Quantitative susceptibility mapping and susceptibility-based distortion correction of echo planar images

Thesis (Ph. D. in Medical Engineering)--Harvard-MIT Program in Health Sciences and Technology, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 143-153).The field of medical image analysis continues to expand as magnetic resonance imaging (MRI) technology advances through increases in field strength and the development of new image acquisition and reconstruction methods. The advent of echo planar imaging (EPI) has allowed volumetric data sets to be obtained in a few seconds, making it possible to image dynamic physiological processes in the brain. In order to extract meaningful information from functional and diffusion data, clinicians and neuroscientists typically combine EPI data with high resolution structural images. Image registration is the process of determining the correct correspondence. Registration of EPI and structural images is difficult due to distortions in EPI data. These distortions are caused by magnetic field perturbations that arise from changes in magnetic susceptibility throughout the object of interest. Distortion is typically corrected by acquiring an additional scan called a fieldmap. A fieldmap provides a direct measure of the magnetic perturbations, allowing distortions to be easily computed and corrected. Fieldmaps, however, require additional scan time, may not be reliable in the presence of significant motion or respiration effects, and are often omitted from clinical protocols. In this thesis, we develop a novel method for correcting distortions in EPI data and registering the EPI to structural MRI. A synthetic fieldmap is computed from a tissue/air segmentation of a structural image using a perturbation method and subsequently used to unwarp the EPI data. Shim and other missing parameters are estimated by registration. We obtain results that are similar to those obtained using fieldmnaps, however, neither fieldmaps nor knowledge of shim coefficients is required. In addition, we describe a method for atlas-based segmentation of structural images for calculation of synthetic fieldmaps. CT data sets are used to construct a probabilistic atlas of the head and corresponding MRI is used to train a classifier that segments soft tissue, air, and bone. Synthetic fieldmap results agree well with acquired fieldmaps: 90% of voxel shifts show subvoxel disagreement with those computed from acquired fieldmaps. In addition, synthetic fieldmaps show statistically significant improvement following inclusion of the atlas. In the second part of this thesis, we focus on the inverse problem of reconstructing quantitative magnetic susceptibility maps from acquired fieldmaps. Iron deposits change the susceptibility of tissue, resulting in magnetic perturbations that are detectable with high resolution fieldmaps. Excessive iron deposition in specific regions of the brain is associated with neurodegenerative disorders such as Alzheimer's and Parkinson's disease. In addition, iron is known to accumulate at varying rates throughout the brain in normal aging. Developing a non-invasive method to calculate iron concentration may provide insight into the role of iron in the pathophysiology of neurodegenerative disease. Calculating susceptibility maps from measured fieldmaps is difficult, however, since iron-related field inhomogeneity may be obscured by larger field perturbations, or 'biasfields', arising from adjacent tissue/air boundaries. In addition, the inverse problem is ill-posed, and fieldmap measurements are only valid in limited anatomical regions. In this dissertation, we develop a novel atlas-based susceptibility mapping (ASM) technique that requires only a single fieldmap acquisition and successfully inverts a spatial formulation of the forward field model. We derive an inhomogeneous wave equation that relates the Laplacian of the observed field to the D'Alembertian of susceptibility, and eliminates confounding biasfields. The tissue/air atlas we constructed for susceptibility-based distortion correction is applied to resolve ambiquity in the forward model arising from the ill-posed inversion. We include fourier-based modeling of external susceptibility sources and the associated biasfield in a variational approach, allowing for simultaneous susceptibility estimation and biasfield elimination. Results show qualitative improvement over two methods commonly used to infer underlying susceptibility values and quantitative susceptibility estimates show stronger correlation with postmortem iron concentrations than competing methods.by Clare Poynton.Ph.D.in Medical Engineerin

