58 research outputs found
Molecular Imaging to Monitor Left Ventricular Remodeling in Heart Failure
Purpose of Review: Cardiovascular diseases are the leading cause of deaths worldwide. Many complex cellular and molecular pathways lead to myocardial remodeling after ischemic insults. Anatomy, function, and viability of the myocardium can be assessed by modern medical imaging techniques by both visualizing and quantifying damages. Novel imaging techniques aim for a precise and accurate visualization of the myocardium and for the detection of alternations at the molecular level.Recent Findings: Magnetic resonance imaging assesses anatomy, function, and tissue characterization of the myocardium non-invasively with high spatial resolution, sensitivity, and specificity. Using hyperpolarized magnetic resonance imaging, molecular and metabolic conditions can be assessed non-invasively. Single photon-emission tomography and positron-emission tomography are the most sensitive techniques to detect biological processes in the myocardium. Cardiac perfusion, metabolism, and viability are the most common clinical targets. In addition, molecular-targeted imaging of biological processes involved in heart failure, such as myocardial innervation, inflammation, and extracellular matrix remodeling, is feasible.Summary: Novel imaging techniques can provide a precise and accurate visualization of the myocardium and for the detection of alternations at molecular level.</div
Diagnosis and prognosis of cardiovascular diseases by means of texture analysis in magnetic resonance imaging
Cardiovascular diseases constitute the leading global cause of morbidity and
mortality. Magnetic resonance imaging (MRI) has become the gold standard technique
for the assessment of patients with myocardial infarction. However, limitations still
exist thus new alternatives are open to investigation. Texture analysis is a technique
that aims to quantify the texture of the images that are not always perceptible by the
human eye. It has been successfully applied in medical imaging but applications to
cardiac MRI (CMR) are still scarce. Therefore, the purpose of this thesis was to apply
texture analysis in conventional CMR images for the assessment of patients with
myocardial infarction, as an alternative to current methods.
Three applications of texture analysis and machine learning techniques were studied:
i) Detection of infarcted myocardium in late gadolinium enhancement (LGE) CMR.
Segmentation of the infarcted myocardium is routinely performed using image
intensity thresholds. The inclusion of texture features to aid the segmentation
was analyzed obtaining overall good results. The method was developed using
10 LGE CMR datasets and tested on a separate dataset comprising 5 cases that
were acquired with a completely different scanner than that used for training.
Therefore, this preliminary study showed the transferability of texture analysis
which is important for clinical applicability.
ii) Differentiation of acute and chronic myocardial infarction using LGE CMR and
standard pre-contrast cine CMR. In this study, two different feature selection
techniques and six different machine learning classifiers were studied and
compared. The best classification was achieved using a polynomial SVM
obtaining an overall AUC of 0.87 ± 0.06 in LGE CMR. Interestingly, results on
cine CMR in which infarctions are visually imperceptible in most cases were also
good (AUC = 0.83 ± 0.08).
iii) Detection of infarcted non-viable segments in cine CMR. This study was
motivated by the findings of the previous one. It demonstrated that texture
analysis can be used to distinguish non-viable, viable and remote segments using
standard pre-contrast cine CMR solely. This was the most relevant contribution
of this thesis as it can be used as hypothesis for future work aiming to accurately
delineate the infarcted myocardium as a gadolinium-free alternative that will have potential advantages.
