Synthetic dataset generation for the analysis and the evaluation of image-based hemodynamics of the human aorta.
Here, we consider the issue of generating a suitable controlled environment for the evaluation of phase contrast (PC) MRI measurements. The computational framework, tailored to build synthetic datasets, is based on a two-step approach, i.e., define and implement (1) an accurate CFD model and (2) an...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 50; no. 2; pp. 145 - 155 |
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| Autores principales: | , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Feb2012
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104512672&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104512672 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Feb2012 vid: 50 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104512672 NLM22194021 2011450349 10.1007/s11517-011-0854-8 NLM22194021 104512672 ppf: 145 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Synthetic dataset generation for the analysis and the evaluation of image-based hemodynamics of the human aorta. aug: au: Morbiducci U Ponzini R Rizzo G Biancolini ME Iannaccone F Gallo D Redaelli A Morbiducci, Umberto Ponzini, Raffaele Rizzo, Giovanna Biancolini, Marco Evanghelos Iannaccone, Francesco Gallo, Diego Redaelli, Alberto affil: Department of Mechanics, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy sug: subj: Aorta, Thoracic Physiology Blood Flow Velocity Physiology Hemodynamics Physiology Human Image Processing, Computer Assisted Methods Magnetic Resonance Imaging Methods Models, Biological ab: Here, we consider the issue of generating a suitable controlled environment for the evaluation of phase contrast (PC) MRI measurements. The computational framework, tailored to build synthetic datasets, is based on a two-step approach, i.e., define and implement (1) an accurate CFD model and (2) an image generator able to mime the overall outcomes of a PC MRI acquisition starting from datasets retrieved by the computational model. About 20 different datasets were built by changing relevant image parameters (pixel size, slice thickness, time frames per cardiac cycle). Focusing our attention on the thoracic aorta, synthetic images were processed in order to: (1) verify to which extent the fluid dynamics into the aortic arch is influenced by the image parameters; (2) establish the effect of spatial and temporal interpolation. Our study demonstrates that the integral scale of the aortic bulk flow could be described satisfactorily even when using images which are nowadays acquirable with MRI scanners. However, attention must be paid to near-wall velocities that can be affected by large inaccuracy. In detail, in bulk flow regions error values are well bounded (below 5% for most of the analyzed resolutions), while errors greater than 100% are systematically present at the vessel's wall. Moreover, also the data interpolation process can be responsible for large inaccuracies in new data generation, due to the inherent complexity of the flow field in some connected regions. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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