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Objectives The ELANA® Heart Bypass creates a standardized sutureless anastomosis. Hereby, we investigate the influence of arteriotomy and graft size on coronary hemodynamics. Methods A computational fluid dynamics (CFD) model was developed. Arteriotomy size (standard 1.43 mm2; varied 0.94 – 3.6 mm2) and graft diameter (standard 2.5 mm; varied 1.5 – 5.0 mm) were independent parameters. Outcome parameters were coronary pressure and flow, and fractional flow reserve (FFR). Results The current size ELANA (arteriotomy 1.43 mm2) presented an estimated FFR 0.65 (39 mL/min). Enlarging arteriotomy increased FFR, coronary pressure, and flow. All reached a maximum once the arteriotomy (2.80 mm2) surpassed the coronary cross-sectional area (2.69 mm2, i.e. 1.85 mm diameter), presenting an estimated FFR 0.75 (46 mL/min). Increasing graft diameter was positively related to FFR, coronary pressure, and flow. Conclusion The ratio between the required minimal coronary diameter for application and the ELANA arteriotomy size effectuates a pressure drop that could be clinically relevant. Additional research and eventual lengthening of the anastomosis is advised. Graphical abstract

Accurate 3-D geometries of arteries and veins are important clinical data for diagnosis of arterial disease and intervention planning. Automatic segmentation of vessels in the transverse view suffers from the low lateral resolution and contrast. Convolutional neural networks are a promising tool for automatic segmentation of medical images, outperforming the traditional segmentation methods with high robustness. In this study, we aim to create a general, robust, and accurate method to segment the lumen–wall boundary of healthy central and peripheral vessels in large field-of-view freehand ultrasound (US) datasets. Data were acquired using the freehand US, in combination with a probe tracker. A total of ±36 000 cross-sectional images, acquired in the common, internal, and external carotid artery ( ${N} = 37$ ), in the radial, ulnar artery, and cephalic vein ( ${N} = 12$ ), and in the femoral artery ( ${N} = 5$ ) were included. To create masks (of the lumen) for training data, a conventional automatic segmentation method was used. The neural networks were trained on: 1) data of all vessels and 2) the carotid artery only. The performance was compared and tested using an open-access dataset. The recall, precision, DICE, and intersection over union (IoU) were calculated. Overall, segmentation was successful in the carotid and peripheral arteries. The Multires U-net architecture performs best overall with DICE = 0.93 when trained on the total dataset. Future studies will focus on the inclusion of vascular pathologies.

Survivors of myocardial infarction are at risk of life-threatening ventricular tachycardias (VTs) later in their lives. Current guidelines for implantable cardioverter defibrillators (ICDs) implantation to prevent VT-related sudden cardiac death is solely based on symptoms and left ventricular ejection fraction. Catheter ablation of scar-related VTs is performed following ICD therapy, reducing VTs, painful shocks, anxiety, depression and worsening heart failure. We postulate that better prediction of the occurrence and circuit of VT, will improve identification of patients at risk for VT and boost preventive ablation, reducing mortality and morbidity. For this purpose, multiple time-evolving aspects of the underlying pathophysiology, including the anatomical substrate, triggers and modulators, should be part of VT prediction models. We envision digital twins as a solution combining clinical expertise with three prediction approaches: evidence-based medicine (clinical practice), data-driven models (data science) and mechanistic models (biomedical engineering). This paper aims to create a mutual understanding between experts in the different fields by providing a comprehensive description of the clinical problem and the three approaches in an understandable manner, leveraging future collaborations and technological innovations for clinical decision support. Moreover, it defines open challenges and gains for digital twin solutions and discusses the potential of hybrid modelling.

Microfluidic designs are versatile examples of technology miniaturisation that find their applications in various cell biology research, especially to investigate the influence of environmental signals on cellular response dynamics. Multicellular systems operate in intricate cellular microenvironments where environmental signals govern well-orchestrated and robust responses, the understanding of which can be realized with integrated microfluidic systems. In this study, we present a fully automated and integrated microfluidic chip that can deliver input signals to single and isolated suspension or adherent cells in a precisely controlled manner. In respective analyses of different single cell types, we observe, in real-time, the temporal dynamics of caspase 3 activation during DMSO-induced apoptosis in single cancer cells (K562) and the translocation of STAT-1 triggered by interferon γ (IFNγ) in single fibroblasts (NIH3T3). Our investigations establish the employment of our versatile microfluidic system in probing temporal single cell signaling networks where alternations in outputs uncover signal processing mechanisms.