The three proposed applications were successfully performed obtaining promising
results. In conclusion, texture analysis can be successfully applied to conventional
CMR images and provides a potential quantitative alternative to existing methods
Texture Analysis of Late Gadolinium Enhanced Cardiac Magnetic Resonance Images for Characterizing Myocardial Fibrosis and Infarction
Le tiers de la population aux Ătats-Unis est affectĂ© par des cardiomyopathies. Lorsque le muscle du coeur, le myocarde, est altĂ©rĂ© par la maladie, la santĂ© du patient est dĂ©tĂ©riorĂ©e et peut mĂȘme entrainer la mort. Les maladies ischĂ©miques sont le rĂ©sultat dâartĂšres coronariennes bloquĂ©es (stĂ©nose), limitant lâapport sanguin vers le myocarde. Les cardiomyopathies non-ischĂ©miques sont les maladies dues Ă dâautres causes que des stĂ©noses. Les fibres de collagĂšne (fibrose) sâinfiltrent dans le muscle cardiaque dans le but de maintenir la forme et les fonctions cardiaques lorsque la structure du myocarde est affectĂ©e par des cardiomyopathies. Ce principe, nĂ©cessaire au fonctionnement du coeur en prĂ©sence de maladies, devient mal adaptĂ© et mĂšne Ă des altĂ©rations du myocarde aux consĂ©quences nĂ©gatives, par exemple lâaugmentation de la rigiditĂ© du myocarde. Une partie du diagnostic clinique lors de cardiomyopathies consiste Ă Ă©valuer la fibrose dans le coeur avec diffĂ©rentes modalitĂ©s dâimagerie. Les fibres de collagĂšne sâinfiltrent et sâaccumulent dans la zone extracellulaire du myocarde ou peuvent remplacer progressivement les cardiomyocytes compromises. Lâinfiltration de fibrose dans le myocarde peut possiblement ĂȘtre rĂ©versible, ce qui rend sa dĂ©tection particuliĂšrement importante pour le clinicien.
DiffĂ©rents tests diagnostiques existent pour aider le clinicien Ă Ă©tablir lâĂ©tat du patient en prĂ©sence de cardiomyopathies. Lâimagerie par rĂ©sonance magnĂ©tique (IRM) est une modalitĂ© dâimagerie qui offre une haute rĂ©solution pour la visualisation du myocarde. Parmi les sĂ©quences disponibles avec cette modalitĂ©, lâimagerie par rehaussement tardif (RT) augmente le contraste du signal existant entre les tissus sains et les tissues malades du myocarde. Il sâagit dâimages en pondĂ©ration T1 avec administration dâagent de contraste qui se propage dans la matrice extracellulaire et rĂ©sulte en un rehaussement du signal Ă cet endroit. Les images IRM RT permettent dâĂ©valuer la prĂ©sence et lâĂ©tendue des dommages au myocarde. Le clinicien peut Ă©valuer la sĂ©vĂ©ritĂ© des cardiomyopathies et poser un pronostique Ă lâaide de ces images. La dĂ©tection de fibrose diffuse dans ces images peut informer le clinicien sur lâĂ©tat du patient et est un important marqueur de cardiomyopathies.
Il est important dâĂ©tablir lâoccurrence de lâinfarctus en prĂ©sence de maladies ischĂ©miques. En effet, lâapproche interventionnelle varie selon que le clinicien fait face Ă une ischĂ©mie aigue ou chronique. Lors du diagnostic, Il serait donc bĂ©nĂ©fique de diffĂ©rencier les infarctus du myocarde aigu de ceux chronique. Ceci sâest avĂ©rĂ© difficile Ă lâaide des images IRM RT oĂč lâintensitĂ© du signal ou la taille des rĂ©gions sont similaires dans les deux types dâischĂ©mie.
Le but de la prĂ©sente thĂšse est donc dâappliquer les mĂ©thodes dâanalyse de texture Ă des images IRM RT afin de dĂ©tecter la prĂ©sence de fibrose diffuse dans le myocarde et de plus de dĂ©terminer lâĂąge de lâinfarctus du myocarde. La premiĂšre Ă©tude portait sur la dĂ©tection de fibrose diffuse dans le myocarde Ă lâaide de lâanalyse de texture appliquĂ©e Ă des images IRM RT afin dâĂ©tablir si un lien existe entre la variation du signal dâintensitĂ© et la structure sous-jacente du myocarde. La prĂ©sence de collagĂšne dans le myocarde augmente avec lâĂąge et nous avons utilisĂ© un modĂšle animal de rats jeunes et ĂągĂ©s. Nous avons fait une Ă©tude ex-vivo afin dâobtenir des images IRM RT de haute rĂ©solution avec absence de mouvement et ainsi permettre une comparaison des images avec des coupes histologiques des coeurs imagĂ©s. Des images IRM RT ont Ă©tĂ© acquises sur vingt-quatre animaux. Les coupes histologiques ont Ă©tĂ© traitĂ©es avec la mĂ©thode utilisant un marqueur âpicrosirius redâ qui donne une teinte rouge au collagĂšne. La quantification de la fibrose obtenue avec les images IRM RT a Ă©tĂ© comparĂ©e Ă la quantification obtenue sur les coupes histologiques. Ces quantifications ont de plus Ă©tĂ© comparĂ©es Ă lâanalyse de texture appliquĂ©e aux images IRM RT. La mĂ©thode de texture a Ă©tĂ© appliquĂ©e en crĂ©ant des cartes de texture basĂ©es sur la valeur de Contraste, cette mesure Ă©tant obtenue par des calculs statistiques sur la matrice de cooccurrence. Les rĂ©gions montrant une plus grande complexitĂ© de signal dâintensitĂ© sur les images IRM RT ont Ă©tĂ© rehaussĂ©es avec les cartes de textures. Un calcul de rĂ©gression linĂ©aire a permis dâĂ©tudier le lien entre les diffĂ©rentes mĂ©thodes de quantification. Nous avons trouvĂ©s que la quantification de fibrose dans le myocarde Ă lâaide de lâanalyse de texture appliquĂ©e sur des images IRM RT concordait avec le niveau de collagĂšne identifiĂ© avec les images IRM et avec les coupes histologiques. De plus, nous avons trouvĂ©s que lâanalyse de texture rehausse la prĂ©sence de fibrose diffuse dans le myocarde.
