In Vitro Assessment of Flow Variability in an Intracranial Aneurysm Model Using 4D Flow MRI and Tomographic PIV

Validation of Dynamic 3D MRI for Urodynamics Assessment Using an Anatomically Realistic In Vitro Model of the Bladder

Lowery urinary tract symptoms (LUTS) affect a large majority of the aging population. 3D Dynamic MRI shows promise as a noninvasive diagnostic tool that can assess bladder anatomy and function (urodynamics) while overcoming challenges associated with current urodynamic assessment methods. However, validation of this technique remains an unmet need. In this study, an anatomically realistic, bladder-mimicking in vitro flow model was created and used to systematically benchmark 3D dynamic MRI performance using a highly controllable syringe pump. Time-resolved volumes of the synthetic bladder model were obtained during simulated filling and voiding events and used to calculate volumetric flowrate. During MRI acquisitions, pressure during each event was recorded and used to create PV loops for work assessment. Error between control and MRI-derived volume for voiding and filling events exhibited 3.36% and 4.66% differences, respectively. A slight increase in average error was observed for MRI-derived flowrate when compared to the control flowrate (4.90% and 7.67% for voiding and filling, respectively). Overall, average error in segmented volumes increased with decreasing volume flowrate. Pressure drops were observed during voiding. Pressure increased during filling. Enhanced validation of novel 3D MRI urodynamics is achieved by using high-resolution PIV for visualizing and quantifying velocity inside the bladder model, which is not currently possible with 3D Dynamic MRI.

Common mechanisms of physiological and pathological rupture events in biology: novel insights into mammalian ovulation and beyond

Ovulation is a cyclical biological rupture event fundamental to fertilisation and endocrine function. During this process, the somatic support cells that surround the germ cell undergo a remodelling process that culminates in breakdown of the follicle wall and release of a mature egg. Ovulation is driven by known proteolytic and inflammatory pathways as well as structural alterations to the follicle vasculature and the fluid-filled antral cavity. Ovulation is one of several types of systematic remodelling that occur in the human body that can be described as rupture. Although ovulation is a physiological form of rupture, other types of rupture occur in the human body which can be pathological, physiological, or both. In this review, we use intracranial aneurysms and chorioamniotic membrane rupture as examples of rupture events that are pathological or both pathological and physiological, respectively, and compare these to the rupture process central to ovulation. Specifically, we compared existing transcriptomic profiles, immune cell functions, vascular modifications, and biomechanical forces to identify common processes that are conserved between rupture events. In our transcriptomic analysis, we found 12 differentially expressed genes in common among two different ovulation data sets and one intracranial aneurysm data set. We also found three genes that were differentially expressed in common for both ovulation data sets and one chorioamniotic membrane rupture data set. Combining analysis of all three data sets identified two genes (Angptl4 and Pfkfb4) that were upregulated across rupture systems. Some of the identified genes, such as Rgs2, Adam8, and Lox, have been characterised in multiple rupture contexts, including ovulation. Others, such as Glul, Baz1a, and Ddx3x, have not yet been characterised in the context of ovulation and warrant further investigation as potential novel regulators. We also identified overlapping functions of mast cells, macrophages, and T cells in the process of rupture. Each of these rupture systems share local vasoconstriction around the rupture site, smooth muscle contractions away from the site of rupture, and fluid shear forces that initially increase and then decrease to predispose one specific region to rupture. Experimental techniques developed to study these structural and biomechanical changes that underlie rupture, such as patient-derived microfluidic models and spatiotemporal transcriptomic analyses, have not yet been comprehensively translated to the study of ovulation. Review of the existing knowledge, transcriptomic data, and experimental techniques from studies of rupture in other biological systems yields a better understanding of the physiology of ovulation and identifies avenues for novel studies of ovulation with techniques and targets from the study of vascular biology and parturition.

