269 research outputs found

    Multimodal MRI analysis using deep learning methods

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    Magnetic resonance imaging (MRI) has been widely used in scientific and clinical research. It is a non-invasive medical imaging technique that reveals anatomical structures and provides useful information for investigators to explore aging and pathological processes. Different MR modalities offer different useful properties. Automatic MRI analysis algorithms have been developed to address problems in many applications such as classification, segmentation, and disease diagnosis. Segmentation and labeling algorithms applied to brain MRIs enable evaluations of the volumetric changes of specific structures in neurodegenerative diseases. Reconstruction of fiber orientations using diffusion MRI is beneficial to obtain better understanding of the underlying structures. In this thesis, we focused on development of deep learning methods for MRI analysis using different image modalities. Specifically, we applied deep learning techniques on different applications, including segmentation of brain structures and reconstruction of tongue muscle fiber orientations. For segmentation of brain structures, we developed an end-to-end deep learning algorithm for ventricle parcellation of brains with ventriculomegaly using T1-w MR images. The deep network provides robust and accurate segmentation results in subjects with high variability in ventricle shapes and sizes. We developed another deep learning method to automatically parcellate the thalamus into a set of thalamic nuclei using T1-w MRI and features from diffusion MRI. The algorithm incorporates a harmonization step to make the network adapt to input images with different contrasts. We also studied the strains associated with tongue muscles during speech production using multiple MRI modalities. To enable this study, we first developed a deep network to reconstruct crossing tongue muscle fiber orientations using diffusion MRI. The network was specifically designed for the human tongue and accounted for the orthogonality property of the tongue muscles. Next, we proposed a comprehensive pipeline to analyze the strains associated with tongue muscle fiber orientations during speech using diffusion MRI, and tagged and cine MRI. The proposed pipeline provides a solution to analyze the cooperation between muscle groups during speech production

    Recommended Implementation of Quantitative Susceptibility Mapping for Clinical Research in The Brain: A Consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group

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    This article provides recommendations for implementing quantitative susceptibility mapping (QSM) for clinical brain research. It is a consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available give rise to the need in the neuroimaging community for guidelines on implementation. This article describes relevant considerations and provides specific implementation recommendations for all steps in QSM data acquisition, processing, analysis, and presentation in scientific publications. We recommend that data be acquired using a monopolar 3D multi-echo GRE sequence, that phase images be saved and exported in DICOM format and unwrapped using an exact unwrapping approach. Multi-echo images should be combined before background removal, and a brain mask created using a brain extraction tool with the incorporation of phase-quality-based masking. Background fields should be removed within the brain mask using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity-based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of whole brain as a region of interest in the analysis, and QSM results should be reported with - as a minimum - the acquisition and processing specifications listed in the last section of the article. These recommendations should facilitate clinical QSM research and lead to increased harmonization in data acquisition, analysis, and reporting

    The impact of head orientation with respect to B0 on diffusion tensor MRI measures

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    Diffusion tensor MRI (DT-MRI) remains the most commonly used approach to characterise white matter (WM) anisotropy. However, DT estimates may be affected by tissue orientation w.r.t. B→0 due to local gradients and intrinsic T2 orientation dependence induced by the microstructure. This work aimed to investigate whether and how diffusion tensor MRI-derived measures depend on the orientation of the head with respect to the static magnetic field, B→0 ⁠. By simulating WM as two compartments, we demonstrated that compartmental T2 anisotropy can induce the dependence of diffusion tensor measures on the angle between WM fibres and the magnetic field. In in vivo experiments, reduced radial diffusivity and increased axial diffusivity were observed in white matter fibres perpendicular to B→0 compared to those parallel to B→0 ⁠. Fractional anisotropy varied by up to 20% as a function of the angle between WM fibres and the orientation of the main magnetic field. To conclude, fibre orientation w.r.t. B→0 is responsible for up to 7% variance in diffusion tensor measures across the whole brain white matter from all subjects and head orientations. Fibre orientation w.r.t. B→0 may introduce additional variance in clinical research studies using diffusion tensor imaging, particularly when it is difficult to control for (e.g. fetal or neonatal imaging, or when the trajectories of fibres change due to e.g. space occupying lesions)

