Academic literature on the topic 'Brain, Magnetic Resonance Imaging, Neuroscience, Diffusion-weighted MRI'

This dissertation describes the development and implementation of advanced slice-accelerated (SMS) MRI methods for imaging blood perfusion and water diffusion in the human brain. Since its introduction in 1977, Echo-Planar Imaging (EPI) paved the way toward a detailed assessment of the structural and functional properties of the human brain. Currently, EPI is one of the most important MRI techniques for neuroscientific studies and clinical applications. Despite its high prevalence in modern medical imaging, EPI still suffers from sub-optimal time efficiency - especially when high isotropic resolutions are required to adequately resolve sophisticated structures as the human brain. The utilization of novel slice-acceleration methods can help to overcome issues related to low temporal efficiency of EPI acquisitions. The aim of the four studies outlining this thesis is to overcome current limitations of EPI by developing methods for slice-accelerated MRI. The first experimental work of this thesis describes the development of a slice-accelerated MRI sequence for dynamic susceptibility contrast imaging. This method for assessing blood perfusion is commonly employed for brain tumor classifications in clinical practice. Following up, the second project of this thesis aims to extend SMS imaging to diffusion MRI at 7 Tesla. Here, a specialized acquisition method was developed employing various methods to overcome problems related to increased energy deposition and strong image distortion. The increased energy depositions for slice-accelerated diffusion MRI are due to specific radiofrequency (RF) excitation pulses. High energy depositions can limit the acquisition speed of SMS imaging, if high slice-acceleration factors are employed. Therefore, the third project of this thesis aimed at developing a specialized RF pulse to reduce the amount of energy deposition. The increased temporal efficiency of SMS imaging can be employed to acquire higher amounts of imaging data for signal averaging and more stable model fits. This is especially true for diffusion MRI measurements, which suffer from intrinsically low signal-to-noise ratios. However, the typically acquired magnitude MRI data introduce a noise bias in diffusion images with low signal-to-noise ratio. Therefore, the last project of this thesis aimed to resolve the pressing issue of noise bias in diffusion MRI. This was achieved by transforming the diffusion magnitude data into a real-valued data representation without noise bias. In combination, the developed methods enable rapid MRI measurements with high temporal efficiency. The diminished noise bias widens the scope of applications of slice- accelerated MRI with high temporal efficiency by enabling true signal averaging and unbiased model fits. Slice-accelerated imaging for the assessment of water diffusion and blood perfusion represents a major step in the field of neuroimaging. It demonstrates that cur- rent limitations regarding temporal efficiency of EPI can be overcome by utilizing modern data acquisition and reconstruction strategies.

Diffusion-weighted (DW) magnetic resonance imaging is an important neuroimaging technique that has successful applications in diagnosis of ischemic stroke and methods based on diffusion tensor imaging (DTI). Tensor measures have been used for detecting changes in tissue microstructure and for non-invasively tracing white matter connections in vivo. The most common image acquistion strategy is to use a DW single-shot echo-planar imaging (ss-EPI) pulse sequence, which is attractive due to its robustness to motion artefacts and high imaging speed. However, this sequence has limited achievable spatial resolution and suffers from geometric distortion and blurring artefacts. Readout-segmented echo-planar imaging (rs-EPI) is a DW sequence that is capable of acquiring high-resolution images by segmenting the acquisition of k- space into multiple shots. The fast, short readouts reduce distortion and blurring and the problem of artefacts due to motion-induced phase changes between shots can be overcome with navigator techniques. The rs-EPI sequence has two main shortcomings. (i) The method is slow to produce image volumes, which is limiting for clinical scans due to patient welfare and prevents us from acquiring very many directions in DTI. (ii) The sequence (like other diffusion techniques) is far from the optimum repetition time (TR) for acquiring data with the highest possible signal-to-noise ratio (SNR) in a given time. The work in this thesis seeks to address both of these important issues using a range of approaches. In Chapter 4 a partial Fourier extension is presented, which addresses point (i) by reducing the number of readout segments acquired and estimating the missing data. This allows reductions in scan time by approximately 40% and the reliability of the images is demonstrated in comparisons with the original images. The application of a simultaneous multi-slice scheme to rs-EPI, to address points (i) and (ii), is described in Chapter 5. Using the slice-accelerated rs-EPI sequence, tractography data were compared to ss-EPI data and high-resolution trace-weighted data were acquired in clinically relevant scan times. Finally, a 3D multi-slab extension that addresses point (i) is presented in Chapter 6. A 3D sequence could also allow higher resolution in the slice direction than 2D multi-slice methods, which are limited by the difficulties in exciting thin, accurate slices. A 3D version of rs-EPI was simulated and implemented and a k-space acquisition synchronised to the cardiac cycle showed substantial improvements in image artefacts compared to a conventional k-space acquisition.