La seconde Ă©tude a pour but de discriminer les infarctus aigus du myocarde de ceux qui sont chroniques sur des images IRM RT de patients souffrant de cardiomyopathies ischĂ©miques. Vingt-deux patients ont subi lâimagerie IRM (12 avec infarctus aigu du myocarde et 12 avec infarctus chronique). Une segmentation des images a permis dâisoler les diffĂ©rentes zones du myocarde, soit la zone dâinfarctus, la zone grise au rebord de lâinfarctus et la zone du myocarde sain, dans les deux groupes de patients. Lâanalyse de texture sâest faite dans ces rĂ©gions en comparant les valeurs obtenues dans les deux groupes. Nous avons obtenu plus de valeurs de texture discriminantes dans la zone grise, en comparaison avec la rĂ©gion du myocarde sain, oĂč aucune valeur de texture nâĂ©tait significativement diffĂ©rente, et Ă la zone dâinfarctus, oĂč seule la valeur de texture statistique Moyenne Ă©tait diffĂ©rentes dans les deux groupes. La zone grise a dĂ©jĂ fait lâobjet dâĂ©tudes ayant Ă©tablis cette rĂ©gion comme composĂ©e de cardiomyocytes sains entremĂȘlĂ©s avec des fibres de collagĂšne. Notre Ă©tude montre que cette rĂ©gion peut exhiber des diffĂ©rences structurelles entre les infarctus aigus du myocarde et ceux qui sont chroniques et que lâanalyse de texture a rĂ©ussi Ă les dĂ©tecter.
LâĂ©tude de la prĂ©sence de collagĂšne dans le myocarde est importante pour le clinicien afin quâil puisse faire un diagnostic adĂ©quat du patient et pour quâil puisse faire un choix de traitement appropriĂ©. Nous avons montrĂ©s que lâanalyse de texture sur des images IRM RT de patients peut diffĂ©rencier et mĂȘme permettre la classification des ischĂ©mies aigues des ischĂ©mies chroniques, ce qui nâĂ©tait pas possible avec uniquement ce type dâimages. Nous avons de plus dĂ©montrĂ©s que lâanalyse de texture dâimages IRM RT permettait dâĂ©valuer le contenu de fibrose diffuse dans un modĂšle animal de haute rĂ©solution avec validation histologique. Une telle relation entre les rĂ©sultats dâanalyse de texture dâimages IRM RT et la structure sous-jacente du myocarde nâavait pas Ă©tĂ© Ă©tudiĂ©e dans la littĂ©rature.
