Mechanical properties of calvarial bones in a mouse model for craniosynostosis

The mammalian cranial vault largely consists of five flat bones that are joined together along their edges by soft fibrous tissues called sutures. Premature closure of the cranial sutures, craniosynostosis, can lead to serious clinical pathology unless there is surgical intervention. Research into the genetic basis of the disease has led to the development of various animal models that display this condition, e.g. mutant type Fgfr2C342Y/+ mice which display early fusion of the coronal suture (joining the parietal and frontal bones). However, whether the biomechanical properties of the mutant and wild type bones are affected has not been investigated before. Therefore, nanoindentation was used to compare the elastic modulus of cranial bone and sutures in wild type (WT) and Fgfr2C342Y/+mutant type (MT) mice during their postnatal development. Further, the variations in properties with indentation position and plane were assessed. No difference was observed in the elastic modulus of parietal bone between the WT and MT mice at postnatal (P) day 10 and 20. However, the modulus of frontal bone in the MT group was lower than the WT group at both P10 (1.39±0.30 vs. 5.32±0.68 GPa; p<0.05) and P20 (5.57±0.33 vs. 7.14±0.79 GPa; p<0.05). A wide range of values was measured along the coronal sutures for both the WT and MT samples, with no significant difference between the two groups. Findings of this study suggest that the inherent mechanical properties of the frontal bone in the mutant mice were different to the wild type mice from the same genetic background. These differences may reflect variations in the degree of biomechanical adaptation during skull growth, which could have implications for the surgical management of craniosynostosis patients

Hybrid foetus with an FE head for a pregnant occupant model for vehicle safety investigations

‘Expecting’, a computational pregnant occupant model, developed to simulate the dynamic response to crash impacts, possesses anthropometric properties of a fifth percentile female at around the 38th week of pregnancy. The model is complete with a finite element uterus and a multi-body foetus which is a novel feature in models of this kind. In this paper, the effect of incorporating a foetus with a finite element head into ‘Expecting’ is investigated. The finite element head was developed using detailed anatomic geometry and projected material properties. Then it was integrated with the ‘Expecting’ model and validated using the lap belt loading and the rigid bar impact tests. The model is then used to simulate frontal impacts at a range of crash severities with seatbelt and airbag, seatbelt only, airbag only as well as no restraint cases to investigate the risk of placental abruption and compare it with the model featuring the original multi-body foetus. The maximum strains developed in the utero-placental interface are used as the main criteria for foetus safety. The results show comparable strain levels to those from the multi-body foetus. It is, therefore, recommended to use the multi-body foetus in simulations as the computation time is more favourable

Evaluation System for Craniosynostosis Surgeries with Computer Simulation and Statistical Modelling

Craniosynostosis is a pathology in infants when one or more sutures prematurely closed, leading to abnormal skull shape. It has been classified according to the specific suture that has been closed, each of which has a typical skull shape. Surgery is the common treatment to correct the deformed skull shape and to reduce the excessive intracranial pressure. Since every case is unique, the cranial facial teams have difficulties to select an optimum solution for a specific patient from multiple options. In addition, there is not an appropriate quantified measurement existed currently to help cranial facial team to quantitatively evaluate their surgeries. We aimed to develop a head model of a craniosynostosis patient, which allows neurosurgeons to perform any potential surgeries on it so as to simulate the postoperative head development. Therefore, neurosurgeons could foresee the surgical results and is able to select the optimal one. In this thesis, we have developed a normal head model, and built mathematical models for possible dynamic behaviors. We also modified this model by closing one or two sutures to simulate common types of craniosynostosis. The abnormal simulation results showed a qualitative match with real cases and the normal simulation indicated a higher growth rate of cranial index than clinical data. We believed that this discrepancy caused by the rigidity of our skull plates, which will be adapted to deformable object in the future. In order to help neurosurgeons to better evaluate a surgery, we hope to develop an algorithm to quantify the level of deformity of a skull. We have designed a set of work flow and targeted curvatures as the key role. A training data was carefully selected to search for an optimal system to characterize different shapes. A set of test data was used to validate our algorithm to assess the performance of the optimal system. With a stable evaluating system, we can evaluate a surgery by comparing the preoperative and postoperative skulls from the patient. An effective surgery can be considered if the postoperative skull shifted toward normal shape from preoperative shape

An automatic pipeline for PET/MRI attenuation correction validation in the brain

PURPOSE: Challenges in PET/MRI quantitative accuracy for neurological uses arise from PET attenuation correction accuracy. We proposed and evaluated an automatic pipeline to assess the quantitative accuracy of four MRI-derived PET AC methods using analytically simulated PET brain lesions and ROIs as ground truth for PET activity. METHODS: Our proposed pipeline, integrating a synthetic lesion insertion tool and the FreeSurfer neuroimaging framework, inserts simulated spherical and brain ROIs into PET projection space, reconstructing them via four PET MRAC techniques. Utilizing an 11-patient brain PET dataset, we compared the quantitative accuracy of four MRACs (DIXON, DIXONbone, UTE AC, and DL-DIXON) against the gold standard PET CTAC, evaluating MRAC to CTAC activity bias in spherical lesions and brain ROIs with and without background activity against original (lesion free) PET reconstructed images. RESULTS: The proposed pipeline yielded accurate results for spherical lesions and brain ROIs, adhering to the MRAC to CTAC pattern of original brain PET images. Among the MRAC methods, DIXON AC exhibited the highest bias, followed by UTE, DIXONBone, and DL-DIXON showing the least. DIXON, DIXONbone, UTE, and DL-DIXON showed MRAC to CTAC biases of - 5.41%, - 1.85%, - 2.74%, and 0.08% respectively for ROIs inserted in background activity; - 7.02%, - 2.46%, - 3.56%, and - 0.05% for lesion ROIs without background; and - 6.82%, - 2.08%, - 2.29%, and 0.22% for the original brain PET images\u27 16 FreeSurfer brain ROIs. CONCLUSION: The proposed pipeline delivers accurate results for synthetic spherical lesions and brain ROIs, with and without background activity consideration, enabling the evaluation of new attenuation correction approaches without utilizing measured PET emission data. Additionally, it offers a consistent method to generate realistic lesion ROIs, potentially applicable in assessing further PET correction techniques

