RT Journal Article ID 365c268f24267f35 A1 Sakly, Houneida A1 Mahmoudi, Ramzi A1 Akil, Mohamed A1 Said, Mourad A1 Tagina, Moncef T1 MOVING TOWARDS A 5D CARDIAC MODEL JF Journal of Flow Visualization and Image Processing JO JFV YR 2019 FD 2019-01-03 VO 26 IS 1 SP 19 OP 48 K1 cardiac pathologies K1 MRI K1 blood flow sequences K1 segmentation K1 coupling K1 fifth dimension K1 hybrid parallelization K1 CPU K1 GPU K1 complexity of calculation AB 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. PB Begell House LK https://www.dl.begellhouse.com/journals/52b74bd3689ab10b,6368d04933435128,365c268f24267f35.html