Notre mĂ©thode pourra ĂȘtre amĂ©liorĂ©e en effectuant dâautres calculs statistiques sur la matrice de cooccurrence, en testant dâautres mĂ©thodes dâanalyse de texture et en appliquant notre mĂ©thode Ă de nouvelles sĂ©quences dâacquisition IRM, tel les images en pondĂ©ration T1. Dâautres amĂ©liorations possibles pourraient porter sur une Ă©valuation de matrice de cooccurrence avec voisinage circulaire suivant la forme du myocarde sur les tranches dâimages IRM RT. Plusieurs matrice de cooccurrence pourraient aussi ĂȘtre Ă©valuĂ©es en fonction de la position dans lâespace du voisinage afin dâintĂ©grer une composante directionnelle dans les calculs de texture. Dâautres Ă©tudes sont nĂ©cessaires afin dâĂ©tablir si une analyse de texture des images IRM RT pourrait diffĂ©rencier le stade de la fibrose pour un mĂȘme patient lors dâune Ă©tude de suivi. De mĂȘme, dâautres Ă©tudes sont nĂ©cessaires afin de valider lâutilisation de texture sur des scanners IRM diffĂ©rents. Ătablir lâĂąge de lâinfarctus du myocarde permettra de planifier les interventions thĂ©rapeutiques et dâĂ©valuer le pronostique pour le patient.----------ABSTRACT
A third of the United States population is affected by cardiomyopathies. Impairment of the heart muscle, the myocardium, puts the patientâs health at risk and could ultimately lead to death. Ischemic cardiomyopathies result from lack of blood (ischemia) reaching the myocardium from blocked coronary arteries. Non-ischemic cardiomyopathies are diseases from other etiology than ischemia. Often collagen fibers infiltrate the heart (fibrosis), as a means to maintain its shape and function in the presence of disease that affects the myocardial cellular structure. This necessary phenomenon ultimately becomes maladaptive and results in the heartâs impairment. Part of the heartâs involvement in disease can be assessed through the analysis of myocardial fibrosis. Cardiomyopathy diagnosis involves the investigation of the presence of myocardial fibrosis, either infiltrative, defined as the increased presence of collagen protein in the extracellular space, or replacement fibrosis, when collagen fibers progressively replace diseased cardiomyocytes. The infiltrative fibrosis is believed to be reversible in some instances and consequently, myocardial fibrosis analysis has decisional impact on the interventional procedure that would benefit the health of the patient. The heart contracts and relaxes as it pumps blood to the rest of the body, an action directly impaired by myocardial damage. Any myocardial involvement should be assessed by the clinician to identify the severity of the myocardial damage, establish a prognosis and plan therapeutic intervention.
Different diagnostic tests are required to image the myocardium and help the clinician in the diagnostic process. Cardiac magnetic resonance (CMR) imaging has emerged as a high resolution imaging modality that offers precise structural analysis of the heart. Among the different imaging sequences available with CMR, late gadolinium enhancement (LGE) shows the myocardium and enhances any impairments that may exist with the use of a contrast agent. It is a T1-weighted image with extracellular contrast agent (CA) administration. Increased signal intensity in the infarct scar is created from the CA dynamics. LGE CMR imaging offers information on the scar size and its location. The clinician can estimate the severity of the disease and establish prognosis with LGE CMR images.
In ischemic cardiomyopathy, it is important to establish the occurrence of the infarction and know the age of the infarct to plan surgical intervention. Differentiation of acute from chronic MI is therefore important in the diagnostic process. In LGE CMR the level of signal intensity or the size of infarction are both similar in acute or in chronic MI. It has therefore been challenging to distinguish acute MI from chronic MI scars with LGE CMR images alone.
The aim of this thesis was to investigate texture analysis of LGE CMR images to determine if acute MI could be distinguished from chronic MI and to detect increased presence of diffuse myocardial fibrosis in the myocardium. The first study was performed to investigate if texture analysis of LGE CMR images could detect variations in the presence of diffuse myocardial fibrosis and if the underlying myocardial structure could be related to the texture measures. Collagen content increased with aging and we used an animal model of young versus old rat. An ex-vivo animal model was necessary to allow for higher image resolution in LGE CMR images and to perform validation of our texture measures with histology images. Twenty four animals were scanned for LGE CMR images and texture analysis was applied to the heart images. Histology slices were stained with picrosirius red and collagen fibers were isolated based on their color content. LGE CMR quantification was compared to histological slices of the heart stained with the picrosirius red method. Texture analysis of LGE CMR images was also compared to the original LGE CMR image quantification and to histology. Texture analysis was done by creating contrast texture maps extracted from Haralickâs gray level co-occurrence matrix (GLCM). Regions of complex signal intensity combination were enhanced in LGE CMR images and in contrast texture maps. Regression analysis was performed to assess the level of agreement between the different analysis methods. We found that LGE CMR images could assess the different levels of collagen content in the different aged animal model, and that moreover texture analysis enhanced those differences. The location of enhancement from texture analysis images corresponded to location of increased collagen content in the old compared to the young rat hearts. Histological validation was shown for texture analysis applied to LGE CMR images to assess myocardial fibrosis.
