Ziejewski, Mariusz

ABSTRACT Accurately predicting vertebral fracture risk in metastatic spines remains a critical challenge in clinical practice. This study developed and validated a QCT‐based finite element analysis (QCT/FEA) approach to investigate the combined effects of baseline bone strength and tumor size on vertebral structural integrity. Areal bone mineral density (aBMD) was also calculated from QCT data to evaluate the reduction in bone density with increasing defect size. Nine cadaveric vertebral bodies were analyzed under varying tumor sizes (0%, 20%, 35%, and 50%). The results demonstrated a strong correlation between experimentally measured and computationally predicted failure forces ( r = 0.97, p < 0.001) and aBMD values ( r = 0.96, p < 0.001). Vertebral strength decreased linearly with increasing tumor size. Importantly, the study revealed that baseline vertebral strength plays a crucial role in fracture risk assessment, often surpassing the impact of tumor size alone. Tumor size reduced vertebral strength at a rate 84% faster than bone density ( p = 0.009), highlighting a greater impact of tumor defects on bone fracture force than on bone density. These findings suggest that relying solely on tumor size for fracture risk prediction may be insufficient. Incorporating baseline bone strength into predictive models significantly enhances accuracy and reliability, providing valuable insights for clinical decision‐making and personalized treatment strategies. This study underscores the importance of advanced computational tools in improving vertebral fracture risk assessment in metastatic spine cases.

Abstract Magnetic resonance elastography (MRE) is commonly used as an image-based alternative for palpation of the internal organs of human body. The presence of tumor or other kind of pathologies in biological tissues can increase its stiffness. Therefore, while MRE technique is capable to provide a quantitative measurement, the qualitative description of the tissue stiffness could be potentially informative as well for physicians. MRE can be divided into several steps including the generation of waves in the tissue, measuring the field displacement of the tissue by magnetic resonance imaging devices, and then applying the constitutive based inversion algorithms to measure the material properties of the tissue. The inversion algorithms are dependent to the constitutive model in use, and moreover, it could be computationally expensive. To overcome this hindrance, in this paper, we propose a machine learning framework for categorizing the tissue stiffness based on the magnetic resonance elastography finite element simulation data. In our finite element simulation, the shear waves are generated in an axisymmetrical model by applying harmonic displacement at the center of the model with the known excitation frequency. To obtain the field displacement of the model, in the first step, the natural frequencies of the system will be calculated through numerical Block-Lanczos eigensolver algorithm. Thereafter, a transient dynamic modal analysis is carried out to find the corresponding displacement response of the tissue in different time steps of the simulation. To obtain the training dataset, ten simulations with the pre-assigned linear elastic modulus in the range of 2 to 6 kPa is conducted and the displacement of the tissue in three points at the end of the first and second cycle will be recorded as the features of the dataset. Each instance of the dataset is labelled as “Low“ or “High”, corresponding to its stiffness quantitative value lying in ranges of 2–4 kPa or 4–6 kPa. A machine learning classifying algorithm, a logistic regression hypothesis will be trained on this dataset. The trained hypothesis will be then tested on six new unseen simulation data with known elastic modulus values. The trained logistic regression was able to classify the tissue stiffness with the perfect accuracy score of 1.0. The findings of this study can be used for qualitative description of the tissue stiffness that can be beneficial for pathology diagnosis and moreover, it eliminates the need on the usage of inversion algorithms which leads to reduction in the computational complexity of tissue characterization.

Abstract A numerical investigation is conducted on the injury-related biomechanical parameters of the human head under blunt impacts. The objective of this research is twofold; first to understand the role of the employed finite element (FE) head model — with its specific components, shape, size, material properties, and mesh size — in predicting tissue responses of the brain, and second to investigate the fidelity of pressure response in validating FE head models. Accordingly, two independently established and validated FE head models are impacted in two directions under two impact severities and their predicted responses in terms of intracranial pressure (ICP) and shear stress are compared. The coup-counter ICP peak values are less sensitive to head model, mesh size, and the brain material. In all cases, maximum ICPs occur on the outer surface, vanishing linearly toward the center of the brain. Hence, it is concluded that different head models may simply reproduce the results of ICP variations due to impact. Shear stress prediction, however, is mainly affected by the head model, direction and severity of impact, and the brain material.