    On harmonisation of brain MRI data across scanners and sites

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    Magnetic resonance imaging (MRI) of the brain has revolutionised neuroscience by opening unique opportunities for studying unknown aspects of brain organisation, function and pathology-induced dysfunction. Despite the huge potential, MRI measures can be limited in their consistency, reproducibility and accuracy which subsequently restricts their quantifiability. Nuisance non-biological factors, such as hardware, software, calibration differences between scanners and post-processing options can contribute or drive trends in neuroimaging features to an extent that interferes with biological variability and obstructs scientific explorations and clinical applications. Such lack of consistency, or harmonisation across neuroimaging datasets poses a great challenge for our capabilities in quantitative MRI. This thesis contributes to better understanding and addressing it. We specifically build a new resource for comprehensively mapping the extent of the problem and objectively evaluating neuroimaging harmonisation approaches. We use a travelling heads paradigm consisting of multimodal MRI data of 10 travelling subjects, each scanned at 5 different sites on 6 different 3.0T scanners from all the 3 major vendors and using 5 imaging modalities. We use this dataset to explore the between-scanner variability of hundreds of imaging-extracted features and compare these to within-scanner (within-subject) variability and biological (between-subject) variability. We identify subsets of features that are/are not reliable across scanners and use our resource as a testbed to enable new investigations which until now have been relatively unexplored. Specifically, we identify optimal pipeline processing steps that minimise between-scanner variability in extracted features (implicit harmonisation). We also test the performance of post-processing harmonisation tools (explicit harmonisation) and specifically check their efficiency in reducing between-scanner variability against baseline gold standards provided by our data. Our explorations allow us to come up with good practice suggestions on processing steps and sets of features where results are more consistent and reproducible and also set references for future studies in this field

    Validation of Deep Learning techniques for quality augmentation in diffusion MRI for clinical studies

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    The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise

    Diffusion-tensor MRI methods to study and evaluate muscle architecture

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    The thesis describes the development of various approaches for measuring muscle architectural parameters using Diffusion Tensor MR Imaging (DTI). It also illustrates how to apply them to study changes in muscle architecture after an injury prevention program.In Chapter 2, because manual segmentation of muscles is cumbersome, we validated a semi-automatic framework for estimating DTI indices in upper leg muscles. This method reduced segmentation time by a factor of three in a cross-sectional study design and can be used fully automatically in a longitudinal assessment of changes in DTI indices.Chapter 3 was a feasibility study measuring fiber orientation changes with DTI in calf muscles and sub-compartments of the Soleus and Tibialis Anterior during plantarflexion and dorsiflexion. Differences in fiber orientations corresponded to the known agonist-antagonist function of the muscles. This shows that DTI can be utilized to assess changes in muscle orientation due to posture or training.In Chapter 4, we compared DTI fiber tractography for Vastus Lateralis fiber architecture assessment with 3D ultrasonography (3D-US). We discovered that both methods have their advantages and disadvantages, with the agreement between the two techniques being moderate.Finally, in Chapter 5, we examined the effects of a hamstring injury prevention exercise on the muscle architectural parameters of basketball players. DTI was employed to quantify changes in fiber orientation and length using tractography and fiber orientation maps. It was observed that the Semitendinosus fascicle length increased after the Nordics exercise, while the Biceps Femoris long head fiber orientation decreased following the Divers intervention

    Computational experimental design for quantitative MRI

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    This thesis presents contributions to the field of quantitative MRI (qMRI) computational experimental design (CED). qMRI experiments are constructed from experimental ‘building blocks’ (e.g. acquisition protocol, model selection, parameter estimation) which, when combined, map tissue properties to quantitative biomarkers. Each of these blocks presents experimental choices: which acquisition protocol, which model, which parameter estimation method. Together, these choices form experimental designs. CED is the in-silico process by which such designs are tailored to suit specific imaging applications. This work addresses three limitations with current CED practices. The first is that they are too narrow in their scope: they are unduly focused on acquisition protocol. qMRI is underpinned by model fitting, which relies on an appropriate choice of signal model and fitting method. This choice cannot be taken for granted: model fitting both depends on and influences the quality of the acquired data. This work argues that CED should not focus on acquisition protocol alone, but rather consider all experimental components in an end-to-end, holistic manner. The second limitation relates to the experimental evaluation metrics currently used in CED. Experiments are assessed on their ability to generate close-to-groundtruth biomarker estimates, rather than on these estimates’ ability to solve real-world tasks (e.g. tissue classification); there is a disconnect between evaluation and application. This work address this by proposing a CED method which assesses experiments on their task performance, and validates its assessments on two clinical datasets. The final limitation relates to the parameter estimation methods available to CED. Existing methods are task-agnostic; they cannot be tailored to the needs of a specific qMRI experiment. This work takes advantage of machine learning techniques to, for the first time, make this possible: by changing training labels, parameter estimation performance is shown to be adjusted in a task-specific manner