Correction of spatial distortion in magnetic resonance imaging

Dissertation to Obtain the Degree of Master in Biomedical EngineeringMagnetic Resonance Imaging (MRI) has been a major investigation and research focus among scientific and medical communities. So, new hardware with superior magnetic fields and faster sequences has been developed. However, these improvements result in intensity and spatial distortions, particularly in fast sequences, as Echo Plana Imaging (EPI), used in functional and diffusion-weighed MRI (fMRI and DW-MRI). Therefore, correction of spatial distortion is useful to obtain a higher quality in this kind of images. This project contains two major parts. The first part consists in simulating MRI data required for assessing the performance of Registration methods and optimizing parameters. To assess the methods five evaluation metrics were calculated between the corrected data and an undistorted EPI, namely: Root Mean Square (RMS); Normalized Mutual Information (NMI), Squared Correlation Coefficient(SCC); Euclidean Distance of Centres of Mass (CM) and Dice Coefficient of segmented images. In brief, this part validates the applied Registration correction method. The project’s second part includes correction of real images, obtained at a Clinical Partner. Real images are diffusion weighted MRI data with different b-values (gradient strength coefficient), allowing performance assessment of different methods on images with increasing b-values and decreasing SNR. The methods tested on real data were Registration, Field Map correction and a new proposed pipeline, which consists in performing a Field Map correction after a registration process. To assess the accuracy of these methods on real data, we used the same evaluation metrics, as for simulated data, except RMS and Dice Coefficient. At the end, it was concluded that Registration-based methods are better than Field Map, and that the new proposed pipeline produces some improvements in the registration. Regarding the influence of b-value on the correction, it is important to say that the methods performed using images with higher b’s showed more improvements in regarding metric values, but the behaviour is similar for all b-values

Image processing methods for human brain connectivity analysis from in-vivo diffusion MRI

The structural connectivity of the brain is considered to encode species-wise and subject-wise patterns that will unlock large areas of understanding of the human brain. Currently, diffusion MRI of the living brain enables to map the microstructure of tissue, allowing to track the pathways of fiber bundles connecting the cortical regions across the brain. These bundles are summarized in a network representation called connectome that is analyzed using graph theory. The extraction of the connectome from diffusion MRI requires a large processing flow including image enhancement, reconstruction, segmentation, registration, diffusion tracking, etc. Although a concerted effort has been devoted to the definition of standard pipelines for the connectome extraction, it is still crucial to define quality assessment protocols of these workflows. The definition of quality control protocols is hindered by the complexity of the pipelines under test and the absolute lack of gold-standards for diffusion MRI data. Here we characterize the impact on structural connectivity workflows of the geometrical deformation typically shown by diffusion MRI data due to the inhomogeneity of magnetic susceptibility across the imaged object. We propose an evaluation framework to compare the existing methodologies to correct for these artifacts including whole-brain realistic phantoms. Additionally, we design and implement an image segmentation and registration method to avoid performing the correction task and to enable processing in the native space of diffusion data. We release PySDCev, an evaluation framework for the quality control of connectivity pipelines, specialized in the study of susceptibility-derived distortions. In this context, we propose Diffantom, a whole-brain phantom that provides a solution to the lack of gold-standard data. The three correction methodologies under comparison performed reasonably, and it is difficult to determine which method is more advisable. We demonstrate that susceptibility-derived correction is necessary to increase the sensitivity of connectivity pipelines, at the cost of specificity. Finally, with the registration and segmentation tool called regseg we demonstrate how the problem of susceptibility-derived distortion can be overcome allowing data to be used in their original coordinates. This is crucial to increase the sensitivity of the whole pipeline without any loss in specificity

Image processing methods for human brain connectivity analysis from in-vivo diffusion MRI

In this PhD Thesis proposal, the principles of diffusion MRI (dMRI) in its application to the human brain mapping of connectivity are reviewed. The background section covers the fundamentals of dMRI, with special focus on those related to the distortions caused by susceptibility inhomogeneity across tissues. Also, a deep survey of available correction methodologies for this common artifact of dMRI is presented. Two methodological approaches to improved correction are introduced. Finally, the PhD proposal describes its objectives, the research plan, and the necessary resources

Simulation-based evaluation of susceptibility distortion correction methods in diffusion MRI for connectivity analysis

Connectivity analysis on diffusion MRI data of the whole-brain suffers from distortions caused by the standard echo-planar imaging acquisition strategies. These images show characteristic geometrical deformations and signal destruction that are an important drawback limiting the success of tractography algorithms. Several retrospective correction techniques are readily available. In this work, we use a digital phantom designed for the evaluation of connectivity pipelines. We subject the phantom to a “theoretically correct” and plausible deformation that resembles the artifact under investigation. We correct data back, with three standard methodologies (namely fieldmap-based, reversed encoding-based, and registration- based). Finally, we rank the methods based on their geometrical accuracy, the dropout compensation, and their impact on the resulting connectivity matrices

The development and application of a simulation system for diffusion-weighted MRI

