors. Background phase correction was achieved using phase estimation on time-averaged reconstructions. In vivo data were acquired in 6 healthy subjects during free breathing. Additional two-dimensional (2D) phase-contrast spoiled gradient-echo (2D PC-GRE) breath-hold data were obtained for reference to compare flow values in the ascending aorta, descending aorta, and pulmonary trunk.

Velocity data acquired with 3D PC-bSSFP compared well with 2D PC-GRE (root mean square error = 3.96 cm/s), with minor underestimation of velocities (-0.52 cm/s). Cardiac phase-dependent signal-to-noise ratios normalized for differences in scan time and resolution between 3D PC-bSSFP and 2D PC-GRE demonstrate relatively steady values for 3D PC-bSSFP when compared to 2D PC-bSSFP with some reduction during phases of high flow.

Free-running, referenceless, four-dimensional-flow MRI using radial 3D PC-bSSFP is feasible on a lower-field 0.6T system, producing adequate flow quantification while yielding simultaneously reasonable cine images for concurrent flow and functional assessment of the heart and great vessels.

In the field of cardiovascular imaging, four-dimensional (4D) flow cardiovascular magnetic resonance (CMR) provides non-invasive assessment of blood flow. Dual velocity encoding (dual-VENC) strategies have emerged to obtain quantitative information on both low and high blood flow velocities simultaneously. However, these strategies often encounter difficulties in coping with large velocity ranges. This work presents a dual-VENC 4D flow CMR sequence that utilizes the coprime rule to define the VENC ratio.

A dual-VENC 4D flow CMR sequence and reconstruction algorithm were developed and validated in vitro at two different field strengths, using a flow phantom generating realistic complex flow patterns. A digital twin of the phantom allowed comparison of the MRI measurements with computational fluid dynamics (CFD) simulations. Three patients with different cardiac pathologies were scanned in order to evaluate the in vivo feasibility of the proposed method.

The results of the in vitro acquisitions demonstrated significant improvement in velocity-to-noise ratio (VNR) with respect to single-VENC acquisitions (110±3%) and conventional dual-VENC de-aliasing approach (75±3%). Furthermore, the effectiveness of aliasing correction was demonstrated even when both sets of images from the dual-VENC acquisition presented velocity aliasing artifacts. We observed a high degree of agreement between the measured and simulated velocity fields.

The strength of this approach lies in the fact that, unlike the conventional de-aliasing method, no data is discarded. The final image is obtained by a weighted average of the VENClow and VENChigh datasets. Consequently, setting the value of the VENChigh to prevent aliasing is no longer necessary, and higher VNR gains are possible.

Phase contrast MRI (PC-MRI) is used clinically to measure velocities in the body, but systematic background phase errors caused by magnetic field imperfections corrupt the velocity measurements with offsets that limit clinical utility. This work aims to minimize systematic background phase errors in PC-MRI, thereby maximizing the accuracy of velocity measurements.

The MRI scanner's background phase errors from eddy currents and mechanical oscillations were modeled using the gradient impulse response function (GIRF). Gradient waveforms were then numerically optimized using the GIRF to create pulse sequences that minimize the background phase errors. The pulse sequences were tested in a static phantom where the predicted response could be compared directly to the measured background velocity. The optimized acquisitions were then tested in human subjects, where flow rates and background errors were compared to conventional PC-MRI.

When using the GIRF-optimized gradient waveforms, the predicted background phase was within 0.6 [95% CI = -3.4, 5.4] mm/s of the measured background phase in a static phantom. Excellent agreement was seen for in vivo blood flow values (flow rate agreementr 2 $$ {r}^2 $$ = 0.96), and the background phase was reduced by 78.8± $$ \pm $$ 18.7%.

This work shows that using a GIRF to model the effects of magnetic field imperfections combined with numerically optimized gradient waveforms enables PC-MRI waveforms to be designed to produce a minimal background phase in the most time-efficient manner. The methodology could be adapted for other MRI sequences where similar magnetic field errors affect measurements.