Four-Dimensional Flow MR Imaging: Technique and Advances

4D Flow MRI is an advanced imaging technique for comprehensive non-invasive assessment of the cardiovascular system. The capture of the blood velocity vector field throughout the cardiac cycle enables measures of flow, pulse wave velocity, kinetic energy, wall shear stress, and more. Advances in hardware, MRI data acquisition and reconstruction methodology allow for clinically feasible scan times. The availability of 4D Flow analysis packages allows for more widespread use in research and the clinic and will facilitate much needed multi-center, multi-vendor studies in order to establish consistency across scanner platforms and to enable larger scale studies to demonstrate clinical value.

Dynamic Analysis of the Coracohumeral Ligament Using Ultra-Sonography in Shoulder Contracture

The coracohumeral ligament (CHL) is related to the range of motion of the shoulder joint. The evaluation of the CHL using ultrasonography (US) has been reported on the elastic modulus and thickness of the CHL, but no dynamic evaluation method has been established. We aimed to quantify the movement of the CHL by applying Particle Image Velocimetry (PIV), a technique used in the field of fluid engineering, to cases of shoulder contracture using the US. The subjects were eight patients, with 16 shoulders. The coracoid process was identified from the body surface, and a long-axis US image of the CHL parallel to the subscapularis tendon was drawn. The shoulder joint was moved from 0 degrees of internal/external rotation to 60 degrees of internal rotation at a rhythm of one reciprocation every 2 s. The velocity of the CHL movement was quantified by the PIV method. The mean magnitude velocity of CHL was significantly faster on the healthy side. The maximum magnitude velocity was significantly faster on the healthy side. The results suggest that the PIV method is helpful as a dynamic evaluation method, and in patients with shoulder contracture, the CHL velocity was significantly decreased.

Developing an in vitro validated 3D in silico internal carotid artery sidewall aneurysm model

Introduction: Direct quantification of hemodynamic factors applied to a cerebral aneurysm (CA) remains inaccessible due to the lack of technologies to measure the flow field within an aneurysm precisely. This study aimed to develop an in vitro validated 3D in silico patient-specific internal carotid artery sidewall aneurysm (ICASA) model which can be used to investigate hemodynamic factors on the CA pathophysiology. Methods: The validated ICASA model was developed by quantifying and comparing the flow field using particle image velocimetry (PIV) measurements and computational fluid dynamics (CFD) simulations. Specifically, the flow field characteristics, i.e., blood flowrates, normalized velocity profiles, flow streamlines, and vortex locations, have been compared at representative time instants in a cardiac pulsatile period in two designated regions of the ICASA model, respectively. One region is in the internal carotid artery (ICA) inlet close to the aneurysm sac, the other is across the middle of the aneurysmal sac. Results and Discussion: The results indicated that the developed computational fluid dynamics model presents good agreements with the results from the parallel particle image velocimetry and flowrate measurements, with relative differences smaller than 0.33% in volumetric flow rate in the ICA and relative errors smaller than 9.52% in averaged velocities in the complex aneurysmal sac. However, small differences between CFD and PIV in the near wall regions were observed due to the factors of slight differences in the 3D printed model, light reflection and refraction near arterial walls, and flow waveform uncertainties. The validated model not only can be further employed to investigate hemodynamic factors on the cerebral aneurysm pathophysiology statistically, but also provides a typical model and guidance for other professionals to evaluate the hemodynamic effects on cerebral aneurysms.

Deep learning-based recognition and segmentation of intracranial aneurysms under small sample size

