Impact of physiological noise correction on detecting blood oxygenation level-dependent contrast in the breast.

Physiological fluctuations are expected to be a dominant source of noise in blood oxygenation level-dependent (BOLD) magnetic resonance imaging (MRI) experiments to assess tumour oxygenation and angiogenesis. This work investigates the impact of various physiological noise regressors: retrospective image correction (RETROICOR), heart rate (HR) and respiratory volume per unit time (RVT), on signal variance and the detection of BOLD contrast in the breast in response to a modulated respiratory stimulus. BOLD MRI was performed at 3 T in ten volunteers at rest and during cycles of oxygen and carbogen gas breathing. RETROICOR was optimized using F-tests to determine which cardiac and respiratory phase terms accounted for a significant amount of signal variance. A nested regression analysis was performed to assess the effect of RETROICOR, HR and RVT on the model fit residuals, temporal signal-to-noise ratio, and BOLD activation parameters. The optimized RETROICOR model accounted for the largest amount of signal variance ([Formula: see text]  =  3.3  ±  2.1%) and improved the detection of BOLD activation (P  =  0.002). Inclusion of HR and RVT regressors explained additional signal variance, but had a negative impact on activation parameter estimation (P  <  0.001). Fluctuations in HR and RVT appeared to be correlated with the stimulus and may contribute to apparent BOLD signal reactivity.This work was supported by the NIHR Cambridge Biomedical Research Centre, the Cambridge Experimental Cancer Medicine Centre and the CRUK-EPSRC Cancer Imaging Centre in Cambridge and Manchester (C197/A16465 and C8742/A18097)

Developing advanced MR imaging to assess spinal cord function and tract integrity.

The overall purpose of this thesis is to develop a way to match diffusion and functional acquisition techniques in the spinal cord (SC) in order to offer a comprehensive assessment of factors responsible for functional and structural integrity. I began by optimising a pipeline to acquire and process spinal functional data and I finished by matching the functional information with that derived from diffusion imaging (DI) performed during the same scan session as fM RI. In order to characterize the interactions between local structural connections (derived from DI) and functional activation of the SC it has been necessary to develop an imaging protocol that acquires transverse SC images with both modalities, matching their spatial and geometrical characteristics. This is because transverse cord images possess the relevant anatomical information in terms of grey-white matter structure and allow better localisation of the functional response and structural properties within the spinal cord. My main contribution to the field has been: 1. To demonstrate that it is possible to use the “ZOOM” sequence for spinal fM RI 2. To characterize the signal obtained and the comparison of different image analysis approaches 3. To propose a final pipeline for acquisition and analysis of spinal fM RI 4. To demonstrate that there is a dependency of pathological functional and structural changes The same ZOOM-EPI sequence has been applied for all the functional studies reported in this thesis. The outcome of the optimisation for spinal fMRI has been matched by a DI protocol, using standard DI parameters for spinal microstructural characterization and constitutes the final MR protocol used in a pilot study including a group of healthy controls and a group of patients affected by multiple sclerosis (MS). Based on the gathered experience and results from data acquired and analysed over the years I have concluded with some recommendations for future studies and development strategies for structural and functional MRI of the spinal cor

Development of an MRI Template and Analysis Pipeline for the Spinal Cord and Application in Patients with Spinal Cord Injury

La moelle épinière est un organe fondamental du corps humain. Étant le lien entre le cerveau et le système nerveux périphérique, endommager la moelle épinière, que ce soit suite à un trauma ou une maladie neurodégénérative, a des conséquences graves sur la qualité de vie des patients. En effet, les maladies et traumatismes touchant la moelle épinière peuvent affecter l’intégrité des neurones et provoquer des troubles neurologiques et/ou des handicaps fonctionnels. Bien que de nombreuses voies thérapeutiques pour traiter les lésions de la moelle épinière existent, la connaissance de l’étendue des dégâts causés par ces lésions est primordiale pour améliorer l’efficacité de leur traitement et les décisions cliniques associées. L’imagerie par résonance magnétique (IRM) a démontré un grand potentiel pour le diagnostic et pronostic des maladies neurodégénératives et traumas de la moelle épinière. Plus particulièrement, l’analyse par template de données IRM du cerveau, couplée à des outils de traitement d’images automatisés, a permis une