Pulmonary valvular regurgitation in postoperative patients with repaired tetralogy of Fallot (rTOF) significantly impairs exercise capacity and causes right heart failure. Quantitative evaluation of the pulmonary valvular regurgitation fraction (PRF) by cardiac magnetic resonance (CMR) is commonly used to determine the indication for surgical or catheter interventions, but less commonly using echocardiography.

We retrospectively investigated the feasibility and validation of vector flow mapping (VFM) for the quantification of PRF (VFM-PRF) in 34 pediatric patients with rTOF, comparing it to CMR-derived PRF (CMR-PRF) and other qualitative or semiquantitative echocardiographic indices. Each predictive value for CMR-PRF ≥40% was assessed using receiver operating characteristic curves. VFM-PRF and CMR-PRF showed good agreement, with a correlation coefficient of 0.90 and the highest predictive value for CMR-PRF ≥40%, resulting in an area under the curve of 0.93. Other conventional echocardiographic parameters demonstrated poor predictive accuracy.

This is the first report to demonstrate the accurate quantification of PRF by echocardiography using VFM in pediatric patients with rTOF, showing good agreement with CMR results. Particularly in children, VFM may be clinically useful in determining the indication for reintervention for pulmonary valve replacement, offering a possible alternative to CMR, which often requires deep sedation and general anesthesia.

Thoracic aortopathies result in aneurysmal expansion of the aorta that can lead to rapidly fatal aortic dissection or rupture. Despite the availability of abundant non-invasive imaging tools, the greatest contemporary challenge in the management of thoracic aortic aneurysm (TAA) is the lack of reliable metrics for risk stratification, with absolute aortic diameter, growth rate, and syndromic factors remaining the primary determinants by which prophylactic surgical intervention is adjudged. Advanced cardiovascular magnetic resonance (CMR) techniques present a potential key to unlocking insights into TAA that could guide disease surveillance and surgical intervention. CMR has the capacity to encapsulate the aorta as a complex biomechanical structure, permitting the determination of aortic volume, morphology, composition, distensibility, and fluid dynamics in a time-efficient manner. Nevertheless, current standard-of-care imaging protocols do not harness its full capacity. This state-of-the-art review explores the emerging role of CMR in the assessment of TAA and presents a blueprint for the required paradigm shift away from aortic size as the sole metric for risk-stratifying TAA.

Computational fluid dynamics (CFD) is a tool that allows for the analysis of otherwise unobservable blood flow patterns. In the context of medicine, CFD enables researchers to better understand acute and chronic pathophysiology as well as utilize modeling tools to predict blood flow patterns in response to surgical intervention. Such a tool is particularly useful in the field of congenital heart disease (CHD), where complex geometries and patient-specific pathology are common. Research applying CFD to study CHDs has significantly grown in the last twenty years, with new methodologies and recommendations being published at an even faster pace in the last decade. Many currently available reviews are focused on a particular area of progress or on the technical approaches to CFD geared toward the clinician. This review focuses on CFD application within the major domains of CHD research, specifically single ventricle defects and aortic coarctation, reviewing consensus seminal work while highlighting more recent avenues of study. Balancing discussion of CFD parameters with potential clinical implications of study results, this review not only aims to provide cardiovascular professionals context for the technical advancements being made in the field but also a sense of contemporary CFD's utility in clinical practice.

Atrial disease or myopathy is a growing concept in cardiovascular medicine, particularly in the context of atrial fibrillation, as well as amyloidosis and heart failure. Among cardiac imaging modalities, cardiovascular magnetic resonance (CMR) is particularly well suited for a comprehensive assessment of atrial myopathy, including tissue characterization and hemodynamics. The goal of this review article is to describe clinical applications and make recommendations on pulse sequences as well as imaging parameters to assess the left atrium and left atrial appendage. Furthermore, we aimed to create an overview of current and promising future emerging applications of left atrium-specific CMR pulse sequences focusing on both electrophysiologic (EP) and non-EP applications.