Vascular ultrasound imaging is inherently hampered by low lateral resolution and contrast. Steering of the ultrasound beams can be used to overcome these limitations in superficial artery imaging because the aperture-to-depth ratio is relatively high. However, in arteries located at larger depths, the steered beams do not overlap for larger steering angles. In this study, the ultrasound probe is physically translated over the abdomen to create large angles between acquisitions, while maintaining overlap on the abdominal aorta. In one phantom setup and 11 volunteers, 2-D cross-sectional multi-perspective ultrasound images of the abdominal aorta were acquired using seven angles between –45° and +45°. Automatic registration of the recorded images was performed by automatic feature detection of the aorta and spine. This automatic detection was successful in 62 out of 77 image sets. Compounded multi-perspective images showed an increase of contrast-to-noise ratios from 0.6 ± 0.1 to 1.2 ± 0.2 over the entire heart cycle in volunteers.

Purpose: Rupture of an arterosclerotic plaque in the carotid artery is a major cause of stroke. Biomechanical analysis of plaques is under development aiming to aid the clinician in the assessment of plaque vulnerability. Patient-specific three-dimensional (3D) geometry assessment of the carotid artery, including the bifurcation, is required as input for these biomechanical models. This requires a high-resolution, 3D, noninvasive imaging modality such as ultrasound (US). In this study, a high-resolution two-dimensional (2D) linear array in combination with a magnetic probe tracking device and automatic segmentation method was used to assess the geometry of the carotid artery. The advantages of using this system over a 3D ultrasound probe are its higher resolution (spatial and temporal) and its larger field of view. Methods: A slow sweep (v = ± 5 mm/s) was made over the subject’s neck so that the full geometry of the bifurcated geometry of the carotid artery is captured. An automated segmentation pipeline was developed. First, the Star-Kalman method was used to approximate the center and size of the vessels for every frame. Images were filtered with a Gaussian high-pass filter before conversion into the 2D monogenic signals, and multiscale asymmetry features were extracted from these data, enhancing low lateral wall-lumen contrast. These images, in combination with the initial ellipse contours, were used for an active deformable contour model to segment the vessel lumen. To segment the lumen–plaque boundary, Otsu’s automatic thresholding method was used. Distension of the wall due to the change in blood pressure was removed using a filter approach. Finally, the contours were converted into a 3D hexahedral mesh for a patient-specific solid mechanics model of the complete arterial wall. Results: The method was tested on 19 healthy volunteers and on 3 patients. The results were compared to manual segmentation performed by three experienced observers. Results showed an average Hausdorff distance of 0.86 mm and an average similarity index of 0.91 for the common carotid artery (CCA) and 0.88 for the internal and external carotid artery. For the total algorithm, the success rate was 89%, in 4 out of 38 datasets the ICA and ECA were not sufficient visible in the US images. Accurate 3D hexahedral meshes were successfully generated from the segmented images. Conclusions: With this method, a subject-specific biomechanical model can be constructed directly from a hand-held 2D US measurement, within 10 min, with a minimal user input. The performance of the proposed segmentation algorithm is comparable to or better than algorithms previously described in literature. Moreover, the algorithm is able to segment the CCA, ICA, and ECA including the carotid bifurcation in transverse B-mode images in both healthy and diseased arteries.

Significance: Intraplaque hemorrhage (IPH) is an important indicator of plaque vulnerability. Early detection could aid the prevention of stroke. Aim: We aim to detect IPH with single wavelength PA imaging in vivo and to improve image quality. Approach: We developed a singular value decomposition (SVD)-based filter to detect the nonstationary and stationary components in ultrasound data. A PA mask was created to detect stationary (IPH) sources. The method was tested ex vivo using phantoms and in vivo in patients. Results: The flow and IPH channels were successfully separated in the phantom data. We can also detect the PA signals from IPH and reject signals from the carotid lumen in vivo. Generalized contrast-to-noise ratio improved in both ex vivo and in vivo in US imaging. Conclusions: SVD-based filtering can successfully detect IPH using a single laser wavelength, opening up opportunities for more economical and cost-effective laser sources.

