arXiv subjects

Adam Wittek

Publications and source records attributed to Adam Wittek.

At least 19 recordsLinked to original sources

arXiv2026

On the validity of using idealised sample geometries for interpreting mechanical tests of very soft tissues

Mechanical characterisation of soft tissues often relies on inverse analysis of experimental data in which constitutive models are calibrated to match experimental force-displacement curves, yet the vast majority of such studies use idealised (nominal) sample geometries even though experimental samples unavoidably deviate from these nominal shapes because of imperfections in excision and mounting. The influence of these geometric simplifications on the material parameters determined through inverse analysis remains poorly quantified. We investigate the appropriateness of using idealised sample geometries in mechanical characterisation of brain tissue. Magnetic resonance imaging (MRI) was used to reconstruct the exact (real) geometry of each nominally cuboidal tissue sample. We determined a stress parameter (the shear modulus) by modelling, using the finite element method, tensile, compressive, and shear tests of brain tissue samples with both the MRI-based (real) and idealised cuboidal geometries, enabling a controlled comparison of geometry. Idealised geometries consistently yielded a lower stress parameter. The discrepancy in shear modulus between the real and idealised geometries varied across loading modes, averaging approximately 10% in shear and 48% under axial loading, predominantly arising from the compressive response. These discrepancies can be attributed to the inability of idealised-geometry models to accurately represent contact interactions and predict strain distributions, particularly under compressive loading. Idealisation of sample geometry may introduce systematic bias in the mechanical characterisation of very soft tissues; therefore, the actual measured sample geometry should be used in inverse analysis to identify constitutive models and their parameters.

cs.CE↗

arXiv2026

Stacked Ensemble Learning for Abdominal Aortic Aneurysm Segmentation in CT Angiography

Abdominal aortic aneurysm (AAA) rupture risk assessment increasingly relies on patient-specific biomechanical computations, which require accurate three-dimensional aneurysm geometry from computed tomography angiography (CTA). Manual and semi-automated segmentation remain time-consuming and observer-dependent, limiting their use in large-scale clinical workflows. In this study, we developed a stacked ensemble framework for automated AAA seg-mentation from CTA images. We used 40 anonymised contrast-enhanced CTA scans from AAA patients and generated reference segmentations using the nnInteractive extension in 3D Slicer. We partitioned the dataset into 32 training cases and 8 held-out test cases. Three nnUNetv2 configurations, Default, DA5, and ResEncL, were trained as base learners, and their voxel-wise probability out-puts were combined using an L2-regularised logistic regression meta-model trained from out-of-sample cross-validation predictions. We evaluated segmentation performance using Dice Coefficient and Separation Distance, a mean boundary-to-boundary distance measure introduced in this study to quantify average surface agreement. On the held-out test set, the ensemble achieved the highest mean Dice Coefficient of 0.9752 and the lowest mean Separation Distance of 0.4598 mm, indicating improved volumetric overlap and average boundary agreement compared with the individual base learners. Overall, stacked ensemble learning provided small but meaningful improvements in AAA segmentation, particularly for boundary accuracy relevant to downstream patient-specific bio-mechanical computations.

cs.CE↗

arXiv2025

Mechanical behaviour of brain-skull interface (meninges) under shear loading through experiment and finite element modelling: Preliminary results

The brain-skull interface (meninges) plays a critical role in governing brain motion during head impacts, yet computational models often simplify this interface using idealized contact conditions due to limited experimental data. This study presents an improved protocol combining experimental testing and computational modelling to determine the mechanical properties of the brain-skull interface under shear loading. Brain tissue and brain-skull complex samples were extracted from sheep cadaver heads and subjected to shear loading. Magnetic resonance imaging (MRI) was used to obtain accurate 3D geometries of the samples, which were then used to create computational grids (meshes) for simulation of the experiments using finite element (FE) models to determine subject-specific properties of the brain tissue and brain-skull interface. A second-order Ogden hyperelastic model was used for the brain tissue, and a cohesive layer was employed to model the brain-skull interface. Our results indicate that a cohesive layer captures the force-displacement and damage initiation of the brain-skull interface. The calibrated cohesive properties showed consistent patterns across samples, with maximum normal tractions ranging from 2.8-3.4 kPa and maximum tangential tractions from 1.8-2.1 kPa. This framework provides a foundation for improving the biofidelity of computational head models used in injury prediction and neurosurgical planning by replacing arbitrary boundary conditions with formulations derived from experimental data on brain-skull interface (meninges) biomechanical behaviour.

