Probing white-matter microstructure with higher-order diffusion tensors and susceptibility tensor MRI.

Diffusion MRI has become an invaluable tool for studying white matter microstructure and brain connectivity. The emergence of quantitative susceptibility mapping and susceptibility tensor imaging (STI) has provided another unique tool for assessing the structure of white matter. In the highly ordered white matter structure, diffusion MRI measures hindered water mobility induced by various tissue and cell membranes, while susceptibility sensitizes to the molecular composition and axonal arrangement. Integrating these two methods may produce new insights into the complex physiology of white matter. In this study, we investigated the relationship between diffusion and magnetic susceptibility in the white matter. Experiments were conducted on phantoms and human brains in vivo. Diffusion properties were quantified with the diffusion tensor model and also with the higher order tensor model based on the cumulant expansion. Frequency shift and susceptibility tensor were measured with quantitative susceptibility mapping and susceptibility tensor imaging. These diffusion and susceptibility quantities were compared and correlated in regions of single fiber bundles and regions of multiple fiber orientations. Relationships were established with similarities and differences identified. It is believed that diffusion MRI and susceptibility MRI provide complementary information of the microstructure of white matter. Together, they allow a more complete assessment of healthy and diseased brains

Learning-based Single-step Quantitative Susceptibility Mapping Reconstruction Without Brain Extraction

Quantitative susceptibility mapping (QSM) estimates the underlying tissue magnetic susceptibility from MRI gradient-echo phase signal and typically requires several processing steps. These steps involve phase unwrapping, brain volume extraction, background phase removal and solving an ill-posed inverse problem. The resulting susceptibility map is known to suffer from inaccuracy near the edges of the brain tissues, in part due to imperfect brain extraction, edge erosion of the brain tissue and the lack of phase measurement outside the brain. This inaccuracy has thus hindered the application of QSM for measuring the susceptibility of tissues near the brain edges, e.g., quantifying cortical layers and generating superficial venography. To address these challenges, we propose a learning-based QSM reconstruction method that directly estimates the magnetic susceptibility from total phase images without the need for brain extraction and background phase removal, referred to as autoQSM. The neural network has a modified U-net structure and is trained using QSM maps computed by a two-step QSM method. 209 healthy subjects with ages ranging from 11 to 82 years were employed for patch-wise network training. The network was validated on data dissimilar to the training data, e.g. in vivo mouse brain data and brains with lesions, which suggests that the network has generalized and learned the underlying mathematical relationship between magnetic field perturbation and magnetic susceptibility. AutoQSM was able to recover magnetic susceptibility of anatomical structures near the edges of the brain including the veins covering the cortical surface, spinal cord and nerve tracts near the mouse brain boundaries. The advantages of high-quality maps, no need for brain volume extraction and high reconstruction speed demonstrate its potential for future applications.Comment: 26 page

Quantitative Susceptibility Mapping: Contrast Mechanisms and Clinical Applications.

Quantitative susceptibility mapping (QSM) is a recently developed MRI technique for quantifying the spatial distribution of magnetic susceptibility within biological tissues. It first uses the frequency shift in the MRI signal to map the magnetic field profile within the tissue. The resulting field map is then used to determine the spatial distribution of the underlying magnetic susceptibility by solving an inverse problem. The solution is achieved by deconvolving the field map with a dipole field, under the assumption that the magnetic field is a result of the superposition of the dipole fields generated by all voxels and that each voxel has its unique magnetic susceptibility. QSM provides improved contrast to noise ratio for certain tissues and structures compared to its magnitude counterpart. More importantly, magnetic susceptibility is a direct reflection of the molecular composition and cellular architecture of the tissue. Consequently, by quantifying magnetic susceptibility, QSM is becoming a quantitative imaging approach for characterizing normal and pathological tissue properties. This article reviews the mechanism generating susceptibility contrast within tissues and some associated applications