Hypoplastic left heart syndrome (HLHS) is a congenital heart disease responsible for 23% of infant cardiac deaths each year in the United States. HLHS patients are born with an underdeveloped left heart, requiring several surgeries to reconstruct the aorta and create a single-ventricle circuit known as the Fontan circulation. While survival into early adulthood is becoming more common, Fontan patients often have a reduced cardiac output, putting them at risk for a multitude of complications. These patients are monitored using chest and neck magnetic resonance imaging (MRI), but their scans do not capture energy loss, pressure, wave intensity or haemodynamics beyond the imaged region. This study develops a framework for predicting these missing features by combining imaging data and computational fluid dynamics (CFD) models. Predicted features from models of HLHS patients are compared with those from control patients with a double outlet right ventricle (DORV). We infer patient-specific parameters through the proposed framework. In the calibrated model, we predict pressure, flow, wave intensity (WI) and wall shear stress (WSS). Results reveal that HLHS patients have lower compliance than DORV patients, resulting in lower WSS and higher WI in the ascending aorta and increased WSS and decreased WI in the descending aorta.

The aim of this study was the initial investigation of 4D-Flow MRI and Vector Ultrasound as novel imaging techniques in the in-vitro analysis of hemodynamics in anatomical models. Specifically, by looking at the hemodynamic performance of state-of-the-art surgical heart valves in a 3D-printed aortic arch.

The mock circulatory loop simulated physiological, pulsatile flow. Two mechanical and three biological aortic valves prostheses were compared in a 3D-printed aortic arch. 4D magnetic resonance imaging and vector flow Doppler ultrasound served as imaging methods. Hemodynamic parameters such as wall shear stress, flow velocities and pressure gradients were analyzed.

The flow analysis revealed characteristic flow-patterns in the 3D-printed aortic arch. The blood-flow in the arch presented complex patterns, including the formation of helixes and vortices. Higher proximal peak velocities and lower flow volumes were found for biological valves.

The mock circulatory loop in combination with modern radiological imaging provides a sufficient basis for the hemodynamic comparison of aortic valves.

A four-dimensional phase-contrast magnetic resonance imaging sequence with respiratory-controlled adaptive k-space reordering (ReCAR-4DPC) offers potential benefits of improved scan efficiency and motion robustness. The purpose of this study was to evaluate the reproducibility of flow measurement using this technique and to compare hemodynamic metrics obtained to two-dimensional phase contrast MRI (2DPC)-derived metrics of the thoracic aorta. ReCAR-4DPC was performed with identical scan parameters in 15 healthy volunteers (6M,9F, mean [range] 37 [23-47] years) and 11 patients with thoracic aortic dissection (6M,5F, 56 [31-81] years) and acquisition time was recorded. Peak systolic velocity (PSV), average flow (AF) and net forward volume (NFV) were quantified by two readers for ReCAR-4DPC at ascending, descending and diaphragmatic aorta levels. Reference standard 2DPC measurements at the same levels were performed by a separate experienced cardiovascular radiologist. ReCAR-4DPC intra-reader agreement, inter-reader agreement, inter-scan repeatability and concordance with 2DPC-derived metrics (all segments combined) were evaluated with Lin's concordance correlation coefficient (LCCC) and reduced major axis regression. The overall average ± SD MRI acquisition time of all subjects was 11:59 ± 3:57 min, with shorter average times (9:37 ± 1:57 min) in healthy volunteers compared to patients (15:13 ± 3:44 min). There was near-perfect intra-reader, inter-reader and inter-scan concordance (LCCC for all metrics > 0.97, > 0.98 and > 0.92 respectively) for ReCAR-4DPC. Concordance with 2DPC was also high (LCCC all > 0.89), with overall minimally lower PSV, AF and NFV values derived from ReCAR-4DPC compared to reference 2DPC derived metrics. ReCAR-4DPC is a reproducible and relatively fast approach for comprehensive measurement of thoracic aortic flow metrics, with robust correlation to conventional 2DPC.

