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Register a new userA generative model of realistic brain cells with application to numerical simulation of diffusion-weighted MR signal
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In this work, we introduce a novel computational framework that we developed to use numerical simulations to investigate the complexity of brain tissue at a microscopic level with a detail never realised before. Directly inspired by the advances in computational neuroscience for modelling brain cells, we propose a generative model that enables us to simulate molecular diffusion within realistic digitalised brain cells, such as neurons and glia, in a completely controlled and flexible fashion. We validate our new approach by showing an excellent match between the morphology and simulated DW-MR signal of the generated digital model of brain cells and those of digital reconstruction of real brain cells from available open-access databases. We demonstrate the versatility and potentiality of the framework by showing a select set of examples of relevance for the DW-MR community. Further development is ongoing, which will support even more realistic conditions like dense packing of numerous 3D complex cell structures and varying cell surface permeability.
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Magnetic resonance imaging technique known as DWI (diffusion-weighted imaging) allows measurement of water diffusivity on a pixel basis for evaluating pathology throughout the body and is now routinely incorporated into many body MRI protocols, mainly in oncology. Indeed water molecules motion reflects the interactions with other molecules, membranes, cells, and in general the interactions with the environment. Microstructural changes as e.g. cellular organization and/or integrity then affect the motion of water molecules, and consequently alter the water diffusion properties measured by DWI. Then DWI technique can be used to extract information about tissue organization at the cellular level indirectly from water motion. In general the signal intensity in DWI can be quantified by using a parameter known as ADC (Apparent Diffusion Coefficient) emphasizing that it is not the real diffusion coefficient, which is a measure of the average water molecular motion. In the simplest models, the distribu- tion of a water molecule diffusing in a certain period of time is considered to have a Gaussian form with its width proportional to the ADC. However, water in biological structures often displays non-Gaussian diffusion behavior, consequently the DWI signal shows a more complex behavior that need to be modeled following different approaches. In this work we explore the possibility to quantify the degree to which water diffusion in biologic tissues is non-Gaussian introducing the AKC parameter (Apparent Kurtosis Coefficient). In this work we have realized DWI non-Gaussian diffusion maps to be used in the clinical routine along with standard ADC maps, giving to the radiologist another tool to explore how much structure inside a voxel is organized. In particular in this work some prostate DWI examples have been analyzed and will be shown.
Purpose: To investigate the effect of realistic microstructural geometry on the susceptibility-weighted magnetic resonance (MR) signal in white matter (WM), with application to demyelination. Methods: Previous work has modeled susceptibility-weighted signals under the assumption that axons are cylindrical. In this work, we explore the implications of this assumption by considering the effect of more realistic geometries. A three-compartment WM model incorporating relevant properties based on literature was used to predict the MR signal. Myelinated axons were modeled with several cross-sectional geometries of increasing realism: nested circles, warped/elliptical circles and measured axonal geometries from electron micrographs. Signal simulations from the different microstructural geometries were compared to measured signals from a Cuprizone mouse model with varying degrees of demyelination. Results: Results from simulation suggest that axonal geometry affects the MR signal. Predictions with realistic models were significantly different compared to circular models under the same microstructural tissue properties, for simulations with and without diffusion. Conclusion: The geometry of axons affects the MR signal significantly. Literature estimates of myelin susceptibility, which are based on fitting biophysical models to the MR signal, are likely to be biased by the assumed geometry, as will any derived microstructural properties.
Mathematical modelling of ionic electrodiffusion and water movement is emerging as a powerful avenue of investigation to provide new physiological insight into brain homeostasis. However, in order to provide solid answers and resolve controversies, the accuracy of the predictions is essential. Ionic electrodiffusion models typically comprise non-trivial systems of non-linear and highly coupled partial and ordinary differential equations that govern phenomena on disparate time scales. Here, we study numerical challenges related to approximating these systems. We consider a homogenized model for electrodiffusion and osmosis in brain tissue and present and evaluate different associated finite element-based splitting schemes in terms of their numerical properties, including accuracy, convergence, and computational efficiency for both idealized scenarios and for the physiologically relevant setting of cortical spreading depression (CSD). We find that the schemes display optimal convergence rates in space for problems with smooth manufactured solutions. However, the physiological CSD setting is challenging: we find that the accurate computation of CSD wave characteristics (wave speed and wave width) requires a very fine spatial and fine temporal resolution.
The head down tilt (HDT) position is commonly used to simulate vascular and tissue fluid dynamics during spaceflights. In HDT position, the cerebral autoregulation faces difficulties to adjust the vascular tone while the cephalad fluid shifts may yield increased intracranial pressures and altered mechanical properties. Recent MRI T2 mapping in HDT position have shown fluid overpressure in the brain and resulting loss of water contents in the CSF and orbital compartments. Brain MRE was performed here in similar HDT conditions. It was sensitive enough to provide new insights on the overall mechanical response of brain tissues in microgravity analogous conditions. Summary of Main Findings/Short Synopsis Brain fluid overpressure and resulting loss of water contents in CSF and orbital compartments were confirmed by T2 mapping in head down tilt position. The overall brain mechanical response in such microgravity analogous conditions, cerebral tissue stiffening, was revealed by whole brain MRE.
We propose a method for extracting physics-based biomarkers from a single multiparametric Magnetic Resonance Imaging (mpMRI) scan bearing a glioma tumor. We account for mass effect, the deformation of brain parenchyma due to the growing tumor, which on its own is an important radiographic feature but its automatic quantification remains an open problem. In particular, we calibrate a partial differential equation (PDE) tumor growth model that captures mass effect, parameterized by a single scalar parameter, tumor proliferation, migration, while localizing the tumor initiation site. The single-scan calibration problem is severely ill-posed because the precancerous, healthy, brain anatomy is unknown. To address the ill-posedness, we introduce an ensemble inversion scheme that uses a number of normal subject brain templates as proxies for the healthy precancer subject anatomy. We verify our solver on a synthetic dataset and perform a retrospective analysis on a clinical dataset of 216 glioblastoma (GBM) patients. We analyze the reconstructions using our calibrated biophysical model and demonstrate that our solver provides both global and local quantitative measures of tumor biophysics and mass effect. We further highlight the improved performance in model calibration through the inclusion of mass effect in tumor growth models -- including mass effect in the model leads to 10% increase in average dice coefficients for patients with significant mass effect. We further evaluate our model by introducing novel biophysics-based features and using them for survival analysis. Our preliminary analysis suggests that including such features can improve patient stratification and survival prediction.
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