Patient-specific computational biomechanics of the brain without segmentation and meshing

2012 ◽  
Vol 29 (2) ◽  
pp. 293-308 ◽  
Author(s):  
Johnny Y. Zhang ◽  
Grand Roman Joldes ◽  
Adam Wittek ◽  
Karol Miller
Keyword(s):  
Patient Specific ◽  
Computational Biomechanics ◽  
The Brain

Numerical Analysis of the VAD Outflow Cannula Positioning on the Blood Flow in the Patient–Specific Brain Supplying Arteries

2020 ◽  
Vol 22 (2) ◽  
pp. 619-636 ◽  
Author(s):  
Zbigniew Tyfa ◽  
Damian Obidowski ◽  
Krzysztof Jóźwik
Keyword(s):  
Blood Flow ◽  
Computer Aided Design ◽  
Volume Flow Rate ◽  
Flow Distribution ◽  
Primary Objective ◽  
Patient Specific ◽  
Blood Flow Distribution ◽  
Reconstruction Process ◽  
Outflow Cannula ◽  
The Brain

AbstractThe primary objective of this research can be divided into two separate aspects. The first one was to verify whether own software can be treated as a viable source of data for the Computer Aided Design (CAD) modelling and Computational Fluid Dynamics CFD analysis. The second aspect was to analyze the influence of the Ventricle Assist Device (VAD) outflow cannula positioning on the blood flow distribution in the brain-supplying arteries. Patient-specific model was reconstructed basing on the DICOM image sets obtained with the angiographic Computed Tomography. The reconstruction process was performed in the custom-created software, whereas the outflow cannulas were added in the SolidWorks software. Volumetric meshes were generated in the Ansys Mesher module. The transient boundary conditions enabled simulating several full cardiac cycles. Performed investigations focused mainly on volume flow rate, shear stress and velocity distribution. It was proven that custom-created software enhances the processes of the anatomical objects reconstruction. Developed geometrical files are compatible with CAD and CFD software – they can be easily manipulated and modified. Concerning the numerical simulations, several cases with varied positioning of the VAD outflow cannula were analyzed. Obtained results revealed that the location of the VAD outflow cannula has a slight impact on the blood flow distribution among the brain supplying arteries.

scholarly journals Transfer of rhodamine-123 into the brain and cerebrospinal fluid of fetal, neonatal and adult rats

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Liam M. Koehn ◽  
Katarzyna M. Dziegielewska ◽  
Mark D. Habgood ◽  
Yifan Huang ◽  
Norman R. Saunders
Keyword(s):  
Cerebrospinal Fluid ◽  
Fetal Brain ◽  
Maternal Blood ◽  
Adult Brain ◽  
Rhodamine 123 ◽  
Patient Specific ◽  
Adult Rats ◽  
Blood Brain ◽  
The Brain ◽  
Brain Barriers

Abstract Background Adenosine triphosphate binding cassette transporters such as P-glycoprotein (PGP) play an important role in drug pharmacokinetics by actively effluxing their substrates at barrier interfaces, including the blood-brain, blood-cerebrospinal fluid (CSF) and placental barriers. For a molecule to access the brain during fetal stages it must bypass efflux transporters at both the placental barrier and brain barriers themselves. Following birth, placental protection is no longer present and brain barriers remain the major line of defense. Understanding developmental differences that exist in the transfer of PGP substrates into the brain is important for ensuring that medication regimes are safe and appropriate for all patients. Methods In the present study PGP substrate rhodamine-123 (R123) was injected intraperitoneally into E19 dams, postnatal (P4, P14) and adult rats. Naturally fluorescent properties of R123 were utilized to measure its concentration in blood-plasma, CSF and brain by spectrofluorimetry (Clariostar). Statistical differences in R123 transfer (concentration ratios between tissue and plasma ratios) were determined using Kruskal-Wallis tests with Dunn’s corrections. Results Following maternal injection the transfer of R123 across the E19 placenta from maternal blood to fetal blood was around 20 %. Of the R123 that reached fetal circulation 43 % transferred into brain and 38 % into CSF. The transfer of R123 from blood to brain and CSF was lower in postnatal pups and decreased with age (brain: 43 % at P4, 22 % at P14 and 9 % in adults; CSF: 8 % at P4, 8 % at P14 and 1 % in adults). Transfer from maternal blood across placental and brain barriers into fetal brain was approximately 9 %, similar to the transfer across adult blood-brain barriers (also 9 %). Following birth when placental protection was no longer present, transfer of R123 from blood into the newborn brain was significantly higher than into adult brain (3 fold, p < 0.05). Conclusions Administration of a PGP substrate to infant rats resulted in a higher transfer into the brain than equivalent doses at later stages of life or equivalent maternal doses during gestation. Toxicological testing of PGP substrate drugs should consider the possibility of these patient specific differences in safety analysis.

