Landmark Localization, Feature Matching and Biomarker Discovery from Magnetic Resonance Images

The work presented in this thesis proposes several methods that can be roughly divided into three different categories: I) landmark localization in medical images, II) feature matching for image registration, and III) biomarker discovery in neuroimaging. The first part deals with the identification of anatomical landmarks. The motivation stems from the fact that the manual identification and labeling of these landmarks is very time consuming and prone to observer errors, especially when large datasets must be analyzed. In this thesis we present three methods to tackle this challenge: A landmark descriptor based on local self-similarities (SS), a subspace building framework based on manifold learning and a sparse coding landmark descriptor based on data-specific learned dictionary basis. The second part of this thesis deals with finding matching features between a pair of images. These matches can be used to perform a registration between them. Registration is a powerful tool that allows mapping images in a common space in order to aid in their analysis. Accurate registration can be challenging to achieve using intensity based registration algorithms. Here, a framework is proposed for learning correspondences in pairs of images by matching SS features and random sample and consensus (RANSAC) is employed as a robust model estimator to learn a deformation model based on feature matches. Finally, the third part of the thesis deals with biomarker discovery using machine learning. In this section a framework for feature extraction from learned low-dimensional subspaces that represent inter-subject variability is proposed. The manifold subspace is built using data-driven regions of interest (ROI). These regions are learned via sparse regression, with stability selection. Also, probabilistic distribution models for different stages in the disease trajectory are estimated for different class populations in the low-dimensional manifold and used to construct a probabilistic scoring function.Open Acces

Myelin water fraction estimation using small- tip fast recovery MRI

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155960/1/mrm28259.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155960/2/mrm28259-sup-0001-Supinfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155960/3/mrm28259_am.pd

Advances in Quantitative MRI: Acquisition, Estimation, and Application

Quantitative magnetic resonance imaging (QMRI) produces images of potential MR biomarkers: measurable tissue properties related to physiological processes that characterize the onset and progression of specific disorders. Though QMRI has potential to be more diagnostic than conventional qualitative MRI, QMRI poses challenges beyond those of conventional MRI that limit its feasibility for routine clinical use. This thesis first seeks to address two of those challenges. It then applies these solutions to develop a new method for myelin water imaging, a challenging application that may be specifically indicative of certain white matter (WM) disorders. One challenge that presently precludes widespread clinical adoption of QMRI involves long scan durations: to disentangle potential biomarkers from nuisance MR contrast mechanisms, QMRI typically requires more data than conventional MRI and thus longer scans. Even allowing for long scans, it has previously been unclear how to systematically tune the "knobs" of MR acquisitions to reliably enable precise biomarker estimation. Chapter 4 formalizes these challenges as a min-max optimal acquisition design problem and solves this problem to design three fast steady-state (SS) acquisitions for precise T1/T2 estimation, a popular QMRI application. The resulting optimized acquisition designs illustrate that acquisition design can enable new biomarker estimation techniques from established MR pulse sequences, a fact that subsequent chapters exploit. Another QMRI challenge involves the typically nonlinear dependence of MR signal models on the underlying biomarkers: these nonlinearities cause conventional likelihood-based estimators to either scale very poorly with the number of unknowns or risk producing suboptimal estimates due to spurious local minima. Chapter 5 instead introduces a fast, general method for dictionary-free QMRI parameter estimation via regression with kernels (PERK). PERK first uses prior distributions and the nonlinear MR signal model to simulate many parameter-measurement pairs. Inspired by machine learning, PERK then takes these pairs as labeled training points and learns from them a nonlinear regression function using kernel functions and convex optimization. Chapter 5 demonstrates PERK for T1/T2 estimation using one of the acquisitions optimized in Chapter 4. Simulations as well as single-slice phantom and in vivo experiments demonstrated that PERK and two well-suited maximum-likelihood (ML) estimators produce comparable T1/T2 estimates, but PERK is consistently at least 140x faster. Similar comparisons to an ML estimator in a more challenging problem (Chapter 6) suggest that this 140x acceleration factor will increase by several orders of magnitude for full-volume QMRI estimation problems involving more latent parameters per voxel. Chapter 6 applies ideas developed in previous chapters to design a new fast method for imaging myelin water content, a potential biomarker for healthy myelin. It first develops a two-compartment dual-echo steady-state (DESS) signal model and then uses a Bayesian variation of acquisition design (Chapter 4) to optimize a new DESS acquisition for precise myelin water imaging. The precision-optimized acquisition is as fast as conventional SS myelin water imaging acquisitions, but enables 2-3x better expected coefficients of variation in fast-relaxing fraction estimates. Simulations demonstrate that PERK (Chapter 5) and ML fast-relaxing fraction estimates from the proposed DESS acquisition exhibit comparable root mean-squared errors, but PERK is more than 500x faster. In vivo experiments are to our knowledge the first to demonstrate lateral WM myelin water content estimates from a fast (3m15s) SS acquisition that are similar to conventional estimates from a slower (32m4s) MESE acquisition.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147486/1/gnataraj_1.pd

Advancing Statistical Inference For Population Studies In Neuroimaging Using Machine Learning