The manual identification and segmentation of intracranial aneurysms (IAs) involved in the 3D reconstruction procedure are labor-intensive and prone to human errors. To meet the demands for routine clinical management and large cohort studies of IAs, fast and accurate patient-specific IA reconstruction becomes a research Frontier. In this study, a deep-learning-based framework for IA identification and segmentation was developed, and the impacts of image pre-processing and convolutional neural network (CNN) architectures on the framework's performance were investigated. Three-dimensional (3D) segmentation-dedicated architectures, including 3D UNet, VNet, and 3D Res-UNet were evaluated. The dataset used in this study included 101 sets of anonymized cranial computed tomography angiography (CTA) images with 140 IA cases. After the labeling and image pre-processing, a training set and test set containing 112 and 28 IA lesions were used to train and evaluate the convolutional neural network mentioned above. The performances of three convolutional neural networks were compared in terms of training performance, segmentation performance, and segmentation efficiency using multiple quantitative metrics. All the convolutional neural networks showed a non-zero voxel-wise recall (V-Recall) at the case level. Among them, 3D UNet exhibited a better overall segmentation performance under the relatively small sample size. The automatic segmentation results based on 3D UNet reached an average V-Recall of 0.797 ± 0.140 (3.5% and 17.3% higher than that of VNet and 3D Res-UNet), as well as an average dice similarity coefficient (DSC) of 0.818 ± 0.100, which was 4.1%, and 11.7% higher than VNet and 3D Res-UNet. Moreover, the average Hausdorff distance (HD) of the 3D UNet was 3.323 ± 3.212 voxels, which was 8.3% and 17.3% lower than that of VNet and 3D Res-UNet. The three-dimensional deviation analysis results also showed that the segmentations of 3D UNet had the smallest deviation with a max distance of +1.4760/-2.3854 mm, an average distance of 0.3480 mm, a standard deviation (STD) of 0.5978 mm, a root mean square (RMS) of 0.7269 mm. In addition, the average segmentation time (AST) of the 3D UNet was 0.053s, equal to that of 3D Res-UNet and 8.62% shorter than VNet. The results from this study suggested that the proposed deep learning framework integrated with 3D UNet can provide fast and accurate IA identification and segmentation.

Advances in machine learning applications for cardiovascular 4D flow MRI

Four-dimensional flow magnetic resonance imaging (MRI) has evolved as a non-invasive imaging technique to visualize and quantify blood flow in the heart and vessels. Hemodynamic parameters derived from 4D flow MRI, such as net flow and peak velocities, but also kinetic energy, turbulent kinetic energy, viscous energy loss, and wall shear stress have shown to be of diagnostic relevance for cardiovascular diseases. 4D flow MRI, however, has several limitations. Its long acquisition times and its limited spatio-temporal resolutions lead to inaccuracies in velocity measurements in small and low-flow vessels and near the vessel wall. Additionally, 4D flow MRI requires long post-processing times, since inaccuracies due to the measurement process need to be corrected for and parameter quantification requires 2D and 3D contour drawing. Several machine learning (ML) techniques have been proposed to overcome these limitations. Existing scan acceleration methods have been extended using ML for image reconstruction and ML based super-resolution methods have been used to assimilate high-resolution computational fluid dynamic simulations and 4D flow MRI, which leads to more realistic velocity results. ML efforts have also focused on the automation of other post-processing steps, by learning phase corrections and anti-aliasing. To automate contour drawing and 3D segmentation, networks such as the U-Net have been widely applied. This review summarizes the latest ML advances in 4D flow MRI with a focus on technical aspects and applications. It is divided into the current status of fast and accurate 4D flow MRI data generation, ML based post-processing tools for phase correction and vessel delineation and the statistical evaluation of blood flow.

Smartphone-based particle image velocimetry for cardiovascular flows applications: A focus on coronary arteries

An experimental set-up is presented for the in vitro characterization of the fluid dynamics in personalized phantoms of healthy and stenosed coronary arteries. The proposed set-up was fine-tuned with the aim of obtaining a compact, flexible, low-cost test-bench for biomedical applications. Technically, velocity vector fields were measured adopting a so-called smart-PIV approach, consisting of a smartphone camera and a low-power continuous laser (30 mW). Experiments were conducted in realistic healthy and stenosed 3D-printed phantoms of left anterior descending coronary artery reconstructed from angiographic images. Time resolved image acquisition was made possible by the combination of the image acquisition frame rate of last generation commercial smartphones and the flow regimes characterizing coronary hemodynamics (velocities in the order of 10 cm/s). Different flow regimes (Reynolds numbers ranging from 20 to 200) were analyzed. The smart-PIV approach was able to provide both qualitative flow visualizations and quantitative results. A comparison between smart-PIV and conventional PIV (i.e., the gold-standard experimental technique for bioflows characterization) measurements showed a good agreement in the measured velocity vector fields for both the healthy and the stenosed coronary phantoms. Displacement errors and uncertainties, estimated by applying the particle disparity method, confirmed the soundness of the proposed smart-PIV approach, as their values fell within the same range for both smart and conventional PIV measured data (≈5% for the normalized estimated displacement error and below 1.2 pixels for displacement uncertainty). In conclusion, smart-PIV represents an easy-to-implement, low-cost methodology for obtaining an adequately robust experimental characterization of cardiovascular flows. The proposed approach, to be intended as a proof of concept, candidates to become an easy-to-handle test bench suitable for use also outside of research labs, e.g., for educational or industrial purposes, or as first-line investigation to direct and guide subsequent conventional PIV measurements.