Methods for seeing inside volumetric images are increasingly important with the rapid advancements in 3D and 4D (3D + time) biomedical imaging techniques. Here, we report a novel volume clipping method and its open-source implementation which enables unprecedented 4D visualization and analysis of embryonic mouse heart development with data from optical coherence tomography (OCT). Clipping a volume to extract information inside has long been a vital approach in biomedical image analysis; however, it is challenging to make a dynamic non-planar cutaway view that is simultaneously smooth, adjustable, efficient to compute, easy to control, and interactive in real time. We addressed this challenge by applying the thin plate spline (TPS) to create a new way of volume clipping, called the clipping spline. Specifically, the clipping spline produces a cutaway view by generating a binary mask based on the unique TPS surface that intersects with a set of 3D control points while having minimal curvature. We implemented this method in an open-source platform where the clipping spline can be interactively controlled for real-time, adjustable, and dynamic cutaway views into a volume. We also developed an algorithm that automatically connects and interpolates different sets of control points over time, providing 4D volume clipping. In addition to characterizing the clipping spline, we demonstrate its application by revealing a series of never-before-seen dynamics and processes of embryonic mouse heart development based on OCT data. We also show a TPS-based method for tracking the embryonic myocardium with control points over two timescales (heartbeat and development). Our results indicate that the clipping spline promises to be broadly used in volumetric biomedical image visualization and analysis, especially by the OCT community.

An automatic method is presented for estimating 4D flow MRI velocity measurement uncertainty in each voxel. The velocity distance (VD) metric, a statistical distance between the measured velocity and local error distribution, is introduced as a novel measure of 4D flow MRI velocity measurement quality.

The method uses mass conservation to assess the local velocity error variance and the standardized difference of means (SDM) velocity to estimate the velocity error correlations. VD is evaluated as the Mahalanobis distance between the local velocity measurement and the local error distribution. The uncertainty model is validated synthetically and tested in vitro under different flow resolutions and noise levels. The VD's application is demonstrated on two in vivo thoracic vasculature 4D flow datasets.

Synthetic results show the proposed uncertainty quantification method is sensitive to aliased regions across various velocity-to-noise ratios and assesses velocity error correlations in four- and six-point acquisitions with correlation errors at or under 3.2%. In vitro results demonstrate the method's sensitivity to spatial resolution, venc settings, partial volume effects, and phase wrapping error sources. Applying VD to assess in vivo 4D flow MRI in the aorta demonstrates the expected increase in measured velocity quality with contrast administration and systolic flow.

The proposed 4D flow MRI uncertainty quantification method assesses velocity measurement error owing to sources including noise, intravoxel phase dispersion, and velocity aliasing. This method enables rigorous comparison of 4D flow MRI datasets obtained in longitudinal studies, across patient populations, and with different MRI systems.

Atrial fibrillation (AF) is the most prevalent arrhythmia in world-wild places and is associated with the development of severe secondary complications such as heart failure and stroke. Emerging evidence shows that the modified hemodynamic environment associated with AF can cause altered flow patterns in left atrial and even systemic blood associated with left atrial appendage thrombosis. Recent advances in magnetic resonance imaging (MRI) allow for the comprehensive visualization and quantification of in vivo aortic flow pattern dynamics. In particular, the technique of 4- dimensional flow MRI (4D flow MRI) offers the opportunity to derive advanced hemodynamic measures such as velocity, vortex, endothelial cell activation potential, and kinetic energy. This review introduces 4D flow MRI for blood flow visualization and quantification of hemodynamic metrics in the setting of AF, with a focus on AF and associated secondary complications.

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.

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.

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%.

PWV, WSS, and net flow demonstrated good agreement across protocols. The WSS threshold should be adjusted for WH4D estimates to optimally match AT4D-derived output. Reproducibility analysis showed good test-retest agreement. This study demonstrates the reproducibility of certain hemodynamic parameters across two 4D flow MRI protocol.

Left atrial (LA) myopathy is thought to be associated with silent brain infarctions (SBI) through changes in blood flow hemodynamics leading to thrombogenesis. 4D-flow MRI enables in-vivo hemodynamic quantification in the left atrium (LA) and LA appendage (LAA).

To determine whether LA and LAA hemodynamic and volumetric parameters are associated with SBI.

Prospective observational study.

A single-site cohort of 125 Participants of the multiethnic study of atherosclerosis (MESA), mean age: 72.3 ± 7.2 years, 56 men.