meilleure compréhension des mécanismes sous-jacents de maladies comme l’Alzheimer et la Sclérose en Plaques. Extraire automatiquement des informations pertinentes d’images IRM au sein de régions spécifiques de la moelle épinière présente toutefois de plus grands défis que dans le cerveau. Il n’existe en effet qu’un nombre limité de template de la moelle épinière dans la littérature, et aucun ne couvre toute la moelle épinière ou n’est lié à un template existant du cerveau. Ce manque de template et d’outils automatisés rend difficile la tenue de larges études d’analyse de la moelle épinière sur des populations variées. L’objectif de ce projet est donc de proposer un nouveau template IRM couvrant toute la moelle épinière, recalé avec un template existant du cerveau, et intégrant des atlas de la structure interne de la moelle épinière (e.g., matière blanche et grise, tracts de la matière blanche). Ce template doit venir avec une série d’outils automatisés permettant l’extraction d’information IRM au sein de régions spécifiques de la moelle épinière. La question générale de recherche de ce projet est donc « Comment créer un template générique de la moelle épinière, qui permettrait l’analyse non biaisée et reproductible de données IRM de la moelle épinière ? ». Plusieurs contributions originales ont été proposées pour répondre à cette question et vont être décrites dans les prochains paragraphes. La première contribution de ce projet est le développement du logiciel Spinal Cord Toolbox (SCT). SCT est un logiciel open-source de traitement d’images IRM multi-parametrique de la moelle épinière (De Leener, Lévy, et al., 2016). Ce logiciel intègre notamment des outils pour la détection et la segmentation automatique de la moelle épinière et de sa structure interne (i.e., matière blanche et matière grise), l’identification et la labellisation des niveaux vertébraux, le recalage d’images IRM multimodales sur un template générique de la moelle épinière (précédemment le template MNI-Poly-AMU, maintenant le template PAM50, proposé içi). En se basant sur un atlas de la moelle, SCT intègre également des outils pour extraire des données IRM de régions spécifiques de la moelle épinière, comme la matière blanche et grise et les tracts de la matière blanche, ainsi que sur des niveaux vertébraux spécifiques. D’autres outils additionnels ont aussi été proposés, comme des outils de correction de mouvement et de traitement basiques d’images appliqués le long de la moelle épinière. Chaque outil intégré à SCT a été validé sur un jeu de données multimodales. La deuxième contribution de ce projet est le développement d’une nouvelle méthode de recalage d’images IRM de la moelle épinière (De Leener, Mangeat, et al., 2017). Cette méthode a été développée pour un usage particulier : le redressement d’images IRM de la moelle épinière, mais peut également être utilisé pour recaler plusieurs images de la moelle épinière entre elles, tout en tenant compte de la distribution vertébrale de chaque sujet. La méthode proposée se base sur une approximation globale de la courbure de la moelle épinière dans l’espace et sur la résolution analytique des champs de déformation entre les deux images. La validation de cette nouvelle méthode a été réalisée sur une population de sujets sains et de patients touchés par une compression de la moelle épinière. La contribution majeure de ce projet est le développement d’un système de création de template IRM de la moelle épinière et la proposition du template PAM50 comme template de référence pour les études d’analyse par template de données IRM de la moelle épinière. Le template PAM50 a été créé à partir d’images IRM tiré de 50 sujets sains, et a été généré en utilisant le redressement d’images présenté ci-dessus et une méthode de recalage d’images itératif non linéaire, après plusieurs étapes de prétraitement d’images. Ces étapes de prétraitement incluent la segmentation automatique de la moelle épinière, l’extraction manuelle du bord antérieur du tronc cérébral, la détection et l’identification des disques intervertébraux, et la normalisation d’intensité le long de la moelle. Suite au prétraitement, la ligne centrale moyenne de la moelle et la distribution vertébrale ont été calculées sur la population entière de sujets et une image initiale de template a été générée. Après avoir recalé toutes les images sur ce template initial, le template PAM50 a été créé en utilisant un processus itératif de recalage d’image, utilisé pour générer des templates de cerveau. Le PAM50 couvre le tronc cérébral et la moelle épinière en entier, est disponible pour les contrastes IRM pondérés en T1, T2 et T2*, et intègre des cartes probabilistes et atlas de la structure interne de la moelle épinière. De plus, le PAM50 a été recalé sur le template ICBM152 du cerveau, permettant ainsi la tenue d’analyse par template simultanément dans le cerveau et dans la moelle épinière. Finalement, plusieurs résultats complémentaires ont été présentés dans cette dissertation. Premièrement, une