Reproducibility of coronary artery diameter assessments in magnetic resonance coronary angiography: phantom study

This report describes the development of a deformable model for the automatic delineation of coronary artery cross-sectional areas with magnetic resonance imaging. The method is validated with coronary artery phantoms of varying diameters and images with different levels of signal-to-noise ratios. The reproducibility of the technique was examined with simulated geometrical shifts and motions during data acquisition. The experimental results indicate a very high reproducibility and low inter-observers variability of the technique, suggesting its suitability for non-invasive assessment of serial changes of vessel dilatation following pharmacological intervention

Automated Assessment of Aortic and Main Pulmonary Arterial Diameters using Model-Based Blood Vessel Segmentation for Predicting Chronic Thromboembolic Pulmonary Hypertension in Low-Dose CT Lung Screening

Chronic thromboembolic pulmonary hypertension (CTEPH) is characterized by obstruction of the pulmonary vasculature by residual organized thrombi. A morphological abnormality inside mediastinum of CTEPH patient is enlargement of pulmonary artery. This paper presents an automated assessment of aortic and main pulmonary arterial diameters for predicting CTEPH in low-dose CT lung screening. The distinctive feature of our method is to segment aorta and main pulmonary artery using both of prior probability and vascular direction which were estimated from mediastinal vascular region using principal curvatures of four-dimensional hyper surface. The method was applied to two datasets, 64 low-dose CT scans of lung cancer screening and 19 normal-dose CT scans of CTEPH patients through the training phase with 121 low-dose CT scans. This paper demonstrates effectiveness of our method for predicting CTEPH in low-dose CT screening

Segmentation of the skull in MRI volumes using deformable mode

In this paper, we present a new approach for segmenting regions of bone in MRI volumes using a deformable model . Our method takes into account the partial volume effects that occur with MRI data, thus permitting a precise segmentation of these bone regions . Partial volume is estimated, in a narrow band around the deformable model, at each iteration of the propagation of the model . Segmentation of the skull in medical imagery is an important stage in applications that require the construction of realistic models of the head . Such models are used, for example, to simulate the behavior of electro- magnetic fields in the head and to model the electrical activity of the cortex in EEG and MEG data . Our segmentation method begins with a pre-segmentation stage, in which a preliminary segmentation of the skull is constructed using a region-growing method . The surface which bounds the pre-segmented skull region offers an automatic 3D initialization of the deformable model . This surface is propagated (in 3D) in the direction of its normal . This propagation is achieved using level set method, thus permitting changes to occur in the topology of the surface as it evolves, an essential capability for our problem . The speed at which the surface evolves is a function of the estimated partial volume. This provides a sub-voxel accuracy in the resulting segmentation .Dans ce papier, nous présentons une méthode de segmentation par modèle déformable des régions osseuses de la tête à partir de données IRM 3D. Cette segmentation prend en compte l'effet du volume partiel présent en IRM permettant ainsi une segmentation précise de l'os. La segmentation du crâne est une étape importante dans les applications nécessitant la construction d'un modèle réaliste de la tête. Ce type de modèle est utilisé, entre autres, pour la simulation du comportement d'un champ électromagnétique dans les tissus de la tête, ainsi que pour la modélisation de l'activité électrique du cortex en EEG et MEG. La méthode de segmentation proposée commence par une pré-segmentation du crâne avec une technique de croissance de région. Le résultat de la pré-segmentation est ensuite raffiné par la propagation, en 3D, de la surface de la région pré-segmentée dans le sens de la normale à cette surface. La propagation est réalisée par la détection des courbes de niveau d'une hypersurface, permettant ainsi des changements de topologie avantageux dans notre cas. L'effet du volume partiel est pris en considération lors de la formulation du terme de vitesse de la surface propagée, ce qui permet de réaliser une segmentation sub-voxelique du crâne

Specular reflection removal and bloodless vessel segmentation for 3-D heart model reconstruction from single view images

