@conference{Tarroni2013,

title = {Automated MRI-based biventricular segmentation using 3D narrow-band statistical level-sets},

author = { G. Tarroni and D. Marsili and F. Veronesi and C. Corsi and A.R. Patel and V. Mor-Avi and C. Lamberti},

issn = {2325-8861},

year  = {2013},

date = {2013-01-01},

booktitle = {Computing in Cardiology, 2013},

pages = {627--630},

abstract = {The goal of this study was to develop a near-automated technique for the segmentation of left ventricular (LV) endo- and epicardial as well as right ventricular (RV) endocardial contours from cardiac magnetic resonance (CMR) images. The newly developed technique was tested against conventional manual tracing. Our approach is based on a 3D narrow-band statistical level-set algorithm (applied to a stack of CMR short-axis images) followed by several refinement steps. This technique was tested on steady-state free precession (SSFP) CMR images acquired during 10-15 sec breathholds in 6 patients, including a total of 120 images. Computational time was around 3 min for a stack of 10 slices. For performance evaluation, an experienced interpreter manually traced ventricular contours on all the images. Quantitative error metrics (Hausdorff distance, HD; mean absolute distance, MAD, Dice coefficient, DC) were computed between automatically identified and manually traced contours. Bland-Altman and linear regression analyses were also performed between automatically and manually computed ventricular volumes. The results (MAD: LV Endo = 1.3±0.7 px, RV Endo = 1.7±1.2 px, LV Epi = 1.5±0.7 px) indicate that fast and accurate identification of LV and RV contours using 3D narrow-band statistical level-sets is feasible.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Narang2012,

title = {Lipid sub-fractionation predicts worsening myocardial perfusion reserve in patients with low-density lipoprotein less than 100mg/dL: a regadenoson dardiac magnetic resonance study.},

author = { Akhil Narang and Chattanong Yodwut and Giacomo Tarroni and Emily Estep and Kristen M Turner and Benjamin H Freed and Nicole M Bhave and Cristiana Corsi and Michael H Davidson and Roberto Lang and Victor Mor-Avi and Amit R Patel},

url = {http://www.jcmr-online.com/content/14/S1/P70},

doi = {10.1186/1532-429X-14-S1-P70},

issn = {1532-429X},

year  = {2012},

date = {2012-02-01},

booktitle = {Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance},

journal = {Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance},

volume = {14 Suppl 1},

number = {Suppl 1},

pages = {P70},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Freed2012,

title = {Regadenoson cardiovascular magnetic resonance myocardial perfusion imaging predicts need for future revascularization},

author = { Benjamin H Freed and Kristen M Turner and Chattanong Yodwut and Giacomo Tarroni and Emily Estep and Nicole M Bhave and Akhil Narang and Sara Tanaka and Cristiana Corsi and Etienne Gayat and Peter Czobor and Kevin Cavanaugh and Roberto Lang and Victor Mor-Avi and Amit R Patel},

url = {http://www.jcmr-online.com/content/14/S1/P7},

doi = {10.1186/1532-429X-14-S1-P7},

issn = {1532-429X},

year  = {2012},

date = {2012-01-01},

booktitle = {Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance},

journal = {Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance},

volume = {14},

number = {Suppl 1},

pages = {P7},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Tarroni2012,

title = {Automated tracking of deformable objects based on non-rigid registration of cardiac images},

author = { G Tarroni and A. R. Patel and C Yodwut and R. M. Lang and C Lamberti and V Mor-Avi and C Corsi},

url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6420488},

year  = {2012},

date = {2012-01-01},

booktitle = {Computing in Cardiology, 2012},

journal = {Computing in Cardiology, 2012},

pages = {693--696},

abstract = {We developed an automated technique for non-rigid image registration as a basis for tracking the heart in contrast-enhanced cardiac magnetic resonance (CMR) image sequences. The goal of the present work was to validate this technique against conventional manual analysis. Our approach is based on a multi-scale extension of the normalized 2D cross-correlation algorithm in combination with level-set methods. This technique was tested on short-axis CMR (Philips 1.5T) image sequences obtained in 11 patients at the mid level of the left ventricle during first pass of a gadolinium bolus. Myocardial identification required around 5s for a 60-frame sequence. To validate the technique, myocardial boundaries were manually traced on all frames by an experienced interpreter. Comparison between automatically registered and manually traced boundaries was performed by computing Hausdorff distance (2.1±1.4px), mean absolute distance (0.9±0.7px), root mean square distance (1.0±0.8px) and Dice coefficient (0.8±0.1). These results indicate that the proposed technique allows fast and accurate non-rigid image registration, and may thus be successfully used for deformable object tracking in cardiac image sequences.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Corsi2011,

title = {Automatic quantification of cardiac scar extent from late gadolinium enhancement magnetic resonance imaging},

author = { C. Corsi and G. Tarroni and A. Tornani and S. Severi and C. Lamberti},

url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6164588},

year  = {2011},

date = {2011-01-01},

booktitle = {Computing in Cardiology, 2011},

journal = {Computing in Cardiology, 2011},

volume = {38},

pages = {405--408},

abstract = {Late gadolinium enhancement cardiac magnetic resonance imaging (LGE-CMRI) is the technique of choice to detect myocardial scars and assess myocardial viability. In clinical practice, this analysis is performed qualitatively or by manually tracing the enhanced area in each acquired slice. The purpose of this study was to test and validate a technique for automated localization and quantification of scar extent. CMRI data in patients with previous myocardial infarction were analyzed using custom software from which the myocardium was automatically identified from steady-state free precession images and registered on LGE-CMRI data. Scar tissue was defined as myocardium with signal intensity ≥80% of its maximum and quantified on each slice. Scar location and extent were assessed and compared with expert analysis. Preliminary results showed that automatic localization of scar from LGE-CMRI is feasible and scar quantification is accurate and reliable.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Tarroni2011,

title = {Clinical Validation of an Automated Technique for MRI Based Quantification of Myocardial Perfusion},

author = { G Tarroni and C Corsi and P F Antkowiak and F Veronesi and C M Kramer and F H Epstein and C Lamberti and A R Patel and V Mor-Avi},

year  = {2011},

date = {2011-01-01},

booktitle = {Computing in Cardiology, 2011},

journal = {Computing in Cardiology, 2011},

volume = {38},

pages = {837--840},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Tarroni2011a,

title = {Automated frame-by-frame segmentation and non-rigid registration of MRI myocardial perfusion data at rest and stress},

author = { G. Tarroni and A.R. Patel and P.F. Antkoviak and F.H. Epstein and C.M. Kramer and C. Lamberti and R.M. Lang and V. Mor-Avi and C. Corsi},

year  = {2011},

date = {2011-01-01},

booktitle = {International Journal of CARS},

journal = {International Journal of CARS},

volume = {6},

number = {Suppl 1},

pages = {S25},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}