1,061 research outputs found
Automated image segmentation and registration of vessel wall MRI for quantitative assessment of carotid artery vessel wall dimensions and plaque composition
The main goal of this thesis was to develop methods for automated segmentation, registration and classification of the carotid artery vessel wall and plaque components using multi-sequence MR vessel wall images to assess atherosclerosis. First, a general introduction into atherosclerosis and different stages of the disease were described including the importance to differentiate between stable and vulnerable plaques. Several non-invasive imaging techniques were discussed and the advantages of multi-sequence MRI were highlighted. Different novel automated image segmentation and registration techniques for analysis of the MRI images have been developed. A 3D vessel model to automatically segment the vessel wall was presented. Automated image registration was applied to correct for patient movement during the acquisition of an MRI scan and between MRI scans. The last topic is the automatic classification of the different plaque components which can be present inside the vessel wall. All techniques were developed and validated using relevant patient data and reference standards. The work presented is an important contribution to the automated analysis of multi-sequence MR vessel wall imaging of the carotid artery. These techniques can speed up the current manual analysis and are potentially more accurate and more reproducible.ASCI research school. Bontius Stichting inz. Doelfonds beeldverwerking. Library of the University of Leiden. Medis medical imaging systems bv, Leiden. Pie Medical Imaging BV, MaastrichtUBL - phd migration 201
Functional pulmonary MRI with ultra-fast steady-state free precession
To date, computed tomography and nuclear medicine techniques are still the reference standard for lung imaging, but radiation exposure is a major concern; especially in case of longitudinal examinations and in children. Therefore, radiation-free imaging is an urgent necessity. Pulmonary magnetic resonance imaging (MRI) is radiation-free, but poses challenges since the low proton density and the presence of strong mesoscopic susceptibility variations considerably reduce the detectable MR signal. As a result, the lung typically appears as a “black hole” with conventional MRI techniques. Recently, ultra-fast balanced steady-state free precession (ufSSFP) methods were proposed for ameliorated lung morphological imaging. In this thesis, ufSSFP is employed to develop and improve several pulmonary functional imaging methods, which can be used in clinical settings using standard MR scanners and equipment.
At every breath, the lung expands and contracts, and at every heartbeat, the blood is pumped through the arteries to reach the lung parenchyma. This creates signal modulations associated with pulmonary blood perfusion and ventilation that are detectable by MRI. The second chapter of this thesis focuses on the optimization of time-resolved two-dimensional (2D) ufSSFP for perfusion-weighted and ventilation-weighted imaging of the lung. Subsequently, in the third chapter, three-dimensional (3D) multi-volumetric ufSSFP breath-hold imaging is used to develop a lung model and retrieve the measure α, a novel ventilation-weighted quantitative parameter.
Oxygen-enhanced MRI exploits the paramagnetic properties of oxygen dissolved in the blood, acting as a weak T1-shortening contrast agent. When breathing pure oxygen, it reaches only ventilated alveoli of the parenchyma and dissolves only in functional and perfused regions. How ufSSFP imaging in combination with a lung model can be used to calculate robust 3D oxygen enhancement maps is described in the fourth chapter. In addition, in the fifth chapter, 2D inversion recovery ufSSFP imaging is employed to map the T1 and T2 relaxation times of the lung, the change of the relaxation times after hyperoxic conditions, as well as the physiological oxygen wash-in and wash-out time (related to the time needed to shorten T1 after oxygen breathing).
The objective of the last chapter of this thesis is the application of 3D ufSSFP imaging before and after intravenous gadolinium-based contrast agent administration for the investigation of signal enhancement ratio (SER) mapping: a rapid technique to visualize perfusion-related diseases of the lung parenchyma.