Three Dimensional (3D) human heart model is attracting attention for its role in medical images for education and clinical purposes. Analysing 2D images to obtain meaningful information requires a certain level of expertise. Moreover, it is time consuming and requires special devices to obtain aforementioned images. In contrary, a 3D model conveys much more information. 3D human heart model reconstruction from medical imaging devices requires several input images, while reconstruction from a single view image is challenging due to the colour property of the heart image, light reflections, and its featureless surface. Lights and illumination condition of the operating room cause specular reflections on the wet heart surface that result in noises forming of the reconstruction process. Image-based technique is used for the proposed human heart surface reconstruction. It is important the reflection is eliminated to allow for proper 3D reconstruction and avoid imperfect final output. Specular reflections detection and correction process examine the surface properties. This was implemented as a first step to detect reflections using the standard deviation of RGB colour channel and the maximum value of blue channel to establish colour, devoid of specularities. The result shows the accurate and efficient performance of the specularities removing process with 88.7% similarity with the ground truth. Realistic 3D heart model reconstruction was developed based on extraction of pixel information from digital images to allow novice surgeons to reduce the time for cardiac surgery training and enhancing their perception of the Operating Theatre (OT). Cardiac medical imaging devices such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) images, or Echocardiography provide cardiac information. However,these images from medical modalities are not adequate, to precisely simulate the real environment and to be used in the training simulator for cardiac surgery. The propose method exploits and develops techniques based on analysing real coloured images taken during cardiac surgery in order to obtain meaningful information of the heart anatomical structures. Another issue is the different human heart surface vessels. The most important vessel region is the bloodless, lack of blood, vessels. Surgeon faces some difficulties in locating the bloodless vessel region during surgery. The thesis suggests a technique of identifying the vessels’ Region of Interest (ROI) to avoid surgical injuries by examining an enhanced input image. The proposed method locates vessels’ ROI by using Decorrelation Stretch technique. This Decorrelation Stretch can clearly enhance the heart’s surface image. Through this enhancement, the surgeon become enables effectively identifying the vessels ROI to perform the surgery from textured and coloured surface images. In addition, after enhancement and segmentation of the vessels ROI, a 3D reconstruction of this ROI takes place and then visualize it over the 3D heart model. Experiments for each phase in the research framework were qualitatively and quantitatively evaluated. Two hundred and thirteen real human heart images are the dataset collected during cardiac surgery using a digital camera. The experimental results of the proposed methods were compared with manual hand-labelling ground truth data. The cost reduction of false positive and false negative of specular detection and correction processes of the proposed method was less than 24% compared to other methods. In addition, the efficient results of Root Mean Square Error (RMSE) to measure the correctness of the z-axis values to reconstruction of the 3D model accurately compared to other method. Finally, the 94.42% accuracy rate of the proposed vessels segmentation method using RGB colour space achieved is comparable to other colour spaces. Experimental results show that there is significant efficiency and robustness compared to existing state of the art methods

Etude morphologique et métrologique des sinus de Valsalva par traitement d'images tomographiques

L'objectif de cette thèse est l'élaboration et l'application de traitements d'images pour permettre une étude objective et fiable des sinus de Valsalva, importantes cavités de la base de l'aorte. Les méthodes proposées s'appliquent aux séquences ciné-IRM et aux examens de scanner sans qu'il n'y ait à modifier le paramétrage entre deux examens. Pour cela, nous avons d'abord étudié la morphologie de cette zone anatomique puis détaillé les différentes propriétés communes à toutes les images de sinus. Ceux-ci font en l'occurrence partie des principaux organes clairs et peu mobiles. Nous avons donc développé un algorithme qui détecte ces éléments et caractérise chacun d'entre eux par une trajectoire unique. Divers outils de morphologie mathématique ont été utilisés à cette occasion, tout comme pour l'extraction du contour des sinus dans chaque image. L'étape de segmentation repose elle sur la reconstruction géodésique, qui s'avère plus efficace et surtout plus robuste que l'usage de contours actifs usuels. L'intérieur des sinus forme un domaine simplement connexe et étoilé. Grâce à ce postulat, nous avons conçu une nouvelle reconstruction, nommée transformée en aurore, qui limite la propagation des intensités aux supports radiaux et présente les résultats dans un repère polaire pour une meilleure lecture des contours.Les points caractéristiques des sinus ont également été détectés, par étude de rayons et détermination de points dominants. Ces points fournissent les éléments nécessaires à une mesure automatique des sinus, mesure cohérente avec les mesures actuellement réalisées manuellement et les variations intra et inter-observateurs de celles-ci. D'autres outils sont enfin esquissés pour modéliser le contour par coniques, classer les images d'examens cinétiques en fonction du moment du cycle et suivre le mouvement des valves dans ces mêmes examens.L'ensemble de ces travaux ont amené à la réalisation d'un logiciel d'aide au diagnostic qui intègre nos méthodes et dont l'interface est également présentée dans le présent mémoire.This Phd thesis deals with the design and the use of image processing tools in order to allow a reliable and objective study of the sinuses of Valsalva which are important cavities of the aortic root. The proposed methods can be applied on cine-MR sequences and CT examinations without any change in the settings between two examinations.Firstly, we studied the morphology of this anatomical area and its constant properties in all images of the dataset. Sinuses are one of the main bright organs with limited movements. Hence a new algorithm has been designed. It detects and characterizes each bright organ by a single trajectory. Various tools of mathematical morphology are used for this step, as for the extraction of the contour of the sinuses in each image.The segmentation step is based on the geodesic reconstruction, which is more effective and more robust than the usual active contours. The shape depicting the sinuses is simply connected and a star domain. With this assumption, a new reconstruction is proposed, called the Aurora transform. This transform limits the spread of intensities only on the radial lines and shows its results in a polar space for a better reading of edges.The relevant points of the sinuses are also detected by a study of radii and the determination of dominant points along edges. An automatic measurement of the sinuses is deduced from these points. The values are very close to the manual measures currently done according to the intra-and inter-observer variations.Some other tools are finally outlined. They includes the modeling of edges by conics, the image classification depending on the time of the cycle in sequences and the tracking of the aortic valves in these examinations.This work led to the devlopement of a diagnostic aid software based on our methods. Its interface is also presented herein.DIJON-BU Doc.électronique (212319901) / SudocSudocFranceF