Modern neuroimaging techniques allow us to investigate the brain in vivo and in high resolution, providing us with high dimensional information regarding the structure and the function of the brain in health and disease. Statistical analysis techniques transform this rich imaging information into accessible and interpretable knowledge that can be used for investigative as well as diagnostic and prognostic purposes. A prevalent area of research in neuroimaging is group comparison, i.e., the comparison of the imaging data of two groups (e.g. patients vs. healthy controls or people who respond to treatment vs. people who don\u27t) to identify discriminative imaging patterns that characterize different conditions. In recent years, the neuroimaging community has adopted techniques from mathematics, statistics, and machine learning to introduce novel methodologies targeting the improvement of our understanding of various neuropsychiatric and neurodegenerative disorders. However, existing statistical methods are limited by their reliance on ad-hoc assumptions regarding the homogeneity of disease effect, spatial properties of the underlying signal and the covariate structure of data, which imposes certain constraints about the sampling of datasets. 1. First, the overarching assumption behind most analytical tools, which are commonly used in neuroimaging studies, is that there is a single disease effect that differentiates the patients from controls. In reality, however, the disease effect may be heterogeneously expressed across the patient population. As a consequence, when searching for a single imaging pattern that characterizes the difference between healthy controls and patients, we may only get a partial or incomplete picture of the disease effect. 2. Second, and importantly, most analyses assume a uniform shape and size of disease effect. As a consequence, a common step in most neuroimaging analyses it to apply uniform smoothing of the data to aggregate regional information to each voxel to improve the signal to noise ratio. However, the shape and size of the disease patterns may not be uniformly represented across the brain. 3. Lastly, in practical scenarios, imaging datasets commonly include variations due to multiple covariates, which often have effects that overlap with the searched disease effects. To minimize the covariate effects, studies are carefully designed by appropriately matching the populations under observation. The difficulty of this task is further exacerbated by the advent of big data analyses that often entail the aggregation of large datasets collected across many clinical sites. The goal of this thesis is to address each of the aforementioned assumptions and limitations by introducing robust mathematical formulations, which are founded on multivariate machine learning techniques that integrate discriminative and generative approaches. Specifically, 1. First, we introduce an algorithm termed HYDRA which stands for heterogeneity through discriminative analysis. This method parses the heterogeneity in neuroimaging studies by simultaneously performing clustering and classification by use of piecewise linear decision boundaries. 2. Second, we propose to perform regionally linear multivariate discriminative statistical mapping (MIDAS) toward finding the optimal level of variable smoothing across the brain anatomy and tease out group differences in neuroimaging datasets. This method makes use of overlapping regional discriminative filters to approximate a matched filter that best delineates the underlying disease effect. 3. Lastly, we develop a method termed generative discriminative machines (GDM) toward reducing the effect of confounds in biased samples. The proposed method solves for a discriminative model that can also optimally generate the data when taking into account the covariate structure. We extensively validated the performance of the developed frameworks in the presence of diverse types of simulated scenarios. Furthermore, we applied our methods on a large number of clinical datasets that included structural and functional neuroimaging data as well as genetic data. Specifically, HYDRA was used for identifying distinct subtypes of Alzheimer\u27s Disease. MIDAS was applied for identifying the optimally discriminative patterns that differentiated between truth-telling and lying functional tasks. GDM was applied on a multi-site prediction setting with severely confounded samples. Our promising results demonstrate the potential of our methods to advance neuroimaging analysis beyond the set of assumptions that limit its capacity and improve statistical power

Dictionary-Free MR Fingerprinting Parameter Estimation Via Inverse Regression

International audienceMR Fingerprint requires an exhaustive search in a dictionary, which even for moderately sized problems, becomes costly and possibly intractable. In this work, we propose an alternative approach: instead of an exhaustive search for every signal, we use the dictionary to learn the functional relationship between signals and parameters. This allows the direct estimation of parameters without the need of searching through the dictionary. The comparison between a standard grid search and the proposed approach suggest that MR Fingerprinting could benefit from a regression approach to limit dictionary size and fasten computation time

Transfer learning of deep neural network representations for fMRI decoding

Background: Deep neural networks have revolutionised machine learning, with unparalleled performance in object classification. However, in brain imaging (e.g., fMRI), the direct application of Convolutional Neural Networks (CNN) to decoding subject states or perception from imaging data seems impractical given the scarcity of available data. New method: In this work we propose a robust method to transfer information from deep learning (DL) features to brain fMRI data with the goal of decoding. By adopting Reduced Rank Regression with Ridge Regularisation we establish a multivariate link between imaging data and the fully connected layer (fc7) of a CNN. We exploit the reconstructed fc7 features by performing an object image classification task on two datasets: one of the largest fMRI databases, taken from different scanners from more than two hundred subjects watching different movie clips, and another with fMRI data taken while watching static images. Results: The fc7 features could be significantly reconstructed from the imaging data, and led to significant decoding performance. Comparison with existing methods: The decoding based on reconstructed fc7 outperformed the decoding based on imaging data alone. Conclusion: In this work we show how to improve fMRI-based decoding benefiting from the mapping between functional data and CNN features. The potential advantage of the proposed method is twofold: the extraction of stimuli representations by means of an automatic procedure (unsupervised) and the embedding of high-dimensional neuroimaging data onto a space designed for visual object discrimination, leading to a more manageable space from dimensionality point of view