Our second study aimed at discriminating acute versus chronic MI from LGE CMR patient images alone through the use of texture analysis. Twenty two patients who had LGE CMR images were included in our study (12 acute and 12 chronic MI). Regional segmentation was performed and texture features were compared in those regions between both groups of patient. Texture analysis resulted in significantly different values between the two groups. More specifically the peri-infarct zone had the most number of discriminative features compared to the remote myocardium which had none and to the infarct core where only the mean features was significantly different. The border zone has been shown to be composed of healthy cardiomyocytes intermingled with the scarâs collagen fibers. Our study indicates this region might exhibit structural differences in the myocardium in acute from chronic MI patients that texture analysis of LGE CMR images can detect.
Characterization of myocardial collagen content is important while clinicians analyze the state of the patient since it influences the course of action required to treat cardiomyopathies. LGE CMR images have been thoroughly used and validated to characterize focal myocardial scar, however it was limited in characterizing the age of infarction or quantifying diffuse collagen content. We have shown texture analysis of LGE CMR images alone can differentiate and even classify, acute from chronic MI patients, which was not previously possible. Characterization of myocardial infarction according to age will prove important in planning therapeutic interventions in clinical practice. Moreover, we have established texture analysis as a means to characterize the myocardium and detect variation in fibrosis content from high resolution LGE CMR images with histology validation. To our knowledge, such a relation between texture analysis of LGE CMR images and the underlying myocardial structure had not been done previously.
Improvements could be done to our method, as we can increase the number of texture features that were analyzed from the GLCM, include other texture analysis methods such as the run-length matrix, and apply our method to other CMR imaging sequences such as T1 mapping. Adapting the GLCM to the heart could also be investigated, such as considering circular GLCM computation to consider the round shape of the myocardium in the short axis LGE CMR image slices. Directional GLCM could also be computed individually and analyzed for any myocardial or collagen fiber orientation indication. Further analysis is also required to establish if texture analysis could differentiate the age of MI in the same individual through a follow-up study. The measures of texture analysis from LGE CMR images obtained through different CMR scanners remains to be investigated as well. Knowing the age of infarct and evaluating the presence of diffuse myocardial fibrosis will help the clinician plan therapeutic interventions and establish a prognosis for the patient
Planification de lâablation radiofrĂ©quence des arythmies cardiaques en combinant modĂ©lisation et apprentissage automatique
Cardiac arrhythmias are heart rhythm disruptions which can lead to sudden cardiac death. They require a deeper understanding for appropriate treatment planning. In this thesis, we integrate personalized structural and functional data into a 3D tetrahedral mesh of the biventricular myocardium. Next, the Mitchell-Schaeffer (MS) simplified biophysical model is used to study the spatial heterogeneity of electrophysiological (EP) tissue properties and their role in arrhythmogenesis. Radiofrequency ablation (RFA) with the elimination of local abnormal ventricular activities (LAVA) has recently arisen as a potentially curative treatment for ventricular tachycardia but the EP studies required to locate LAVA are lengthy and invasive. LAVA are commonly found within the heterogeneous scar, which can be imaged non-invasively with 3D delayed enhanced magnetic resonance imaging (DE-MRI). We evaluate the use of advanced image features in a random forest machine learning framework to identify areas of LAVA-inducing tissue. Furthermore, we detail the datasetâs inherent error sources and their formal integration in the training process. Finally, we construct MRI-based structural patient-specific heart models and couple them with the MS model. We model a recording