1.5T. Cardiac MRI: Cine balanced steady state free precession (bSSFP) and 4D-flow sequences. Brain MRI: T1- and T2-weighted SE and FLAIR.

Presence of SBI was determined from brain MRI by neuroradiologists according to routine diagnostic criteria in all participants without a history of stroke based on the MESA database. Minimum and maximum LA volumes and ejection fraction were calculated from bSSFP data. Blood stasis (% of voxels <10 cm/sec) and peak velocity (cm/sec) in the LA and LAA were assessed by a radiologist using an established 4D-flow workflow.

Student's t test, Mann-Whitney U test, one-way ANOVA, chi-square test. Multivariable stepwise logistic regression with automatic forward and backward selection. Significance level P < 0.05.

26 (20.8%) had at least one SBI. After Bonferroni correction, participants with SBI were significantly older and had significantly lower peak velocities in the LAA. In multivariable analyses, age (per 10-years) (odds ratio (OR) = 1.99 (95% confidence interval (CI): 1.30-3.04)) and LAA peak velocity (per cm/sec) (OR = 0.87 (95% CI: 0.81-0.93)) were significantly associated with SBI.

Older age and lower LAA peak velocity were associated with SBI in multivariable analyses whereas volumetric-based measures from cardiac MRI or cardiovascular risk factors were not. Cardiac 4D-flow MRI showed potential to serve as a novel imaging marker for SBI.

3 TECHNICAL EFFICACY: Stage 2.

Portal vein thrombosis (PVT) is thought to arise from stagnant blood flow, yet conclusive evidence is lacking. Relative residence time (RRT) assessed using 4D Flow MRI may offer insight into portal flow stagnation.

To explore the relationship between RRT values and the presence of PVT in cirrhotic participants.

Prospective.

Forty-eight participants with liver cirrhosis (27 males, median age 67 years [IQR: 57-73]) and 20 healthy control participants (12 males, median age 45 years [IQR: 40-54]).

3 T/4D Flow MRI.

Laboratory (liver and kidney function test results and platelet count) and clinical data (presence of tumors and other imaging findings), and portal hemodynamics derived from 4D Flow MRI (spatiotemporally averaged RRT [RRT-mean], flow velocity, and flow rate) were analyzed.

We used multivariable logistic regression, adjusted by selected covariates through the Lasso method, to explore whether RRT-mean is an independent risk factor for PVT. The area under the ROC curve (AUC) was also calculated to assess the model's discriminative ability. P < 0.05 indicated statistical significance.

The liver cirrhosis group consisted of 16 participants with PVT and 32 without PVT. Higher RRT-mean values (odds ratio [OR] 11.4 [95% CI: 2.19, 118]) and lower platelet count (OR 0.98 per 1000 μL [95% CI: 0.96, 0.99]) were independent risk factors for PVT. The incorporation of RRT-mean (AUC, 0.77) alongside platelet count (AUC, 0.75) resulted in an AUC of 0.84. When including healthy control participants, RRT-mean had an adjusted OR of 12.4 and the AUC of the combined model (RRT-mean and platelet count) was 0.90.

Prolonged RRT values and low platelet count were significantly associated with the presence of PVT in cirrhotic participants. RRT values derived from 4D Flow MRI may have potential clinical relevance in the management of PVT.

2 TECHNICAL EFFICACY: Stage 2.

Arterial stiffness has emerged as a prominent marker of risk for cardiovascular diseases. Few studies are interested in predicting symptomatic or asymptomatic arterial stiffness from hemodynamics and biomechanics parameters. Machine learning models can be used as an intelligent tool for arterial stiffness detection based on hemodynamic and biomechanical parameters. Indeed, in the case of arterial stiffness hemodynamics and biomechanics parameters present significant change, such as an increase in age, local wave velocity, arterial elastance, Young's modulus, reflected wave amplitude, decrease in arterial compliance, reflected wave arrival time, and reflection coefficient. This study aims to assess the impact of artificial intelligence using machine-learning algorithms for the detection of arterial stiffness. The ability of various machine-learning approaches can be investigated to predict wall stiffness in the carotid artery and to evaluate the risk of cardiovascular events. A mathematical model developed in previous work was used to determine hemodynamic and biomechanical parameters. Accuracy, sensitivity, and specificity are calculated to evaluate the performance of the proposed models. All used classifiers demonstrated high performance in predicting arterial stiffness, notably with the Support Vector Machine, Artificial Neural Network, and Decision Tree classifiers achieving exceptional accuracies of 100%. In this study, the potential of machine learning based on hemodynamic parameters for the prediction of symptomatic and asymptomatic arterial stiffness was demonstrated.

