BME2023 Paper Submission & Registration
9th Dutch Bio-Medical Engineering Conference

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14:30   Vascular I
Chair: Mirunalini Thirugnanasambandam
15 mins
Multi-aperture high frame rate 3D ultrasound imaging of Abdominal Aortic Aneurysm phantoms
Larissa Jansen, Hans-Martin Schwab, Richard Lopata
Abstract: Imaging abdominal aortic aneurysms (AAAs) using conventional 3D ultrasound (US) imaging is challenging, because the field of view and temporal resolution are limited. Furthermore, due to the physics of US, the AAA wall is best visible in regions where the angle of incidence of the US beam with respect to the vessel surface is small (< 20°). Acquisition from multiple perspectives can help to increase the field of view, increase wall visibility, and improve lateral resolution. These approaches require adequate spatial and temporal registration, and image fusion. Hence it would be more convenient to have a large footprint probe or ultimately a flexible patch that can be attached to the abdomen. However, such large aperture probes are not yet available. Therefore, in this study we propose to use a multi-aperture high frame rate approach for enhanced functional imaging of AAAs. An AAA mimicking phantom was made from polyvinyl alcohol by using a custom mould. This phantom was attached to a circulatory mock-loop setup that mimics the circulatory system. A flow pump was used to pump water into the phantom with a predefined pulsatile flow pattern. A bistatic interleaved high frame rate sparse aperture transmit-receive sequence was implemented for two 1024¬-element Vermon matrix probes, which were each connected to a vantage-256 Verasonics US system. Using a translation stage, data was gathered with the two probes at different positions along the vessel length and with different probe orientations. Imaging was performed in a high-quality mode and high frame rate mode by changing the number of compound angles. After spatial and temporal registration of the multi-aperture data, image reconstruction was performed to obtain a large 3D + time dataset suitable for functional imaging. With the multi-aperture approach proposed, large field of view high frame rate 3D data of AAA phantoms were obtained, from which a more complete AAA geometry can be assessed. Furthermore, the different imaging modes allow for various applications such as geometry assessment, strain imaging and flow measurements. Future work involves imaging patient specific aneurysm geometries and improve imaging of the bifurcation towards the iliac artery.
15 mins
Data-driven generation of inlet velocity profiles for CFD modelling in thoracic aortic aneurysms
Selene Pirola
Abstract: Computational fluid dynamics (CFD) has emerged as a powerful tool to investigate development and growth of aortic aneurysms. Our previous work1 showed that inlet boundary conditions (IBC) are crucial to accurately reproduce blood flow features in the ascending aorta. However, the availability of in vivo measurements to be used as IBC is limited. This hinders progress of research on ascending aortic disease. With this work2, we aim to address this issue by proposing a data-driven generative model of 4D aortic velocity profiles suitable for use in CFD modelling of the ascending aorta. By exploiting principal component analysis (PCA), a statistical shape model (SSM) of 4D aortic inlet velocity profiles was developed starting from 4D flow magnetic resonance imaging scans of 30 subjects with ascending thoracic aortic aneurysm. Using the SSM, a dataset of 500 synthetic cases was generated. Velocity profiles from both the clinical and synthetic cohorts were extensively characterized by computing flow morphology descriptors (e.g., flow jet angle - FJA) of both spatial and temporal features. The synthetic dataset was then further refined by excluding generated profiles which presented flow descriptors outside the physiological range observed in the clinical cohort. This selection resulted in the acceptance of 437 synthetic profiles with realistic properties. T-tests and Mann–Whitney U test confirmed that no statistically significant differences existed between the two cohorts. Statistically significant correlations were found between PCA principal modes of variation and flow descriptors in the synthetic cohort: e.g., mode 1 strongly correlated (r=0.94, p<0.0001) with the spatial heterogeneity of the velocity magnitude (quantified by the flow dispersion index3). The average velocity profile obtained by the conducted PCA qualitatively resembled a parabolic-shaped profile but was quantitatively characterized by more complex features – such as 13° FJA at peak systole and non-null in-plane velocity. This further supports the need for more realistic IBC for ascending thoracic aorta simulations. Therefore, to allow for the computational research community to benefit from more realistic IBCs, we have released2 the 437 generated synthetic profiles. We believe that the present work will allow to replace the common practice of prescribing idealized IBCs in numerical simulations of blood flow with more realistic conditions. 1Pirola S, et al. APL Bioeng. 2018;2(2):026101. 2Saitta S, et al. arXiv 2022, 3Youssefi P, et al. J Biomech Eng. 2018;140(1):011002.