A statistical approach to the inverse problem in magnetoencephalography

Magnetoencephalography (MEG) is an imaging technique used to measure the magnetic field outside the human head produced by the electrical activity inside the brain. The MEG inverse problem, identifying the location of the electrical sources from the magnetic signal measurements, is ill-posed, that is, there are an infinite number of mathematically correct solutions. Common source localization methods assume the source does not vary with time and do not provide estimates of the variability of the fitted model. Here, we reformulate the MEG inverse problem by considering time-varying locations for the sources and their electrical moments and we model their time evolution using a state space model. Based on our predictive model, we investigate the inverse problem by finding the posterior source distribution given the multiple channels of observations at each time rather than fitting fixed source parameters. Our new model is more realistic than common models and allows us to estimate the variation of the strength, orientation and position. We propose two new Monte Carlo methods based on sequential importance sampling. Unlike the usual MCMC sampling scheme, our new methods work in this situation without needing to tune a high-dimensional transition kernel which has a very high cost. The dimensionality of the unknown parameters is extremely large and the size of the data is even larger. We use Parallel Virtual Machine (PVM) to speed up the computation.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS716 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Incorporation of biomechanical child cadaver neck behaviour in a child model and injury prediction in vehicle frontal crash

This research was completed in an effort to improve the biofidelity of a finite element child model and the accuracy of injury predictions in forward facing child restraint seats during numerical simulations of frontal crashes. After material alterations to the child model, neck tensile force was found to be within the range of cadaver tests and the rotation-moment curves were in good agreement with the corridor of the pediatric cadaver head/neck complex tests. The altered child model has illustrated more accurate biomechanical responses and kinematics; its biofidelity has been improved. The upper and lower neck tensile forces of the child model were reduced by approximately 35% and 41%, respectively. Tensile deformation of the child neck was increased by 2.75 times while rotational deformation increased by 37%. The percentage error of the maximum displacements of the child head was reduced from approximately 16% to 13.5%

A Scientific Approach to Understanding the Head Trauma Endured by a Mixed Martial Arts Fighter

The purpose of this research is to gain some insight on the type of head trauma an athlete may encounter during mixed martial arts (MMA) competition. These athletes endure continuous blows to the head throughout their training and fighting career. The knowledge obtained from this research may assist MMA athletes and trainers in assessing the way they train, how they compete and, more importantly, how long they choose to compete in their amateur or professional MMA career. The analysis is performed by first creating a three-dimensional solid model of the human head based on geometric coordinates originally obtained from a cadaver. The geometry is then imported into a Finite Element Analysis (FEA) software and validated by simulating a benchmark model based on experimental results. This research utilizes experimental data provided by the National Geographic on impact loads of various MMA striking techniques applied to the already validated geometry and FEA model to obtain the resulting pressure that occurs in the brain of the human head. These results are subsequently analyzed to determine how severe this trauma may be to an athlete. Key points such as ways to further improve the FEA results are also discussed

Dual-3DM3-AD : Mixed Transformer based Semantic Segmentation and Triplet Pre-processing for Early Multi-Class Alzheimer’s Diagnosis

Alzheimer’s Disease (AD) is a widespread, chronic, irreversible, and degenerative condition, and its early detection during the prodromal stage is of utmost importance. Typically, AD studies rely on single data modalities, such as MRI or PET, for making predictions. Nevertheless, combining metabolic and structural data can offer a comprehensive perspective on AD staging analysis. To address this goal, this paper introduces an innovative multi-modal fusion-based approach named as Dual-3DM3-AD. This model is proposed for an accurate and early Alzheimer’s diagnosis by considering both MRI and PET image scans. Initially, we pre-process both images in terms of noise reduction, skull stripping and 3D image conversion using Quaternion Non-local Means Denoising Algorithm (QNLM), Morphology function and Block Divider Model (BDM), respectively, which enhances the image quality. Furthermore, we have adapted Mixed-transformer with Furthered U-Net for performing semantic segmentation and minimizing complexity. Dual-3DM3-AD model is consisted of multi-scale feature extraction module for extracting appropriate features from both segmented images. The extracted features are then aggregated using Densely Connected Feature Aggregator Module (DCFAM) to utilize both features. Finally, a multi-head attention mechanism is adapted for feature dimensionality reduction, and then the softmax layer is applied for multi-class Alzheimer’s diagnosis. The proposed Dual-3DM3-AD model is compared with several baseline approaches with the help of several performance metrics. The final results unveil that the proposed work achieves 98% of accuracy, 97.8% of sensitivity, 97.5% of specificity, 98.2% of f-measure, and better ROC curves, which outperforms other existing models in multi-class Alzheimer’s diagnosis.© 2024 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/fi=vertaisarvioitu|en=peerReviewed