cs.CE↗

arXiv2025

On the Appropriateness of Linear Stress Recovery in Biomechanical Analysis of Abdominal Aortic Aneurysm

Abdominal aortic aneurysm (AAA) wall stress is a candidate rupture risk marker but is typically computed from single-phase images without known cardiac phase. Linear stress recovery methods, which solve a single geometrically linear equilibrium problem on the imaged, already-loaded geometry, have been validated for static stress estimation, but their robustness to unknown imaging phase remains unexplored. We investigated whether imaging phase materially biases 99th percentile stress recovered linearly, and whether linear recovery agrees with non-linear analysis under matched loads. Two patient-specific AAAs from a public 4D-CTA cohort (Case 1: 5.5% strain; Case 2: 4.5% strain) were analyzed. For each, we analyzed diastolic and synthetic systolic geometry, the latter generated by warping the diastolic mesh via displacements from non-linear hyperelastic analysis. Linear stresses were recovered on both geometries under systolic pressure and compared via 99th-percentile maximum principal stress, stress distributions, and 3D stress differential contours. Linear stresses under pulse pressure were compared against non-linear stresses. 99th-percentile stresses from linear recovery on diastolic vs synthetic systolic geometries under systolic pressure differed by 8.6% (Case 1) and 3.5% (Case 2), within segmentation uncertainty. 99th-percentile stresses from linear recovery and non-linear analysis under pulse pressure agreed closely: 0% difference (Case 1) and 1.1% (Case 2), with nearly identical distributions. These findings support linear stress recovery for patient-specific AAA analysis in clinical settings with static single-phase imaging, offering a computationally efficient alternative without compromising accuracy or requiring patient-specific wall properties.

physics.med-ph↗

arXiv2025

Impact of Geometric Uncertainty on the Computation of Abdominal Aortic Aneurysm Wall Strain

Abdominal aortic aneurysm (AAA) is a life-threatening condition characterized by permanent enlargement of the aorta, often detected incidentally during imaging for unrelated conditions. Current management relies primarily on aneurysm diameter and growth rate, which may not reliably predict patient-specific rupture risk. Computation of AAA wall stress and strain has the potential to improve individualized risk assessment, but these analyses depend on image-derived geometry, which is subject to segmentation uncertainty and lacks a definitive ground truth for the wall boundary. While the effect of geometric uncertainty on wall stress has been studied, its influence on wall strain remains unclear. In this study, we assessed the impact of geometric uncertainty on AAA wall strain computed using deformable image registration of time-resolved 3D computed tomography angiography (4D-CTA). Controlled perturbations were applied to the wall geometry along the surface normal, parameterized by the standard deviation for random variation and the mean for systematic inward or outward bias, both scaled relative to wall thickness. Results show that uncertainties in AAA wall geometry reduce the accuracy of computed strain, with inward bias (toward the blood lumen and intraluminal thrombus) consistently causing greater deviations than outward bias (toward regions external to the aortic wall). Peak strain is more sensitive but less robust, whereas the 99th percentile strain remains more stable under perturbations. We concluded that, for sufficiently accurate strain estimation, geometric uncertainty should remain within one wall thickness (typically 1.5 mm).

cs.CE↗

arXiv2025

4D-CTA Image and geometry dataset for kinematic analysis of abdominal aortic aneurysms