Diffusion-weighted magnetic resonance imaging (MRI) has allowed unprecedented non-invasive mapping of brain neural connectivity in vivo by means of fiber tractography applications. Fiber tractography has emerged as a useful tool for mapping brain white matter connectivity prior to surgery or in an intraoperative setting. The advent of high angular resolution diffusion-weighted imaging (HARDI) techniques in MRI for fiber tractography has allowed mapping of fiber tracts in areas of complex white matter fiber crossings. Raw HARDI images, as a result of elevated diffusion-weighting, suffer from depressed signal-to-noise ratio (SNR) levels. The accuracy of fiber tractography is dependent on the performance of the various methods extracting dominant fiber orientations from the HARDI-measured noisy diffusivity profiles. These methods will be sensitive to and directly affected by the noise. In the first part of the thesis this issue is addressed by applying an objective and adaptive smoothing to the noisy HARDI data via generalized cross-validation (GCV) by means of the smoothing splines on the sphere method for estimating the smooth diffusivity profiles in three dimensional diffusion space. Subsequently, fiber orientation distribution functions (ODFs) that reveal dominant fiber orientations in fiber crossings are then reconstructed from the smoothed diffusivity profiles using the Funk-Radon transform. Previous ODF smoothing techniques have been subjective and non-adaptive to data SNR. The GCV-smoothed ODFs from our method are accurate and are smoothed without external intervention facilitating more precise fiber tractography. Diffusion-weighted MRI studies in amyotrophic lateral sclerosis (ALS) have revealed significant changes in diffusion parameters in ALS patient brains. With the need for early detection of possibly discrete upper motor neuron (UMN) degeneration signs in patients with early ALS, a HARDI study is applied in order to investigate diffusion-sensitive changes reflected in the diffusion tensor imaging (DTI) measures axial and radial diffusivity as well as the more commonly used measures fractional anisotropy (FA) and mean diffusivity (MD). The hypothesis is that there would be added utility in considering axial and radial diffusivities which directly reflect changes in the diffusion tensors in addition to FA and MD to aid in revealing neurodegenerative changes in ALS. In addition, applying adaptive smoothing via GCV to the HARDI data further facilitates the application of fiber tractography by automatically eliminating spurious noisy peaks in reconstructed ODFs that would mislead fiber tracking.