Hemodynamic characteristics in a cerebral aneurysm model using non-Newtonian blood analogues

This study aims to develop an experimentally validated computational fluid dynamics (CFD) model to estimate hemodynamic characteristics in cerebral aneurysms (CAs) using non-Newtonian blood analogues. Blood viscosities varying with shear rates were measured under four temperatures first, which serves as the reference for the generation of blood analogues. Using the blood analogue, particle image velocimetry (PIV) measurements were conducted to quantify flow characteristics in a CA model. Then, using the identical blood properties in the experiment, CFD simulations were executed to quantify the flow patterns, which were used to compare with the PIV counterpart. Additionally, hemodynamic characteristics in the simplified Newtonian and non-Newtonian models were quantified and compared using the experimentally validated CFD model. Results showed the proposed non-Newtonian viscosity model can predict blood shear-thinning properties accurately under varying temperatures and shear rates. Another developed viscosity model based on the blood analogue can well represent blood rheological properties. The comparisons in flow characteristics show good agreements between PIV and CFD, demonstrating the developed CFD model is qualified to investigate hemodynamic factors within CAs. Furthermore, results show the differences of absolute values were insignificant between Newtonian and non-Newtonian fluids in the distributions of wall shear stress (WSS) and oscillatory shear index (OSI) on arterial walls. However, not only does the simplified Newtonian model underestimate WSS and OSI in most regions of the aneurysmal sac, but it also makes mistakes in identifying the high OSI regions on the sac surface, which may mislead the hemodynamic assessment on the pathophysiology of CAs.

Computational Fontan Analysis: Preserving Accuracy While Expediting Workflow

Background: Postoperative outcomes of the Fontan operation have been linked to geometry of the cavopulmonary pathway, including graft shape after implantation. Computational fluid dynamics (CFD) simulations are used to explore different surgical options. The objective of this study is to perform a systematic in vitro validation for investigating the accuracy and efficiency of CFD simulation to predict Fontan hemodynamics. Methods: CFD simulations were performed to measure indexed power loss (iPL) and hepatic flow distribution (HFD) in 10 patient-specific Fontan models, with varying mesh and numerical solvers. The results were compared with a novel in vitro flow loop setup with 3D printed Fontan models. A high-resolution differential pressure sensor was used to measure the pressure drop for validating iPL predictions. Microparticles with particle filtering system were used to measure HFD. The computational time was measured for a representative Fontan model with different mesh sizes and numerical solvers. Results: When compared to in vitro setup, variations in CFD mesh sizes had significant effect on HFD (P  =  .0002) but no significant impact on iPL (P  =  .069). Numerical solvers had no significant impact in both iPL (P  =  .50) and HFD (P  =  .55). A transient solver with 0.5 mm mesh size requires computational time 100 times more than a steady solver with 2.5 mm mesh size to generate similar results. Conclusions: The predictive value of CFD for Fontan planning can be validated against an in vitro flow loop. The prediction accuracy can be affected by the mesh size, model shape complexity, and flow competition.