Cardiovascular magnetic resonance (CMR) imaging is recommended in patients with congenital heart disease (CHD) in clinical practice guidelines as the imaging standard for a large variety of diseases. As CMR is evolving, novel techniques are becoming available. Some of them are already used clinically, whereas others still need further evaluation. In this statement, the authors give an overview of relevant new CMR techniques for the assessment of CHD. Studies with reference values for these new techniques are listed in the Supplementary data online, supplement.

The intricate neurofluid dynamics and balance is essential in preserving the structural and functional integrity of the brain. Key among these forces are: hemodynamics, such as heartbeat-driven arterial and venous blood flow, and hydrodynamics, such as cerebrospinal fluid (CSF) circulation. The delicate interplay between these dynamics is crucial for maintaining optimal homeostasis within the brain. Currently, the widely accepted framework for understanding brain functions is the Monro-Kellie's doctrine, which posits a constant sum of intracranial CSF, blood flow and brain tissue volumes. However, in recent decades, there has been a growing interest in exploring the dynamic interplay between these elements and the impact of external factors, such as daily changes in body position. CSF circulation in particular plays a crucial role in the context of neurodegeneration and dementia, since its dysfunction has been associated with impaired clearance mechanisms and accumulation of toxic substances. Despite the implementation of various invasive and non-invasive imaging techniques to investigate the intracranial hemodynamic or hydrodynamic properties, a comprehensive understanding of how all these elements interact and are influenced by body position remains wanted. Establishing a comprehensive overview of this topic is therefore crucial and could pave the way for alternative care approaches. In this review, we aim to summarize the existing understanding of intracranial hemodynamic and hydrodynamic properties, fundamental for brain homeostasis, along with factors known to influence their equilibrium. Special attention will be devoted to elucidating the effects of body position shifts, given their significance and remaining ambiguities. Furthermore, we will explore recent advancements in imaging techniques utilized for real time and non-invasive measurements of dynamic body fluid properties in-vivo.

Brain aneurysms, characterized by abnormal bulging in blood vessels, pose significant risks if ruptured, necessitating precise neuroanatomical knowledge and advanced neurosurgical techniques for effective management. This article delves into the intricate neuroanatomy relevant to brain aneurysms, including the vascular structures and critical regions involved. It provides a comprehensive overview of the pathophysiology of aneurysm formation and progression. The discussion extends to modern neurosurgical approaches for treating brain aneurysms, such as microsurgical clipping, endovascular coiling, and flow diversion techniques. Emphasis is placed on preoperative planning, intraoperative navigation, and postoperative care, highlighting the importance of a multidisciplinary approach. By integrating neuroanatomical insights with cutting-edge surgical practices, this article aims to enhance the understanding and treatment outcomes of brain aneurysms.

Although arterial stiffness is known as a biomarker for cardiovascular events and stroke, there is limited information in the literature regarding the stiffness of intracranial aneurysms. In this study, we aim to assess the stiffness of intracranial aneurysms using 4D Flow MRI.

A total of 27 aneurysms in 25 patients with internal carotid artery aneurysms were included in this study. Using 4D Flow MRI, we measured the arterial pulse wave form during a cardiac cycle at planes proximal and distal to the target aneurysm. The damping of these waveforms through the aneurysm was defined as the aneurysm damping index (ADI) and compared to the contralateral side. We also investigated the clinical factors related to the ADI.

