Suscripción a Biblioteca: Guest
Journal of Flow Visualization and Image Processing

Publicado 4 números por año

ISSN Imprimir: 1065-3090

ISSN En Línea: 1940-4336

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 0.6 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.6 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00013 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.14 SJR: 0.201 SNIP: 0.313 CiteScore™:: 1.2 H-Index: 13

Indexed in

MOVING TOWARDS A 5D CARDIAC MODEL

Volumen 26, Edición 1, 2019, pp. 19-48
DOI: 10.1615/JFlowVisImageProc.2018027194
Get accessGet access

SINOPSIS

The process of medical diagnosis requires several steps to identify the types of cardiac pathologies. The segmentation step is used to determine the measurements for cardiac abnormalities on the short axis of the 4D acquired in MRI, but this phase remains limited on the blood flow sequences. The MRI modality allows the experts to quantify the stenosis and the regurgitation of aortic blood flow. The parameters extracted from the flow sequences, after segmentation, make it possible to identify the valvular pathologies, but they are not sufficient to complete the medical prognosis as well as the lack of precision of these measurements. In this paper, we propose to make a coupling between the 4D cardiac cuts with their study of blood flow through the technique of registration and reconstruction. The interest of the purpose of the blood flow as the fifth dimension is to improve the accuracy of the cardiac parameters and the extracting of measurements for valvulopathies in the process of assistance to the decision for experts. An example was introduced in this context through the development of cloud services for patient-specific simulations of blood flows through aortic valves as well as an OsiriX software for 5D visualization that combines a 4D sequence and the functional flow dimension. In this framework, we proposed a processing chain to lead towards a 5D solution. Another problem raised is the choice of the appropriate architecture to solve the problem of hybrid parallelization for processing these cardiac images. To test the constraints of time of the 5D concept, we need a GPU graphics processor acquired in MRI, as well as a CPU processor to perform the complexity of calculation and the operations applied to the algorithms of image processing.

CITADO POR
  1. Sakly Houneida, Said Mourad, Tagina Moncef, Reconstruction of 5D cardiac MRI through the blood flow registration: simulation of the fifth dimension and assessment of the left ventricular ejection fraction, Network Modeling Analysis in Health Informatics and Bioinformatics, 9, 1, 2020. Crossref

  2. Sakly Houneida, Said Mourad, Tagina Moncef, Medical Decision Making for Cardiac MRI with CFD “Detection of Severe Stenosis Using a 5D Model of the Descending Aorta”, BioMedInformatics, 2, 1, 2021. Crossref

  3. Miled Malek, Messaoud Mohammed Anouar Ben, Bouzid Aicha, Lip reading of words with lip segmentation and deep learning, Multimedia Tools and Applications, 2022. Crossref

  4. Miled Malek, Messaoud Mohamed Anouar Ben, Lip segmentation with hybrid model, 2022 6th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 2022. Crossref

  5. Mahmoudi Ramzi, Slama Sana, Benameur Narjes, Boukhris Khouloud, Hmida Badii, Bedoui Mohamed Hedi, Notes on Fifth Dimension Modelling in Cardiovascular System Using Artificial Intelligence-Based Tools, in Information Systems and Technologies, 468, 2022. Crossref

Portal Digitalde Biblioteca Digital eLibros Revistas Referencias y Libros de Ponencias Colecciones Precios y Políticas de Suscripcione Begell House Contáctenos Language English 中文 Русский Português German French Spain