Semiautomated Skeletonization of the Pulmonary Arterial Tree in Micro-CT Images

We present a simple and robust approach that utilizes planar images at different angular rotations combined with unfiltered back-projection to locate the central axes of the pulmonary arterial tree. Three-dimensional points are selected interactively by the user. The computer calculates a sub- volume unfiltered back-projection orthogonal to the vector connecting the two points and centered on the first point. Because more x-rays are absorbed at the thickest portion of the vessel, in the unfiltered back-projection, the darkest pixel is assumed to be the center of the vessel. The computer replaces this point with the newly computer-calculated point. A second back-projection is calculated around the original point orthogonal to a vector connecting the newly-calculated first point and user-determined second point. The darkest pixel within the reconstruction is determined. The computer then replaces the second point with the XYZ coordinates of the darkest pixel within this second reconstruction. Following a vector based on a moving average of previously determined 3- dimensional points along the vessel\u27s axis, the computer continues this skeletonization process until stopped by the user. The computer estimates the vessel diameter along the set of previously determined points using a method similar to the full width-half max algorithm. On all subsequent vessels, the process works the same way except that at each point, distances between the current point and all previously determined points along different vessels are determined. If the difference is less than the previously estimated diameter, the vessels are assumed to branch. This user/computer interaction continues until the vascular tree has been skeletonized

Testing Foundations of Biological Scaling Theory Using Automated Measurements of Vascular Networks

Scientists have long sought to understand how vascular networks supply blood and oxygen to cells throughout the body. Recent work focuses on principles that constrain how vessel size changes through branching generations from the aorta to capillaries and uses scaling exponents to quantify these changes. Prominent scaling theories predict that combinations of these exponents explain how metabolic, growth, and other biological rates vary with body size. Nevertheless, direct measurements of individual vessel segments have been limited because existing techniques for measuring vasculature are invasive, time consuming, and technically difficult. We developed software that extracts the length, radius, and connectivity of in vivo vessels from contrast-enhanced 3D Magnetic Resonance Angiography. Using data from 20 human subjects, we calculated scaling exponents by four methods--two derived from local properties of branching junctions and two from whole-network properties. Although these methods are often used interchangeably in the literature, we do not find general agreement between these methods, particularly for vessel lengths. Measurements for length of vessels also diverge from theoretical values, but those for radius show stronger agreement. Our results demonstrate that vascular network models cannot ignore certain complexities of real vascular systems and indicate the need to discover new principles regarding vessel lengths

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

Signal Processing Methods for Quantitative Power Doppler Microvascular Angiography

Operator-dependent instrument settings and the likelihood of image artifacts are two challenges for reliably using three-dimensional (3-D) power Doppler angiography in flow depiction and quantification applications. To address the operator-dependent settings challenge, an automated method for wall filter cut-off selection, the wall filter selection curve (WFSC) method, was developed using flow-phantom images. The flow-phantom WFSCs guided the development of a theoretical signal model relating color pixel density (CPD) and wall filter cut-off frequency. Simulations using the theoretical model were used to define criteria for the WFSC method to be applied to unprocessed power Doppler signals from 3-D vasculature. The adapted WFSC method was combined with a 3-D skeletonization and vessel network reconstruction method to present a two-stage processing method aimed at improving vascular detection, visualization and quantification. The two-stage method was evaluated using two in vivo models; a murine tumor model was used to test the performance of the method in a flow quantification application and a chick embryo chorioallantoic membrane (CAM) model was used to evaluate the method’s value for flow depiction applications. Applying the WFSC method to flow-phantom images improved vessel delineation and vascular quantification to within 3% of the vascular volume fraction of the phantom. Criteria for the WFSC method from the simulations were to assess at least 100 cut-off frequencies and that the CPD variability should be less than 5% to ensure quantification accuracy. Large variations in the cut-off frequency selected using the WFSC among images acquired at different time points and across different animals in the murine tumor model signified the relevance of spatially and temporally adjusting the cut-off frequency. The two-stage method improved visualization of the vascular network and significantly reduced artifacts in both the tumor and CAM models in comparison to images using conventional Doppler processing. In the CAM model, vessel diameters measured in two-stage processed images were more accurate than measurements in images exported from a commercial scanner. The proposed signal processing methods increase accuracy and robustness of qualitative and quantitative studies using 3-D power Doppler angiography to assess vascular networks for flow depiction and quantification

Vascular Complexity Evaluation Using a Skeletonization Approach and 3D LED-Based Photoacoustic Images

