On the Real-Time Performance, Robustness and Accuracy of Medical Image Non-Rigid Registration

Three critical issues about medical image non-rigid registration are performance, robustness and accuracy. A registration method, which is capable of responding timely with an accurate alignment, robust against the variation of the image intensity and the missing data, is desirable for its clinical use. This work addresses all three of these issues. Unacceptable execution time of Non-rigid registration (NRR) often presents a major obstacle to its routine clinical use. We present a hybrid data partitioning method to parallelize a NRR method on a cooperative architecture, which enables us to get closer to the goal: accelerating using architecture rather than designing a parallel algorithm from scratch. to further accelerate the performance for the GPU part, a GPU optimization tool is provided to automatically optimize GPU execution configuration.;Missing data and variation of the intensity are two severe challenges for the robustness of the registration method. A novel point-based NRR method is presented to resolve mapping function (deformation field) with the point correspondence missing. The novelty of this method lies in incorporating a finite element biomechanical model into an Expectation and Maximization (EM) framework to resolve the correspondence and mapping function simultaneously. This method is extended to deal with the deformation induced by tumor resection, which imposes another challenge, i.e. incomplete intra-operative MRI. The registration is formulated as a three variable (Correspondence, Deformation Field, and Resection Region) functional minimization problem and resolved by a Nested Expectation and Maximization framework. The experimental results show the effectiveness of this method in correcting the deformation in the vicinity of the tumor. to deal with the variation of the intensity, two different methods are developed depending on the specific application. For the mono-modality registration on delayed enhanced cardiac MRI and cine MRI, a hybrid registration method is designed by unifying both intensity- and feature point-based metrics into one cost function. The experiment on the moving propagation of suspicious myocardial infarction shows effectiveness of this hybrid method. For the multi-modality registration on MRI and CT, a Mutual Information (MI)-based NRR is developed by modeling the underlying deformation as a Free-Form Deformation (FFD). MI is sensitive to the variation of the intensity due to equidistant bins. We overcome this disadvantage by designing a Top-to-Down K-means clustering method to naturally group similar intensities into one bin. The experiment shows this method can increase the accuracy of the MI-based registration.;In image registration, a finite element biomechanical model is usually employed to simulate the underlying movement of the soft tissue. We develop a multi-tissue mesh generation method to build a heterogeneous biomechanical model to realistically simulate the underlying movement of the brain. We focus on the following four critical mesh properties: tissue-dependent resolution, fidelity to tissue boundaries, smoothness of mesh surfaces, and element quality. Each mesh property can be controlled on a tissue level. The experiments on comparing the homogeneous model with the heterogeneous model demonstrate the effectiveness of the heterogeneous model in improving the registration accuracy

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

SEARCHING NEUROIMAGING BIOMARKERS IN MENTAL DISORDERS WITH GRAPH AND MULTIMODAL FUSION ANALYSIS OF FUNCTIONAL CONNECTIVITY

Mental disorders such as schizophrenia (SZ), bipolar (BD), and major depression disorders (MDD) can cause severe symptoms and life disruption. They share some symptoms, which can pose a major clinical challenge to their differentiation. Objective biomarkers based on neuroimaging may help to improve diagnostic accuracy and facilitate optimal treatment for patients. Over the last decades, non-invasive in-vivo neuroimaging techniques such as magnetic resonance imaging (MRI) have been increasingly applied to measure structure and function in human brains. With functional MRI (fMRI) or structural MRI (sMRI), studies have identified neurophysiological deficits in patients’ brain from different perspective. Functional connectivity (FC) analysis is an approach that measures functional integration in brains. By assessing the temporal coherence of the hemodynamic activity among brain regions, FC is considered capable of characterizing the large-scale integrity of neural activity. In this work, we present two data analysis frameworks for biomarker detection on brain imaging with FC, 1) graph analysis of FC and 2) multimodal fusion analysis, to better understand the human brain. Graph analysis reveals the interaction among brain regions based on graph theory, while the multimodal fusion framework enables us to utilize the strength of different imaging modalities through joint analysis. Four applications related to FC using these frameworks were developed. First, FC was estimated using a model-based approach, and revealed altered the small-world network structure in SZ. Secondly, we applied graph analysis on functional network connectivity (FNC) to differentiate BD and MDD during resting-state. Thirdly, two functional measures, FNC and fractional amplitude of low frequency fluctuations (fALFF), were spatially overlaid to compare the FC and spatial alterations in SZ. And finally, we utilized a multimodal fusion analysis framework, multi-set canonical correlation analysis + joint independent component analysis (mCCA+jICA) to link functional and structural abnormalities in BD and MDD. We also evaluated the accuracy of predictive diagnosis through classifiers generated on the selected features. In summary, via the two frameworks, our work has made several contributions to advance FC analysis, which improves our understanding of underlying brain function and structure, and our findings may be ultimately useful for the development of biomarkers of mental disease