The techniques presented in this thesis using optimized ufSSFP pulse sequences demonstrated potential to reveal new insights on pulmonary function as well as quantification, and might become part of the future standard for the evaluation and follow-up of several lung pathologies
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,
LĂ©vy, 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 (LĂ©vy 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
Image synthesis for the attenuation correction and analysis of PET/MR data
While magnetic resonance imaging (MRI) provides high-resolution anatomical information, positron emission tomography (PET) provides functional information. Combined PET/MR scanners are expected to offer a new range of clinical applications but efforts are still necessary to mitigate some limitations of this promising technology. One of the factors limiting the use of PET/MR scanners, especially in the case of neurology studies, is the imperfect attenuation correction, leading to a strong bias of the PET activity. Exploiting the simultaneous acquisition of both modalities, I explored a new family of methods to synthesise X-ray computed tomography (CT) images from MR images. The synthetic images are generated through a multi-atlas information propagation scheme, locally matching the MRI-derived patient's morphology to a database of MR/CT image pairs, using a local image similarity measure. The proposed algorithm provides a significant improvement in PET reconstruction accuracy when compared with the current correction, allowing an unbiased analysis of the PET images. A similar image synthesis scheme was then used to better identify abnormalities in cerebral glucose metabolism measured by [18]F-fluorodeoxyglucose (FDG) PET. This framework consists of creating a subject-specific healthy PET model based on the propagation of morphologically-matched PET images, and comparing the subject's PET image to the model via a Z-score. By accounting for inter-subject morphological differences, the proposed method reduces the variance of the normal population used for comparison in the Z-score, thus increasing the sensitivity. To demonstrate that the applicability of the proposed CT synthesis method is not limited to PET/MR attenuation correction, I redesigned the synthesis process to derive tissue attenuation properties from MR images in the head & neck and pelvic regions to facilitate MR-based radiotherapy treatment planning
Multi-scale imaging and modelling of bone
The multi-level organization of bone facilitates the exploitation of in-vivo micro-scale information which is currently lacking for clinical applications. The three sub-projects presented in this thesis investigate the human skeletal system at multiple scales using magnetic resonance imaging (MRI) with the aim of providing new techniques for extracting finer scale information in-vivo. At the whole organ level, human knee joint kinematics was studied using a combined MRI strategy. This new strategy enables the in-vivo investigation of tibiofemoral locomotion under body weight-bearing conditions by modelling the knee flexion angle as a function of the femur and tibia cartilage surfaces in contact. The resultant "contact" trajectory may potentially be used to understand the mechanical cause of cartilage degeneration and as a biomarker to detect abnormalities in the lower limb. At the molecular level, in-vivo MR diffusion tensor imaging (DTI) has been performed for the first time in the human tibia epiphysis. By tracking the water molecules inside the red marrow, the organization of trabecular bone network may be understood as the streamlines formed by anisotropic diffusion trajectories. This sub-project aims to understand the organization of trabecular bone networks non-invasively, which is usually performed ex-vivo through biopsies. The feasibility and reproducibility of DTI is studied. Finally, a new MR imaging protocol named multi-directional sub-pixel enhancement (mSPENT) is proposed and developed to quantify the trabecular bone structural arrangement at the meso-scale. By modulating a dephasing gradient to manipulate the underlying spin system inside each voxel, the resulting mSPENT image contrast varies with gradient at different directions based on the magnetization at the corresponding voxel. A tensor-based method is further developed to model this contrast change, leading to a localized quantification of tissue structural orientation beyond the conventional MR imaging resolution
CHARACTERIZATION OF BRAIN TISSUE MICROSTRUCTURES WITH DIFFUSION MRI
Diffusion MRI is a useful medical imaging tool for noninvasive mapping of the neuroanatomy and brain connectivity. In this dissertation, we worked on developing diffusion MRI techniques to probe brain tissue microstructures from various perspectives.
Spatial resolution of the diffusion MRI is the key to obtain accurate microstructural information. In Chapter 2 and 3, we focused on developing high-resolution in vivo diffusion MRI techniques, such as 3D fast imaging sequence and a localized imaging approach using selective excitation RF pulses. We demonstrated the power of the superior resolution in delineating complex microstructures in the live mouse brain. With the high resolution diffusion MRI data, we were able to map the intra-hippocampal connectivity in the mouse brain, which showed remarkable similarity with tracer studies (Chapter 4). Using the localized fast imaging technique, we were the first to achieve in utero diffusion MRI of embryonic mouse brain, which revealed the microstructures in the developing brains and the changes after inflammatory injury (Chapter 5).
The second half of the dissertation explores the restricted water diffusion at varying diffusion times and microstructure scales, using the oscillating gradient spin-echo (OGSE) diffusion MRI. We showed in the live normal mouse brains that unique tissue contrasts can be obtained at different oscillating frequency. We demonstrated in a neonatal mouse model of hypoxia-ischemia, that in the edema brain tissues, diffusion MRI signal changed much faster with oscillating frequency compared to the normal tissue, indicating significant changes in cell size associated with cytotoxic edema (Chapter 6). In the mild injury mice, OGSE showed exquisite sensitivity in detecting subtle injury in the hippocampus, which may relate to microstructural changes in smaller scales, such as the subcellular organelles (Chapter 7). Finally, we addressed the technical issues of OGSE diffusion MRI, and proposed a new hybrid OGSE sequence with orthogonally placed pulsed and oscillating gradients to suppress the perfusion related pseudo-diffusion (Chapter 8).
In conclusion, we developed in vivo high-resolution diffusion techniques, and time-dependent diffusion measurements to characterize brain tissue microstructures in the normal and diseased mouse brains. The knowledge gained from this dissertation study may advance our understanding on microstructural basis of diffusion MRI
Advanced perfusion quantification methods for dynamic PET and MRI data modelling
The functionality of tissues is guaranteed by the capillaries, which supply the microvascular
network providing a considerable surface area for exchanges between blood and tissues.
