http://www.cnr.it/ontology/cnr/individuo/prodotto/ID325676

Automatic extraction of three-dimensional thoracic aorta geometric model from phase contrast MRI for morphometric and hemodynamic characterization (Articolo in rivista)

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Automatic extraction of three-dimensional thoracic aorta geometric model from phase contrast MRI for morphometric and hemodynamic characterization (Articolo in rivista) (literal)

Anno

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

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Paola Volonghi 1, Daniele Tresoldi 1, Marcello Cadioli 2, Antonio M. Usuelli 1, Raffaele Ponzini 3, Umberto Morbiducci 4, Antonio Esposito 5 and Giovanna Rizzo 1,* (2015)
Automatic extraction of three-dimensional thoracic aorta geometric model from phase contrast MRI for morphometric and hemodynamic characterization
in Magnetic resonance in medicine (Print)
(literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

Paola Volonghi 1, Daniele Tresoldi 1, Marcello Cadioli 2, Antonio M. Usuelli 1, Raffaele Ponzini 3, Umberto Morbiducci 4, Antonio Esposito 5 and Giovanna Rizzo 1,* (literal)

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Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

. 1Institute of Molecular Bioimaging and Physiology, CNR, Segrate (MI), Italy 2. Philips Healthcare, Monza, Italy 3. CINECA, Segrate (MI), Italy 4. Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy 5. Department of Radiology, Scientific Institute H.S. Raffaele, Milan, Italy (literal)

Titolo

Automatic extraction of three-dimensional thoracic aorta geometric model from phase contrast MRI for morphometric and hemodynamic characterization (literal)

Abstract

Purpose To propose and assess a new method that automatically extracts a three-dimensional (3D) geometric model of the thoracic aorta (TA) from 3D cine phase contrast MRI (PCMRI) acquisitions. Methods The proposed method is composed of two steps: segmentation of the TA and creation of the 3D geometric model. The segmentation algorithm, based on Level Set, was set and applied to healthy subjects acquired in three different modalities (with and without SENSE reduction factors). Accuracy was evaluated using standard quality indices. The 3D model is characterized by the vessel surface mesh and its centerline; the comparison of models obtained from the three different datasets was also carried out in terms of radius of curvature (RC) and average tortuosity (AT). Results In all datasets, the segmentation quality indices confirmed very good agreement between manual and automatic contours (average symmetric distance < 1.44 mm, DICE Similarity Coefficient > 0.88). The 3D models extracted from the three datasets were found to be comparable, with differences of less than 10% for RC and 11% for AT. Conclusion Our method was found effective on PCMRI data to provide a 3D geometric model of the TA, to support morphometric and hemodynamic characterization of the aorta. Magn Reson Med, 2015. © 2015 Wiley Periodicals, Inc. (literal)

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