15 mins
The influence of plaque structural stress and wall shear stress on human coronary plaque progression
Aikaterini Tziotziou, Jolanda Wentzel, Ali Akyildiz
Abstract: Atherosclerosis is one of the most widespread diseases in our cardiovascular system and a primary cause of death as its progression and rupture can lead to myocardial attack or stroke [1]. Atherosclerotic plaque progression over time in coronary arteries is affected by local hemodynamic and biomechanical factors, such as the Wall Shear Stress (WSS) and the Plaque Structural Stress (PSS) [2]. Low WSS is known to be associated with plaque progression but the association between PSS, and its combination with WSS, towards plaque progression has not been well-established yet [2]. In this work, we study the effect of PSS and WSS on human coronary plaque progression. Forty non-stented, non-culprit coronary arteries (IMPACT study) [3] were imaged at two time points (baseline and one-year follow-up) using combined near infrared spectroscopy intravascular ultrasound (NIRS-IVUS) and optical coherence tomography (OCT). The 2D plaque geometries were extracted from the combined imaging data. PSS (max principal stress) in patient-specific cross-sections was calculated via ABAQUS, by using the material properties of individual plaque components [2, 4] and the backward incremental method [4] to quantify and incorporate the initial stresses. The individual and combined impact of PSS and WSS on plaque progression was studied using Linear Mixed Models in SPSS. The 3D artery geometries were divided into 1,5mm/45o sectors and the statistical analyses were sector-based. The plaque burden and wall thickness change were used as parameters to quantify plaque progression. For the statistical analysis, the arterial sectors were divided into three tertiles (low, mid, high) with respect to PSS and WSS and categorised as healthy and diseased based on 0.5mm thickness threshold, including NIRS+ and NIRS- sectors. The effect of PSS and WSS individually, as well as when combined, on the plaque burden and wall thickness change were statistically significant. Specifically, low WSS was associated with plaque progression, while low and high PSS was associated with increased plaque burden and wall thickness change in diseased and healthy sectors, respectively. The analysis of the combined effect of PSS and WSS on plaque progression showed that healthy sectors of high PSS with low WSS and diseased sectors of low PSS with low WSS had the greatest plaque burden and wall thickness increase. We hope that this study will provide great insights for better understanding the plaque growth mechanisms and developing plaque progression prediction models. 1. Shah, P et al., Thrombosis, 2015:634983, 2015. 2. Costopoulos, C et al., Eur Heart J., 40:1411-1422, 2019. 3. Hartman, E et al., J. of Cardiovasc. Trans. Res., 14:416–425, 2021. 4. Akyildiz, A C et al., Computer. Methods in Biomech. and Biomed. Eng., 19: 771–779, 2016.
15 mins
Identification of endarterectomy plaques composition using deep learning on multispectral photoacoustic images
Camilo Cano-Barrera, Nastaran Mohammadian Rad, Marc van Sambeek, Richard Lopata, Min Wu
Abstract: Multispectral photoacoustic imaging (sPAI) is an emerging modality that allows real-time, non-invasive, and radiation-free measurements of tissue, benefitting from their optical contrast. sPAI is ideal for morphology assessment in arterial plaques, where plaque composition provides relevant information on the progression of the plaques and their vulnerability. However, since sPAI is affected by spectral coloring, general spectroscopy unmixing techniques don't provide reliable identification of the sample composition. In this study, we demonstrate the capabilities of deep learning (DL) techniques for the classification of plaque composition from sPAI. We performed ex-vivo sPAI on carotid endarterectomy samples (n=9) in a water tank at multiple acquisition angles (around 180 degrees with a step of 30 degrees). Images were acquired at wavelengths from 500 nm to 1300 nm (in steps of 5 nm) to capture the main spectral features of the different chromophores in the plaques. We transformed the raw sPAI modulated signal into a feature space. The features vector was used as input to train a two-layer convolutional neural network (CNN) architecture to label distinct components in human carotid plaque samples. Histology is used to segment regions of interest within the plaques and label the spectra of plaque materials. The test set is compared with the histology and the spectral unmixing results using an established blind unmixing method. Results show the feasibility of automatic multi-label image segmentation of sPAI using deep learning to differentiate constituent regions within a plaque. The plaque compositions such as lipids, collagen, smooth muscle cells, and hemorrhages can be well identified, and the decomposition results generally agree with the corresponding histological staining. In regions with low amplitude signals and clutter, the network outperforms state-of-the-art blind unmixing techniques. Moreover, with the proposed DL-based classification approach, there is no need to perform an extensive fluence correction to identify multiple materials, which is usually required in most unmixing techniques. All the results demonstrate the great potential to characterize plaque composition with the DL-based unmixing method. In future work, we will expand the training set by scanning plaques with surrounding tissue and exploring U-net networks to exploit the spatial information of the samples.