Quality Control for 4D Flow MR Imaging

In recent years, 4D flow MRI has become increasingly important in clinical applications for the blood vessels in the whole body, heart, and cerebrospinal fluid. 4D flow MRI has advantages over 2D cine phase-contrast (PC) MRI in that any targeted area of interest can be analyzed post-hoc, but there are some factors to be considered, such as ensuring measurement accuracy, a long imaging time and post-processing complexity, and interobserver variability.Due to the partial volume phenomenon caused by low spatial and temporal resolutions, the accuracy of flow measurement in 4D flow MRI is reduced. For spatial resolution, it is recommended to include at least four voxels in the vessel of interest, and if possible, six voxels. In large vessels such as the aorta, large voxels can be secured and SNR can be maintained, but in small cerebral vessels, SNR is reduced, resulting in reduced accuracy. A temporal resolution of less than 40 ms is recommended. The velocity-to-noise ratio (VNR) of low-velocity blood flow is low, resulting in poor measurement accuracy. The use of dual velocity encoding (VENC) or multi-VENC is recommended to avoid velocity wrap around and to increase VNR. In order to maintain sufficient spatio-temporal resolution, a longer imaging time is required, leading to potential patient movement during examination and a corresponding decrease in measurement accuracy.For the clinical application of new technologies, including various acceleration techniques, in vitro and in vivo accuracy verification based on existing accuracy-validated 2D cine PC MRI and 4D flow MRI, as well as accuracy verification on the conservation of mass' principle, should be performed, and intraobserver repeatability, interobserver reproducibility, and test-retest reproducibility should be checked.

The Biofabrication of Diseased Artery In Vitro Models

As the leading causes of global death, cardiovascular diseases are generally initiated by artery-related disorders such as atherosclerosis, thrombosis, and aneurysm. Although clinical treatments have been developed to rescue patients suffering from artery-related disorders, the underlying pathologies of these arterial abnormalities are not fully understood. Biofabrication techniques pave the way to constructing diseased artery in vitro models using human vascular cells, biomaterials, and biomolecules, which are capable of recapitulating arterial pathophysiology with superior performance compared with conventional planar cell culture and experimental animal models. This review discusses the critical elements in the arterial microenvironment which are important considerations for recreating biomimetic human arteries with the desired disorders in vitro. Afterward, conventionally biofabricated platforms for the investigation of arterial diseases are summarized, along with their merits and shortcomings, followed by a comprehensive review of advanced biofabrication techniques and the progress of their applications in establishing diseased artery models.

Imaging Modalities for Intracranial Aneurysm: More Than Meets the Eye

Intracranial aneurysms (IA) are often asymptomatic and have a prevalence of 3 to 5% in the adult population. The risk of IA rupture is low, however when it occurs half of the patients dies from subarachnoid hemorrhage (SAH). To avoid this fatal evolution, the main treatment is an invasive surgical procedure, which is considered to be at high risk of rupture. This risk score of IA rupture is evaluated mainly according to its size and location. Therefore, angiography and anatomic imaging of the intracranial aneurysm are crucial for its diagnosis. Moreover, it has become obvious in recent years that several other factors are implied in this complication, such as the blood flow complexity or inflammation. These recent findings lead to the development of new IA imaging tools such as vessel wall imaging, 4D-MRI, or molecular MRI to visualize inflammation at the site of IA in human and animal models. In this review, we will summarize IA imaging techniques used for the patients and those currently in development.

Influence of Spatial Resolution and Compressed SENSE Acceleration Factor on Flow Quantification with 4D Flow MRI at 3 Tesla