This article presents a dataset used in the article "Kinematics of Abdominal Aortic Aneurysms", published in the Journal of Biomechanics. The dataset is publicly available for download from the Zenodo data repository (https://doi.org/10.5281/zenodo.15477710). The dataset includes time-resolved 3D computed tomography angiography (4D-CTA) images of abdominal aortic aneurysm (AAA) captured throughout the cardiac cycle from ten patients diagnosed with AAA, along with ten patient-specific AAA geometries extracted from these images. Typically, the 4D-CTA dataset for each patient contains ten electrocardiogram (ECG)-gated 3D-CTA image frames acquired over a cardiac cycle, capturing both the systolic and diastolic phases of the AAA configuration. For method verification, the dataset also includes synthetic ground truth data generated from Patient 1's 3D-CTA AAA image in the diastolic phase. The ground truth data includes the patient-specific finite element (FE) biomechanical model and a synthetic systolic 3D-CTA image. The synthetic systolic image was generated by warping Patient 1's diastolic 3D-CTA image using the realistic displacement field obtained from the AAA biomechanical FE model. The images were acquired at Fiona Stanley Hospital in Western Australia and provided to the researchers at the Intelligent Systems for Medicine Laboratory at The University of Western Australia (ISML-UWA), where image-based AAA kinematic analysis was performed. Our dataset enabled the analysis of AAA wall displacement and strain throughout the cardiac cycle using a non-invasive, in vivo, image registration-based approach. The use of widely adopted, open-source file formats (NRRD for images and STL for geometries) facilitates broad applicability and reusability in AAA biomechanics studies that require patient-specific geometry and information about AAA kinematics during cardiac cycle.

cs.CE↗

arXiv2025

Personalizing the meshed SPL/NAC Brain Atlas for patient-specific scientific computing using SynthMorph

Developing personalized computational models of the human brain remains a challenge for patient-specific clinical applications and neuroscience research. Efficient and accurate biophysical simulations rely on high-quality personalized computational meshes derived from patient's segmented anatomical MRI scans. However, both automatic and manual segmentation are particularly challenging for tissues with limited visibility or low contrast. In this work, we present a new method to create personalized computational meshes of the brain, streamlining the development of computational brain models for clinical applications and neuroscience research. Our method uses SynthMorph, a state-of-the-art anatomy-aware, learning-based medical image registration approach, to morph a comprehensive hexahedral mesh of the open-source SPL/NAC Brain Atlas to patient-specific MRI scans. Each patient-specific mesh includes over 300 labeled anatomical structures, more than any existing manual or automatic methods. Our registration-based method takes approximately 20 minutes, significantly faster than current state-of-the-art mesh generation pipelines, which can take up to two hours. We evaluated several state-of-the-art medical image registration methods, including SynthMorph, to determine the most optimal registration method to morph our meshed anatomical brain atlas to patient MRI scans. Our results demonstrate that SynthMorph achieved high DICE similarity coefficients and low Hausdorff Distance metrics between anatomical structures, while maintaining high mesh element quality. These findings demonstrate that our registration-based method efficiently and accurately produces high-quality, comprehensive personalized brain meshes, representing an important step toward clinical translation.

cs.CE↗

arXiv2025

Towards personalised assessment of abdominal aortic aneurysm structural integrity

Abdominal aortic aneurysm (AAA) is a life-threatening condition characterized by the progressive dilation of the aorta, which can lead to rupture if undetected or untreated. Stress-based rupture risk estimation using computational biomechanics has been widely studied; however, it requires wall strength data that cannot be measured in humans in vivo. To overcome this limitation, the goal of this study is to present a new method for biomechanical assessment of AAA via simultaneous consideration of tension and strain in AAA wall. We present a patient-specific, non-invasive method for assessing the structural integrity of the AAA wall using only time-resolved 3D computed tomography angiography (4D-CTA) images and blood pressure data. The proposed approach integrates wall strain (throughout the cardiac cycle) and wall tension analysis to compute a novel index, the Relative Structural Integrity Index (RSII), which quantifies local wall stiffness independently of wall thickness, wall material properties, and blood pressure measurement conditions. We applied our method to twenty patients from three different hospitals to extract visual RSII maps over the AAA wall of each individual patient and to compare the RSII values between aneurysmal and non-aneurysmal aortas in one patient. Our results primarily show similar RSII values across all patients, indicating the consistency of the method. Additionally, we observed patterns consistent with experimental findings reported in the literature: AAA walls exhibited higher stiffness than healthy aortic walls, while localized low-stiffness zones in the AAA wall were predominantly found in the most dilated regions.

cs.CE↗

arXiv2024

Hexahedral mesh of anatomical atlas for construction of computational human brain models: Applications to modeling biomechanics and bioelectric field propagation