catheter using a dipole approach and generate distinct normal and LAVA-like electrograms at locations where they have been found in clinics. This enriches our predictions of the locations of LAVA-inducing tissue obtained through image-based learning. Confidence maps can be generated and analyzed prior to RFA to guide the intervention. These contributions have led to promising results and proofs of concepts.Les arythmies sont des perturbations du rythme cardiaque qui peuvent entrainer la mort subite et requiĂšrent une meilleure comprĂ©hension pour planifier leur traitement. Dans cette thĂšse, nous intĂ©grons des donnĂ©es structurelles et fonctionnelles Ă un maillage 3D tĂ©traĂ©drique biventriculaire. Le modĂšle biophysique simplifiĂ© de Mitchell-Schaeffer (MS) est utilisĂ© pour Ă©tudier lâhĂ©tĂ©rogĂ©nĂ©itĂ© des propriĂ©tĂ©s Ă©lectrophysiologiques (EP) du tissu et leur rĂŽle sur lâarythmogĂ©nĂšse. Lâablation par radiofrĂ©quence (ARF) en Ă©liminant les activitĂ©s ventriculaires anormales locales (LAVA) est un traitement potentiellement curatif pour la tachycardie ventriculaire, mais les Ă©tudes EP requises pour localiser les LAVA sont longues et invasives. Les LAVA se trouvent autour de cicatrices hĂ©tĂ©rogĂšnes qui peuvent ĂȘtre imagĂ©es de façon non-invasive par IRM Ă rehaussement tardif. Nous utilisons des caractĂ©ristiques dâimage dans un contexte dâapprentissage automatique avec des forĂȘts alĂ©atoires pour identifier des aires de tissu qui induisent des LAVA. Nous dĂ©taillons les sources dâerreur inhĂ©rentes aux donnĂ©es et leur intĂ©gration dans le processus dâapprentissage. Finalement, nous couplons le modĂšle MS avec des gĂ©omĂ©tries du coeur spĂ©cifiques aux patients et nous modĂ©lisons le cathĂ©ter avec une approche par un dipĂŽle pour gĂ©nĂ©rer des Ă©lectrogrammes normaux et des LAVA aux endroits oĂč ils ont Ă©tĂ© localisĂ©s en clinique. Cela amĂ©liore la prĂ©diction de localisation du tissu induisant des LAVA obtenue par apprentissage sur lâimage. Des cartes de confiance sont gĂ©nĂ©rĂ©es et peuvent ĂȘtre utilisĂ©es avant une ARF pour guider lâintervention. Les contributions de cette thĂšse ont conduit Ă des rĂ©sultats et des preuves de concepts prometteurs
Knowledge discovery on the integrative analysis of electrical and mechanical dyssynchrony to improve cardiac resynchronization therapy
Cardiac resynchronization therapy (CRT) is a standard method of treating heart failure by coordinating the function of the left and right ventricles. However, up to 40% of CRT recipients do not experience clinical symptoms or cardiac function improvements. The main reasons for CRT non-response include: (1) suboptimal patient selection based on electrical dyssynchrony measured by electrocardiogram (ECG) in current guidelines; (2) mechanical dyssynchrony has been shown to be effective but has not been fully explored; and (3) inappropriate placement of the CRT left ventricular (LV) lead in a significant number of patients.
In terms of mechanical dyssynchrony, we utilize an autoencoder to extract new predictive features from nuclear medicine images, characterizing local mechanical dyssynchrony and improving the CRT response rate. Although machine learning can identify complex patterns and make accurate predictions from large datasets, the low interpretability of these black box methods makes it difficult to integrate them with clinical decisions made by physicians in the healthcare setting. Therefore, we use visualization techniques to enable physicians to understand the physical meaning of new features and the reasoning behind the clinical decisions made by the artificial intelligent model.
For electrical dyssynchrony, we use short-time Fourier transform (STFT) to transform one-dimensional waveforms into two-dimensional frequency-time spectra. And transfer learning is used to leverage the knowledge learned from a large arrhythmia ECG dataset of related medical conditions to improve patient selection for CRT with limited data. This improves prediction accuracy, reduces the time and resources required, and potentially leads to better patient outcomes. Furthermore, an innovative approach is proposed for using three-dimensional spatial VCG information to describe the characteristics of electrical dyssynchrony, locate the latest activation site, and combine it with the latest mechanical contraction site to select the optimal LV lead position.