étude de validation de la répétabilité et reproductibilité de mesures de l’aire de section de la moelle épinière a été menée sur une population de patients touchés par la sclérose en plaques. Les résultats démontrent une haute fiabilité des mesures ainsi que la possibilité de détecter des changements très subtiles de l’aire de section transverse de la moelle, importants pour mesurer l’atrophie de la moelle épinière précoce due à des maladies neurodégénératives comme la sclérose en plaques. Deuxièmement, un nouveau biomarqueur IRM des lésions de la moelle épinière a été proposé, en collaboration avec Allan Martin, de l’Université de Toronto. Ce biomarqueur, calculé à partir du ratio d’intensité entre la matière blanche et grise sur des images IRM pondérées en T2*, utilise directement les développements proposés dans ce projet, notamment en utilisant le recalage du template de la moelle épinière et les méthodes de segmentation de la moelle. La faisabilité d’extraire des mesures de données IRM multiparamétrique dans des régions spécifiques de la moelle épinière a également été démontrée, permettant d’améliorer le diagnostic et pronostic de lésions et compression de la moelle épinière. Finalement, une nouvelle méthode d’extraction de la morphométrie de la moelle épinière a été proposée et utilisée sur une population de patients touchés par une compression asymptomatique de la moelle épinière, démontrant de grandes capacités de diagnostic (> 99%). Le développement du template PAM50 comble le manque de template de la moelle épinière dans la littérature mais présente cependant plusieurs limitations. En effet, le template proposé se base sur une population de 50 sujets sains et jeunes (âge moyen = 27 +- 6.5) et est donc biaisée vers cette population particulière. Adapter les analyses par template pour un autre type de population (âge, race ou maladie différente) peut être réalisé directement sur les méthodes d’analyse mais aussi sur le template en lui-même. Tous le code pour générer le template a en effet été mis en ligne (https://github.com/neuropoly/template) pour permettre à tout groupe de recherche de développer son propre template. Une autre limitation de ce projet est le choix d’un système de coordonnées basé sur la position des vertèbres. En effet, les vertèbres ne représentent pas complètement le caractère fonctionnel de la moelle épinière, à cause de la différence entre les niveaux vertébraux et spinaux. Le développement d’un système de coordonnées spinal, bien que difficile à caractériser dans des images IRM, serait plus approprié pour l’analyse fonctionnelle de la moelle épinière. Finalement, il existe encore de nombreux défis pour automatiser l’ensemble des outils développés dans ce projet et les rendre robuste pour la majorité des contrastes et champs de vue utilisés en IRM conventionnel et clinique. Ce projet a présenté plusieurs développements importants pour l’analyse de données IRM de la moelle épinière. De nombreuses améliorations du travail présenté sont cependant requises pour amener ces outils dans un contexte clinique et pour permettre d’améliorer notre compréhension des maladies affectant la moelle épinière. Les applications cliniques requièrent notamment l’amélioration de la robustesse et de l’automatisation des méthodes d’analyse d’images proposées. La caractérisation de la structure interne de la moelle épinière, incluant la matière blanche et la matière grise, présente en effet de grands défis, compte tenu de la qualité et la résolution des images IRM standard acquises en clinique. Les outils développés et validés au cours de ce projet ont un grand potentiel pour la compréhension et la caractérisation des maladies affectant la moelle épinière et aura un impact significatif sur la communauté de la neuroimagerie.----------ABSTRACT The spinal cord plays a fundamental role in the human body, as part of the central nervous system and being the vector between the brain and the peripheral nervous system. Damaging the spinal cord, through traumatic injuries or neurodegenerative diseases, can significantly affect the quality of life of patients. Indeed, spinal cord injuries and diseases can affect the integrity of neurons, and induce neurological impairments and/or functional disabilities. While various treatment procedures exist, assessing the extent of damages and understanding the underlying mechanisms of diseases would improve treatment efficiency and clinical decisions. Over the last decades, magnetic resonance imaging (MRI) has demonstrated a high potential for the diagnosis and prognosis of spinal cord injury and neurodegenerative diseases. Particularly, template-based analysis of brain MRI data has been very helpful for the understanding of neurological diseases, using automated analysis of large groups of patients. However, extracting MRI information within specific regions of the spinal cord with minimum bias and using automated tools is still a challenge. Indeed, only a limited number of MRI template of the spinal cord exists, and