Diffusion-weighted MRI (DW-MRI) is a powerful, non-invasive imaging technique that allows us to infer the structure of biological tissue. It is particularly well suited to the brain, and is used by clinicians and researchers studying its structure in health and disease. High quality data is required to accurately characterise tissue structure with DW-MRI. Obtaining such data requires the careful optimisation of the image acquisition and processing pipeline, in order to maximise image quality and minimise artefacts. This thesis extends an existing MRI simulator to create a simulation system capable of producing realistic DW-MR data, with artefacts, and applies it to improve the acquisition and processing of such data. The simulator is applied in three main ways. Firstly, a novel framework for evaluating post-processing techniques is proposed and applied to assess commonly used strategies for the correction of motion, eddy-current and susceptibility artefacts. Secondly, it is used to explore the often overlooked susceptibility-movement interaction. It is demonstrated that this adversely impacts analysis of DW-MRI data, and a simple modification to the acquisition scheme is suggested to mitigate its impact. Finally, the simulation is applied to develop a new tool to perform automatic quality control. Simulated data is used to train a classifier to detect movement artefacts in data, with performance approaching that of a classifier trained on real data whilst requiring much less manually-labelled training data. It is hoped that both the findings in this thesis and the simulation tool itself will benefit the DW-MRI community. To this end, the tool is made freely available online to aid the development and validation of methods for acquiring and processing DW-MRI data

Towards efficient neurosurgery: Image analysis for interventional MRI

Interventional magnetic resonance imaging (iMRI) is being increasingly used for performing imageguided neurosurgical procedures. Intermittent imaging through iMRI can help a neurosurgeon visualise the target and eloquent brain areas during neurosurgery and lead to better patient outcome. MRI plays an important role in planning and performing neurosurgical procedures because it can provide highresolution anatomical images that can be used to discriminate between healthy and diseased tissue, as well as identify location and extent of functional areas. This is of significant clinical utility as it helps the surgeons maximise target resection and avoid damage to functionally important brain areas. There is clinical interest in propagating the pre-operative surgical information to the intra-operative image space as this allows the surgeons to utilise the pre-operatively generated surgical plans during surgery. The current state of the art neuronavigation systems achieve this by performing rigid registration of pre-operative and intra-operative images. As the brain undergoes non-linear deformations after craniotomy (brain shift), the rigidly registered pre-operative images do not accurately align anymore with the intra-operative images acquired during surgery. This limits the accuracy of these neuronavigation systems and hampers the surgeon’s ability to perform more aggressive interventions. In addition, intra-operative images are typically of lower quality with susceptibility artefacts inducing severe geometric and intensity distortions around areas of resection in echo planar MRI images, significantly reducing their utility in the intraoperative setting. This thesis focuses on development of novel methods for an image processing workflow that aims to maximise the utility of iMRI in neurosurgery. I present a fast, non-rigid registration algorithm that can leverage information from both structural and diffusion weighted MRI images to localise target lesions and a critical white matter tract, the optic radiation, during surgical management of temporal lobe epilepsy. A novel method for correcting susceptibility artefacts in echo planar MRI images is also developed, which combines fieldmap and image registration based correction techniques. The work developed in this thesis has been validated and successfully integrated into the surgical workflow at the National Hospital for Neurology and Neurosurgery in London and is being clinically used to inform surgical decisions

Investigating the specificity of the neurologic pain signature against breathlessness and finger opposition

Brain biomarkers of pain, including pain-predictive “signatures” based on brain activity, can provide measures of neurophysiological processes and potential targets for interventions. A central issue relates to the specificity of such measures, and understanding their current limits will both advance their development and explore potentially generalizable properties of pain to other states. Here, we used 2 data sets to test the neurologic pain signature (NPS), an established pain neuromarker. In study 1, brain activity was measured using high-field functional magnetic resonance imaging (7T fMRI, N = 40) during 5 to 25 seconds of experimental breathlessness (induced by inspiratory resistive loading), conditioned breathlessness anticipation, and finger opposition. In study 2, we assessed anticipation and breathlessness perception (3T, N = 19) under blinded saline (placebo) and remifentanil administration. The NPS responded to breathlessness, anticipation, and finger opposition, although no direct comparisons with painful events were possible. Local NPS patterns in anterior or midinsula, S2, and dorsal anterior cingulate responded to breathlessness and finger opposition and were reduced by remifentanil. Local NPS responses in the dorsal posterior insula did not respond to any manipulations. Therefore, significant global NPS activity alone is not specific for pain, and we offer insight into the overlap between NPS responses, breathlessness, and somatomotor demand