ADI assessment was successful in all cases. The average ADI was 1.18±0.28, which was significantly larger than 1.0 (P = 0.0027 [t-test]). The ADI on the aneurysm side was larger than on the contralateral side (1.19±0.30 vs 1.05±0.17, P = 0.029 [t-test]). On multivariate analysis, the use of beta-blockers (β=0.46, P = 0.015) and smoking history (β=-0.22, P = 0.024) showed a significant correlation with ADI.

We have proposed a novel method to observe arterial pulse wave dumping through intracranial aneurysm using 4D Flow MRI. The damping can be quantitatively observed, and the ADI has correlations with clinical factors such as antihypertensive drugs and smoking. Further studies should focus more on evaluating aneurysm stiffness and its clinical applications.

Cerebrovascular imaging assessments are particularly challenging in adolescent cohorts, where not all modalities are appropriate, and rapid brain maturation alters hemodynamics at both macro- and microvascular scales. In a preliminary sample of healthy adolescents (n=12, 8-25 years), we investigated relationships between 4D flow MRI-derived blood velocity and blood flow in bilateral anterior, middle, and posterior cerebral arteries and BOLD cerebrovascular reactivity in associated vascular territories. As hypothesized, higher velocities in large arteries are associated with an earlier response to a vasodilatory stimulus (cerebrovascular reactivity delay) in the downstream territory. Higher blood flow through these arteries is associated with a larger BOLD response to a vasodilatory stimulus (cerebrovascular reactivity amplitude) in the associated territory. These trends are consistent in a case study of adult moyamoya disease. In our small adolescent cohort, macrovascular-microvascular relationships for velocity/delay and flow/CVR change with age, though underlying mechanisms are unclear. Our work emphasizes the need to better characterize this key stage of human brain development, when cerebrovascular hemodynamics are changing, and standard imaging methods offer limited insight into these processes. We provide important normative data for future comparisons in pathology, where combining macro- and microvascular assessments may better help us prevent, stratify, and treat cerebrovascular disease.

Wall Shear Stress (WSS) is one of the most important parameters used in cardiovascular fluid mechanics, and it provides a lot of information like the risk level caused by any vascular occlusion. Since WSS cannot be measured directly and other available relevant methods have issues like low resolution, uncertainty and high cost, this study proposes a novel method by combining computational fluid dynamics (CFD), fluid-structure interaction (FSI), conditional generative adversarial network (cGAN) and convolutional neural network (CNN) to predict coronary artery occlusion risk using only noninvasive images accurately and rapidly. First, a cGAN model called WSSGAN was developed to predict the WSS contours on the vessel wall by training and testing the model based on the calculated WSS contours using coupling CFD-FSI simulations. Then, an 11-layer CNN was used to classify the WSS contours into three grades of occlusions, i.e. low risk, medium risk and high risk. To verify the proposed method for predicting the coronary artery occlusion risk in a real case, the patient's Magnetic Resonance Imaging (MRI) images were converted into a 3D geometry for use in the WASSGAN model. Then, the predicted WSS contours by the WSSGAN were entered into the CNN model to classify the occlusion grade.

Cardiac magnetic resonance imaging (MRI) is widely used for non-invasive assessment of cardiac morphology, function, and tissue characteristics due to its exquisite soft-tissue contrast. However, it remains time-consuming and requires proficiency, making it costly and limiting its widespread use. Traditional cardiac MRI is inefficient as signal acquisition is often limited to specific cardiac phases and requires complex view planning, parameter adjustments, and management of both respiratory and cardiac motion. Recent efforts have aimed to make cardiac MRI more efficient and accessible. Among these innovations, the free-running framework enables 5D whole-heart imaging without the need for an electrocardiogram signal, respiratory breath-holding, or complex planning. It uses a fully self-gated approach to extract cardiac and respiratory signals directly from the acquired image data, allowing for more efficient coverage in time and space without the need for electrocardiogram gating, triggering, navigators, or breath-holds. This review provides a comprehensive overview of the free-running framework, detailing its history, concepts, recent improvements, and clinical applications.

Maximum diameter measurements are used to assess the rupture risk of abdominal aortic aneurysms (AAAs); however, these are not precise enough to predict all ruptures. Four-dimensional (4D) flow MRI-derived parameters provide additional information by visualizing hemodynamics in AAAs but merit further investigation before they are clinically applicable.