none covers the full spinal cord, thereby preventing large multi-centric template-based analysis of the spinal cord. Moreover, no template integrates both the spinal cord and the brain region, thereby preventing simultaneous cerebrospinal studies. The objective of this project was to propose a new MRI template of the full spinal cord, which allows simultaneous brain and spinal cord studies, that integrates atlases of the spinal cord internal structures (e.g., white and gray matter, white matter pathways) and that comes with tools for extracting information within these subregions. More particularly, the general research question of the project was “How to create generic MRI templates of the spinal cord that would enable unbiased and reproducible template-based analysis of spinal cord MRI data?”. Several original contributions have been made to answer this question and to enable template-based analysis of spinal cord MRI data. The first contribution was the development of the Spinal Cord Toolbox (SCT), a comprehensive and open-source software for processing multi-parametric MRI data of the spinal cord (De Leener, Lévy, et al., 2016). SCT includes tools for the automatic segmentation of the spinal cord and its internal structure (white and gray matter), vertebral labeling, registration of multimodal MRI data (structural and non-structural) on a spinal cord MRI template (initially the MNI-Poly-AMU template, later the PAM50 template), co-registration of spinal cord MRI images, as well as the robust extraction of MRI metric within specific regions of the spinal cord (i.e., white and gray matter, white matter tracts, gray matter subregions) and specific vertebral levels using a spinal cord atlas (Lévy et al., 2015). Additional tools include robust motion correction and image processing along the spinal cord. Each tool included in SCT has been validated on a multimodal dataset. The second contribution of this project was the development of a novel registration method dedicated to spinal cord images, with an interest in the straightening of the spinal cord, while preserving its topology (De Leener, Mangeat et al., 2017). This method is based on the global approximation of the spinal cord and the analytical computation of deformation fields perpendicular to the centerline. Validation included calculation of distance measurements after straightening on a population of healthy subjects and patients with spinal cord compression. The major contribution of this project was the development of a framework for generating MRI template of the spinal cord and the PAM50 template, an unbiased and symmetrical MRI template of the brainstem and full spinal cord. Based on 50 healthy subjects, the PAM50 template was generated using an iterative nonlinear registration process, after applying normalization and straightening of all images. Pre-processing included segmentation of the spinal cord, manual delineation of the brainstem anterior edge, detection and identification of intervertebral disks, and normalization of intensity along the spinal cord. Next, the average centerline and vertebral distribution was computed to create an initial straight template space. Then, all images were registered to the initial template space and an iterative nonlinear registration framework was applied to create the final symmetrical template. The PAM50 covers the brainstem and the full spinal cord, from C1 to L2, is available for T1-, T2- and T2*-weighted contrasts, and includes probabilistic maps of the white and the gray matter and atlases of the white matter pathways and gray matter subregions. Additionally, the PAM50 template has been merged with the ICBM152 brain template, thereby allowing for simultaneous cerebrospinal template-based analysis. Finally, several complementary results, focused on clinical validation and applications, are presented. First, a reproducibility and repeatability study of cross-sectional area measurements using SCT (De Leener, Granberg, Fink, Stikov, & Cohen-Adad, 2017) was performed on a Multiple Sclerosis population (n=9). The results demonstrated the high reproducibility and repeatability of SCT and its ability to detect very subtle atrophy of the spinal cord. Second, a novel biomarker of spinal cord injury has been proposed. Based on the T2*-weighted intensity ratio between the white and the gray matter, this new biomarker is computed by registering MRI images with the PAM50 template and extracting metrics using probabilistic atlases. Additionally, the feasibility of extracting multiparametric MRI metrics from subregions of the spinal cord has been demonstrated and the diagnostic potential of this approach has been assessed on a degenerative cervical myelopathy (DCM) population. Finally, a method for extracting shape morphometrics along the spinal cord has been proposed, including spinal cord flattening, indentation and torsion. These metrics demonstrated high capabilities for the diagnostic of asymptomatic spinal cord compression (AUC=99.8% for flattening, 