    Cultivate Quantitative Magnetic Resonance Imaging Methods to Measure Markers of Health and Translate to Large Scale Cohort Studies

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    Magnetic Resonance Imaging (MRI) is an indispensable tool in healthcare and research, with a growing demand for its services. The appeal of MRI stems from its non-ionizing radiation nature, ability to generate high-resolution images of internal organs and structures without invasive procedures, and capacity to provide quantitative assessments of tissue properties such as ectopic fat, body composition, and organ volume. All without long term side effects. Nine published papers are submitted which show the cultivation of quantitative measures of ectopic fat within the liver and pancreas using MRI, and the process of validating whole-body composition and organ volume measurements. All these techniques have been translated into large-scale studies to improve health measurements in large population cohorts. Translating this work into large-scale studies, including the use of artificial intelligence, is included. Additionally, an evaluation accompanies these published studies, appraising the evolution of these quantitative MRI techniques from the conception to their application in large cohort studies. Finally, this appraisal provides a summary of future work on crowdsourcing of ground truth training data to facilitate its use in wider applications of artificial intelligence.In conclusion, this body of work presents a portfolio of evidence to fulfil the requirements of a PhD by published works at the University of Salford

    Fiber-orientation independent component of R2* obtained from single-orientation MRI measurements in simulations and a post-mortem human optic chiasm

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    The effective transverse relaxation rate (R2*) is sensitive to the microstructure of the human brain like the g-ratio which characterises the relative myelination of axons. However, the fibre-orientation dependence of R2* degrades its reproducibility and any microstructural derivative measure. To estimate its orientation-independent part (R2,iso*) from single multi-echo gradient-recalled-echo (meGRE) measurements at arbitrary orientations, a second-order polynomial in time model (hereafter M2) can be used. Its linear time-dependent parameter, β1, can be biophysically related to R2,iso* when neglecting the myelin water (MW) signal in the hollow cylinder fibre model (HCFM). Here, we examined the performance of M2 using experimental and simulated data with variable g-ratio and fibre dispersion. We found that the fitted β1 can estimate R2,iso* using meGRE with long maximum-echo time (TEmax ≈ 54 ms), but not accurately captures its microscopic dependence on the g-ratio (error 84%). We proposed a new heuristic expression for β1 that reduced the error to 12% for ex vivo compartmental R2 values. Using the new expression, we could estimate an MW fraction of 0.14 for fibres with negligible dispersion in a fixed human optic chiasm for the ex vivo compartmental R2 values but not for the in vivo values. M2 and the HCFM-based simulations failed to explain the measured R2*-orientation-dependence around the magic angle for a typical in vivo meGRE protocol (with TEmax ≈ 18 ms). In conclusion, further validation and the development of movement-robust in vivo meGRE protocols with TEmax ≈ 54 ms are required before M2 can be used to estimate R2,iso* in subjects

    Fiber-orientation independent component of R2* obtained from single-orientation MRI measurements in simulations and a post-mortem human optic chiasm

    Get PDF
    The effective transverse relaxation rate (R2*) is sensitive to the microstructure of the human brain like the g-ratio which characterises the relative myelination of axons. However, the fibre-orientation dependence of R2* degrades its reproducibility and any microstructural derivative measure. To estimate its orientation-independent part (R2,iso*) from single multi-echo gradient-recalled-echo (meGRE) measurements at arbitrary orientations, a second-order polynomial in time model (hereafter M2) can be used. Its linear time-dependent parameter, β1, can be biophysically related to R2,iso* when neglecting the myelin water (MW) signal in the hollow cylinder fibre model (HCFM). Here, we examined the performance of M2 using experimental and simulated data with variable g-ratio and fibre dispersion. We found that the fitted β1 can estimate R2,iso* using meGRE with long maximum-echo time (TEmax ≈ 54 ms), but not accurately captures its microscopic dependence on the g-ratio (error 84%). We proposed a new heuristic expression for β1 that reduced the error to 12% for ex vivo compartmental R2 values. Using the new expression, we could estimate an MW fraction of 0.14 for fibres with negligible dispersion in a fixed human optic chiasm for the ex vivo compartmental R2 values but not for the in vivo values. M2 and the HCFM-based simulations failed to explain the measured R2*-orientation-dependence around the magic angle for a typical in vivo meGRE protocol (with TEmax ≈ 18 ms). In conclusion, further validation and the development of movement-robust in vivo meGRE protocols with TEmax ≈ 54 ms are required before M2 can be used to estimate R2,iso* in subjects
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