Aims Pressure loss versus transvalvular flow analysis challenges physiologic models of current aortic valve stenosis. New conceptual frameworks are needed to explain these real-world observations. Methods and results A patient-specific, 3D-printed, silicon model of a stenotic valve was placed inside an in-vitro haemodynamic model of the circulatory system. Instantaneous pressure and flow in the aorta and left ventricle were simulated according to measured patient specific parameters. Thereafter, a realistic transcatheter aortic valve was implanted (TAVI) in the model. Simulated post-TAVI mean pressure gradients resembled patient observations (3.7 ± 0.7 mmHg vs 6.7 ± 2.3 mmHg), but pre-TAVI measurements underestimated the pressure gradient (35.1 ± 0.6 mmHg vs 45.3 ± 1.5 mmHg). Conclusion Patient-specific 3D-printed stenotic aortic valve models could simulate baseline haemodynamics. A TAVI procedure was successfully performed on the 3D silicone rubber valve in a physiologic in-vitro model. Pre-TAVI haemodynamics in the model underestimated in-patient mean pressure gradient, whereas post TAVI pressure gradient was predicted correctly with the TAVI valve inside the 3D printed model. This study shows that these types of models could be used to study AS hemodynamics with the TAVI valve inside the 3D printed model. Improvements in the 3D-printed model, like addition of calcification and fine-tuning of the haemodynamic model, could further enhance accuracy of the simulation.

Recent studies have shown the efficacy of myocardial strain estimated using speckle tracking echocardiography (STE) in predicting response to cardiac resynchronisation therapy. This study focuses on circumferential strain patterns, comparing STE-acquired strains to tagged-magnetic resonance imaging (MRI-T). Second, the effect of regularisation was examined. Two-dimensional parasternal ultrasound (US) and MRI-T data were acquired in the left ventricular short-axis view of canines before (n = 8) and after (n = 9) left bunch branch block (LBBB) induction. US-based strain analysis was performed on Digital Imaging and Communications in Medicine data at the mid-level using three overall methods ("Commercial software," "Basic block-matching," "regularised block-matching"). Moreover, three regularisation approaches were implemented and compared. MRI-T analysis was performed using SinMod. Normalised regional circumferential strain curves, based on standard six or septal/lateral segments, were analysed and cross-correlated with MRI-T data. Systolic strain (SS) and septal rebound stretch (SRS) were calculated and compared. Overall agreement of normalised circumferential strain was good between all methods on a global and regional level. All STE methods showed a bias (>= 4% strain) toward higher SS estimates. Pre-LBBB, septal and lateral segment correlation was excellent between the Basic (mean rho = 0.96) and regularised (mean rho = 0.97) methods and MRI-T. The Commercial method showed a significant discrepancy between the two walls (septal rho = 0.94, lateral rho = 0.68). Correlation with MRI-T reduced between pre- and post-LBBB (Commercial rho = 0.79, Basic rho = 0.82, mean regularised rho = 0.86). Septal strain patterns and SRS varied with the STE software and type of regularisation, with all STE methods estimating nonzero SRS values pre-LBBB. Absolute values showed moderate agreement, with a bias for higher strain from STE. SRS varied with the type of software and extra regularisation applied. Open efforts are needed to understand the underlying causes of differences between STE methods before standardisation can be achieved. This is particularly important given the apparent clinical value of strain-based parameters such as SRS. (C) 2019 The Author(s). Published by Elsevier Inc. on behalf of World Federation for Ultrasound in Medicine & Biology.