99.3% for indentation, and 98.4% for torsion). The development of the PAM50 template enables unbiased template-based analysis of the spinal cord. However, the PAM50 template has several limitations. Indeed, the proposed template has been generated with multimodal MRI images from 50 healthy and young individuals (age = 27+/- 6.5 y.o.). Therefore, the template is specific to this particular population and could not be directly usable for age- or disease-specific populations. One solution is to open-source the templategeneration code so that research groups can generate and use their own spinal cord MRI template. The code is available on https://github.com/neuropoly/template. While this project introduced a generic referential coordinate system, based on vertebral levels and the pontomedullary junction as origin, one limitation is the choice of this coordinate system. Another coordinate system, based spinal segments would be more suitable for functional analysis. However, the acquisition of MRI images with high enough resolution to delineate the spinal roots is still challenging. Finally, several challenges in the automation of spinal cord MRI processing remains, including the robust detection and identification of vertebral levels, particularly in case of small fields-of-view. This project introduced key developments for the analysis of spinal cord MRI data. Many more developments are still required to bring them into clinics and to improve our understanding of diseases affecting the spinal cord. Indeed, clinical applications require the improvement of the robustness and the automation of the proposed processing and analysis tools. Particularly, the detection and segmentation of spinal cord structures, including vertebral labeling and white/gray matter segmentation, is still challenging, given the lowest quality and resolution of standard clinical MRI acquisition. The tools developed and validated here have the potential to improve our understanding and the characterization of diseases affecting the spinal cord and will have a significant impact on the neuroimaging community

Assessment of physiological noise modelling methods for functional imaging of the spinal cord.

The spinal cord is the main pathway for information between the central and the peripheral nervous systems. Non-invasive functional MRI offers the possibility of studying spinal cord function and central sensitisation processes. However, imaging neural activity in the spinal cord is more difficult than in the brain. A significant challenge when dealing with such data is the influence of physiological noise (primarily cardiac and respiratory), and currently there is no standard approach to account for these effects. We have previously studied the various sources of physiological noise for spinal cord fMRI at 1.5T and proposed a physiological noise model (PNM) (Brooks et al., 2008). An alternative de-noising strategy, selective averaging filter (SAF), was proposed by Deckers et al. (2006). In this study we reviewed and implemented published physiological noise correction methods at higher field (3T) and aimed to find the optimal models for gradient-echo-based BOLD acquisitions. Two general techniques were compared: physiological noise model (PNM) and selective averaging filter (SAF), along with regressors designed to account for specific signal compartments and physiological processes: cerebrospinal fluid (CSF), motion correction (MC) parameters, heart rate (HR), respiration volume per time (RVT), and the associated cardiac and respiratory response functions. Functional responses were recorded from the cervical spinal cord of 18 healthy subjects in response to noxious thermal and non-noxious punctate stimulation. The various combinations of models and regressors were compared in three ways: the model fit residuals, regression model F-tests and the number of activated voxels. The PNM was found to outperform SAF in all three tests. Furthermore, inclusion of the CSF regressor was crucial as it explained a significant amount of signal variance in the cord and increased the number of active cord voxels. Whilst HR, RVT and MC explained additional signal (noise) variance, they were also found (in particular HR and RVT) to have a negative impact on the parameter estimates (of interest)--as they may be correlated with task conditions e.g. noxious thermal stimuli. Convolution with previously published cardiac and respiratory impulse response functions was not found to be beneficial. The other novel aspect of current study is the investigation of the influence of pre-whitening together with PNM regressors on spinal fMRI data. Pre-whitening was found to reduce non-white noise, which was not accounted for by physiological noise correction, and decrease false positive detection rates

Investigating chemotherapy induced peripheral neuropathy (CIPN) and its treatment, using functional Magnetic Resonance Imaging (fMRI)