Numerical simulations rely on constructing accurate and detailed models to produce reliable results - a task that is often challenging. This task becomes notably more difficult when the model is of the human brain. We create an anatomically comprehensive hexahedral mesh of the human brain using an open-source digital brain atlas. Digital atlases are valuable tools currently used by medical professionals, medical students, and researchers for gathering, presenting, and discovering knowledge about the human brain. We demonstrate that the atlas can be used to efficiently create an accurate and detailed hexahedral finite element mesh of the brain for scientific computing. We present two case studies. The first case study constructs a biomechanical model of the brain to compute brain deformations and predict traumatic brain injury risk due to violent impact. In the second case study, we construct a bioelectrical model of the brain to solve the electroencephalography (EEG) forward problem, a frequent simulation process used in electrophysiology to study electromagnetic fields generated by the nervous system. We demonstrate efficient and accurate model construction using the meshed anatomical brain atlas, as well as emphasize the importance of effective communication and contextual analysis of results for enabling multi-disciplinary scientific computing research.

cs.CE↗

arXiv2024

Towards Determining Mechanical Properties of Brain-Skull Interface Under Tension and Compression

Computational biomechanics models of the brain have become an important tool for investigating the brain responses to mechanical loads. The geometry, loading conditions, and constitutive properties of such brain models are well-studied and generally accepted. However, there is a lack of experimental evidence to support models of the layers of tissues (brain-skull interface) connecting the brain with the skull which determine boundary conditions for the brain. We present a new protocol for determining the biomechanical properties of the brain-skull interface and present the preliminary results (for a small number of tissue samples extracted from sheep cadaver heads). The method consists of biomechanical experiments using brain tissue and brain-skull complex (consisting of the brain tissue, brain-skull interface, and skull bone) and comprehensive computer simulation of the experiments using the finite element (FE) method. Application of the FE simulations allowed us to abandon the traditionally used approaches that rely on analytical formulations that assume cuboidal (or cylindrical) sample geometry when determining the parameters that describe the biomechanical behaviour of the brain tissue and brain-skull interface. In the simulations, we used accurate 3D geometry of the samples obtained from magnetic resonance images (MRIs). Our results indicate that the behaviour of the brain-skull interface under compressive loading appreciably differs from that under tension. Rupture of the interface was clearly visible for tensile load while no obvious indication of mechanical failure was observed under compression. These results suggest that assuming a rigid connection or frictionless sliding contact between the brain tissue and skull bone, the approaches often used in computational biomechanics models of the brain, may not accurately represent the mechanical behaviour of the brain-skull interface.

cs.CE↗

arXiv2024

An efficient pipeline to compute patient-specific cerebral aneurysm wall tension

Cerebral aneurysm rupture, leading to subarachnoid hemorrhage with a high mortality rate, disproportionately affects younger populations, resulting in a significant loss of productive life years. A significant proportion of these deaths is due to aneurysmal re-bleeding within the first three days following the initial bleed, prior to treatment. While early aneurysm treatment is recommended, there is no consensus on the ideal timing, and emergency treatment offers only an incremental benefit at a significant cost. Although various multivariable prediction models have been proposed to provide personalized risk assessments, no validated patient-specific predictor is available to rationalize emergency treatment. Furthermore, no model has yet incorporated emerging computational biomechanics-based biomarkers such as wall tension. In this paper, we proposed and validated an efficient semi-automatic pipeline to compute patient-specific cerebral aneurysm wall tension as a potential biomarker for the likelihood of re-bleeding. Our pipeline uses the patient's computed tomography angiography (CTA) image obtained at the time of subarachnoid hemorrhage diagnosis to create a patient-specific biomechanical model of the cerebral aneurysm using the finite element method. A distinctive feature of our approach is the straightforward model creation and wall tension computation using shell finite elements, without requiring patient-specific material properties or aneurysm wall thickness. Our non-invasive, patient-specific method for cerebral aneurysm wall tension can potentially provide individualized risk prediction and enhance clinical decision-making.

cs.CE↗

arXiv2024

Abdominal aortic aneurysm wall stress: A 7-line code in MATLAB and a one-click software application