Algorithmic assessment of cardiac viability using magnetic resonance imaging

MRI is a non-invasive imaging method which produces high resolution images of human tissues from inside the human body. Due to its outstanding ability, it is quickly becoming a major tool for medical and clinical studies, including high profile areas such as neurology, oncology, cardiology and etc. MRI technology developed relatively slowly compared to other methods such as x-ray. A decade ago, it took more than 5 minutes to construct an MR image. However more recently, with several significant inventions such as echo planar imaging and steady state free procession techniques, the acquisition time of MRI has significantly reduced. At present, it is possible to capture dozens of MR images in a second. Those techniques are generally called ultra-fast MRI. The fast MR acquisition techniques enable us to extend our studies to the moving tissues such as the myocardium. Using the ultra-fast MRI, multiple images can be acquired during a cardiac cycle allowing the construction of cardiac cinematographic MR images. Cardiac motion can therefore be revealed. Abnormal cardiac motion is often related to cardiac diseases such as ischaemic myocardium and myocardial infarction. With advanced MRI techniques, cardiac diseases can be more specifically defined. For example, the late contrast enhanced MRI highlights acute myocardial infarction. The first-pass perfusion MRI suggests the existence of ischaemic myocardium. At the present time the majority of the analysis of MR images can be performed either qualitatively or quantitatively. The qualitative assessment is an eye-ball assessment of the images on a MRI workstation, which is subjective and inaccurate. The quantitative assessment of MR image relies on the computer technologies of both hardware and software. In recent years, the demands for the quantitative assessment of MR images have increased sharply. Many so-called computer aided diagnosis systems were developed to process data either more accurately or more efficiently. In this study, we developed an algorithmic method to analyse the late contrast enhanced MR images, revealing the so-called hibernating myocardium. The algorithm is based on an efficient and robust image registration algorithm. Using the image registration algorithm, we are able to integrate the static late contrast enhanced MR image with its corresponding cardiac cinematography MR images, and so constructing cardiac CINE late enhanced MR images. Our algorithm was tested on 20 subjects. In each of the subject, the mean left ventricle diastolic volume and systolic volume was measured by planimetry from both the original CINE images and the constructed late enhanced CINE images. The results are: left ventricle diastolic volume (original / constructed) = 206 / 215 ml, p = 0.35. Left ventricle systolic volume (original / constructed) = 129 / 123 ml, p = 0.33. With our algorithm, the cardiac motion and the myocardial infarction can therefore be studied simultaneously to locate the hibernating myocardium which moves abnormally. The accurate location of the hibernating myocardium is important because it could turn into the irreversible myocardial infarction. On the other hand, with proper medical treatment or cardiac surgery, the hibernating myocardium could be revitalised. The experimental results show there are no significant differences between the artificial cine late contrast enhanced MR images and the original cinematography MR images in left ventricle diastolic volume, left ventricle systolic volume. The method therefore appears promising as an improved cardiac viability assessment tool. In addition, we extended the method to a semi-automatic cardiac contour definition algorithm, which has produced a satisfactory result in contour definition for cardiac cinematography MR images from 34 subjects including 20 healthy volunteers and 14 patients. Although it is a semi-automatic method, the diagnosis time could be significantly reduced compared to the manual method. The algorithm was preliminarily tested on 10 first-pass perfusion MR sequences and 10 aortic MR sequences. The experimental results were satisfactory. Although, minor manual correction is required on some occasions, we believe our method could be clinically useful for the study of cardiac cinematography MR images, first-pass perfusion MR images and aortic MR images