scholarly journals Influence of Patient-Specific Head Modeling on EEG Source Imaging

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Yohan Céspedes-Villar ◽  
Juan David Martinez-Vargas ◽  
G. Castellanos-Dominguez
Keyword(s):  
Neurological Diseases ◽  
Structural Data ◽  
Problem Formulation ◽  
Patient Specific ◽  
Source Imaging ◽  
Eeg Source Imaging ◽  
Deep Sources ◽  
The Individual ◽  
Tissue Compartments ◽  
The Brain

Electromagnetic source imaging (ESI) techniques have become one of the most common alternatives for understanding cognitive processes in the human brain and for guiding possible therapies for neurological diseases. However, ESI accuracy strongly depends on the forward model capabilities to accurately describe the subject’s head anatomy from the available structural data. Attempting to improve the ESI performance, we enhance the brain structure model within the individual-defined forward problem formulation, combining the head geometry complexity of the modeled tissue compartments and the prior knowledge of the brain tissue morphology. We validate the proposed methodology using 25 subjects, from which a set of magnetic-resonance imaging scans is acquired, extracting the anatomical priors and an electroencephalography signal set needed for validating the ESI scenarios. Obtained results confirm that incorporating patient-specific head models enhances the performed accuracy and improves the localization of focal and deep sources.

scholarly journals Simulation of atherosclerotic plaque growth using computational biomechanics and patient-specific data

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Dimitrios S. Pleouras ◽  
Antonis I. Sakellarios ◽  
Panagiota Tsompou ◽  
Vassiliki Kigka ◽  
Savvas Kyriakidis ◽  
...  
Keyword(s):  
Disease Progression ◽  
Plaque Burden ◽  
Patient Specific ◽  
High Density Lipoproteins ◽  
Computational Biomechanics ◽  
Plaque Area ◽  
Causes Of Mortality ◽  
Plaque Growth ◽  
Atherosclerotic Process ◽  
The One

Abstract Atherosclerosis is the one of the major causes of mortality worldwide, urging the need for prevention strategies. In this work, a novel computational model is developed, which is used for simulation of plaque growth to 94 realistic 3D reconstructed coronary arteries. This model considers several factors of the atherosclerotic process even mechanical factors such as the effect of endothelial shear stress, responsible for the initiation of atherosclerosis, and biological factors such as the accumulation of low and high density lipoproteins (LDL and HDL), monocytes, macrophages, cytokines, nitric oxide and formation of foams cells or proliferation of contractile and synthetic smooth muscle cells (SMCs). The model is validated using the serial imaging of CTCA comparing the simulated geometries with the real follow-up arteries. Additionally, we examine the predictive capability of the model to identify regions prone of disease progression. The results presented good correlation between the simulated lumen area (P < 0.0001), plaque area (P < 0.0001) and plaque burden (P < 0.0001) with the realistic ones. Finally, disease progression is achieved with 80% accuracy with many of the computational results being independent predictors.

scholarly journals EEG Synchronization Analysis for Seizure Prediction: A Study on Data of Noninvasive Recordings

Processes ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 846 ◽  
Author(s):  
Paolo Detti ◽  
Giampaolo Vatti ◽  
Garazi Zabalo Manrique de Lara
Keyword(s):  
Prediction Method ◽  
Control Device ◽  
Patient Specific ◽  
Seizure Prediction ◽  
Classification Approach ◽  
Machine Learning Methods ◽  
Eeg Data ◽  
Epileptic Patients ◽  
The University ◽  
The Brain

Objective: Epilepsy is a neurological disorder arising from anomalies of the electrical activity in the brain, affecting ~65 million individuals worldwide. Prediction methods, typically based on machine learning methods, require a large amount of data for training, in order to correctly classify seizures with small false alarm rates. Methods: In this work, we present a new database containing EEG scalp signals of 14 epileptic patients acquired at the Unit of Neurology and Neurophysiology of the University of Siena, Italy. Furthermore, a patient-specific seizure prediction method, based on the detection of synchronization patterns in the EEG, is proposed and tested on the data of the database. The use of noninvasive EEG data aims to explore the possibility of developing a noninvasive monitoring/control device for the prediction of seizures. The prediction method employs synchronization measures computed over all channel pairs and a computationally inexpensive threshold-based classification approach. Results and conclusions: The experimental analysis, performed by inspection and by the proposed threshold-based classifier on all the patients of the database, shows that the features extracted by the synchronization measures are able to detect preictal and ictal states and allow the prediction of the seizures few minutes before the seizure onsets.

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