Four-dimensional (4D) flow MRI allows quantifying flow in blood vessels–non invasively and in vivo. The clinical use of 4D flow MRI in small vessels, however, is hampered by long examination times and limited spatial resolution. Compressed SENSE (CS-SENSE) is a technique that can accelerate 4D flow dramatically. Here, we investigated the effect of spatial resolution and CS acceleration on flow measurements by using 4D flow MRI in small vessels in vitro at 3 T. We compared the flow in silicon tubes (inner diameters of 2, 3, 4, and 5 mm) measured with 4D flow MRI, accelerated with four CS factors (CS = 2.5, 4.5, 6.5, and 13) and three voxel sizes (0.5, 1, and 1.5 mm³) to 2D flow MRI and a flow sensor. Additionally, the velocity field in an aneurysm model acquired with 4D flow MRI was compared to the one simulated with computational fluid dynamics (CFD). A strong correlation was observed between flow sensor, 2D flow MRI, and 4D flow MRI (rho > 0.94). The use of fewer than seven voxels per vessel diameter (nROI) resulted in an overestimation of flow in more than 5% of flow measured with 2D flow MRI. A negative correlation (rho = −0.81) between flow error and nROI were found for CS = 2.5 and 4.5. No statistically significant impact of CS factor on differences in flow rates was observed. However, a trend of increased flow error with increased CS factor was observed. In an aneurysm model, the peak velocity and stagnation zone were detected by CFD and all 4D flow MRI variants. The velocity difference error in the aneurysm sac did not exceed 11% for CS = 4.5 in comparison to CS = 2.5 for all spatial resolutions. Therefore, CS factors from 2.5–4.5 can appear suitable to improve spatial or temporal resolution for accurate quantification of flow rate and velocity. We encourage reporting the number of voxels per vessel diameter to standardize 4D flow MRI protocols.

Motion Analysis of Triangular Fibrocartilage Complex by Using Ultrasonography Images: Preliminary Analysis

The triangular fibrocartilage complex (TFCC) is a significant stabilizer of the distal radioulnar joint. Diagnosing TFCC injury is currently difficult, but ultrasonography (US) has emerged as a low-cost, minimally invasive diagnostic tool. We aimed to quantitatively analyze TFCC by performing motion analysis by using US. Twelve healthy volunteers, comprising 24 wrists (control group), and 15 patients with TFCC Palmer type 1B injuries (injury group) participated. The US transducer was positioned between the ulnar styloid process and triquetrum and was tilted ulnarly 30° from the vertical line. The wrist was then actively moved from 10° of radial deviation to 20° of ulnar deviation in a 60-rounds-per-minute rhythm that was paced by a metronome. The articular disc displacement velocity magnitude was analyzed by using particle image velocimetry fluid measurement software. The mean area of the articular discs was larger on ulnar deviation in the control group. The mean articular disc area on radial deviation was larger in the injury group. The average articular disc velocity magnitude for the injury group was significantly higher than that for the control group. The results suggest that patients with TFCC injury lose articular disc cushioning and static stability, and subsequent abnormal motion can be analyzed by using US.

Intracranial Flow Velocity Quantification Using Non-Contrast Four-Dimensional Flow MRI: A Prospective Comparative Study with Transcranial Doppler Ultrasound

Four-dimensional (4D) flow magnetic resonance imaging (MRI) allows three-dimensional velocity encoding to measure blood flow in a single scan, regardless of the intracranial artery direction. We compared blood flow velocity quantification by non-contrast 4D flow MRI and by transcranial Doppler ultrasound (TCD), the most widely used modality for measuring velocity. Twenty-two patients underwent both TCD and non-contrast 4D flow MRI. The mean time interval between TCD and non-contrast 4D flow MRI was 0.7 days. Subsegmental velocities were measured bilaterally in the middle cerebral and basilar arteries using TCD and non-contrast 4D flow MRI. Intracranial velocity measurements using TCD and non-contrast 4D flow MRI demonstrated a strong correlation in the bilateral M1, especially at the proximal segment (right r = 0.74, left r = 0.78; all p < 0.001). Mean velocities acquired with 4D flow MRI were approximately 8 to 10% lower than those acquired with TCD according to the location of M1. Intracranial arterial flow measurements estimated using non-contrast 4D flow MRI and TCD showed strong correlation. 4D flow MRI enables simultaneous assessment of vascular morphology and quantitative hemodynamic measurement, providing three-dimensional blood flow visualization. 4D flow MRI is a clinically useful sequence with a promising role in cerebrovascular disease.

