Learning task-optimal image registration with applications in localizing structure and function in the cerebral cortex

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 127-141).In medical image analysis, registration is necessary to establish spatial correspondences across two or more images. Registration is rarely the end-goal, but instead, the results of image registration are used in other tasks, such as voxel-based morphometry, functional group analysis, image segmentation and tracking. In this thesis, we argue that the quality of image registration should be evaluated in the context of the application. Consequently, we develop a framework for learning registration cost functions optimized for specific tasks. We demonstrate that by taking into account the application, we not only achieve better registration, but also potentially resolve certain ambiguities and ill-posed nature of image registration. We first develop a generative model for joint registration and segmentation of images. By jointly modeling registration and the application of image segmentation, we demonstrate improvements in parcellation of the cerebral cortex into different structural units. In this thesis, we work with spherical representations of the human cerebral cortex. Consequently, we develop a fast algorithm for registering spherical images. Application to the cortex shows that our algorithm achieves state-of-the-art accuracy, while being an order of magnitude faster than competing diffeomorphic, landmark-free algorithms. Finally, we consider the problem of automatically determining the "free" parameters of registration cost functions.(cont.) Registration is usually formulated as an optimization problem with multiple tunable parameters that are manually set. By introducing a second layer of optimization over and above the usual registration, this thesis provides the first effective approach to optimizing thousands of registration parameters to improve alignment of a new image as measured by an application-specific performance measure. Much previous work has been devoted to developing generic registration algorithms, which are then specialized to particular imaging modalities (e.g., MR), particular imaging targets (e.g., cardiac) and particular post- registration analyses (e.g., segmentation). Our framework provides a principled method for adapting generic algorithms to specific applications. For example, we estimate the optimal weights or cortical folding template of the generic weighted Sum of Squared Differences dissimilarity measure for localizing underlying cytoarchitecture and functional regions of the cerebral cortex. The generality of the framework suggests potential applications to other problems in science and engineering formulated as optimization problems.by B.T. Thomas Yeo.Ph.D

JOSA: Joint surface-based registration and atlas construction of brain geometry and function

Surface-based cortical registration is an important topic in medical image analysis and facilitates many downstream applications. Current approaches for cortical registration are mainly driven by geometric features, such as sulcal depth and curvature, and often assume that registration of folding patterns leads to alignment of brain function. However, functional variability of anatomically corresponding areas across subjects has been widely reported, particularly in higher-order cognitive areas. In this work, we present JOSA, a novel cortical registration framework that jointly models the mismatch between geometry and function while simultaneously learning an unbiased population-specific atlas. Using a semi-supervised training strategy, JOSA achieves superior registration performance in both geometry and function to the state-of-the-art methods but without requiring functional data at inference. This learning framework can be extended to any auxiliary data to guide spherical registration that is available during training but is difficult or impossible to obtain during inference, such as parcellations, architectonic identity, transcriptomic information, and molecular profiles. By recognizing the mismatch between geometry and function, JOSA provides new insights into the future development of registration methods using joint analysis of the brain structure and function.Comment: A. V. Dalca and B. Fischl are co-senior authors with equal contribution. arXiv admin note: text overlap with arXiv:2303.0159

Dynamic Thermal Imaging for Intraoperative Monitoring of Neuronal Activity and Cortical Perfusion

Neurosurgery is a demanding medical discipline that requires a complex interplay of several neuroimaging techniques. This allows structural as well as functional information to be recovered and then visualized to the surgeon. In the case of tumor resections this approach allows more fine-grained differentiation of healthy and pathological tissue which positively influences the postoperative outcome as well as the patient's quality of life. In this work, we will discuss several approaches to establish thermal imaging as a novel neuroimaging technique to primarily visualize neural activity and perfusion state in case of ischaemic stroke. Both applications require novel methods for data-preprocessing, visualization, pattern recognition as well as regression analysis of intraoperative thermal imaging. Online multimodal integration of preoperative and intraoperative data is accomplished by a 2D-3D image registration and image fusion framework with an average accuracy of 2.46 mm. In navigated surgeries, the proposed framework generally provides all necessary tools to project intraoperative 2D imaging data onto preoperative 3D volumetric datasets like 3D MR or CT imaging. Additionally, a fast machine learning framework for the recognition of cortical NaCl rinsings will be discussed throughout this thesis. Hereby, the standardized quantification of tissue perfusion by means of an approximated heating model can be achieved. Classifying the parameters of these models yields a map of connected areas, for which we have shown that these areas correlate with the demarcation caused by an ischaemic stroke segmented in postoperative CT datasets. Finally, a semiparametric regression model has been developed for intraoperative neural activity monitoring of the somatosensory cortex by somatosensory evoked potentials. These results were correlated with neural activity of optical imaging. We found that thermal imaging yields comparable results, yet doesn't share the limitations of optical imaging. In this thesis we would like to emphasize that thermal imaging depicts a novel and valid tool for both intraoperative functional and structural neuroimaging