Intersubject Regularity in the Intrinsic Shape of Human V1

Previous studies have reported considerable intersubject variability in the three-dimensional geometry of the human primary visual cortex (V1). Here we demonstrate that much of this variability is due to extrinsic geometric features of the cortical folds, and that the intrinsic shape of V1 is similar across individuals. V1 was imaged in ten ex vivo human hemispheres using high-resolution (200 μm) structural magnetic resonance imaging at high field strength (7 T). Manual tracings of the stria of Gennari were used to construct a surface representation, which was computationally flattened into the plane with minimal metric distortion. The instrinsic shape of V1 was determined from the boundary of the planar representation of the stria. An ellipse provided a simple parametric shape model that was a good approximation to the boundary of flattened V1. The aspect ration of the best-fitting ellipse was found to be consistent across subject, with a mean of 1.85 and standard deviation of 0.12. Optimal rigid alignment of size-normalized V1 produced greater overlap than that achieved by previous studies using different registration methods. A shape analysis of published macaque data indicated that the intrinsic shape of macaque V1 is also stereotyped, and similar to the human V1 shape. Previoud measurements of the functional boundary of V1 in human and macaque are in close agreement with these results

Axon diameters and myelin content modulate microscopic fractional anisotropy at short diffusion times in fixed rat spinal cord

Mapping tissue microstructure accurately and noninvasively is one of the frontiers of biomedical imaging. Diffusion Magnetic Resonance Imaging (MRI) is at the forefront of such efforts, as it is capable of reporting on microscopic structures orders of magnitude smaller than the voxel size by probing restricted diffusion. Double Diffusion Encoding (DDE) and Double Oscillating Diffusion Encoding (DODE) in particular, are highly promising for their ability to report on microscopic fractional anisotropy ({\mu}FA), a measure of the pore anisotropy in its own eigenframe, irrespective of orientation distribution. However, the underlying correlates of {\mu}FA have insofar not been studied. Here, we extract {\mu}FA from DDE and DODE measurements at ultrahigh magnetic field of 16.4T in the aim to probe fixed rat spinal cord microstructure. We further endeavor to correlate {\mu}FA with Myelin Water Fraction (MWF) derived from multiexponential T2 relaxometry, as well as with literature-based spatially varying axonal diameters. In addition, a simple new method is presented for extracting unbiased {\mu}FA from three measurements at different b-values. Our findings reveal strong anticorrelations between {\mu}FA (derived from DODE) and axon diameter in the distinct spinal cord tracts; a moderate correlation was also observed between {\mu}FA derived from DODE and MWF. These findings suggest that axonal membranes strongly modulate {\mu}FA, which - owing to its robustness towards orientation dispersion effects - reflects axon diameter much better than its typical FA counterpart. The {\mu}FA exhibited modulations when measured via oscillating or blocked gradients, suggesting selective probing of different parallel path lengths and providing insight into how those modulate {\mu}FA metrics. Our findings thus shed light into the underlying microstructural correlates of {\mu}FA and are (...

Quantifying MRI frequency shifts due to structures with anisotropic magnetic susceptibility using pyrolytic graphite sheet

Magnetic susceptibility is an important source of contrast in magnetic resonance imaging (MRI), with spatial variations in the susceptibility of tissue affecting both the magnitude and phase of the measured signals. This contrast has generally been interpreted by assuming that tissues have isotropic magnetic susceptibility, but recent work has shown that the anisotropic magnetic susceptibility of ordered biological tissues, such as myelinated nerves and cardiac muscle fibers, gives rise to unexpected image contrast. This behavior occurs because the pattern of field variation generated by microstructural elements formed from material of anisotropic susceptibility can be very different from that predicted by modelling the effects in terms of isotropic susceptibility. In MR images of tissue, such elements are manifested at a sub-voxel length-scale, so the patterns of field variation that they generate cannot be directly visualized. Here, we used pyrolytic graphite sheet which has a large magnetic susceptibility anisotropy to form structures of known geometry with sizes large enough that the pattern of field variation could be mapped directly using MRI. This allowed direct validation of theoretical expressions describing the pattern of field variation from anisotropic structures with biologically relevant shapes (slabs, spherical shells and cylindrical shells)

Development of Methodologies for Diffusion-weighted Magnetic Resonance Imaging at High Field Strength