An abdominal aortic aneurysm (AAA) is a life-threatening condition characterized by the irreversible dilation of the lower aorta, usually detected incidentally during imaging for other health issues. Current clinical practice for managing AAA relies on a one-size-fits-all approach, based on the aneurysm's maximum diameter and growth rate, which can lead to underestimation or overestimation of AAA rupture risk. Patient-specific AAA wall stress, computed using biomechanical models derived from medical images without needing patient-specific material properties, has been widely investigated for developing individualized AAA rupture risk predictors. Therefore, AAA wall stress, determined reliably and quickly, has the potential to enhance patient-specific treatment plans. This paper presents a 7-line code, written in MATLAB using the Partial Differential Equation Toolbox, for AAA wall stress computations via finite element analysis. The code takes AAA wall geometry as input and outputs stress components over the AAA wall domain. Additionally, we present a one-click standalone software application for AAA wall stress computation, developed based on our 7-line code using MATLAB Compiler. After verification, we used our code to compute AAA wall stress in ten patients. Our analysis indicated that the 99th percentile of maximum principal stress across all patients ranged from 0.307 MPa to 0.466 MPa, with an average of 0.380 MPa and a standard deviation of 0.048 MPa. Moreover, for every case, the MATLAB simulation time was less than a minute on a laptop workstation.

cs.CE↗

arXiv2024

Kinematics of Abdominal Aortic Aneurysms

A search in Scopus within "Article title, Abstract, Keywords" unveils 2,444 documents focused on the biomechanics of Abdominal Aortic Aneurysm (AAA), mostly on AAA wall stress. Only 24 documents investigated AAA kinematics, an important topic that could potentially offer significant insights into the biomechanics of AAA. In this paper, we present an image-based approach for patient-specific, in vivo, and non-invasive AAA kinematic analysis using patient's time-resolved 3D computed tomography angiography (4D-CTA) images, with an objective to measure wall displacement and strain during the cardiac cycle. Our approach relies on regularized deformable image registration for estimating wall displacement, estimation of the local wall strain as the ratio of its normal displacement to its local radius of curvature, and local surface fitting with non-deterministic outlier detection for estimating the wall radius of curvature. We verified our approach against synthetic ground truth image data created by warping a 3D-CTA image of AAA using a realistic displacement field obtained from a finite element biomechanical model. We applied our approach to assess AAA wall displacements and strains in ten patients. Our kinematic analysis results indicated that the 99th percentile of circumferential wall strain, among all patients, ranged from 2.62% to 5.54%, with an average of 4.45% and a standard deviation of 0.87%. We also observed that AAA wall strains are significantly lower than those of a healthy aorta. Our work demonstrates that the registration-based measurement of AAA wall displacements in the direction normal to the wall is sufficiently accurate to reliably estimate strain from these displacements.

cs.CE↗

arXiv2024

Towards Full Automation of Geometry Extraction for Biomechanical Analysis of Abdominal Aortic Aneurysm; Neural Network-Based versus Classical Methodologies

Background: For the clinical adoption of stress-based rupture risk estimation in abdominal aortic aneurysms (AAAs), a fully automated pipeline, from clinical imaging to biomechanical stress computation, is essential. To this end, we investigated the impact of AI-based image segmentation methods on stress computation results in the walls of AAAs. We compared wall stress distributions and magnitudes calculated from geometry models obtained from classical semi-automated segmentation versus automated neural network-based segmentation. Method: 16 different AAA contrast-enhanced computed tomography (CT) images were semi-automatically segmented by an analyst, taking between 15 and 40 minutes of human effort per patient, depending on image quality. The same images were automatically segmented using PRAEVAorta2 commercial software by NUREA (https://www.nurea-soft.com/), developed based on artificial intelligence (AI) algorithms, and automatically post-processed with an in-house MATLAB code, requiring only 1-2 minutes of computer time per patient. Aneurysm wall stress calculations were automatically performed using the BioPARR software (https://bioparr.mech.uwa.edu.au/). Results: Compared to the classical semi-automated segmentation, the automatic neural network-based segmentation leads to equivalent stress distributions, and slightly higher peak and 99th percentile maximum principal stress values. However, our statistical analysis indicated that the differences in AAA wall stress obtained using the two segmentation methods are not statistically significant and fall well within the typical range of inter-analyst and intra-analyst variability. Conclusions: Our findings are a steppingstone toward a fully automated pipeline for biomechanical analysis of AAAs, starting with CT scans and concluding with wall stress assessment.

cs.CE↗

arXiv2024

Open Meshed Anatomy: Towards a comprehensive finite element hexahedral mesh derived from open atlases

