Validation of a fast method for quantifying left ventricular torsion
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Validation of a fast method for quantifying left ventricular torsion Meral L Reyhan* and Daniel B Ennis Address: University of California, Los Angeles, CA, 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):P252
doi:10.1186/1532-429X-12-S1-P252
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/P252 © 2010 Reyhan and Ennis; licensee BioMed Central Ltd.
Purpose To develop and validate a quantitative method, requiring limited user interaction, for the fast calculation of left ventricular (LV) torsion.
Introduction LV torsion is an important measure of LV performance [1,2]. Torsion is the difference in the angle of rotation between apical and basal slices. Rotations in image space directly correspond to rotations in Fourier space; therefore it is possible to exploit this property and measure LV torsion in k-space.
k-space Cross Correlation The only user interaction needed for processing is contouring of the LV epicardium in the first time frame. After masking, the LV epicardial edge was smoothed to reduce ringing in k-space before Fourier transformation. The kspace magnitude data for each frame was 2D cross-correlated with a rotated version (-1° to 1° with step-size of 0.25°) of the frame immediately after it within the same slice. The maximum of the cross-correlation from all
Methods Short-axis tagged images were acquired at 1.5 T in six beagle dogs using the 3D fast gradient echo pulse sequence with the following parameters: 180 × 180 × 128-160 mm field of view (FOV), 384 × 128 × 32 acquisition matrix, 12° imaging flip angle, ± 62.5 kHz receiver bandwidth, TE/TR = 3.4/8.0 ms, and five pixel tag spacing. Tissue tags in two short axis slices were tracked semi-automatically using the FindTags [3] software to define "gold standard" results. Tissue Tag Tracking Estimates of LV rotation at each slice level in each time frame were obtained from the rotation of horizontal and vertical tag intersections about the LV centroid determined using FindTags.
Figure 1
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Journal of Cardiovascular Magnetic Resonance 2010, 12(Suppl 1):P252
http://jcmr-online.com/content/12/S1/P252
Conclusion We have developed a fast, user friendly, and accurate algorithm for computing global LV torsion. Refinements in image acquisition and k-space processing should improve the overall accuracy.
References 1. 2. 3.
Stuber M: Circulation 1999, 100(4):361-368. Sorger JM: JCMR 2003, 5(4):521-530. Guttman MA: IEEE Comp Graph Appl 1997, 17(1):30-38.
Figure 2
tested rotations defined the angle of rotation between the first frame and each subsequent fram
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