Objectives To date, the ultimate decision to treat iliac artery stenoses in patients suffering from symptomatic peripheral arterial disease is based on the patient’s symptoms and on visual inspection of angiographical images. The primary aim of this study was to investigate the accuracy of geometry-based methods (i.e. visual inspection and quantitative vascular analysis (Viewforum version R7.2v1 Advanced vessel analysis, Philips Healthcare, Best, The Netherlands) of 3D rotational angiography) to identify the severity of equivocal iliac artery stenosis in peripheral arterial disease patients with intra-arterial hyperemic pressure measurements (gold standard) as a reference. Methods Twenty patients with symptomatic iliac artery stenoses were subjected to 3D rotational angiography. Intra-arterial pressure measurements under hyperemic conditions were performed across 24 visually identified iliac artery stenoses. Three experienced interventional-radiologists retrospectively estimated the lumen diameter reduction by visual inspection. Furthermore, quantitative vascular analysis was performed on the 3D rotational angiography data. Geometry-based estimates were classified into two groups: lumen diameter reduction of Results Visual inspection and quantitative vascular analysis correctly identified hemodynamically significant stenoses in, respectively, 83% and 67% of the 24 iliac artery stenoses. Quantitative vascular analysis-based identification of hemodynamic significant stenoses (Δp ≥ 10 mmHg) could be optimized by lowering the threshold to a 42% lumen diameter reduction which improved the accuracy from 67% to 83%. Conclusions Visual inspection of 3D rotational angiography by experienced interventional-radiologists has an 83% accuracy to identify hemodynamic significant iliac artery stenoses (Δ p ≥10 mmHg). The use of quantitative vascular analysis software did not improve accuracy.

Objectives In this study the influence of surrounding tissues including the presence of the spine on wall stress analysis and mechanical characterization of abdominal aortic aneurysms using ultrasound imaging has been investigated. Methods Geometries of 7 AAA patients and 11 healthy volunteers were acquired using 3-D ultrasound and converted to finite element based models. Model complexity of externally unsupported (aorta-only) models was complemented with inclusion of both soft tissue around the aorta and a spine support dorsal to the aorta. Computed 3-D motion of the aortic wall was verified by means of ultrasound speckle tracking. Resulting stress, strain, and estimated shear moduli were analyzed to quantify the effect of adding surrounding material supports. Results An improved agreement was shown between the ultrasound measurements and the finite element tissue and spine models compared to the aorta-only models. Peak and 99-percentile Von Mises stress showed an overall decrease of 23–30%, while estimated shear modulus decreased with 12–20% after addition of the soft tissue. Shear strains in the aortic wall were higher in areas close to the spine compared to the anterior region. Conclusions Improving model complexity with surrounding tissue and spine showed a homogenization of wall stresses, reduction in homogeneity of shear strain at the posterior side of the AAA, and a decrease in estimated aortic wall shear modulus. Future research will focus on the importance of a patient-specific spine geometry and location.

Aims Using non-invasive 3D ultrasound, peak wall stress (PWS) and aortic stiffness can be evaluated, which may provide additional criteria in abdominal aortic aneurysm (AAA) risk assessment. In this study, these measures were determined in both young and age-matched individuals, and AAA patients while its relation to age, maximum diameter, and growth was assessed statistically. Methods and results Time-resolved 3D-US data were acquired for 30 volunteers and 65 AAA patients. The aortic geometry was segmented, and tracked over the cardiac cycle using 3D speckle tracking to characterize the wall motion. Wall stress analysis was performed using finite element analysis. Model parameters were optimized until the model output matched the measured 3D displacements. A significant increase in aortic stiffness was measured between the age-matched volunteers [median 0.58, interquartile range (IQR) 0.48-0.71 kPa ...m] and the small AAA patients (median 1.84, IQR 1.38-2.46 kPa ...m; P < 0.001). In addition, an increase in aortic stiffness was evaluated between the small (30-39 mm) and large (≥50 mm) AAAs (median 2.72, IQR 1.99-3.14 kPa ...m; P = 0.01). The 99th percentile wall stress showed a positive correlation with diameter (Ï = 0.73, P < 0.001), and significant differences between age-matched volunteers and AAA patients. Conclusion The AAA pathology causes an early and significant increase in aortic stiffness of the abdominal aorta, even after correcting for the expected effect of ageing and differences in arterial pressure. Moreover, some AAAs revealed relatively high PWS, although the maximum diameter was below the threshold for surgical repair. Using the current method, these measures become available during follow-up, which could improve AAA rupture risk assessment.

