scholarly journals Reconstruction of Intima and Adventitia Models into a State Undeformed by a Catheter by Using CT, IVUS, and Biplane X-Ray Angiogram Images

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Jinwon Son ◽  
Young Choi
Keyword(s):  
Blood Vessel ◽  
3D Model ◽  
Flow Analysis ◽  
Ct Image ◽  
X Ray ◽  
Second Stage ◽  
Blood Flow Analysis ◽  
Position And Orientation ◽  
Ivus Image ◽  
Ivus Imaging

The number of studies on blood flow analysis using fluid-structure interaction (FSI) analysis is increasing. Though a 3D blood vessel model that includes intima and adventitia is required for FSI analysis, there are difficulties in generating it using only one type of medical imaging. In this paper, we propose a 3D modeling method for accurate FSI analysis. An intravascular ultrasound (IVUS) image is used with biplane X-ray angiogram images to calculate the position and orientation of the blood vessel. However, these images show that the blood vessel is deformed by the catheter inserted into the blood vessel for IVUS imaging. To eliminate such deformation, a CT image was added and the two models were registered. First, a 3D model of the undeformed intima was generated using a CT image. In the second stage, a model of intima and adventitia deformed by the catheter was generated by combining the IVUS image and the X-ray angiogram images. A 3D model of intima and adventitia with the deformation caused by insertion of the catheter eliminated was generated by matching these 3D blood vessel models in different states. In addition, a 3D blood vessel model including bifurcation was generated using the proposed method.

An approach to automatic blood vessel image registration of microcirculation for blood flow analysis on nude mice

2011 ◽  
Vol 14 (4) ◽  
pp. 319-330 ◽  
Author(s):  
Wen-Chen Lin ◽  
Chih-Chieh Wu ◽  
Geoffrey Zhang ◽  
Tung-Hsin Wu ◽  
Yang-Hsien Lin ◽  
...  
Keyword(s):  
Blood Flow ◽  
Image Registration ◽  
Blood Vessel ◽  
Nude Mice ◽  
Flow Analysis ◽  
Blood Flow Analysis

scholarly journals Training AI-Based Feature Extraction Algorithms, for Micro CT Images, Using Synthesized Data

2021 ◽  
Vol 40 (1) ◽  
Author(s):  
Matthew Konnik ◽  
Bahar Ahmadi ◽  
Nicholas May ◽  
Joseph Favata ◽  
Zahra Shahbazi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Learning Algorithms ◽  
Extraction Process ◽  
Machine Learning Algorithms ◽  
Ct Image ◽  
X Ray ◽  
3D Object ◽  
Automated Feature Extraction ◽  
Position And Orientation

AbstractX-ray computed tomography (CT) is a powerful technique for non-destructive volumetric inspection of objects and is widely used for studying internal structures of a large variety of sample types. The raw data obtained through an X-ray CT practice is a gray-scale 3D array of voxels. This data must undergo a geometric feature extraction process before it can be used for interpretation purposes. Such feature extraction process is conventionally done manually, but with the ever-increasing trend of image data sizes and the interest in identifying more miniature features, automated feature extraction methods are sought. Given the fact that conventional computer-vision-based methods, which attempt to segment images into partitions using techniques such as thresholding, are often only useful for aiding the manual feature extraction process, machine-learning based algorithms are becoming popular to develop fully automated feature extraction processes. Nevertheless, the machine-learning algorithms require a huge pool of labeled data for proper training, which is often unavailable. We propose to address this shortage, through a data synthesis procedure. We will do so by fabricating miniature features, with known geometry, position and orientation on thin silicon wafer layers using a femtosecond laser machining system, followed by stacking these layers to construct a 3D object with internal features, and finally obtaining the X-ray CT image of the resulting 3D object. Given that the exact geometry, position and orientation of the fabricated features are known, the X-ray CT image is inherently labeled and is ready to be used for training the machine learning algorithms for automated feature extraction. Through several examples, we will showcase: (1) the capability of synthesizing features of arbitrary geometries and their corresponding labeled images; and (2) use of the synthesized data for training machine-learning based shape classifiers and features parameter extractors.

Mirsid volcanic tuff, alkaline activation, dissolved species, optimum activation time, optimum activating concentration

2020 ◽  
Vol 71 (2) ◽  
pp. 196-201
Author(s):  
Erika Reisz ◽  
Corneliu-Mircea Davidescu ◽  
Radu Ardelean ◽  
Liviu Costea
Keyword(s):  
Sudden Increase ◽  
Activation Time ◽  
Volcanic Tuff ◽  
X Ray ◽  
Second Stage ◽  
Dissolved Species ◽  
Species Optimum ◽  
Potassium Magnesium ◽  
Exchange Properties ◽  
Activating Solution

The purpose of this article is to study the activation of the Mir�id volcanic tuff with NaOH solutions at various concentrations. To be more specific, the work investigated the evolution of the concentrations of species that passed from the tuff into the activating solutions and the quantities of dissolved species from 100 g tuff. The species found in the activating solution were: potassium, magnesium, aluminium and silicon. The shape of the curves - a sudden increase followed by a plateau or a second stage of slower increase - allowed for setting up the optimal activation time at a half-hour. Another finding was the optimal concentration of 1 N for the activating solution. X-ray diffractograms showed the increase of clinoptilolite content in the tuff, thus improving the adsorbent as well as ion exchange properties by activation with NaOH solutions.

Sign in / Sign up

Export Citation Format

Share Document