What's new and what's next in diffusion MRI preprocessing

Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, bias fields, and spatial normalization. The focus will be on “what’s new” since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on “Mapping the Connectome” in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on “what’s next” in dMRI preprocessing

Neuroimaging of functional and structural alterations in Juvenile Myoclonic Epilepsy and Frontal Lobe Epilepsy

Epilepsy is the commonest neurological disorder and has profound effects on patients, who suffer from epileptic seizures and also from cognitive impairment. The exact mechanisms of cognitive impairment remain unclear. Aim of this study was to analyse in more detail the functional and structural alterations in two different patient groups, juvenile myoclonic epilepsy (JME) and frontal lobe epilepsy (FLE). We recruited and investigated 26 healthy controls, 30 patients with JME and 67 patients with FLE. All participants underwent magnetic resonance imaging (MRI), including structural imaging, five functional MRI paradigms and diffusion tensor imaging (DTI) as well as neuropsychological assessment. In patients with JME we could show motor cortex hyperactivity and an increased functional connectivity between the pre-frontal cognitive cortex and the motor system. This correlated with increased structural connectivity, measured by DTI and also with disease severity: patients with more active epilepsy showed a stronger hyperconnectivity. In FLE, we could show extensive reorganization of cognitive functions, and we could show, that functional MRI can be used as a new diagnostic method, to identify dysfunctional areas, indicative of the seizure onset zone. This is particularly important in patients with nonlesional FLE, where epilepsy surgery may be advisable but is challenged by the absence of a visible surgical target. The study has provided new insights into pathophysiological mechanisms in JME, specifically explaining the characteristic effect of motor seizures triggered by cognitive effort. It has contributed strong evidence that the observed imaging alterations are the cause and not a consequence of JME, by documenting marked structural changes in seizure free patients. For patients with FLE the study showed highly individual effects of chronic epilepsy on cognitive processing in the frontal lobe. These alterations are clinically relevant for both, avoiding complications from surgery, but also to identify pathological alterations not visible in conventional MRI

Robot-assisted fMRI assessment of early brain development

Preterm birth can interfere with the intra-uterine mechanisms driving cerebral development during the third trimester of gestation and often results in severe neuro-developmental impairments. Characterizing normal/abnormal patterns of early brain maturation could be fundamental in devising and guiding early therapeutic strategies aimed at improving clinical outcome by exploiting the enhanced early neuroplasticity. Over the last decade the morphology and structure of the developing human brain has been vastly characterized; however the concurrent maturation of brain function is still poorly understood. Task-dependent fMRI studies of the preterm brain can directly probe the emergence of fundamental neuroscientific notions and also provide clinicians with much needed early diagnostic and prognostic information. To date, task-fMRI studies of the preterm population have however been hampered by methodological challenges leading to inconsistent and contradictory results. In this thesis I present a modular and flexible system to provide clinicians and researchers with a simple and reliable solution to deliver computer-controlled stimulation patterns to preterm infants during task-fMRI experiments. The system is primarily aimed at studying the developing sensori-motor system as preterm infants are often affected by neuro-motor dysfunctions such as cerebral palsy. Wrist and ankle robotic stimulators were developed and firstly used to study the emerging somatosensory “homunculus”. The wrist robotic stimulator was then used to characterize the development of the sensori-motor system throughout the mid-to-late preterm period. An instrumented pacifier system was also developed to explore the potential sensorimotor modulation of early sucking activity; however, despite its clear potential to be employed in future fMRI studies, results have not yet been obtained on preterm infants. Functional difficulties associated with prematurity are likely to extend to multi-sensory integration, and the olfactory system currently remains under-investigated despite its clear developmental importance. A custom olfactometer was developed and used to assess its early functionality.Open Acces

Computing 3D Non-rigid Brain Registration Using Extended Robust Point Matching for Composite Multisubject fMRI Analysis

Abstract. In this paper we present extensions to the Robust Point Matching framework to improve its ability to handle larger point sets with greater computational efficiency. While in the past this methodology has only been used to register either two-dimensional or small synthetic three-dimensional data-sets we demonstrate its first successful application on large real 3D data-sets. We apply this methodology to the problem of forming composite activation maps from functional magnetic resonance images. In particular we demonstrate the superior performance of this algorithm to a pure intensity-based registration in the specific area of the fusiform gyrus. We also demonstrate that the robustness of this method can be useful in the case where part of the brain is missing as a result of incorrect image slice specification.