REGISTRATION AND SEGMENTATION OF BRAIN MR IMAGES FROM ELDERLY INDIVIDUALS

Quantitative analysis of the MRI structural and functional images is a fundamental component in the assessment of brain anatomical abnormalities, in mapping functional activation onto human anatomy, in longitudinal evaluation of disease progression, and in computer-assisted neurosurgery or surgical planning. Image registration and segmentation is central in analyzing structural and functional MR brain images. However, due to increased variability in brain morphology and age-related atrophy, traditional methods for image registration and segmentation are not suitable for analyzing MR brain images from elderly individuals. The overall goal of this dissertation is to develop algorithms to improve the registration and segmentation accuracy in the geriatric population. The specific aims of this work includes 1) to implement a fully deformable registration pipeline to allow a higher degree of spatial deformation and produce more accurate deformation field, 2) to propose and validate an optimum template selection method for atlas-based segmentation, 3) to propose and validate a multi-template strategy for image normalization, which characterizes brain structural variations in the elderly, 4) to develop an automated segmentation and localization method to access white matter integrity (WMH) in the elderly population, and finally 5) to study the default-mode network (DMN) connectivity and white matter hyperintensity in late-life depression (LLD) with the developed registration and segmentation methods. Through a series of experiments, we have shown that the deformable registration pipeline and the template selection strategies lead to improved accuracy in the brain MR image registration and segmentation, and the automated WMH segmentation and localization method provides more specific and more accurate information about volume and spatial distribution of WMH than traditional visual grading methods. Using the developed methods, our clinical study provides evidence for altered DMN connectivity in LLD. The correlation between WMH volume and DMN connectivity emphasizes the role of vascular changes in LLD's etiopathogenesis

The Electrophysiology of Resting State fMRI Networks

Traditional research in neuroscience has studied the topography of specific brain functions largely by presenting stimuli or imposing tasks and measuring evoked brain activity. This paradigm has dominated neuroscience for 50 years. Recently, investigations of brain activity in the resting state, most frequently using functional magnetic resonance imaging (fMRI), have revealed spontaneous correlations within widely distributed brain regions known as resting state networks (RSNs). Variability in RSNs across individuals has found to systematically relate to numerous diseases as well as differences in cognitive performance within specific domains. However, the relationship between spontaneous fMRI activity and the underlying neurophysiology is not well understood. This thesis aims to combine invasive electrophysiology and resting state fMRI in human subjects to better understand the nature of spontaneous brain activity. First, we establish an approach to precisely coregister intra-cranial electrodes to fMRI data (Chapter 2). We then created a novel machine learning approach to define resting state networks in individual subjects (Chapter 3). This approach is validated with cortical stimulation in clinical electrocorticography (ECoG) patients (Chapter 4). Spontaneous ECoG data are then analyzed with respect to fMRI time-series and fMRI-defined RSNs in order to illustrate novel ECoG correlates of fMRI for both local field potentials and band-limited power (BLP) envelopes (Chapter 5). In Chapter 6, we show that the spectral specificity of these resting state ECoG correlates link classic brain rhythms with large-scale functional domains. Finally, in Chapter 7 we show that the frequencies and topographies of spontaneous ECoG correlations specifically recapitulate the spectral and spatial structure of task responses within individual subjects

Characterizing Tool-Selective Areas With Human Neuroimaging

Humans, unlike any other species, use tools to achieve complex goals. New Caledonian Crows, among the best of avian tool-makers, use twigs to retrieve food in crevices, and veined octopuses use coconut shells as shelters. Humans, however, go above and beyond these simple behaviours. Even when compared to orders that are evolutionarily closest to humans such as non-human primates, tool use is indisputably more advanced in humans. Conventionally, neuroimaging researchers who have studied complex tool use in humans do so by presenting pictures of tools and measuring the brain activity evoked by actions potentiated by the tools. This method has revealed tool-selective regions that activate in response to pictures of tools but, critically, also activate in response to real actions with real tools. Though there is overlap between regions that respond to both pictures of tools and to real tool use, it is unclear whether tool pictures are indeed an effective proxy for real tool use. In light of this, the overarching goals of this thesis were, 1) from a methodological perspective, to determine whether different proxies for studying tool use are more effective than using pictures but less technically challenging than using real actions on real tools; and 2) from a theoretical perspective, to determine what these proxies can reveal about tool-related processing, particularly in brain regions involved in visuomotor control. In sum, the results from this thesis revealed, 1) that presenting videos of familiar tool actions is an optimal proxy to study tool use, and 2) that tool-selective regions are areas selective for actions afforded by tools, for the characteristic motion associated with tools, and for familiar tools of which functional associations are well-established. Taken together, this thesis offers support to the notion that tool-selective regions process information with the purpose of predicting upcoming actions and reasoning possible ways to use a tool to interact with a target. In agreement with the affordance perspective, tool-selective regions do so even when there is no intent to act on a tool

Construction of embedded fMRI resting state functional connectivity networks using manifold learning

We construct embedded functional connectivity networks (FCN) from benchmark resting-state functional magnetic resonance imaging (rsfMRI) data acquired from patients with schizophrenia and healthy controls based on linear and nonlinear manifold learning algorithms, namely, Multidimensional Scaling (MDS), Isometric Feature Mapping (ISOMAP) and Diffusion Maps. Furthermore, based on key global graph-theoretical properties of the embedded FCN, we compare their classification potential using machine learning techniques. We also assess the performance of two metrics that are widely used for the construction of FCN from fMRI, namely the Euclidean distance and the lagged cross-correlation metric. We show that the FCN constructed with Diffusion Maps and the lagged cross-correlation metric outperform the other combinations