Diffusion-weighted imaging of small animals at high field strengths is a challenging prospect due to its extreme sensitivity to motion. Periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) was introduced at 9.4T as an imaging method that is robust to motion and distortion. Proton density (PD)-weighted and T2-weighted PROPELLER data were generally superior to that acquired with single-shot, Cartesian and echo planar imaging-based methods in terms of signal-to-noise ratio (SNR), contrast-to-noise ratio and resistance to artifacts. Simulations and experiments revealed that PROPELLER image quality was dependent on the field strength and echo times specified. In particular, PD-weighted imaging at high field led to artifacts that reduced image contrast. In PROPELLER, data are acquired in progressively rotated blades in k-space and combined on a Cartesian grid. PROPELLER with echo truncation at low spatial frequencies (PETALS) was conceived as a postprocessing method that improved contrast by reducing the overlap of k-space data from different blades with different echo times. Where the addition of diffusion weighting gradients typically leads to catastrophic motion artifacts in multi-shot sequences, diffusion-weighted PROPELLER enabled the acquisition of high quality, motion-robust data. Applications in the healthy mouse brain and abdomen at 9.4T and in stroke patients at 3T are presented. PROPELLER increases the minimum scan time by approximately 50%. Consequently, methods were explored to reduce the acquisition time. Two k-space undersampling regimes were investigated by examining image fidelity as a function of degree of undersampling. Undersampling by acquiring fewer k-space blades was shown to be more robust to motion and artifacts than undersampling by expanding the distance between successive phase encoding steps. To improve the consistency of undersampled data, the non-uniform fast Fourier transform was employed. It was found that acceleration factors of up to two could be used with minimal visual impact on image fidelity. To reduce the number of scans required for isotropic diffusion weighting, the use of rotating diffusion gradients was investigated, exploiting the rotational symmetry of the PROPELLER acquisition. Fixing the diffusion weighting direction to the individual rotating blades yielded geometry and anisotropy-dependent diffusion measurements. However, alternating the orientations of diffusion weighting with successive blades led to more accurate measurements of the apparent diffusion coefficient while halving the overall acquisition time. Optimized strategies are proposed for the use of PROPELLER in rapid high resolution imaging at high field strength

Advances in diffusion MRI acquisition and processing in the Human Connectome Project

The Human Connectome Project (HCP) is a collaborative 5-year effort to map human brain connections and their variability in healthy adults. A consortium of HCP investigators will study a population of 1200 healthy adults using multiple imaging modalities, along with extensive behavioral and genetic data. In this overview, we focus on diffusion MRI (dMRI) and the structural connectivity aspect of the project. We present recent advances in acquisition and processing that allow us to obtain very high-quality in-vivo MRI data, whilst enabling scanning of a very large number of subjects. These advances result from 2 years of intensive efforts in optimising many aspects of data acquisition and processing during the piloting phase of the project. The data quality and methods described here are representative of the datasets and processing pipelines that will be made freely available to the community at quarterly intervals, beginning in 2013

Quantitative Assessment Of Magnetic Properties Within White Matter Fibers Using Mri

Quantitative magnetic resonance imaging techniques such as quantitative susceptibility mapping (QSM) have great potential to provide tissue specific and longitudinally applicable biomarkers of disease. Such markers can potentially aid in not only the study of disease progression, but may also one day help inform the treatment of disease. Quantitative magnetic susceptibility mapping (QSM) is a nontrivial and powerful technique that directly measures the magnetization of tissue. The magnetization of tissue in the body is directly related to its composition and structure. The challenge in this work is that both the content and organization of tissue result in orientation dependent relaxation and magnetization, the complexity of which provides an opportunity to assess tissue microstructure and content from magnetic resonance field measurements. In this work, we will evaluate the reconstruction of magnetic susceptibility and relaxation tensors from MR field measurements and assess the contribution of major tissue components, such as iron and myelin, to measured tensor properties. This builds the theoretical and experimental ground work in translating measurements of tissue magnetization for applications in vivo

Noise and error propagation in diffusion tensor imaging

Diffusion Tensor Imaging (DTI) is the in vivo visualization and analysis of white matter fiber tracts by measuring the anisotropy of water molecule diffusion in the brain tissue. DTI has been increasingly used in clinical imaging. Diffusion weighted images are affected by noise from the human subject and the MRI scanner. This thesis studies the error propagation in the calculation of the DTI invariant anisotropy, mainly the Fractional Anisotropy (FA) using four methods and their comparison in terms of error, filtering and computational efficiency using simulated and human brain data. These methods were Diffusion Tensor, Diffusion Ellipsoid, Hasan and Platonic Variance. The results showed similar trends across the simulated and real data sets. Of the four methods used to calculate FA, the Hasan method without diffusion tensor yielded best computational efficiency, but poor noise robustness, whereas the Platonic Variance method was more robust to noise and provided good computational efficiency