In addition, we apply deep reinforcement learning to the decision-making problem of CRT patients. We investigate discrete state space/specific action space models to find the best treatment strategy, improve the reward equation based on the physician\u27s experience, and learn the approximation of the best action-value function that can improve the treatment policy used by clinicians and provide interpretability
Doctor of Philosophy
dissertationImage segmentation entails the partitioning of an image domain, usually two or three dimensions, so that each partition or segment has some meaning that is relevant to the application at hand. Accurate image segmentation is a crucial challenge in many disciplines, including medicine, computer vision, and geology. In some applications, heterogeneous pixel intensities; noisy, ill-defined, or diffusive boundaries; and irregular shapes with high variability can make it challenging to meet accuracy requirements. Various segmentation approaches tackle such challenges by casting the segmentation problem as an energy-minimization problem, and solving it using efficient optimization algorithms. These approaches are broadly classified as either region-based or edge (surface)-based depending on the features on which they operate. The focus of this dissertation is on the development of a surface-based energy model, the design of efficient formulations of optimization frameworks to incorporate such energy, and the solution of the energy-minimization problem using graph cuts. This dissertation utilizes a set of four papers whose motivation is the efficient extraction of the left atrium wall from the late gadolinium enhancement magnetic resonance imaging (LGE-MRI) image volume. This dissertation utilizes these energy formulations for other applications, including contact lens segmentation in the optical coherence tomography (OCT) data and the extraction of geologic features in seismic data. Chapters 2 through 5 (papers 1 through 4) explore building a surface-based image segmentation model by progressively adding components to improve its accuracy and robustness. The first paper defines a parametric search space and its discrete formulation in the form of a multilayer three-dimensional mesh model within which the segmentation takes place. It includes a generative intensity model, and we optimize using a graph formulation of the surface net problem. The second paper proposes a Bayesian framework with a Markov random field (MRF) prior that gives rise to another class of surface nets, which provides better segmentation with smooth boundaries. The third paper presents a maximum a posteriori (MAP)-based surface estimation framework that relies on a generative image model by incorporating global shape priors, in addition to the MRF, within the Bayesian formulation. Thus, the resulting surface not only depends on the learned model of shapes,but also accommodates the test data irregularities through smooth deviations from these priors. Further, the paper proposes a new shape parameter estimation scheme, in closed form, for segmentation as a part of the optimization process. Finally, the fourth paper (under review at the time of this document) presents an extensive analysis of the MAP framework and presents improved mesh generation and generative intensity models. It also performs a thorough analysis of the segmentation results that demonstrates the effectiveness of the proposed method qualitatively, quantitatively, and clinically. Chapter 6, consisting of unpublished work, demonstrates the application of an MRF-based Bayesian framework to segment coupled surfaces of contact lenses in optical coherence tomography images. This chapter also shows an application related to the extraction of geological structures in seismic volumes. Due to the large sizes of seismic volume datasets, we also present fast, approximate surface-based energy minimization strategies that achieve better speed-ups and memory consumption
The nuclear medicine technologist will see you now
Background: It has been estimated that an additional 3500 radiographers alone are needed over the next 5 years. Assistant Practitioners, Advanced Practitioners and Radiologists equals further 2500 positions. A major expansion in the imaging workforce is a must to fulfil the increasing demand for radiology services. Recruitment within existing radiology workforce and training in Nuclear Medicine had proven insufficient. Development of Apprenticeship for Nuclear Medicine degree at Cumbria University was essential. Registration with The Academy for Healthcare Science (AHCS) was guaranteed upon completion.
Methods used: Data analysis from the first University intake in 2017 through 2018, 2019 and the very challenging 2020 cohort of apprentices.
Assessment of the recruitment process including candidate background, experience and education.
Studentsâ journey and feedback from their degree level 6 studies.
Data for the number of graduating students across cohorts.
Retention data of newly qualified professionals in training departments.
Summary: Recruiting candidates internally, ensuring they have a healthcare experience, facilitate retention post qualification.
Fulfilment of University requirements regarding UCAS points proves to be a valuable tool to ensure studies completion.
UHS alone managed to recruit four candidates. Two already qualified with 1st hons degree and working at band 5 level and the other two are determined to progress within the profession upon graduation.