Vasculature analysis is a fundamental aspect in the diagnosis, treatment, outcome evaluation and follow-up of several diseases. The quantitative characterization of the vascular network can be a powerful means for earlier pathologies revealing and for their monitoring. For this reason, non-invasive and quantitative methods for the evaluation of blood vessels complexity is a very important issue. Many imaging techniques can be used for visualizing blood vessels, but many modalities are limited by high costs, the need of exogenous contrast agents, the use of ionizing radiation, a very limited acquisition depth, and/or long acquisition times. Photoacoustic imaging has recently been the focus of much research and is now emerging in clinical applications. This imaging modality combines the qualities of good contrast and the spectral specificity of optical imaging and the high penetration depth and the spatial resolution of acoustic imaging. The optical absorption properties of blood also make it an endogenous contrast agent, allowing a completely non-invasive visualization of blood vessels. Moreover, more recent LED-based photoacoustic imaging systems are more affordable, safe and portable when compared to a laser-based systems. In this chapter we will confront the issue of vessel extraction techniques and how quantitative vascular parameters can be computed on 3D LED-based photoacoustic images using an in vitro vessel phantom model

Customizable tubular model for n-furcating blood vessels and its application to 3D reconstruction of the cerebrovascular system

Understanding the 3D cerebral vascular network is one of the pressing issues impacting the diagnostics of various systemic disorders and is helpful in clinical therapeutic strategies. Unfortunately, the existing software in the radiological workstation does not meet the expectations of radiologists who require a computerized system for detailed, quantitative analysis of the human cerebrovascular system in 3D and a standardized geometric description of its components. In this study, we show a method that uses 3D image data from magnetic resonance imaging with contrast to create a geometrical reconstruction of the vessels and a parametric description of the reconstructed segments of the vessels. First, the method isolates the vascular system using controlled morphological growing and performs skeleton extraction and optimization. Then, around the optimized skeleton branches, it creates tubular objects optimized for quality and accuracy of matching with the originally isolated vascular data. Finally, it optimizes the joints on n-furcating vessel segments. As a result, the algorithm gives a complete description of shape, position in space, position relative to other segments, and other anatomical structures of each cerebrovascular system segment. Our method is highly customizable and in principle allows reconstructing vascular structures from any 2D or 3D data. The algorithm solves shortcomings of currently available methods including failures to reconstruct the vessel mesh in the proximity of junctions and is free of mesh collisions in high curvature vessels. It also introduces a number of optimizations in the vessel skeletonization leading to a more smooth and more accurate model of the vessel network. We have tested the method on 20 datasets from the public magnetic resonance angiography image database and show that the method allows for repeatable and robust segmentation of the vessel network and allows to compute vascular lateralization indices. Graphical abstract: [Figure not available: see fulltext.]</p

Segmentation-based blood flow parameter refinement in cerebrovascular structures using 4D arterial spin labeling MRA

Objective: Cerebrovascular diseases are one of the main global causes of death and disability in the adult population. The preferred imaging modality for the diagnostic routine is digital subtraction angiography, an invasive modality. Time-resolved three-dimensional arterial spin labeling magnetic resonance angiography (4D ASL MRA) is an alternative non-invasive modality, which captures morphological and blood flow data of the cerebrovascular system, with high spatial and temporal resolution. This work proposes advanced medical image processing methods that extract the anatomical and hemodynamic information contained in 4D ASL MRA datasets. Methods: A previously published segmentation method, which uses blood flow data to improve its accuracy, is extended to estimate blood flow parameters by fitting a mathematical model to the measured vascular signal. The estimated values are then refined using regression techniques within the cerebrovascular segmentation. The proposed method was evaluated using fifteen 4D ASL MRA phantoms, with ground-truth morphological and hemodynamic data, fifteen 4D ASL MRA datasets acquired from healthy volunteers, and two 4D ASL MRA datasets from patients with a stenosis. Results: The proposed method reached an average Dice similarity coefficient of 0.957 and 0.938 in the phantom and real dataset segmentation evaluations, respectively. The estimated blood flow parameter values are more similar to the ground-truth values after the refinement step, when using phantoms. A qualitative analysis showed that the refined blood flow estimation is more realistic compared to the raw hemodynamic parameters. Conclusion: The proposed method can provide accurate segmentations and blood flow parameter estimations in the cerebrovascular system using 4D ASL MRA datasets. Significance: The information obtained with the proposed method can help clinicians and researchers to study the cerebrovascular system non-invasively