Multi-scale active shape description in medical imaging

Shape description in medical imaging has become an increasingly important research field in recent years. Fast and high-resolution image acquisition methods like Magnetic Resonance (MR) imaging produce very detailed cross-sectional images of the human body - shape description is then a post-processing operation which abstracts quantitative descriptions of anatomically relevant object shapes. This task is usually performed by clinicians and other experts by first segmenting the shapes of interest, and then making volumetric and other quantitative measurements. High demand on expert time and inter- and intra-observer variability impose a clinical need of automating this process. Furthermore, recent studies in clinical neurology on the correspondence between disease status and degree of shape deformations necessitate the use of more sophisticated, higher-level shape description techniques. In this work a new hierarchical tool for shape description has been developed, combining two recently developed and powerful techniques in image processing: differential invariants in scale-space, and active contour models. This tool enables quantitative and qualitative shape studies at multiple levels of image detail, exploring the extra image scale degree of freedom. Using scale-space continuity, the global object shape can be detected at a coarse level of image detail, and finer shape characteristics can be found at higher levels of detail or scales. New methods for active shape evolution and focusing have been developed for the extraction of shapes at a large set of scales using an active contour model whose energy function is regularized with respect to scale and geometric differential image invariants. The resulting set of shapes is formulated as a multiscale shape stack which is analysed and described for each scale level with a large set of shape descriptors to obtain and analyse shape changes across scales. This shape stack leads naturally to several questions in regard to variable sampling and appropriate levels of detail to investigate an image. The relationship between active contour sampling precision and scale-space is addressed. After a thorough review of modem shape description, multi-scale image processing and active contour model techniques, the novel framework for multi-scale active shape description is presented and tested on synthetic images and medical images. An interesting result is the recovery of the fractal dimension of a known fractal boundary using this framework. Medical applications addressed are grey-matter deformations occurring for patients with epilepsy, spinal cord atrophy for patients with Multiple Sclerosis, and cortical impairment for neonates. Extensions to non-linear scale-spaces, comparisons to binary curve and curvature evolution schemes as well as other hierarchical shape descriptors are discussed

3D human body modelling from range data

This thesis describes the design, implementation and application of an integrated and fully automated system for interpreting whole-body range data. The system is shown to be capable of generating complete surface models of human bodies, and robustly extracting anatomical features for anthropometry, with minimal intrusion on the subject. The ability to automate this process has enormous potential for personalised digital models in medicine, ergonomics, design and manufacture and for populating virtual environments. The techniques developed within this thesis now form the basis of a commercial product. However, the technical difficulties are considerable. Human bodies are highly varied and many of the features of interest are extremely subtle. The underlying range data is typically noisy and is sparse at occluded areas. In addressing these problems this thesis makes five main research contributions. Firstly, the thesis describes the design, implementation and testing of the whole integrated and automated system from scratch, starting at the image capture hardware. At each stage the tradeoffs between performance criteria are discussed, and experiments are described to test the processes developed. Secondly, a combined data-driven and model-based approach is described and implemented, for surface reconstruction from the raw data. This method addresses the whole body surface, including areas where body segments touch, and other occluded areas. The third contribution is a library of operators, designed specifically for shape description and measurement of the human body. The library provides high-level relational attributes, an "electronic tape measure" to extract linear and curvilinear measurements,as well as low-level shape information, such as curvature. Application of the library is demonstrated by building a large set of detectors to find anthropometric features, based on the ISO 8559 specification. Output is compared against traditional manual measurements and a detailed analysis is presented. The discrepancy between these sets of data is only a few per cent on most dimensions, and the system's reproducibility is shown to be similar to that of skilled manual measurers. The final contribution is that the mesh models and anthropometric features, produced by the system, have been used as a starting point to facilitate other research, Such as registration of multiple body images,draping clothing and advanced surface modelling techniques