arXiv subjects

Susann Beier

Publications and source records attributed to Susann Beier.

13 recordsLinked to original sources

arXiv2026

Influence of Plaque Characteristics on Stent Biomechanical Outcomes - A Case Study on Double Kissing Crush Coronary Stenting

Background Double Kissing (DK) Crush is a two-stent technique for complex coronary bifurcation lesions, yet the biomechanical influence of plaque on its performance remains poorly understood. This study developed a computational biomechanical model of the DK-Crush procedure to quantify how plaque presence and composition affect procedural outcomes and the performance of Xience Sierra and Orsiro stents. Methods A population-representative coronary bifurcation was modelled with no plaque, lipid plaque, and fibrous plaque. The complete DK-Crush sequence was simulated using finite element analysis for both stent platforms. Mechanical outcomes included arterial wall stress, malapposition, side branch ostium clearance, and residual stenosis. Post-deployment hemodynamics was assessed using pulsatile computational fluid dynamics, quantifying high shear rate volume and lumen area exposed to low time-averaged endothelial shear stress (TAESS). Results Plaque presence and stiffness reduced lumen restoration, increased arterial wall stress, led to larger high shear rate regions and, for fibrous plaque, increased exposure to low TAESS. Malapposition and ostial clearance depended mainly on stent design. Plaque also altered the relative performance of the two platforms, revealing differences not observed in plaque-free models. Conclusions Plaque characteristics substantially affect DK-Crush biomechanics and modify stent behaviour. Incorporating plaque is therefore essential for realistic computational evaluation of bifurcation stenting.

physics.bio-ph↗

arXiv2025

Machine-Learning Based Detection of Coronary Artery Calcification Using Synthetic Chest X-Rays

Coronary artery calcification (CAC) is a strong predictor of cardiovascular events, with CT-based Agatston scoring widely regarded as the clinical gold standard. However, CT is costly and impractical for large-scale screening, while chest X-rays (CXRs) are inexpensive but lack reliable ground truth labels, constraining deep learning development. Digitally reconstructed radiographs (DRRs) offer a scalable alternative by projecting CT volumes into CXR-like images while inheriting precise labels. In this work, we provide the first systematic evaluation of DRRs as a surrogate training domain for CAC detection. Using 667 CT scans from the COCA dataset, we generate synthetic DRRs and assess model capacity, super-resolution fidelity enhancement, preprocessing, and training strategies. Lightweight CNNs trained from scratch outperform large pretrained networks; pairing super-resolution with contrast enhancement yields significant gains; and curriculum learning stabilises training under weak supervision. Our best configuration achieves a mean AUC of 0.754, comparable to or exceeding prior CXR-based studies. These results establish DRRs as a scalable, label-rich foundation for CAC detection, while laying the foundation for future transfer learning and domain adaptation to real CXRs.

cs.CV↗

arXiv2025

Mechanistic Insights Into How Rewiring and Bifurcation Angle Affect DK-Crush Stent Deployment

Background Double Kissing Crush (DKC) is a preferred two-stent technique for complex coronary bifurcation lesions. Proximal cell rewiring is routinely recommended to reduce technical failure, and DKC is considered effective across various bifurcation angles. However, it remains unclear whether this standard approach is optimal for all patients. This study investigates the interaction between bifurcation angle and rewiring configuration to identify anatomy-specific strategies. Methods Computational modeling of the DKC procedure was used to simulate 12 DKC procedures across three left main bifurcation angles (45{\deg}, 70{\deg}, and 100{\deg}) and four rewiring configurations: proximal-proximal (P-P), proximal-distal (P-D), distal-proximal (D-P), and distal-distal (D-D). Evaluation metrics included stent malapposition, side branch ostium clearance, arterial wall stress, low time-averaged endothelial shear stress, and high shear rates. Results DKC performed in wide bifurcations (100{\deg}) resulted in worse outcomes, with malapposition reaching 18%, side branch clearance down to 23%, and up to twice the exposure to adverse high shear rates compared to narrower angles. In contrast, intermediate (70{\deg}) and narrow (45{\deg}) angles generally resulted in more favorable outcomes, though optimal rewiring varied by angle. Proximal strategies, i.e. P-P and P-D, were most effective at 70{\deg}, while D-D performed best at 45{\deg}. No single strategy was consistently superior across all bifurcation angles. Conclusions DKC outcomes depend on bifurcation angle and can be optimized by tailoring rewiring strategies, challenging the current clinical understanding. These findings support anatomy-specific procedural planning and intravascular imaging to guide rewiring. This study provides a mechanistic rationale to improve clinical decision-making and tailor bifurcation interventions.

