Imagerie médicale | EPFL Graph Search

L'imagerie médicale regroupe les moyens d'acquisition et de restitution d'images du corps humain à partir de différents phénomènes physiques tels que l'absorption des rayons X, la résonance magnétique nucléaire, la réflexion d'ondes ultrasons ou la radioactivité auxquels on associe parfois les techniques d'imagerie optique comme l'endoscopie. Apparues, pour les plus anciennes, au tournant du , ces techniques ont révolutionné la médecine grâce au progrès de l'informatique en permettant de visualiser indirectement l'anatomie, la physiologie ou le métabolisme du corps humain. Développées comme outil diagnostique, elles sont aussi largement utilisées dans la recherche biomédicale pour mieux comprendre le fonctionnement de l'organisme. Elles trouvent aussi des applications de plus en plus nombreuses dans différents domaines tels que la sécurité, l'archéologie et l'art. Historique vignette|upright|Le premier cliché anatomique radiographique par Wilhelm Röntgen. Les débuts de

Dual-Activatable Cell Tracker for Controlled and Prolonged Single-Cell Labeling

Salome Riccarda Püntener, Pablo Rivera Fuentes

Cell trackers are fluorescent chemical tools that facilitate imaging and tracking cells within live organisms. Despite their versatility, these dyes lack specificity, tend to leak outside of the cell, and stain neighboring cells. Here, we report a dual-activatable cell tracker for increased spatial and temporal staining control, especially for single-cell tracking. This probe overcomes the typical problems of current cell trackers: off-target staining, high background signal, and leakage from the intracellular medium. Staining with this dye is not cytotoxic, and it can be used in sensitive primary cells. Moreover, this dye is resistant to harsh fixation and permeabilization conditions and allows for multi-wavelength studies with confocal microscopy and fluorescence-activated cell sorting. Using this cell tracker, we performed in vivo homing experiments in mice with primary splenocytes and tracked a single cell in a heterogeneous, multicellular culture environment for over 20 h. These experiments, in addition to comparative proliferation studies with other cell trackers, demonstrated that the signal from this dye is retained in cells for over 72 h after photoactivation. We envision that this type of probes will facilitate the analysis of single-cell behavior and migration in cell culture and in vivo experiments.

AMER CHEMICAL SOC2020

From volumetric graphical objects to surface registration

Eduardo Solanas Vilar

Relatively recent developments in medical imaging allow the anatomical and functional noninvasive study of internal organs of the body. One of the most complex and interesting organs in our body is the brain. Many efforts are being carried out to try to understand how it works, its internal connections, how different zones interact with each other, why, in some cases, a zone takes in charge a functionality of another damaged one, etc.; the questions are innumerable and the answers, if any, are sparse. Recently, magnetic resonance imaging (MRI) equipments have been developed to capture the functional activity of the brain. Unfortunately, functional images suffer from a lack of spatial resolution. Thus, the need of combining them with anatomical ones, which have a much higher quality. Nowadays these images provide precious information for diagnosis purposes and this is the reason for the need of imaging tools to extract and analyze the information which is present in those images, and which sometimes is not appreciable for the human eye. Acquired MRI images are presented as volumetric sets of data with which one can reconstruct a three dimensional volumetric model of the scanned organ. Nevertheless, sometimes physicians are just interested in the surface of the organ, for example in brain functional activation studies. In addition, many times it is important to be able to carry out group studies, or to locate a lesion with respect to a standard reference. To do such studies it is necessary to perform a transformation of the surfaces in order to put them into correspondence. Such technique is called registration. Unfortunately, volumetric registration does not give satisfactory results for surface registration purposes. The solution is to work directly with surfaces instead of processing the volumetric data. Unfortunately we do not have information about the surfaces, hence the need of computer graphics and computer vision techniques. The present work proposes a number of methods to help in the task of providing information from volumetric objects to surface registration. To achieve that some stages are necessary. The proposed stages are independent one from another so they can be used, or not, depending on the particular need. They can even be used for other purposes than those presented in the present work. The first two stages address the problem of image quality. This is a very important "preprocessing" step because posterior processing will depend on the image quality. Usually MR images are corrupted with different artifacts; among them a spatial-dependent bias field and a Riciandistributed noise. For the former a supervised correction is proposed, and for the later a directional spatial filter using a novel diffusivity function. After both stages the volumetric MRI data is ready to be classified. The purpose of the proposed classification process is to extract the white matter of the brain, given that sulci are better extracted from white matter than from the gray one. So far we have handled volumetric data, the next stage is concerned with the registration of the reconstructed white matter surface. This is achieved by means of a hierarchical representation of brain's sulci using level sets. The hierarchical representation and surface registration are carried out using the theory of partial differential equations (PDE) and level set methods. Validation studies are carried out independently in order to show the performance of the proposed approaches. Also a full scheme is proposed with the aim of presenting a complete approach from volumetric objects to surface registration.

EPFL2004

Breast texture synthesis and estimation of the role of the anatomy and tumor shape in the radiological detection process

Cyril Castella

Breast cancer is the most common, and the number one cause of death by cancer among women. However, when it is sufficiently early detected, heavy treatments can be avoided, and morbidity and mortality can be reduced. This is the reason why randomized clinical trials were started in the 60s, followed in the last decades by screening mammography national programs. The process of breast cancer detection in mammography is complex. Its understanding offers numerous challenges to radiologists and medical physicists. One way to apprehend it is to model this process step-by-step by performing psychophysical experiments with anatomical or synthetic images. In this approach, the images information content is controlled. Since the first experiments with synthetic images created with white noise and simple geometric signals, technical and computational improvements allowed to get ever closer to clinical realism for studying the mechanism of perception of a signal on a radiological image. The present work extends the list of tools that have been used until now in psychophysical experiments in mammography. It proposes a detailed statistical analysis of anatomical images, from which algorithms for breast density classification and realistic breast texture synthesis are developed. In a second phase, psychophysical experiments with simple signals and benign or malignant masses combined with anatomical or synthetic backgrounds are presented. The performance of human observers is analyzed as a function of parameters such as background type, signal type, or uncertainty about the size or the shape of the signal. These results are compared to that of existing or adapted models from the literature, and the different models are evaluated in their ability to predict the performance of human observers for the detection of lesions in such conditions. Each step of this project focused on the objective and reproducible aspect of the image evaluation or of the observer performance. Controlled yet realistic conditions ensure the robustness of the results, as well as their clinical adaptability. Among the main results, synthetic mammographic texture images have been generated and validated. They provide a virtually unlimited database of images with demonstrated visual and statistical realism. Concerning the analysis of the human observers' performance, this work shows that they are sensitive to uncertainty about the signal size but not about its shape, that they use similar detection strategies with real and synthetic images, and that they are mainly sensitive to the anatomical fluctuations in the immediate area around the signal location. These effects, as well as the performance level of human observers for the detection tasks under consideration, could be reproduced by models taking into account human visual system characteristics. The conclusions of this work will be able to guide future studies in the field of detection tasks in mammography or tomosynthesis. This 3D breast imaging technique presents, like mammography, numerous challenges in order to understand and to objectively characterize its clinical potential. Studies with model observers specifically validated for detection tasks in medical imaging provide an excellent alternative in terms of time and costs for answering these questions.