Microcirculation is affected by any pathological condition and any change in the blood supply
can be used as a biomarker for the diagnosis of lesions and the optimization of the treatment.
Nowadays, a number of techniques for the study of perfusion in vivo and in vitro are
available. Among the several imaging modalities developed for the study of microcirculation,
the analysis of the tissue kinetics of intravenously injected contrast agents or tracers is the
most widely used technique. Tissue kinetics can be studied using different modalities: the
positive enhancement of the signal in the computed tomography and in the ultrasound
dynamic contrast enhancement imaging; T1-weighted MRI or the negative enhancement of
T2* weighted MRI signal for the dynamic susceptibility contrast imaging or, finally, the
uptake of radiolabelled tracers in dynamic PET imaging. Here we will focus on the perfusion
quantification of dynamic PET and MRI data. The kinetics of the contrast agent (or the tracer)
can be analysed visually, to define qualitative criteria but, traditionally, quantitative
physiological parameters are extracted with the implementation of mathematical models.
Serial measurements of the concentration of the tracer (or of the contrast agent) in the tissue
of interest, together with the knowledge of an arterial input function, are necessary for the
calculation of blood flow or perfusion rates from the wash-in and/or wash-out kinetic rate
constants. The results depend on the acquisition conditions (type of imaging device, imaging
mode, frequency and total duration of the acquisition), the type of contrast agent or tracer
used, the data pre-processing (motion correction, attenuation correction, correction of the
signal into concentration) and the data analysis method.
As for the MRI, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a
non-invasive imaging technique that can be used to measure properties of tissue
microvasculature. It is sensitive to differences in blood volume and vascular permeability that
can be associated with tumour angiogenesis. DCE-MRI has been investigated for a range of
clinical oncologic applications (breast, prostate, cervix, liver, lung, and rectum) including
cancer detection, diagnosis, staging, and assessment of treatment response. Tumour
microvascular measurements by DCE-MRI have been found to correlate with prognostic
factors (such as tumour grade, microvessel density, and vascular endothelial growth factor
expression) and with recurrence and survival outcomes. Furthermore, DCE-MRI changes
measured during treatment have been shown to correlate with outcome, suggesting a role as
a predictive marker. The accuracy of DCE-MRI relies on the ability to model the
pharmacokinetics of an injected contrast agent using the signal intensity changes on
sequential magnetic resonance images. DCE-MRI data are usually quantified with the
application of the pharmacokinetic two-compartment Tofts model (also known as the
standard model), which represents the system with the plasma and tissue (extravascular
extracellular space) compartments and with the contrast reagent exchange rates between
them. This model assumes a negligible contribution from the vascular space and considers
the system in, what-is-known as, the fast exchange limit, assuming infinitely fast
transcytolemmal water exchange kinetics. In general, the number, as well as any assumption
about the compartments, depends on the properties of the contrast agent used (mainly
gadolinium) together with the tissue physiology or pathology studied. For this reason, the
choice of the model is crucial in the analysis of DCE-MRI data. The value of PET in clinical oncology has been demonstrated with studies in a variety of
cancers including colorectal carcinomas, lung tumours, head and neck tumours, primary and
metastatic brain tumours, breast carcinoma, lymphoma, melanoma, bone cancers, and other
soft-tissue cancers. PET studies of tumours can be performed for several reasons including
the quantification of tumour perfusion, the evaluation of tumour metabolism, the tracing of
radiolabelled cytostatic agents. In particular, the kinetic analysis of PET imaging has showed,
in the past few years, an increasing value in tumour diagnosis, as well as in tumour therapy,
through providing additional indicative parameters. Many authors have showed the benefit
of kinetic analysis of anticancer drugs after labelling with radionuclide in measuring the
specific therapeutic effect bringing to light the feasibility of applying the kinetic analysis to
the dynamic acquisition. Quantification methods can involve visual analysis together with
compartmental modelling and can be applied to a wide range of different tracers. The
increased glycolysis in the most malignancies makes 18F-FDG-PET the most common
diagnostic method used in tumour imaging. But, PET metabolic alteration in the target tissue
can depend by many other factors. For example, most types of cancer are characterized by
increased choline transport and by the overexpression of choline kinase in highly proliferating
cells in response to enhanced demand of phosphatidylcholine (prostate, breast, lung, ovarian
and colon cancers). This effect can be diagnosed with choline-based tracers as the 18Ffluoromethylcholine
(18F-FCH), or the even more stable 18F-D4-Choline. Cellular
proliferation is also imaged with 18F-fluorothymidine (FLT), which is trapped within the
cytosol after being mono phosphorylated by thymidine kinase-1 (TK1), a principal enzyme
in the salvage pathway of DNA synthesis. 18F-FLT has been found to be useful for noninvasive
assessment of the proliferation rate of several types of cancer and showed high
reproducibility and accuracy in breast and lung cancer tumours.