physics.med-ph↗

arXiv2025

LWT-ARTERY-LABEL: A Lightweight Framework for Automated Coronary Artery Identification

Coronary artery disease (CAD) remains the leading cause of death globally, with computed tomography coronary angiography (CTCA) serving as a key diagnostic tool. However, coronary arterial analysis using CTCA, such as identifying artery-specific features from computational modelling, is labour-intensive and time-consuming. Automated anatomical labelling of coronary arteries offers a potential solution, yet the inherent anatomical variability of coronary trees presents a significant challenge. Traditional knowledge-based labelling methods fall short in leveraging data-driven insights, while recent deep-learning approaches often demand substantial computational resources and overlook critical clinical knowledge. To address these limitations, we propose a lightweight method that integrates anatomical knowledge with rule-based topology constraints for effective coronary artery labelling. Our approach achieves state-of-the-art performance on benchmark datasets, providing a promising alternative for automated coronary artery labelling.

eess.IV↗

arXiv2025

Are Ultrathin Stents Optimal for Bifurcation Lesions? Insights from Computational Modelling of Provisional and DK-Crush Techniques

Complex coronary bifurcation lesions remain challenging in percutaneous coronary intervention, with stent design and deployment strategy influencing clinical outcomes. This study compares the mechanical and hemodynamic performance of the ultrathin-strut Orsiro and thin-strut Xience Sierra stent in Provisional Side Branch (PSB) and Double Kissing Crush (DKC) techniques. We used finite element analyses of bifurcation stent deployment to assess malapposition, ostium clearance, and arterial wall stress for both techniques. Computational fluid dynamics simulations quantified the luminal exposure to low Time-Averaged Endothelial Shear Stress (TAESS below 0.4 Pa) and high shear rates (above 1000 1/s). In PSB, Orsiro showed higher malapposition (13.0% vs 9.6%) but improved SB ostium clearance (77% vs 64%) and lower low-TAESS exposure (30.3% vs 33.6%) compared to Xience. Orsiro also produced higher arterial wall stresses, particularly during kissing balloon inflation. In DKC, differences in malapposition and ostium clearance diminished between stents, though Orsiro retained a hemodynamic advantage with lower low-TAESS (28.2% vs 36.3%).Stent design influenced outcomes more strongly in PSB, where anatomical interaction and platform-specific behavior impacted both structural and hemodynamic results. In DKC, procedural complexity minimized those differences, making the stenting technique the primary performance driver. Nonetheless, Orsiro consistently preserved more favorable flow conditions. These findings highlight the need to match device selection with lesion characteristics in PSB, while in DKC, optimizing procedural steps may have a greater impact than the choice of stent platform.

physics.med-ph↗

arXiv2025

Reliability of characterising coronary artery flow with the flow-split outflow strategy: comparison against the multiscale approach

In computational modelling of coronary haemodynamics, imposing patient-specific flow conditions is paramount, yet often impractical due to resource and time constraints, limiting the ability to perform a large number of simulations particularly for diseased cases. We aimed to compare coronary haemodynamics quantified using a simplified flow-split strategy with varying exponents against the clinically verified but computationally intensive multiscale simulations under both resting and hyperaemic conditions in arteries with varying degrees of stenosis. Six patient-specific left coronary artery trees were segmented and reconstructed, including three with severe (>70%) and three with mild (<50%) focal stenoses. Simulations were performed for the entire coronary tree to account for the flow-limiting effects from epicardial artery stenoses. Both a 0D-3D coupled multiscale model and a flow-split approach with four different exponents (2.0, 2.27, 2.33, and 3.0) were used. The resulting prominent haemodynamic metrics were statistically compared between the two methods. Flow-split and multiscale simulations did not significantly differ under resting conditions regardless of the stenosis severity. However, under hyperaemic conditions, the flow-split method significantly overestimated the time-averaged wall shear stress by up to 16.8 Pa (p=0.031) and underestimate the fractional flow reserve by 0.327 (p=0.043), with larger discrepancies observed in severe stenoses than in mild ones. Varying the exponent from 2.0 to 3.0 within the flow-split methods did not significantly affect the haemodynamic results (p>0.141). Flow-split strategies with exponents between 2.0 and 3.0 are appropriate for modelling stenosed coronaries under resting conditions. Multiscale simulations are recommended for accurate modelling of hyperaemic conditions, especially in severely stenosed arteries.

