Magnetic Resonance Elastography of the Brain: from Phantom to Mouse to Man

The overall objective of this study is to develop magnetic resonance elastography: MRE) imaging to better understand brain deformation, brain tissue mechanical properties, and brain-skull interaction in vivo. The findings of this study provide parameters for numerical models of human head biomechanics, as well as data for validation of these models. Numerical simulations offer enormous potential to the study of traumatic brain injury: TBI) and may also contribute to the development of prophylactic devices for high-risk subjects: e.g., military personnel, first-responders, and athletes). Current numerical models have not been adequately parameterized or validated and their predictions remain controversial. This dissertation describes three kinds of MRE experiments, conducted in phantom: physical model), mouse, and man. Phantom studies provide a means to experimentally confirm the accuracy of MRE estimates of viscoelastic parameters in relatively simple materials and geometries. Studies in the mouse provide insight into the dispersive nature of brain tissue mechanical properties at frequencies beyond those that can be measured in humans. Studies in human subjects provide direct measurements of the human brain\u27s response to dynamic extracranial loads, including skull-brain energy transmission and viscoelastic properties

Developing novel diffusion MRI methods for comprehensive analysis of restricted and anisotropic self-diffusion system

Diffusion MRI is a non-invasive imaging technique used to study the microstructural properties of biological tissues by observing the self-diffusion of water molecules. Traditional diffusion MRI methods, based on the pulsed gradient spin-echo sequence, employ magnetic field gradients to encode information about translational motion. However, this approach combines various aspects of diffusion, such as restriction, anisotropy, and flow, into a single observable, leading to interpretation ambiguities, especially in complex heterogeneous materials like living biological tissues.In this thesis, we address these challenges and push the boundaries of diffusion MRI by introducing innovative techniques for studying biological tissue microstructure. Our approach centers around the "double-rotation" technique borrowed from solid-state NMR, which generates modulated gradient waveforms, enabling us to explore the 2D frequency-anisotropy domain in-depth. By integrating this technique with oscillating gradients and tensor-valued encoding, we create a comprehensive methodology for data acquisition. Drawing inspiration from the "model-free" analytical strategies originally designed for studying rotational dynamics in macromolecules, we extend its applicability to MRI techniques for understanding diffusion in biological tissues.Through a series of proof-of-principle experiments, we validate our novel acquisition and analysis strategy across various samples. These experiments encompass the study of isotropic and anisotropic Gaussian diffusion in simple liquids, characterizing anisotropic Gaussian diffusion in a lyotropic liquid crystal with lamellar microstructure, and exploring restricted diffusion in a yeast cell sediment. Additionally, we showcase the effectiveness of our methods on ex vivo mouse brain and tumor tissue, highlighting the practical potential of our approach.Our proposed double-rotation gradient waveforms enable comprehensive sampling of both the frequency and "shape" dimensions of diffusion encoding, providing detailed insights into restriction and anisotropy in heterogeneous materials. The implications of our work extend to model-free investigations, allowing us to understand microstructural changes linked with pathology or normal brain development

Generation of realistic white matter substrates with controllable morphology for diffusion MRI simulations

Numerical phantoms have played a key role in the development of diffusion MRI (dMRI) techniques seeking to estimate features of the microscopic structure of tissue by providing a ground truth for simulation experiments against which we can validate and compare techniques. One common limitation of numerical phantoms which represent white matter (WM) is that they oversimplify the true complex morphology of the tissue which has been revealed through ex vivo studies. It is important to try to generate WM numerical phantoms that capture this realistic complexity in order to understand how it impacts the dMRI signal. This thesis presents work towards improving the realism of WM numerical phantoms by generating fibres mimicking natural fibre genesis. A novel phantom generator is presented which was developed over two works, resulting in Contextual Fibre Growth (ConFiG). ConFiG grows fibres one-by-one, following simple rules motivated by real axonal guidance mechanisms. These simple rules enable ConFiG to generate phantoms with tuneable microstructural features by growing fibres while attempting to meet morphological targets such as user-specified density and orientation distribution. We compare ConFiG to the state-of-the-art approach based on packing fibres together by generating phantoms in a range of fibre configurations including crossing fibre bundles and orientation dispersion. Results demonstrate that ConFiG produces phantoms with up to 20% higher densities than the state-of-the-art, particularly in complex configurations with crossing fibres. We additionally show that the microstructural morphology of ConFiG phantoms is comparable to real tissue, producing diameter and orientation distributions close to electron microscopy estimates from real tissue as well as capturing complex fibre cross sections. ConFiG is applied to investigate the intra-axonal diffusivity and probe assumptions in a family of dMRI modelling techniques based on spherical deconvolution (SD), demonstrating that the microscopic variations in fibres’ shapes affects the diffusion within axons. This leads to variations in the per-fibre signal contrary to the assumptions inherent in SD which may have a knock-on effect in popular techniques such as tractography