This work explores if combining information from multiple Magnetic Resonance Imaging (MRI) modalities provides improved interpretation of brain biological architecture as each MR modality can reveal different characteristics of underlying anatomical structures. Structural MRI provides a means for high-resolution quantitative study of brain morphometry. Diffusion-weighted MR imaging (DWI) allows for low-resolution modeling of diffusivity properties of water molecules. Structural and diffusion-weighted MRI modalities are commonly used for monitoring the biological architecture of the brain in normal development or neurodegenerative disease processes. Structural MRI provides an overall map of brain tissue organization that is useful for identifying distinct anatomical boundaries that define gross organization of the brain. DWI models provide a reflection of the micro-structure of white matter (WM), thereby providing insightful information for measuring localized tissue properties or for generating maps of brain connectivity. Multispectral information from different structural MR modalities can lead to better delineation of anatomical boundaries, but careful considerations should be taken to deal with increased partial volume effects (PVE) when input modalities are provided in different spatial resolutions. Interpretation of diffusion-weighted MRI is strongly limited by its relatively low spatial resolution. PVE's are an inherent consequence of the limited spatial resolution in low-resolution images like DWI. This work develops novel methods to enhance tissue classification by addressing challenges of partial volume effects encountered from multi-modal data that are provided in different spatial resolutions. Additionally, this project addresses PVE in low-resolution DWI scans by introducing a novel super-resolution reconstruction approach that uses prior information from multi-modal structural MR images provided in higher spatial resolution. The major contributions of this work include: 1) Enhancing multi-modal tissue classification by addressing increased PVE when multispectral information come from different spatial resolutions. A novel method was introduced to find pure spatial samples that are not affected by partial volume composition. Once detecting pure samples, we can safely integrate multi-modal information in training/initialization of the classifier for an enhanced segmentation quality. Our method operates in physical spatial domain and is not limited by the constraints of voxel lattice spaces of different input modalities. 2) Enhancing the spatial resolution of DWI scans by introducing a novel method for super-resolution reconstruction of diffusion-weighted imaging data using high biological-resolution information provided by structural MRI data such that the voxel values at tissue boundaries of the reconstructed DWI image will be in agreement with the actual anatomical definitions of morphological data. We used 2D phantom data and 3D simulated multi-modal MR scans for quantitative evaluation of introduced tissue classification approach. The phantom study result demonstrates that the segmentation error rate is reduced when training samples were selected only from the pure samples. Quantitative results using Dice index from 3D simulated MR scans proves that the multi-modal segmentation quality with low-resolution second modality can approach the accuracy of high-resolution multi-modal segmentation when pure samples are incorporated in the training of classifier. We used high-resolution DWI from Human Connectome Project (HCP) as a gold standard for super-resolution reconstruction evaluation to measure the effectiveness of our method to recover high-resolution extrapolations from low-resolution DWI data using three evaluation approaches consisting of brain tractography, rotationally invariant scalars and tensor properties. Our validation demonstrates a significant improvement in the performance of developed approach in providing accurate assessment of brain connectivity and recovering the high-resolution rotationally invariant scalars (RIS) and tensor property measurements when our approach was compared with two common methods in the literature. The novel methods of this work provide important improvements in tools that assist with improving interpretation of brain biological architecture. We demonstrate an increased sensitivity for volumetric and diffusion measures commonly used in clinical trials to advance our understanding of both normal development and disease induced degeneration. The improved sensitivity may lead to a substantial decrease in the necessary sample size required to demonstrate statistical significance and thereby may reduce the cost of future studies or may allow more clinical and observational trials to be performed in parallel.

Dopamine is an important neurotransmitter that is involved in several human functions such as reward, cognition, emotions and movement. Abnormalities of the neurotransmitter itself, or the dopamine receptors through which it exerts its actions, contribute to a wide range of psychiatric and neurological disorders such as Parkinson’s disease and schizophrenia. Thus far, despite the great interest and extensive research, the exact role of dopamine and the causalities of dopamine related disorders are not fully understood. Here we have developed multimodal imaging methods, to investigate the release of dopamine and the distribution of the dopamine D2-like receptor family in-vivo in healthy humans. We use the [¹¹C]PHNO PET ligand, which enables exploration of dopamine-related parameters in striatal regions, and for the first time in extrastriatal regions, that are known to be associated with distinctive functions and disorders. Our methods involve robust approaches for the manual and automated delineation of these brain regions, in terms of structural and functional organisation, using information from structural and diffusion MRI images. These data have been combined with [¹¹C]PHNO PET data for quantitative dopamine imaging. Our investigation has revealed the distribution and the relative density of the D3R and D2R sites of the dopamine D2-like receptor family, in healthy humans. In addition, we have demonstrated that the release of dopamine has a functional rather than a structural specificity and that the relative densities of the D3R and D2R sites do not drive this specificity. We have also shown that the dopamine D3R receptor is primarily distributed in regions that have a central role in reward and addiction. A finding that supports theories that assigns a primarily limbic role to the D3R.