Knowing the precise material properties of intracranial head organs is crucial for studying the biomechanics of head injury. It has been shown that these biological tissues are significantly rate-dependent; hence, their material properties should be determined with respect to the range of deformation rate they experience. In this paper, a validated finite element human head model is used to investigate the biomechanics of the head in impact and blast, leading to traumatic brain injuries (TBI). We simulate the head under various directions and velocities of impacts, as well as helmeted and unhelmeted head under blast shock waves. It is demonstrated that the strain rates for the brain are in the range of 36 to 241 s−1, approximately 1.9 and 0.86 times the resulting head acceleration under impacts and blast scenarios, respectively. The skull was found to experience a rate in the range of 14 to 182 s−1, approximately 0.7 and 0.43 times the head acceleration corresponding to impact and blast cases. The results of these incident simulations indicate that the strain rates for brainstem and dura mater are respectively in the range of 15 to 338 and 8 to 149 s−1. These findings provide a good insight into characterizing the brain tissue, cranial bone, brainstem and dura mater, and also selecting material properties in advance for computational dynamical studies of the human head.

Blast traumatic brain injury (bTBI) may happen due to sudden blast and high-frequency loads. Due to the moral issues and the burden of experimental approaches, using computational methods such as finite element analysis (FEA) can be effective. Several finite element studies have focused on the effects of TBI to anticipate and understand the brain dynamic response. One of the most important factors in every FEA study of bTBI is the accurate modeling of brain tissue material properties. The main goal of this study is a comparison of different brain tissue constitutive models to understand the dynamic response of brain under an identical blast load. The multi-material FE modeling of the human head has several limitations such as its complexity and consequently high computational costs. Therefore, a spherical head model is modeled which suggests more straightforward observation/understanding of the FE modeling of skull (solid), CSF (fluid), and the brain tissue. Three different material models are considered for the brain tissue, namely hyperelastic, viscoelastic, and hyperviscoelastic. Brain dynamic responses are studied in terms of the head kinematics (linear acceleration), intracranial pressure (ICP), shear stress, and maximum mechanical strain. Our results showed that the hyperelastic model predicts larger ICP and shear than other constitutive brain tissue models. However, all material models predicted similar shear strain and head accelerations.

Skull deformation and vibration has been hypothesized to be an injury mechanism when the human head undergoes an impact scenario. The extent that skull deformation may increase the risk of traumatic brain injury, however, is not well understood. This computational study explains whether skull deformation has any impact on the variation of intracranial pressure (ICP). To this end, a finite element head model including major anatomical components of the human head was employed. The head model has been validated against ICP variations on the brain. The impact simulations were carried out using a rigid cylindrical impactor. The scenarios were frontal impacts with the impactor hitting the forehead of the head model at two impact severity levels. In order to examine the effect of skull elasticity on the stress wave propagation inside the cranium under an external applied force, the skull was also taken as a rigid body with the same density as the elastic one, and the result were compared with those obtained with the deformable skull. For the two cases, the variation of ICPs at the coup and countercoup sites were recorded and compared. The results of the study showed that, for the case studies presented here, the deformation of skull didn’t increase the level of ICP inside the brain. It was concluded that the skull rapid body motion might be responsible for brain injuries.

Blast-induced traumatic brain injury (bTBI), is defined as a type of acquired brain injury that occurs upon the interaction of the human head with blast-generated high-pressure shockwaves. Lack of experimental studies due to moral issues, have motivated the researchers to employ computational methods to study the bTBI mechanisms. Accordingly, a nonlinear finite element (FE) analysis was employed to study the interaction of both unprotected and protected head models with explosion pressure waves. The head was exposed to the incoming shockwaves from front, back, and side directions. The main goal was to examine the effects of head protection tools and the direction of blast waves on the tissue and kinematical responses of the brain. Generation, propagation, and interactions of blast waves with the head were modeled using an arbitrary Lagrangian-Eulerian (ALE) method and a fluid-structure interaction (FSI) coupling algorithm. The FE simulations were performed using Ls-Dyna, a transient, nonlinear FE code. Side blast predicted the highest mechanical responses for the brain. Moreover, the protection assemblies showed to significantly alter the blast flow mechanics. Use of faceshield was also observed to be highly effective in the front blast due to hindering of shockwaves.