Hemodynamic Study of a Patient-Specific Intracranial Aneurysm: Comparative Assessment of Tomographic PIV, Stereoscopic PIV, In Vivo MRI and Computational Fluid Dynamics

Introduction

Wall shear stress (WSS) is associated with the growth and rupture of an intracranial aneurysm. To reveal their underlying connections, many image-based computational fluid dynamics (CFD) studies have been conducted. However, the methodological validations using both in vivo medical imaging and in vitro optical flow measurements were rarely accompanied in such studies.

Methods

In the present study, we performed a comparative assessment on the hemodynamics of a patient-specific intracranial saccular aneurysm using in vivo 4D Flow MRI, in silico CFD, in vitro stereoscopic and tomographic particle imaging velocimetry (Stereo-PIV and Tomo-PIV) techniques. PIV experiments and CFD were conducted under steady state corresponding to the peak systole of 4D Flow MRI.

Results

The results showed that all modalities provided similar flow features and overall surface distribution of WSS. However, a large variation in the absolute WSS values was found. 4D Flow MRI estimated a 2- to 4-fold lower peak WSS (3.99 Pa) and a 1.6- to 2-fold lower mean WSS (0.94 Pa) than Tomo-PIV, Stereo-PIV, and CFD. Bland-Altman plots of WSS showed that the differences between PIV-/CFD-based WSS and 4D Flow MRI-based WSS increase with higher WSS magnitude. Such proportional trend was absent in the Bland-Altman comparison of velocity where the resolutions of PIV and CFD datasets were matched to 4D Flow MRI. We also found that because of superior resolution in the out-of-plane direction, WSS estimation by Tomo-PIV was higher than Stereo-PIV.

Conclusions

Our results indicated that the differences in spatial resolution could be the main contributor to the discrepancies between each modality. The findings of this study suggest that with current techniques, care should be taken when using absolute WSS values to perform a quantitative risk analysis of aneurysm rupture.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13239-021-00583-2.

Experimental Validation of Enhanced Magnetic Resonance Imaging (EMRI) Using Particle Image Velocimetry (PIV)

Flow-sensitive four-dimensional Cardiovascular Magnetic Resonance Imaging (4D Flow CMR) has increasingly been utilised to characterise patients' blood flow, in association with patiens' state of health and disease, even though spatial and temporal resolutions still constitute a limit. Computational fluid dynamics (CFD) is a powerful tool that could expand these information and, if integrated with experimentally-obtained velocity fields, would enable to derive a large variety of the flow descriptors of interest. However, the accuracy of the flow parameters is highly influenced by the quality of the input data such as the anatomical model and boundary conditions typically derived from medical images including 4D Flow CMR. We previously proposed a novel approach in which 4D Flow CMR and CFD velocity fields are integrated to obtain an Enhanced 4D Flow CMR (EMRI), allowing to overcome the spatial-resolution limitation of 4D Flow CMR, and enable an accurate quantification of flow. In this paper, the proposed approach is validated in a U bend channel, an idealised model of the human aortic arch. The flow patterns were studied with 4D Flow CMR, CFD and EMRI, and compared with high resolution 2D PIV experiments obtained in pulsatile conditions. The main strengths and limitations of 4D Flow CMR and CFD were illustrated by exploiting the accuracy of PIV by comparing against PIV velocity fields. EMRI flow patterns showed a better qualitative and quantitative agreement with PIV results than the other techniques. EMRI enables to overcome the experimental limitations of MRI-based velocity measurements and the modelling simplifications of CFD, allowing an accurate prediction of complex flow patterns observed experimentally, while satisfying mass and momentum balance equations.

