Level-Set Based Artery-Vein Separation in Blood Pool Agent CE-MR Angiograms

Blood pool agents (BPAs) for contrast-enhanced (CE) magnetic-resonance angiography (MRA) allow prolonged imaging times for higher contrast and resolution. Imaging is performed during the steady state when the contrast agent is distributed through the complete vascular system. However, simultaneous venous and arterial enhancement in this steady state hampers interpretation. In order to improve visualization of the arteries and veins from steady-state BPA data, a semiautomated method for artery-vein separation is presented. In this method, the central arterial axis and central venous axis are used as initializations for two surfaces that simultaneously evolve in order to capture the arterial and venous parts of the vasculature using the level-set framework. Since arteries and veins can be in close proximity of each other, leakage from the evolving arterial (venous) surface into the venous (arterial) part of the vasculature is inevitable. In these situations, voxels are labeled arterial or venous based on the arrival time of the respective surface. The evolution is steered by external forces related to feature images derived from the image data and by internal forces related to the geometry of the level sets. In this paper, the robustness and accuracy of three external forces (based on image intensity, image gradient, and vessel-enhancement filtering) and combinations of them are investigated and tested on seven patient datasets. To this end, results with the level-set-based segmentation are compared to the reference-standard manually obtained segmentations. Best results are achieved by applying a combination of intensity- and gradient-based forces and a smoothness constraint based on the curvature of the surface. By applying this combination to the seven datasets, it is shown that, with minimal user interaction, artery-vein separation for improved arterial and venous visualization in BPA CE-MRA can be achieved

Image processing platform for the analysis of brain vascular patterns

Aquest projecte consisteix en el desenvolupament d'una aplicació web per al suport metge de l'anàlisi d'imatges cerebrovasculars. L'objectiu és crear un prototip obert i modular que serveixi com a exemple i plantilla per al desenvolupament d'altres projectes. L'objectiu és aconseguir una alternativa a les opcions comercials actualment existents d'eines d'anàlisi de dades en la indústria de la salut. L'aplicació es desenvolupa utilitzant el llenguatge Python. L'aplicació permet a l'usuari carregar imatges mèdiques contingudes en fitxers DICOM, aquestes imatges són processades per eliminar el soroll i extreure els vasos sanguinis de la imatge de cara a l'anàlisi. Els resultats es resumeixen en tres gràfics: un anomenat mapa isocronal que reflecteix l'evolució temporal de el flux de la sang, un altre gràfic mostrant l'esquelet de l'estructura o xarxa de sistema vascular, i un últim gràfic que representa dades numèriques extretes com a paràmetres de l'anàlisi de l'esquelet. El framework Dash és usat per implementar la interfície i la interacció amb l'usuari. L'usuari pot carregar dues mostres diferents a el mateix temps i executar una anàlisi per comparar els resultats de les dues mostres en una mateixa pantalla. Finalment l'aplicació s'empaqueta en un contenidor virtual usant la plataforma Docker. Després de provar l'aplicació amb imatges reals de mostra proporcionades per l'Hospital Sant Joan de Déu, els resultats obtinguts són satisfactoris ja que l'aplicació funciona adequadament així com els algoritmes de processat d'imatge aplicats. Malgrat les limitacions de el projecte, el treball realitzat pot servir com a punt de partida per a futurs desenvolupaments.Este proyecto consiste en el desarrollo de una aplicación web para el soporte médico del análisis de imágenes cerebrovasculares. El objetivo es crear un prototipo abierto y modular que sirva como ejemplo y plantilla para el desarrollo de otros proyectos. El objetivo es conseguir una alternativa a las opciones comerciales actualmente existentes de herramientas de análisis de datos en la industria de la salud. La aplicación se desarrolla usando el lenguaje Python. La aplicación permite al usuario cargar imágenes médicas contenidas en ficheros DICOM, esas imágenes son procesadas para eliminar el ruido y extraer los vasos sanguíneos de la imagen de cara al análisis. Los resultados se resumen en tres gráficos: uno llamado mapa isocronal que refleja la evolución temporal del flujo de la sangre, otro gráfico mostrando el esqueleto de la estructura o red del sistema vascular, y un último gráfico que representa datos numéricos extraídos como parámetros del análisis del esqueleto. El framework Dash es usado para implementar la interfaz y la interacción con el usuario. El usuario puede cargar dos muestras diferentes al mismo tiempo y ejecutar un análisis para comparar los resultados de las dos muestras en una misma pantalla. Finalmente la aplicación se empaqueta en un contenedor virtual usando la plataforma Docker. Tras probar la aplicación con imágenes reales de muestra proporcionadas por el Hospital Sant Joan de Déu, los resultados obtenidos son satisfactorios ya que la aplicación funciona adecuadamente así como los algoritmos de procesado de imagen aplicados. Pese a las limitaciones del proyecto, el trabajo realizado puede servir como punto de partida para futuros desarrollos.This project consists in the development of a web application for the support of medical professionals in the analysis of cerebrovascular image data. The objective is to build an open and modular prototype that can serve as an example or template for the development of other projects. The purpose is to have an open alternative to the commercial options currently available for data analysis tools in the health industry market. The application is developed using Python. The application allows the user to load medical images contained in DICOM files, those images are processed for noise removal and binarization in order to build the result graphs. The results are three graphs: an image graph called “isochronal map” reflecting the temporal evolution of the blood flow, an image graph showing the skeleton of the vascular system structure, a box-plot graph representing the numerical branch data extracted from the skeleton. The Dash framework is used to construct the user interface and to implement the user interaction functionalities. The subject can load two different samples at the same time and execute the analysis to compare the results for both samples in the same screen. Finally the application is containerized using Docker to package it and make it multi-platform. The app is tested and the results are satisfactory as the resulting application works properly and so do the image processing algorithms for the input data provided by the Hospital Sant Joan de Déu. Despite its obvious limitations, the work done serves as a starting point for future developments

