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A method to determine regional mechanical left ventricular dyssynchrony based on high temporal resolution short axis SSFP cine images

Authors
Journal
Journal of Cardiovascular Magnetic Resonance
1097-6647
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Volume
14
Identifiers
DOI: 10.1186/1532-429x-14-s1-w18
Keywords
  • Workshop Presentation
Disciplines
  • Medicine

Abstract

A method to determine regional mechanical left ventricular dyssynchrony based on high temporal resolution short axis SSFP cine images WORKSHOP PRESENTATION Open Access A method to determine regional mechanical left ventricular dyssynchrony based on high temporal resolution short axis SSFP cine images Jonathan Suever1*, Brandon K Fornwalt2, Michael Lloyd3, John N Oshinski1,4 From 15th Annual SCMR Scientific Sessions Orlando, FL, USA. 2-5 February 2012 Background Left ventricular (LV) mechanical dyssynchrony has been proposed as a parameter to select patients for cardiac resynchronization therapy (CRT) [Bax et al JACC 2005]. Several recent studies have shown that placing the LV pacing lead in the most delayed regions yields a better response to CRT [Ansalone et al JACC 2002]. However, most imaging-based methods assess global LV dyssyn- chrony providing a single value for the entire LV. Regio- nal maps of LV dyssynchrony are required for planning LV lead placement. The objective of this study was to develop a method to create a map of regional left ventricular mechanical dyssynchrony based on short-axis SSFP cine images. Methods We examined a series of 13 patients that met standard criteria for CRT (QRS>120ms, NYHA HF class III-IV). Patients underwent a CMR exam prior to CRT that included acquisition of high temporal resolution short- axis cine images (60 frames/cardiac cycle). The endocar- dial boundary was delineated in a semi-automated man- ner, and the boundary contour was sampled at 100 equally spaced points (Figure 1A). A time-series of radial motion curves relative to the center of mass was generated for each radial location and over each slice. Results To detect dyssynchronous regions of the LV, a model of “synchronous” contraction must be identified. Our method to identify a synchronous contraction curve used quality threshold (QT) clustering to identify the radial contraction curves that were most alike and most prevalent in the LV by measuring the “distance” betwe

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