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Realistic Simulation of Local Image Appearance of Cardiac Magnetic Resonance Imaging Using a Virtual Phantom Population

Authors :
Tobon-Gomez, Catalina
Sukno, Federico
Bijnens, Bart H.
Huguet, Marina
Frangi, Alejandro
Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB)
Universitat Pompeu Fabra [Barcelona] (UPF)
Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN)
Instituto de Salud Carlos III [Madrid] (ISC)-ministerio de ciencia e innovacion
CETIR Grup Sant Jordi [Barcelona] (CETIR Grup)
CETIR Grup
Institució Catalana de Recerca i Estudis Avançats (ICREA)
Session 04 : Device Delivery
Surgery Planning
and Future Directions
Monteil, Alain
Source :
CI2BM09-MICCAI Workshop on Cardiovascular Interventional Imaging and Biophysical Modelling, CI2BM09-MICCAI Workshop on Cardiovascular Interventional Imaging and Biophysical Modelling, Sep 2009, London, United Kingdom. pp.132-140
Publication Year :
2009
Publisher :
HAL CCSD, 2009.

Abstract

International audience; Magnetic resonance imaging (MRI) simulators have been largely applied to brain studies. However, cardiac applications of these simulators are only emerging. This paper focuses on the realistic simulation of local appearance of cardiac MRI datasets. Simulations are obtained from the MRISIM simulator with XCAT phantom (formerly NCAT) as input. Phantoms are further extended to increase realism of the local appearance of the simulated datasets. The extension is based on: resemblance of partial volume effect by using a higher resolution phantom as input to the simulator, addition of intensity variability of each tissue by increasing the number of labels of the phantom, and inclusion of trabeculae in the ventricular cavities. The clinical database included 40 patients for anatomical measurements and 5 healthy athletes for local grey value statistics. The virtual database included 20 digital phantoms. Histograms from different tissues were obtained from the real datasets and compared to histograms of the simulated datasets by means of Chi-square dissimilarity metric. The addition of sublabels and trabeculae improved the matching of real histograms in 8 out of 11 comparisons. Simulated intensity distributions were improved up to 76% with respect to the original distributions. Our methodology obtained a higher dissimilarity metric for lung and pericardial tissue.

Details

Language :
English
Database :
OpenAIRE
Journal :
CI2BM09-MICCAI Workshop on Cardiovascular Interventional Imaging and Biophysical Modelling, CI2BM09-MICCAI Workshop on Cardiovascular Interventional Imaging and Biophysical Modelling, Sep 2009, London, United Kingdom. pp.132-140
Accession number :
edsair.dedup.wf.001..89e0bab26f7d3b5444738e4ec57d088d