physics.med-ph↗

arXiv2025

High-Intensity Helical Flow: A Double-Edged Sword in Coronary Artery Haemodynamics

The role of Helical Flow (HF) in human coronary arteries remains uncertain, yet its understanding promises unprecedented insights into atherosclerotic processes. In this study, we investigated the effects of HF and key haemodynamic descriptors in 39 patient-specific left coronary artery trees from the ASOCA dataset, including 20 non-stenosed and 19 stenosed cases. Absolute HF intensity $h_2$ correlated with higher Time-Averaged Endothelial Shear Stress (TAESS) in all vessel segments regardless of stenosis (p < 0.05). In stenosed cases, this correlation was so prominent that the vessel area exposed to adversely low TAESS was reduced (< 0.5 Pa, p = 0.0001), while areas of adversely high TAESS increased (> 4.71 Pa, p < 0.05), coinciding with high $h_2$ regions. This suggests that HF in coronary arteries is not always protective as previously thought. It not only mitigates low TAESS, which is associated with long-term plaque development and restenosis, but also exacerbates adversely high TAESS, which is linked to increased plaque vulnerability and acute events. Our findings redefine the current understanding of helical blood flow's role in cardiovascular atherosclerotic disease processes.

physics.med-ph↗

arXiv2023

Exploring the Interplay of Left Coronary Tree Anatomy and Haemodynamics: Implications for Plaque Formation

The link between atherosclerosis and blood flow-induced haemodynamic luminal shear stresses is well established. However, this understanding has not been translated into clinical practice because of the interdependent effects of the complex coronary anatomy and a multitude of potential haemodynamic metrics, which have been challenging to delineate. Thus, this study aims to identify anatomical and haemodynamic differences in coronary trees at different stages of stenoses. A total of 39 left coronary trees were considered, which are publicly available. Each coronary tree was dissected into bifurcations and non-bifurcating segments for comparisons. We calculated a full set of anatomical metrics and performed transient flow simulations to solve the normalised luminal area exposed to Low Time-Average Endothelial Shear Stress (%LowTAESS), High Oscillatory Shear Index (%HighOSI), and High Relative Residence Time (%HighRRT). We statistically investigated the differences between non-stenosed (n=20, Diameter Stenosis DS=0%), moderately (n=12, 0% =70%) stenosed cases, whereby p<0.05* is considered significant. Only the average curvature and %HighOSI differed between the non-stenosed, and moderately or severely stenosed for the coronary trees (p=0.024* and p<0.001*), and non-bifurcating segments (p=0.027* and p<0.001*). %LowTAESS@0.5Pa and %HighRRT@2.5Pa-1 significantly differed between moderately (0% =70\%) stenosed trees (p=0.009* and p=0.012*). Our findings suggest curvature and potentially %HighOSI being critical factors in coronary plaque onset in non-bifurcating segments, whereas %LowTAESS and %HighRRT affect plaque progression after onset.

physics.med-ph↗

arXiv2023

Sex-Specific Variances in Anatomy and Blood Flow of the Left Main Coronary Bifurcation: Implications for Coronary Artery Disease Risk