Computational simulations using methods such as the finite element (FE) method rely on high-quality meshes for achieving accurate results. This study introduces a method for creating a high-quality hexahedral mesh using the Open Anatomy Project's brain atlas. Our atlas-based FE hexahedral mesh of the brain mitigates potential inaccuracies and uncertainties due to segmentation - a process that often requires input of an inexperienced analyst. It accomplishes this by leveraging existing segmentation from the atlas. We further extend the mesh's usability by forming a two-way correspondence between the atlas and mesh. This feature facilitates property assignment for computational simulations and enhances result analysis within an anatomical context. We demonstrate the application of the mesh by solving the electroencephalography (EEG) forward problem. Our method simplifies the mesh creation process, reducing time and effort, and provides a more comprehensive and contextually enriched visualisation of simulation outcomes.

cs.CE↗

arXiv2023

Random boundaries: quantifying segmentation uncertainty in solutions to boundary-value problems

Engineering simulations using boundary-value partial differential equations often implicitly assume that the uncertainty in the location of the boundary has a negligible impact on the output of the simulation. In this work, we develop a novel method for describing the geometric uncertainty in image-derived models and use a naive method for subsequently quantifying a simulation's sensitivity to that uncertainty. A Gaussian random field is constructed to represent the space of possible geometries, based on image-derived quantities such as pixel size, which can then be used to probe the simulation's output space. The algorithm is demonstrated with examples from biomechanics where patient-specific geometries are often segmented from low-resolution, three-dimensional images. These examples show the method's wide applicability with examples using linear elasticity and fluid dynamics. We show that important biomechanical outputs of these example simulations, namely maximum principal stress and wall shear stress, can be highly sensitive to realistic uncertainties in geometry.

q-bio.TO↗

arXiv2022

Automated modeling of brain bioelectric activity within the 3D Slicer environment

Electrocorticography (ECoG) or intracranial electroencephalography (iEEG) monitors electric potential directly on the surface of the brain and can be used to inform treatment planning for epilepsy surgery when paired with numerical modeling. For solving the inverse problem in epilepsy seizure onset localization, accurate solution of the iEEG forward problem is critical which requires accurate representation of the patient's brain geometry and tissue electrical conductivity. In this study, we present an automatic framework for constructing the brain volume conductor model for solving the iEEG forward problem and visualizing the brain bioelectric field on a deformed patient-specific brain model within the 3D Slicer environment. We solve the iEEG forward problem on the predicted postoperative geometry using the finite element method (FEM) which accounts for patient-specific inhomogeneity and anisotropy of tissue conductivity. We use an epilepsy case study to illustrate the workflow of our framework developed and integrated within 3D Slicer.

physics.med-ph↗

arXiv2022

Generation of Patient-specific Structured Hexahedral Mesh of Aortic Aneurysm Wall

Abdominal Aortic Aneurysm (AAA) is an enlargement in the lower part of the main artery Aorta by 1.5 times its normal diameter. AAA can cause death if rupture occurs. Elective surgeries are recommended to prevent rupture based on geometrical measurements of AAA diameter and diameter growth rate. Reliability of these geometric parameters to predict the AAA rupture risk has been questioned, and biomechanical assessment has been proposed to distinguish between patients with high and low risk of rupture. Stress in aneurysm wall is the main variable of interest in such assessment. Most studies use finite element method to compute AAA stress. This requires discretising patient-specific geometry (aneurysm wall and intraluminal thrombus ILT) into finite elements/meshes. Tetrahedral elements are most commonly used as they can be generated in seemingly automated and effortless way. In practice, however, due to complex aneurysm geometry, the process tends to require time consuming mesh optimisation to ensure sufficiently high quality of tetrahedral elements. Furthermore, ensuring solution convergence requires large number of tetrahedral elements, which leads to long computation times. In this study, we focus on generation of hexahedral meshes as they are known to provide converged solution for smaller number of elements than tetrahedral meshes. Generation of hexahedral meshes for continua with complex/irregular geometry, such as aneurysms, requires analyst interaction. We propose a procedure for generating high quality patient-specific hexahedral discretisation of aneurysm wall using the algorithms available in commercial software package for mesh generation. For aneurysm cases, we demonstrate that the procedure facilitates patient-specific mesh generation within timeframe consistent with clinical workflow constraints while requiring only limited input from the analyst.

cs.CE↗