Conclusion: It had been proved that candidates with prior healthcare experience are more likely to successfully complete studies. They perform well within the role and progress guaranteeing retention. Structured training with university input ensured highly qualified workforce registered with AHCS
The role of feature-tracking cardiovascular magnetic resonance in optimising response to cardiac resynchronisation therapy
Cardiac resynchronisation therapy (CRT) forms part of the established treatment for heart failure, but individual response is variable. Deformation imaging permits assessment of myocardial mechanics. Echocardiography-based techniques are unable to refine patient selection for CRT, although can identify preferential late mechanically activated (LMA) targets for lead placement. Feature-tracking (FT) is a rapid cardiac magnetic resonance (CMR) deformation technique performed on standard acquisition, overcoming the limitations of myocardial tagging (MT). This work aims to validate FT-CMR against MT and establish its role in patient selection and left ventricular (LV) lead deployment in the context of CRT.A validation study performed on healthy volunteers and cardiomyopathy patients demonstrated good intra- and inter-observer variability, and reasonable agreement compared with MT. In a retrospective observational study of CRT recipients, greater baseline dyssynchrony did not predict LV reverse remodelling (LVRR) or symptomatic response at 6 months, but low strain was associated with a high risk of cardiovascular mortality. Furthermore, lead deployment over non-scarred, LMA myocardium, assessed using late gadolinium enhancement (LGE) and FT-CMR was associated with better LVRR and long term survival. FT-CMR showed no ability to enhance patient selection for CRT but, coupled with LGE CMR, has a role in guiding LV lead deployment
Analysis of first pass myocardial perfusion imaging with magnetic resonance
Early diagnosis and localisation of myocardial perfusion defects is an important step in the treatment of coronary artery disease. Thus far, coronary angiography is the conventional standard investigation for patients with known or suspected coronary artery disease and it provides information about the presence and location of coronary stenoses. In recent years, the development of myocardial perfusion CMR has extended the role of MR in the evaluation of ischaemic heart disease beyond the situations where there have already been gross myocardial changes such as acute infarction or scarring. The ability to non-invasively evaluate cardiac perfusion abnormalities before pathologic effects occur, or as follow-up to therapy, is important to the management of patients with coronary artery disease. Whilst limited multi-slice 2D CMR perfusion studies are gaining increased clinical usage for quantifying gross ischaemic burden, research is now directed towards complete 3D coverage of the myocardium for accurate localisation of the extent of possible defects. In 3D myocardial perfusion imaging, a complete volumetric data set has to be acquired for each cardiac cycle in order to study the first pass of the contrast bolus. This normally requires a relatively large acquisition window within each cardiac cycle to ensure a comprehensive coverage of the myocardium and reasonably high resolution of the images. With multi-slice imaging, long axis cardiac motion during this large acquisition window can cause the myocardium imaged in different cross- sections to be mis-registered, i.e., some part of the myocardium may be imaged more than twice whereas other parts may be missed out completely. This type of mis-registration is difficult to correct for by using post-processing techniques. The purpose of this thesis is to investigate techniques for tracking through plane motion during 3D myocardial perfusion imaging, and a novel technique for extracting intrinsic relationships between 3D cardiac deformation due to respiration and multiple ID real-time measurable surface intensity traces is developed. Despite the fact that these surface intensity traces can be strongly coupled with each other but poorly correlated with respiratory induced cardiac deformation, we demonstrate how they can be used to accurately predict cardiac motion through the extraction of latent variables of both the input and output of the model. The proposed method allows cross-modality reconstruction of patient specific models for dense motion field prediction, which after initial modelling can be use in real-time prospective motion tracking or correction. In CMR, new imaging sequences have significantly reduced the acquisition window whilst maintaining the desired spatial resolution. Further improvements in perfusion imaging will require the application of parallel imaging techniques or making full use of the information content of the Âż-space data. With this thesis, we have proposed RR-UNFOLD and RR-RIGR for significantly reducing the amount of data that is required to reconstruct the perfusion image series. The methods use prospective diaphragmatic navigator echoes to ensure UNFOLD and RIGR are carried out on a series of images that are spatially registered. An adaptive real-time re-binning algorithm is developed for the creation of static image sub-series related to different levels of respiratory motion. Issues concerning temporal smoothing of tracer kinetic signals and residual motion artefact are discussed, and we have provided a critical comparison of the relative merit and potential pitfalls of the two techniques. In addition to the technical and theoretical descriptions of the new methods developed, we have also provided in this thesis a detailed literature review of the current state-of-the-art in myocardial perfusion imaging and some of the key technical challenges involved. Issues concerning the basic background of myocardial ischaemia and its functional significance are discussed. Practical solutions to motion tracking during imaging, predictive motion modelling, tracer kinetic modelling, RR-UNFOLD and RR-RIGR are discussed, all with validation using patient and normal subject data to demonstrate both the strength and potential clinical value of the proposed techniques.Open acces
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