Studies have shown marked sex disparities in Coronary Artery Diseases (CAD) epidemiology, yet the underlying mechanisms remain unclear. We explored sex disparities in the coronary anatomy and the resulting haemodynamics in patients with suspected, but no significant CAD. Left Main (LM) bifurcations were reconstructed from CTCA images of 127 cases (42 males and 85 females, aged 38 to 81). Detailed shape parameters were measured for comparison, including bifurcation angles, curvature, and diameters, before solving the haemodynamic metrics using CFD. The severity and location of the normalised vascular area exposed to physiologically adverse haemodynamics were statistically compared between sexes for all branches. We found significant differences between sexes in potentially adverse haemodynamics. Females were more likely than males to exhibit adversely low Time Averaged Endothelial Shear Stress along the inner wall of a bifurcation (16.8% vs. 10.7%). Males had a higher percentage of areas exposed to both adversely high Relative Residence Time (6.1% vs 4.2%, p=0.001) and high Oscillatory Shear Index (4.6% vs 2.3%, p<0.001). However, the OSI values were generally small and should be interpreted cautiously. Males had larger arteries (M vs F, LM: 4.0mm vs 3.3mm, LAD: 3.6mm 3.0mm, LCX:3.5mm vs 2.9mm), and females exhibited higher curvatures in all three branches (M vs F, LM: 0.40 vs 0.46, LAD: 0.45 vs 0.51, LCx: 0.47 vs 0.55, p<0.001) and larger inflow angle of the LM trunk (M: 12.9{\deg} vs F: 18.5{\deg}, p=0.025). Haemodynamic differences were found between male and female patients, which may contribute, at least in part, to differences in CAD risk. This work may facilitate a better understanding of sex differences in the clinical presentation of CAD, contributing to improved sex-specific screening, especially relevant for women with CAD who currently have worse predictive outcomes.

physics.med-ph↗

arXiv2023

Assessing Encoder-Decoder Architectures for Robust Coronary Artery Segmentation

Coronary artery diseases are among the leading causes of mortality worldwide. Timely and accurate diagnosis, facilitated by precise coronary artery segmentation, is pivotal in changing patient outcomes. In the realm of biomedical imaging, convolutional neural networks, especially the U-Net architecture, have revolutionised segmentation processes. However, one of the primary challenges remains the lack of benchmarking datasets specific to coronary arteries. However through the use of the recently published public dataset ASOCA, the potential of deep learning for accurate coronary segmentation can be improved. This paper delves deep into examining the performance of 25 distinct encoder-decoder combinations. Through analysis of the 40 cases provided to ASOCA participants, it is revealed that the EfficientNet-LinkNet combination, serving as encoder and decoder, stands out. It achieves a Dice coefficient of 0.882 and a 95th percentile Hausdorff distance of 4.753. These findings not only underscore the superiority of our model in comparison to those presented at the MICCAI 2020 challenge but also set the stage for future advancements in coronary artery segmentation, opening doors to enhanced diagnostic and treatment strategies.

eess.IV↗

arXiv2023

The Road to the Ideal Stent: A Review of Stent Design Optimisation Methods, Findings, and Opportunities

Coronary stent designs have undergone significant transformations in geometry, materials, and drug elution coatings, contributing to the continuous improvement of stenting success over recent decades. However, the increasing use of percutaneous coronary intervention techniques on complex coronary artery disease anatomy continues to be a challenge and pushes the boundary to improve stent designs. Design optimisation techniques especially are a unique set of tools used to assess and balance competing design objectives, thus unlocking the capacity to maximise the performance of stents. This review provides a brief history of metallic and bioresorbable stent design evolution, before exploring the latest developments in performance metrics and design optimisation techniques in detail. This includes insights on different contemporary stent designs, structural and haemodynamic performance metrics, shape and topology representation, and optimisation along with the use of surrogates to deal with the underlying computationally expensive nature of the problem. Finally, an exploration of current key gaps and future possibilities is provided that includes hybrid optimisation of clinically relevant metrics, non-geometric variables such as material properties, and the possibility of personalised stenting devices.

physics.med-ph↗

arXiv2023

MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedback

cs.CV↗

arXiv2022

Computed tomography coronary angiogram images, annotations and associated data of normal and diseased arteries

Computed Tomography Coronary Angiography (CTCA) is a non-invasive method to evaluate coronary artery anatomy and disease. CTCA is ideal for geometry reconstruction to create virtual models of coronary arteries. To our knowledge there is no public dataset that includes centrelines and segmentation of the full coronary tree. We provide anonymized CTCA images, voxel-wise annotations and associated data in the form of centrelines, calcification scores and meshes of the coronary lumen in 20 normal and 20 diseased cases. Images were obtained along with patient information with informed, written consent as part of Coronary Atlas (https://www.coronaryatlas.org/). Cases were classified as normal (zero calcium score with no signs of stenosis) or diseased (confirmed coronary artery disease). Manual voxel-wise segmentations by three experts were combined using majority voting to generate the final annotations. Provided data can be used for a variety of research purposes, such as 3D printing patient-specific models, development and validation of segmentation algorithms, education and training of medical personnel and in-silico analyses such as testing of medical devices.

cs.CV↗