Feasibility of automated frame-by-frame myocardial segmentation as a basis for quantification of first-pass perfusion im
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Feasibility of automated frame-by-frame myocardial segmentation as a basis for quantification of first-pass perfusion images Giacomo Tarroni1, Amit R Patel2, Federico Veronesi1, Claudio Lamberti1, Victor Mor-Avi*2 and Cristiana Corsi1 Address: 1University of Bologna, Bologna, Italy and 2University of Chicago, Chicago, IL, USA * Corresponding author
from 13th Annual SCMR Scientific Sessions Phoenix, AZ, USA. 21-24 January 2010 Published: 21 January 2010 Journal of Cardiovascular Magnetic Resonance 2010, 12(Suppl 1):O45
doi:10.1186/1532-429X-12-S1-O45
Abstracts of the 13th Annual SCMR Scientific Sessions - 2010
Meeting abstracts - A single PDF containing all abstracts in this Supplement is available here. http://www.biomedcentral.com/content/files/pdf/1532-429X-11-S1-infoThis abstract is available from: http://jcmr-online.com/content/12/S1/O45 © 2010 Tarroni et al; licensee BioMed Central Ltd.
Introduction Quantification of first-pass myocardial perfusion from cardiac magnetic resonance (CMR) images relies on the definition of myocardial regions of interest (ROI). This is usually achieved by manually drawing ROIs in one frame and then adjusting their position on subsequent frames. This methodology is tedious and potentially inaccurate. We recently developed a technique based on image noise density distribution for automated dynamic endocardial border detection as a basis for quantification of left ventricular size and function.
ified region-based model based on the probability of density distribution of gray levels. Then epicardial boundaries were detected using a classical edge-based level-set model. Image registration was achieved by two-dimensional cross-correlation to compensate for respiratory motion. Six standard myocardial ROIs were automatically defined and contrast enhancement curves were constructed throughout the image sequence. This approach was tested on 24 slices during first pass perfusion by: (1) visually judging frame-by-frame the accuracy of endo- and epicar-
Purpose The goal of this study is to adapt this technique for automated frame-by-frame myocardial segmentation of firstpass perfusion images and test its clinical feasibility.
Methods LV short-axis images (Philips 1.5 T) were obtained at three levels of the left ventricle during first pass of a Gadolinium-DTPA bolus (0.10 mmol/kg @5 ml/sec). Images were acquired using a hybrid gradient echo/echo planar imaging sequence (3 slices, thickness 10 mm, pixel size 2.5 × 2.5 mm, nonselective 90° saturation pulse followed by 80 ms delay, flip angle 20°, TR = 5.9 ms, TE = 2.7 ms, EPI factor 5, SENSE factor 2). For each slice, after manually placing a seed point inside the LV cavity on a single frame, endocardial boundaries were automatically detected throughout the image sequence by using a mod-
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Journal of Cardiovascular Magnetic Resonance 2010, 12(Suppl 1):O45
http://jcmr-online.com/content/12/S1/O45
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