15 mins
Deep learning and ground truth simulations for super-resolution ultrasound imaging with microbubbles
Nathan Blanken, Alina Kuliesh, Baptiste Heiles, Kartik Jain, Hervé Delingette, Christoph Brune, Michel Versluis, David Maresca, Jelmer M. Wolterink, Guillaume Lajoinie
Abstract: Ultrasound is a cost-effective, non-ionizing, portable imaging technique offering excellent temporal resolution. In blood flow imaging, the poor contrast can be addressed by administering contrast agents in the form of a microbubble suspension. Ultrasound vector flow imaging (echoPIV) has shown an accuracy on par with phase contrast MRI. However, diagnosis of vascular diseases would benefit substantially from further improved resolution, beyond the diffraction limit of ultrasound. Current ultrasound super-resolution methods require long acquisition times and are often based on microbubble localization from reconstructed grey-scale images. Here, we introduce a new super-resolution ultrasound technique based on the deconvolution of raw radio-frequency (RF) ultrasound data using deep learning. As a first step, we perform super-resolution before image reconstruction. Our strategy consists of: i) generating ultrasound RF data together with the ground-truth bubble locations with a custom, fast simulator and ii) training a neural network to detect bubbles within the simulated data. The simulator defines a random spatial microbubble distribution and simulates its response to a propagating plane wave. We use a dilated convolution neural network to recover the bubble locations within a single RF channel. In a future step, we will perform super-resolution and image reconstruction jointly, using end-to-end neural network training on raw ultrasound data. To this end, we have developed a state-of-the-art, physically realistic simulator that captures arterial flow, nonlinear acoustics, and nonlinear microbubble behaviour. Application of a trained neural network to single-element ultrasound data yields an order-of-magnitude gain in axial resolution in the final reconstructed images compared to standard B-mode images. Moreover, our method shows a substantial improvement in localization accuracy compared to other super-resolution methods, owing to accurate simulation of the microbubble response and access to the ground truth locations during training. Our results demonstrate that the application of a convolutional neural network to raw transducer element data is a promising path towards super-resolved imaging of high-density microbubble populations, allowing for real-time, high-resolution blood flow imaging and improved diagnosis of vascular diseases.
15 mins
Experimental characterisation and computational modelling of blood clot fracture
Behrooz Fereidoonnezhad, Anushree Dwivedi, Ray McCarthy, Patrick McGarry
Abstract: Thrombus (blood clot) fragmentation during endovascular stroke treatment, such as mechanical thrombectomy, leads to downstream emboli resulting in poor clinical outcomes. Understanding the fracture behaviour of blood clot is crucial for development of next-generation thrombectomy devices and clinical strategies. In this presentation, I will talk about our recent findings on blood clot fracture. A novel hyperelastic model has been developed to replicate the mechanical behaviour of blood clots in tension and compression. Inverse finite element method and cohesive zone modelling (CZM) have been used to characterise the mixed-mode fracture behaviour of blood clots from the experimental data of compact tension fracture test and lap-shear test. Platelet-contracted and non-contracted blood clot analogues with different compositions have been tested and role of clot contractility and clot composition on the mechanical and fracture behaviour of blood clots have been investigated. The results of this study reveal that fracture resistance of blood clots has a direct relationship with the amount of fibrin fibres and level of platelet contraction. The fracture strength of fibrin-rich clots is significantly higher than RBC-rich clots. Clot contraction has been found to have a significant influence on fracture behaviour of blood clots, highlighting the key role of platelets on fracture resistance of blood clot. Finite element cohesive zone modelling of clot fracture experiments show that rupture resistance in mode II fracture is higher than mode I. This implies that blood clots are more prone to fragment in mode I than mode II. These findings have key importance for design of the next-generation devices for endovascular treatment of stroke patients.

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