The aim of this thesis is the perfusion quantification of dynamic PET and MRI data of patients
with lung, brain, liver, prostate and breast lesions with the application of advanced models.
This study covers a wide range of imaging methods and applications, presenting a novel
combination of MRI-based perfusion measures with PET kinetic modelling parameters in
oncology. It assesses the applicability and stability of perfusion quantification methods,
which are not currently used in the routine clinical practice.
The main achievements of this work include: 1) the assessment of the stability of perfusion
quantification of D4-Choline and 18F-FLT dynamic PET data in lung and liver lesions,
respectively (first applications in the literature); 2) the development of a model selection in
the analysis of DCE-MRI data of primary brain tumours (first application of the extended
shutter speed model); 3) the multiparametric analysis of PET and MRI derived perfusion
measurements of primary brain tumour and breast cancer together with the integration of
immuohistochemical markers in the prediction of breast cancer subtype (analysis of data
acquired on the hybrid PET/MRI scanner).
The thesis is structured as follows:
- Chapter 1 is an introductive chapter on cancer biology. Basic concepts, including the causes
of cancer, cancer hallmarks, available cancer treatments, are described in this first chapter.
Furthermore, there are basic concepts of brain, breast, prostate and lung cancers (which are
the lesions that have been analysed in this work). - Chapter 2 is about Positron Emission Tomography. After a brief introduction on the basics
of PET imaging, together with data acquisition and reconstruction methods, the chapter
focuses on PET in the clinical settings. In particular, it shows the quantification techniques
of static and dynamic PET data and my results of the application of graphical methods,
spectral analysis and compartmental models on dynamic 18F-FDG, 18F-FLT and 18F-D4-
Choline PET data of patients with breast, lung cancer and hepatocellular carcinoma.
- Chapter 3 is about Magnetic Resonance Imaging. After a brief introduction on the basics of
MRI, the chapter focuses on the quantification of perfusion weighted MRI data. In particular,
it shows the pharmacokinetic models for the quantification of dynamic contrast enhanced
MRI data and my results of the application of the Tofts, the extended Tofts, the shutter speed
and the extended shutter speed models on a dataset of patients with brain glioma.
- Chapter 4 introduces the multiparametric imaging techniques, in particular the combined
PET/CT and the hybrid PET/MRI systems. The last part of the chapter shows the applications
of perfusion quantification techniques on a multiparametric study of breast tumour patients,
who simultaneously underwent DCE-MRI and 18F-FDG PET on a hybrid PET/MRI scanner.
Then the results of a predictive study on the same dataset of breast tumour patients integrated
with immunohistochemical markers. Furthermore, the results of a multiparametric study on
DCE-MRI and 18F-FCM brain data acquired both on a PET/CT scanner and on an MR
scanner, separately. Finally, it will show the application of kinetic analysis in a radiomic
study of patients with prostate cancer
19F Magnetic Resonance Imaging of Lung Ventilation Dynamics and Cell Tracking
The Fluorine isotope 19F has great potential in the use of magnetic resonance imaging (MRI) for clinical applications. 19F is inert, naturally abundant, has a close resonance frequency to proton (1H) (allowing most modern MRI scanners to work with the addition of a tuned coil), has negligible presence in the mammalian body (allowing background signal free acquisitions), and the high gyromagnetic ratio provides sufficient magnetic resonance signal to be visible without hyperpolarization. Uses for 19F MRI includes functional lung imaging, diffusion imaging, cell tracking, and oxygenation sensing among others. Although not widely used in the clinical setting at the time of writing this dissertation. The potential improvements 19F MRI could bring to healthcare are vast. 19F lung imaging has been studied in animal and human models, and has shown to be capable of producing sensitive markers for lung diseases such as cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD) by providing spatially localized functional information. In cell tracking, 19F has shown potential in drug delivery monitoring, inflammation imaging, immune cell tracking, and oxygenation measurement with the potential of spatial localization and cell quantification. This dissertation presents my work on human in-vivo multi-breath wash-in/out 19F lung imaging, and the processing of biomarkers more sensitive to CF disease progression over the current gold standard (spirometry). 19F lung MRI was compared to hyperpolarized (HP) Xenon (129Xe) ventilation defect percentage (VDP) analysis. The feasibility of free-breathing 19F lung imaging was explored using a combination of spiral acquisition and denoising. The last two chapters present preliminary work on sequence programming for diffusion imaging and cell tracking at high magnetic fields (9.4T). Preliminary work on oxygen sensing at 9.4T is also explored.Doctor of Philosoph
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