Current aortic stenosis severity grading is based mainly on the local properties of the stenotic valve, such as pressure gradient or jet velocity. Success rates of valve replacement therapy are still suboptimal, so alternative grading of AS should be investigated. We suggest the efficiency of power transfer from the left ventricle to the aorta, as it takes into account heart, valve and circulatory system. Left ventricular and circulatory power were estimated using a 0D model, which was optimised to patient data: left ventricular and aortic pressure, aortic flow and diastolic left ventricular volume. Optimisation was performed using a data assimilation method. These data were available in rest as well as chemically induced exercise for twelve patients. Using this limited data set, we showed that aortic valve efficiency is highly heterogeneous between patients, but also often dependent on the haemodynamic load. This indicates that power transfer efficiency is a highly interesting metric for further research in aortic stenosis.

Tetralogy of Fallot (TOF) is a congenital heart anomaly that causes a drastic reduction in the oxygen level. In this study, we coupled a lumped-parameter model with a patient-specific three-dimensional (3D) model which included a modified Blalock-Taussig (MBT) shunt. By forming a closed loop, we investigated the effects of certain parameters on the flow rates and the pressures at different locations of the developed network. A local sensitivity analysis on an initial zero-dimensional (0D) closed-loop model was conducted. The 0D lumped parameter (LP) model was then refined based on the results of the multiscale 0D-3D model and the local sensitivity analysis was repeated for the refined 0D model. It was shown that the maximum pressure of the pulmonary bed had the highest sensitivity of 94% to the diameter of MBT shunt. We observed that the existence of the flow in the shunt during the diastole caused an elevated wall shear stress (WSS) in the pulmonary artery. In this work, we calculated the flow velocity and pressure field in a 3D patient-specific aorta with an MBT shunt, and then we used the results to increase the accuracy of our LP model to simulate numerous 0D simulations in a significantly shorter time, which is potentially applicable for medical decision-making.

Dynamic imaging of the skeletal muscles used to be strenuous and often impossible to perform manually. Accordingly, long-term dynamic musculoskeletal imaging has not been performed. The feasibility of long-term dynamic musculoskeletal functional analysis using hands-free ultrasound will be demonstrated in ten healthy endurance runners. After every kilometer, the vastus lateralis muscle was imaged whilst running using a fixated probe connected to a smart phone. The image quality was quantified by estimation of the probe-skin contact preservation and the field-of-view stability. Moreover, the pennation angles and muscle thicknesses were computed automatically. Long-term dynamic acquisition was successful in nine out of ten runners. Probe-skin contact loss ranged between 0 and 57% of the gait cycle. The biggest change in field-of-view occurred during the first kilometer with an average decline in complex-wavelet structural similarity index of 0.21, followed by an onward total decrease of 0.09, on average. The mean pennation angle and thickness were approximately constant, with the average fluctuation being 0.94 degrees and 0.11 cm, respectively. The feasibility of long-term musculoskeletal function analysis has been demonstrated, with probe-skin contact loss the main limiting factor. Dynamic, hands-free ultrasound might enable research for a more profound insight in the prevention and rehabilitation of musculoskeletal injuries.

Current decision-making for clinical intervention of abdominal aortic aneurysms (AAAs) is based on the maximum diameter of the aortic wall, but this does not provide patient-specific information on rupture risk. Ultrasound (US) imaging can assess both geometry and deformation of the aortic wall. However, low lateral contrast and resolution are currently limiting the precision of both geometry and local strain estimates. To tackle these drawbacks, a multiperspective scanning mode was developed on a dual transducer US system to perform strain imaging at high frame rates. Experimental imaging was performed on porcine aortas embedded in a phantom of the abdomen, pressurized in a mock circulation loop. US images were acquired with three acquisition schemes: Multiperspective ultrafast imaging, single perspective ultrafast imaging, and conventional line-by-line scanning. Image registration was performed by automatic detection of the transducer surfaces. Multiperspective images and axial displacements were compounded for improved segmentation and tracking of the aortic wall, respectively. Performance was compared in terms of image quality, motion tracking, and strain estimation. Multiperspective compound displacement estimation reduced the mean motion tracking error over one cardiac cycle by a factor 10 compared to conventional scanning. Resolution increased in radial and circumferential strain images, and circumferential signal-to-noise ratio (SNRe) increased by 10 dB. Radial SNRe is high in wall regions moving towards the transducer. In other regions, radial strain estimates remain cumbersome for the frequency used. In conclusion, multiperspective US imaging was demonstrated to improve motion tracking and circumferential strain estimation of porcine aortas in an experimental set-up.