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Innovative MRI techniques in neuroimaging approaches for cerebrovascular diseases and vascular cognitive impairment

Cognitive impairment and dementia are recognized as major threats to public health. Many studies have shown the important role played by challenges to the cerebral vasculature and the neurovascular unit. To investigate the structural and functional characteristics of the brain, MRI has proven an invaluable tool for visualizing the internal organs of patients and analyzing the parameters related to neuronal activation and blood flow in vivo. Different strategies of imaging can be combined to obtain various parameters: (i) measures of cortical and subcortical structures (cortical thickness, subcortical structures volume); (ii) evaluation of microstructural characteristics of the white matter (fractional anisotropy, mean diffusivity); (iii) neuronal activation and synchronicity to identify functional networks across different regions (functional connectivity between specific regions, graph measures of specific nodes); and (iv) structure of the cerebral vasculature and its efficacy in irrorating the brain (main vessel diameter, cerebral perfusion). The high amount of data obtainable from multi-modal sources calls for methods of advanced analysis, like machine-learning algorithms that allow the discrimination of the most informative features, to comprehensively characterize the cerebrovascular network into specific and sensitive biomarkers. By using the same techniques of human imaging in pre-clinical research, we can also investigate the mechanisms underlying the pathophysiological alterations identified in patients by imaging, with the chance of looking for molecular mechanisms to recover the pathology or hamper its progression

Method for Narrow Vascular Structure Segmentation in Ct Data.

International audienc

Feasibility and relevance of discrete vasculature modeling in routine hyperthermia treatment planning

Purpose: To investigate the effect of patient specific vessel cooling on head and neck hyperthermia treatment planning (HTP). Methods and materials: Twelve patients undergoing radiotherapy were scanned using computed tomography (CT), magnetic resonance imaging (MRI) and contrast enhanced MR angiography (CEMRA). 3D patient models were constructed using the CT and MRI data. The arterial vessel tree was constructed from the MRA images using the ‘graph-cut’ method, combining information from Frangi vesselness filtering and region growing, and the results were validated against manually placed markers in/outside the vessels. Patient specific HTP was performed and the change in thermal distribution prediction caused by arterial cooling was evaluated by adding discrete vasculature (DIVA) modeling to the Pennes bioheat equation (PBHE). Results: Inclusion of arterial cooling showed a relevant impact, i.e., DIVA modeling predicts a decreased treatment quality by on average 0.19 °C (T90), 0.32 °C (T50) and 0.35 °C (T20) that is robust against variations in the inflow blood rate (|ΔT| 0.5 °C) were observed. Conclusion: Addition of patient-specific DIVA into the thermal modeling can significantly change predicted treatment quality. In cases where clinically detectable vessels pass the heated region, we advise to perform DIVA modeling

A framework for intracranial saccular aneurysm detection and quantification using morphological analysis of cerebral angiograms

Reliable early prediction of aneurysm rupture can greatly help neurosurgeons to treat aneurysms at the right time, thus saving lives as well as providing significant cost reduction. Most of the research efforts in this respect involve statistical analysis of collected data or simulation of hemodynamic factors to predict the risk of aneurysmal rupture. Whereas, morphological analysis of cerebral angiogram images for locating and estimating unruptured aneurysms is rarely considered. Since digital subtraction angiography (DSA) is regarded as a standard test by the American Stroke Association and American College of Radiology for identification of aneurysm, this paper aims to perform morphological analysis of DSA to accurately detect saccular aneurysms, precisely determine their sizes, and estimate the probability of their ruptures. The proposed diagnostic framework, intracranial saccular aneurysm detection and quantification, first extracts cerebrovascular structures by denoising angiogram images and delineates regions of interest (ROIs) by using watershed segmentation and distance transformation. Then, it identifies saccular aneurysms among segmented ROIs using multilayer perceptron neural network trained upon robust Haralick texture features, and finally quantifies aneurysm rupture by geometrical analysis of identified aneurysmic ROI. De-identified data set of 59 angiograms is used to evaluate the performance of algorithms for aneurysm detection and risk of rupture quantification. The proposed framework achieves high accuracy of 98% and 86% for aneurysm classification and quantification, respectively