Configuration-based electrical properties tomography

Purpose: To introduce phase-based conductivity mapping from a configuration space analysis.Methods: The frequency response function of balanced SSFP (bSSFP) is used to perform a configuration space analysis. It is shown that the transceive phase for conductivity mapping can be directly obtained by a simple fast Fourier transform of a series of phase-cycled bSSFP scans. For validation, transceive phase and off-resonance mapping with fast Fourier transform is compared with phase estimation using a recently proposed method, termed PLANET. Experiments were performed in phantoms and for in vivo brain imaging at 3 T using a quadrature head coil.Results: For fast Fourier transform, aliasing can lead to systematic phase errors. This bias, however, decreases rapidly with increasing sampling points. Interestingly, Monte Carlo simulations revealed a lower uncertainty for the transceive phase and the off-resonance using fast Fourier transform as compared with PLANET. Both methods, however, essentially retrieve the same phase information from a set of phase-cycled bSSFP scans. As a result, configuration-based conductivity mapping was successfully performed using eight phase-cycled bSSFP scans in the phantoms and for brain tissues. Overall, the retrieved values were in good agreement with ex-pectations. Conductivity estimation and mapping of the field inhomogeneities can therefore be performed in conjunction with the estimation of other quantitative pa-rameters, such as relaxation, using configuration theory.Conclusions: Phase-based conductivity mapping can be estimated directly from a simple Fourier analysis, such as in conjunction with relaxometry, using a series of phase-cycled bSSFP scans

Virtual clinical trials in medical imaging: a review

The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities

Spherical Polar Fourier EAP and ODF Reconstruction via Compressed Sensing in Diffusion MRI

International audienceIn diffusion magnetic resonance imaging (dMRI), the Ensemble Average Propagator (EAP), also known as the propagator, describes completely the water molecule diffusion in the brain white matter without any prior knowledge about the tissue shape. In this paper, we describe a new and efficient method to accurately reconstruct the EAP in terms of the Spherical Polar Fourier (SPF) basis from very few diffusion weighted magnetic resonance images (DW-MRI). This approach nicely exploits the duality between SPF and a closely related basis in which one can respectively represent the EAP and the diffusion signal using the same coefficients, and efficiently combines it to the recent acquisition and reconstruction technique called Compressed Sensing (CS). Our work provides an efficient analytical solution to estimate, from few measurements, the diffusion propagator at any radius. We also provide a new analytical solution to extract an important feature characterising the tissue microstructure: the Orientation Distribution Function (ODF). We illustrate and prove the effectiveness of our method in reconstructing the propagator and the ODF on both noisy multiple q-shell synthetic and phantom data

The sensitivity of diffusion MRI to microstructural properties and experimental factors

Diffusion MRI is a non-invasive technique to study brain microstructure. Differences in the microstructural properties of tissue, including size and anisotropy, can be represented in the signal if the appropriate method of acquisition is used. However, to depict the underlying properties, special care must be taken when designing the acquisition protocol as any changes in the procedure might impact on quantitative measurements. This work reviews state-of-the-art methods for studying brain microstructure using diffusion MRI and their sensitivity to microstructural differences and various experimental factors. Microstructural properties of the tissue at a micrometer scale can be linked to the diffusion signal at a millimeter-scale using modeling. In this paper, we first give an introduction to diffusion MRI and different encoding schemes. Then, signal representation-based methods and multi-compartment models are explained briefly. The sensitivity of the diffusion MRI signal to the microstructural components and the effects of curvedness of axonal trajectories on the diffusion signal are reviewed. Factors that impact on the quality (accuracy and precision) of derived metrics are then reviewed, including the impact of random noise, and variations in the acquisition parameters (i.e., number of sampled signals, b-value and number of acquisition shells). Finally, yet importantly, typical approaches to deal with experimental factors are depicted, including unbiased measures and harmonization. We conclude the review with some future directions and recommendations on this topic