OBJECTIVE: Osteoarthritis (OA) is a chronic joint disease characterized by progressive degradation of cartilage. It affects more than 10% of the people aged over 60 years-old worldwide with a rising prevalence due to the increasingly aging population. OA is a major source of pain, disability, and socioeconomic cost. Currently, the lack of effective diagnosis and affordable imaging options for early detection and monitoring of OA presents the clinic with many challenges. Spectroscopic Photoacoustic (sPA) imaging has the potential to reveal changes in cartilage composition with different degrees of damage, based on optical absorption contrast.DESIGN: In this study, the capabilities of sPA imaging and its potential to characterize cartilage damage were explored. To this end, 15 pieces of cartilage samples from patients undergoing a total joint replacement were collected and were imaged ex vivo with sPA imaging at a wide optical spectral range (between 500 nm and 1300 nm) to investigate the photoacoustic properties of cartilage tissue. All the PA spectra of the cartilage samples were analyzed and compared to the corresponding histological results.RESULTS: The collagen related PA spectral changes were clearly visible in our imaging data and were related to different degrees of cartilage damage. The results are in good agreement with histology and the current gold standard, i.e., the Mankin score.CONCLUSIONS: This study demonstrates the potential and possible clinical application of sPA imaging in OA.

Patient outcome in trans‐aortic valve implantation (TAVI) therapy partly relies on a patient's haemodynamic properties that cannot be determined from current diagnostic methods alone. In this study, we predict changes in haemodynamic parameters (as a part of patient outcome) after valve replacement treatment in aortic stenosis patients. A framework to incorporate uncertainty in patient‐specific model predictions for decision support is presented. A 0D lumped parameter model including the left ventricle, a stenotic valve and systemic circulatory system has been developed, based on models published earlier. The unscented Kalman filter (UKF) is used to optimize model input parameters to fit measured data pre‐intervention. After optimization, the valve treatment is simulated by significantly reducing valve resistance. Uncertain model parameters are then propagated using a polynomial chaos expansion approach. To test the proposed framework, three in silico test cases are developed with clinically feasible measurements. Quality and availability of simulated measured patient data are decreased in each case. The UKF approach is compared to a Monte Carlo Markov Chain (MCMC) approach, a well‐known approach in modelling predictions with uncertainty. Both methods show increased confidence intervals as measurement quality decreases. By considering three in silico test‐cases we were able to show that the proposed framework is able to incorporate optimization uncertainty in model predictions and is faster and the MCMC approach, although it is more sensitive to noise in flow measurements. To conclude, this work shows that the proposed framework is ready to be applied to real patient data., In this study, we aim to provide a framework to predict changes in haemodynamic parameters (as a part of patient outcome) after valve replacement treatment in aortic stenosis. The framework involves uncertain input parameter optimization using an unscented Kalman filter approach. Valve replacement treatment is then simulated, and uncertainties are propagated via polynomial chaos expansions. The framework is able to capture measurement quality in prediction confidence intervals and is ready to be applied to patient data. Novelty Statement We developed a lumped parameter model to predict changes in haemodynamic parameters after valve replacement therapy in aortic stenosisWe showed a data assimilation approach (Unscented Kalman Filter) is suitable for parameter optimization for this model based on available clinical data, with estimated optimization uncertaintyWe expanded parameter optimization via Unscented Kalman Filtering via sensitivity‐based weightingWe coupled uncertain parameter optimization with a polynomial chaos expansion approach to propagate uncertain parameters in treatment prediction