To assess the reproducibility of 4D flow MRI-derived hemodynamics, to investigate possible correlations with lumen and maximum diameter, and to explore potential relationships with vorticity and aneurysm growth.

Prospective single-arm study.

A total of 22 (71.5 ± 6.1 years, 20 male) asymptomatic AAA patients with a maximum diameter of at least 30 mm.

A 3.0 T/Free-breathing 4D flow MRI phase-contrast acquisition with retrospective ECG-gating.

Patients underwent two consecutive 4D flow MRI scans 1-week apart. Aortic volumes were segmented from time-averaged phase contrast magnetic resonance angiographies. Reproducibility was assessed by voxelwise analysis after registration. Mean flow velocity, mean wall shear stress (WSS), mean lumen diameter, and qualitative vorticity scores were assessed. In addition, Dixon MRI and retrospective surveillance data were used to study maximum diameter (including thrombus), intraluminal thrombus volume (ILT), and growth rate.

For reproducibility assessment, Bland-Altman analyses, Pearson correlation, Spearman's correlation, and orthogonal regression were conducted. Potential correlations between hemodynamics and vorticity scores were assessed using linear regression. P < 0.05 was considered statistically significant.

Test-retest median Pearson correlation coefficients for flow velocity and WSS were 0.85 (IQR = 0.08) m/sec and 0.82 (IQR = 0.10) Pa, respectively. Mean WSS significantly correlated with mean flow velocity (R = 0.75) and inversely correlated with mean lumen diameter (R = -0.73). No significant associations were found between 4D flow MRI-derived hemodynamic parameters and maximum diameter (flow velocity: P = 0.98, WSS: P = 0.22).

A 4D flow MRI is robust for assessing the hemodynamics within AAAs. No correlations were found between hemodynamic parameters and maximum diameter, ILT volume and growth rate.

2 TECHNICAL EFFICACY: Stage 2.

We present an unsupervised deep learning method to perform flow denoising and super-resolution without high-resolution labels. We demonstrate the ability of a single model to reconstruct three-dimensional stenosis and aneurysm flows, with varying geometries, orientations, and boundary conditions. Ground truth data was generated using computational fluid dynamics, and then corrupted with multiplicative Gaussian noise. Auto-encoders were used to compress the representations of the flow domain geometry and the (possibly noisy and low-resolution) flow field. These representations were used to condition a physics-informed neural network. A physics-based loss was implemented to train the model to recover lost information from the noisy input by transforming the flow to a solution of the Navier-Stokes equations. Our experiments achieved mean squared errors in the true flow reconstruction of O(1.0 × 10-4), and root mean squared residuals of O(1.0 × 10-2) for the momentum and continuity equations. Our method yielded correlation coefficients of 0.971 for the hidden pressure field and 0.82 for the derived wall shear stress field. By performing point-wise predictions of the flow, the model was able to robustly denoise and super-resolve the field to 20× the input resolution.

Background: The utilization of hemodynamic parameters, whose estimation is often cumbersome, can fasten diagnostics and decision-making related to congenital heart diseases. The main goal of this study is to investigate the relationship between hemodynamic and morphometric features of the thoracic aorta and to construct corresponding predictive models. Methods: Multi-slice spiral computed tomography images of the aortas of patients with coarctation diagnoses and patients without cardiac or vascular diseases were evaluated to obtain numerical models of the aorta and branches of the aortic arch. Hemodynamic characteristics were estimated in key subdomains of the aorta and three branches using computational fluid dynamics methods. The key morphometric features (diameters) were calculated at locations in proximity to the domains, where hemodynamic characteristics are evaluated. Results: The functional dependencies for velocities and pressure on the corresponding diameters have been fitted, and a metamodel has been constructed employing the predicted values from these models. Conclusions: The metamodel demonstrated high accuracy in classifying aortas into their respective types, thereby confirming the adequacy of the predicted hemodynamic characteristics by morphometric characteristics. The proposed methodology is applicable to other heart diseases without fundamental changes.