Intact structural connectivity among brain regions is critical to cognition. Structural connectivity forms the substratum for information flow between brain regions, and its plasticity is a hallmark of learning in the brain. Moreover, structural connectivity markers constitute a heritable phenotype. Investigating neuroanatomical connectivity in the human brain is, therefore, critical not only for uncovering the neural underpinnings of behavior but also for understanding connectomic bases of neurodevelopmental and neurodegenerative disorders, such as autism and Alzheimer’s Disease. Diffusion magnetic resonance imaging (dMRI) and tractography are among the only techniques, at present, that enable estimation of anatomical connectivity in the human brain, in-vivo. By tracking the anisotropic diffusion of water molecules in white matter, dMRI and tractography enable post hoc reconstruction of contiguous fascicles between distal brain regions. How accurately can dMRI and tractography track these connections to match ground-truth in the brain? Are structural connections between specific pairs of brain regions informative about subjects’ cognitive capacities, like attention? Could changes in these connections indicate mechanisms of cognitive decline, both in healthy and pathologically aging populations? In this thesis, I report results from three studies, each of which addresses one of these key questions. In the first study, I explored how the midbrain contributes to attention, by combining model-based analysis of behavior with dMRI-tractography. Specifically, I examined the role of the superior colliculus (SC), a vertebrate midbrain structure, in attention. Does the SC control perceptual sensitivity to attended information, does it enable biasing choices toward attended information, or both? I mapped structural connections of the human SC with neocortical regions and found that the strengths of these connections correlated with, and were strongly predictive of, individuals’ choice bias, but not sensitivity. Taken together with previous studies, these results indicate that the human SC may play an evolutionarily conserved role in controlling choice bias during visual attention. In the second study, I developed a novel approach, implemented on GPUs, for pruning structural connectomes, at scale. First, I identified key limitations of a state-of-the-art connectome pruning technique, Linear Fascicle Evaluation (LiFE), and introduced a GPU-based implementation that achieves >100x speedups over conventional CPU-based implementations. Leveraging these speedups, I advanced LiFE’s algorithm by imposing a regularization constraint on estimated fiber weights. This regularized, accelerated, LiFE algorithm (“ReAl-LiFE”) estimates sparser connectomes that also provide more accurate fits to the underlying diffusion signal, and enables rapid and accurate connectome evaluation at scale. In the third study, I demonstrated several real-world applications of the ReAl-LiFE technique for analysis of large datasets. First, I showed that structural connectivity estimated with ReAl-LiFE predicts behavioral scores across a range of cognitive tasks in a cohort with 200 healthy human volunteers from the Human Connectome Project database. Moreover, ReAl-LiFE pruned connection weights provided a more reliable marker for structural connectivity strength than the number of fibers in the unpruned connectome. Second, ReAl-LiFE connection weights effectively predicted both chronological age, as well as age-related decline in cognitive factor scores in a cohort of 101 healthy, aged volunteers whose data were acquired as part of the Tata longitudinal study on aging at IISc. Finally, analyzing nearly 100 dMRI scans from the ADNI database, I showed that ReAl-LiFE outperformed competing approaches in terms of its accuracy with classifying patients with Alzheimer’s Dementia from healthy, age-matched controls, based on cortico-hippocampal connection weights. In summary, these findings show that diffusion MRI and tractography can serve as powerful tools for addressing key questions regarding brain-behavior relationships. In this thesis, I developed a technique to reliably estimate structural connectivity between distal brain regions, identified the role of subcortical structural connections in attention, and showed that cortical connectivity can be used to predict behavioral scores and cognitive decline. Broadly, these results will be relevant for understanding the connectomic basis of various cognitive processes, like attention, in healthy populations, and its dysfunction in diseased patients.