Background Chemotherapy Induced Peripheral Neuropathy (CIPN) is a debilitating neuropathy caused by commonly used chemotherapeutics. Clinically, the problem of CIPN is compounded by difficulties with diagnosis and limited treatment options. The pathophysiology of CIPN remains elusive, with current mechanistic postulates focused mainly on the peripheral nervous system. However, animal and human models of non-CIPN neuropathic conditions have shown the brain to be central to the development and maintenance of painful neuropathy. Moreover, evidence suggests that aberrant activity in key regions of the brain and brainstem could denote individual vulnerability for chronic pain states. The impact of the brain on CIPN development is unknown. Assessment of drug efficacy using brain imaging can provide sensitive readouts and is increasingly used in clinical trials. Aims Firstly, to prospectively explore the structure and function of the brain in cancer patients prior to chemotherapy administration, using functional magnetic resonance imaging (fMRI), in order to determine whether baseline differences exist between patients who progress to CIPN as compared to those who do not. Secondly, to develop a pilot study using fMRI to investigate a topical treatment for CIPN, in order to assess the feasibility of setting up a study with this kind of design. Methods To address the first aim of this thesis a prospective cohort study (the CIPN fMRI Study) was developed. Cancer patients scheduled to receive neurotoxic chemotherapy treatment including oxaliplatin, carboplatin, carbotaxol, or cisplatin, were recruited from three NHS trusts in Scotland, to undergo a high resolution (3 tesla) functional MRI scan, at a single time point prior to commencement of chemotherapy. During the scan structural, resting state and functional data were collected. Functional data involved the presentation of punctate stimuli (using a 256mN von Frey filament), above the patients’ right medial malleolus. While receiving the punctate stimuli, patients viewed images that had neutral or positive emotional content or a baseline coloured image with no content. Sample size was based on previously successful pain fMRI studies and pragmatic estimates. Acute CIPN was defined clinically by common toxicity criteria as necessitating a chemotherapy dose reduction or cessation. Data were analysed using FMRIB’s Software Library (FSL) version 5, 2015. Standard data pre-processing (brain extraction, registration, B0 unwarping, motion correction, and denosiing with FIX) was carried out. Structural analysis was conducted using FIRST. Resting state analysis utilised FSL’s MELODIC tool, and a non-parametric group comparison was made following a dual regression approach. FEAT was used for both first and second level functional analyses. Group comparisons were made using a mixed effects analysis (z threshold 2·3 and 2, regions considered significant at p<0·05, cluster corrected). The group was split by sex to explore known sex differences in pain processing. To address the second aim of this thesis, a pilot fMRI randomised controlled trial (MINT3 Study) was designed. Approvals from ethics and research and development were sought and obtained. Data collection forms were developed. An fMRI experiment was proposed and a single pilot scan was conducted and analysed. Results 30 patients were recruited for the CIPN fMRI study (mean age 60·4 years, [95% Confidence Interval: 57.4-63.4, 17 women). Two patients had lung cancer, nine had gynecological malignancies and 18 had colorectal cancer. 17 patients developed acute CIPN. Structural analysis showed that patients who developed CIPN had a smaller volume of the Nucleus Accumbens (NAc). Resting state analysis did not show clear differences between those who developed CIPN and those who did not. Finally, functional analysis showed that patients who did not develop CIPN had greater activation in the superior frontal gyrus when viewing positive emotional images as compared to those who did progress to CIPN. Region of interest analysis showed that female patients who developed CIPN had greater activity in their mesencephalic pontine reticular formation (MPRF). Male patients who progressed to CIPN had decreased activity in their thalamus. Feasability of the MINT3 study set up and fMRI paradigm was assessed. Interpretation Differences in brain structure and function are evident between patients who developed CIPN and those who did not. Crucially, the regions identified, in particular the NAc, have been postulated to denote a vulnerability for progression to pain states. Although the findings need further confirmation they suggest a paradigm shift in terms of CIPN as a clinical problem. Specifically, it appears that certain individuals can be considered as having increased risk of CIPN development prior to chemotherapy administration. This risk relates to the baseline structure, and function of their brains. Finally, the set up of the MINT3 fMRI study showed that this kind of study design is acceptable in terms of ethical and R&D approvals and a single healthy volunteer pilot