Enhancement of cerebrovascular 4D flow MRI velocity fields using machine learning and computational fluid dynamics simulation data

Blood flow metrics obtained with four-dimensional (4D) flow phase contrast (PC) magnetic resonance imaging (MRI) can be of great value in clinical and experimental cerebrovascular analysis. However, limitations in both quantitative and qualitative analyses can result from errors inherent to PC MRI. One method that excels in creating low-error, physics-based, velocity fields is computational fluid dynamics (CFD). Augmentation of cerebral 4D flow MRI data with CFD-informed neural networks may provide a method to produce highly accurate physiological flow fields. In this preliminary study, the potential utility of such a method was demonstrated by using high resolution patient-specific CFD data to train a convolutional neural network, and then using the trained network to enhance MRI-derived velocity fields in cerebral blood vessel data sets. Through testing on simulated images, phantom data, and cerebrovascular 4D flow data from 20 patients, the trained network successfully de-noised flow images, decreased velocity error, and enhanced near-vessel-wall velocity quantification and visualization. Such image enhancement can improve experimental and clinical qualitative and quantitative cerebrovascular PC MRI analysis.

In Vitro Assessment of Right Ventricular Outflow Tract Anatomy and Valve Orientation Effects on Bioprosthetic Pulmonary Valve Hemodynamics

PURPOSE

The congenital heart defect Tetralogy of Fallot (ToF) affects 1 in 2500 newborns annually in the US and typically requires surgical repair of the right ventricular outflow tract (RVOT) early in life, with variations in surgical technique leading to large disparities in RVOT anatomy among patients. Subsequently, often in adolescence or early adulthood, patients usually require surgical placement of a xenograft or allograft pulmonary valve prosthesis. Valve longevity is highly variable for reasons that remain poorly understood.

METHODS

This work aims to assess the performance of bioprosthetic pulmonary valves in vitro using two 3D printed geometries: an idealized case based on healthy subjects aged 11 to 13 years and a diseased case with a 150% dilation in vessel diameter downstream of the valve. Each geometry was studied with two valve orientations: one with a valve leaflet opening posterior, which is the native pulmonary valve position, and one with a valve leaflet opening anterior.

RESULTS

Full three-dimensional, three-component, phase-averaged velocity fields were obtained in the physiological models using 4D flow MRI. Flow features, particularly vortex formation and reversed flow regions, differed significantly between the RVOT geometries and valve orientations. Pronounced asymmetry in streamwise velocity was present in all cases, while the diseased geometry produced additional asymmetry in radial flows. Quantitative integral metrics demonstrated increased secondary flow strength and recirculation in the rotated orientation for the diseased geometry.

CONCLUSIONS

The compound effects of geometry and orientation on bioprosthetic valve hemodynamics illustrated in this study could have a crucial impact on long-term valve performance.

Particle Image Velocimetry Measurements of the Flow-Diverting Effects of a New Generation of the eCLIPs Implant for the Treatment of Intracranial Bifurcation Aneurysms

Flow diverters (FDs) for the endovascular treatment of intracranial aneurysms are effective for sidewall aneurysms, but their use at a bifurcation is problematic because FDs only partially cover the aneurysm neck and impede flow into a daughter branch; they are thus not employed routinely in this anatomy. eCLIPs was developed as a non-tubular implant to completely cover the neck of an aneurysm and serve as a coil retention device necessary for the adequate treatment of wide-neck bifurcation aneurysms. eCLIPs has shown some flow diversion effects in bifurcation anatomy but not equal to those exhibited by clinically accepted flow diverters in sidewall anatomy. A new generation of eCLIPs implant, the eCLIPs bifurcation flow diverter (eBFD), with higher metal coverage, was developed to achieve a similar flow diversion as a Pipeline Embolization Device (PED), a prototypical FD. Particle image velocimetry was used to capture the fluid dynamics and velocity reduction within silicone aneurysm replicas. A circulatory mimicking loop was developed to circulate the flow through the silicone models. All generations of eCLIPs implants had some flow-diverting effect, with increasing metal coverage density of the implant proportionately increasing the flow diversion effect. The eBFD, with a metal density of 35%, showed greater flow diversion than PED, with 30% metal density, for bifurcation anatomy. The eBFD showed similar reduction of flow in a bifurcation anatomy to PED in a sidewall, both sufficient to permit early thrombosis of the aneurysm. Thus, the eBFD can potentially provide sufficient flow diversion for the treatment of bifurcation aneurysms to avoid adjunctive coiling.