Final Feigenbaum’s Echocardiography DIGITAL

Feigenbaum’s Echocardiography

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Feigenbaum’s Echocardiography

FIGURE 5.13. Illustration of the basic principles of speckle tracking. An apical four-cham- ber view is presented from which a section of the ventricular septum has been expanded ( bordered area ). Within the expanded area, two circular regions of interest are identified. Note the distinctly different acoustic signature within these regions. This illustration is a simplification of the speckle phenomenon and, in reality, substantially smaller regions of interest with more subtle variation in tissue signature based on more fundamental imaging characteristics are utilized. mechanism for tracking discreet myocardial segments from which deformation measurements of strain and strain rate can be calcu- lated. It also provides deƒnition of the myocardial boundary from which the myocardial blood pool and ventricular volumes can be extrapolated. With either tissue tracking or acoustic boundary detection, the utility of the automated edge detection is greatest in high-quality studies and rapidly drops o† with lower-quality images. For patients with signiƒcant degradation of overall visual image quality auto- mated edge detection systems may consistently fail and will provide erroneous information which should not be used. It should be heavily emphasized that blind reliance on any of the available automated edge detection algorithms, whether utiliz- ing two or three-dimensional echocardiography, acoustic boundary detection or speckle tracking, must be visually conƒrmed as accu- rate before the data are utilized. Visual analysis by a skilled echo- cardiographer incorporates endocardial motion and myocardial thickening into the assessment of ventricular function both region- ally and globally. A skilled echocardiographer visually and mentally ƒlters out artifact and other vague extraneous echoes which can be confused for the true endocardial border. At all times the automati- cally detected border should be scrutinized for accuracy against the known location of the endocardial border when analyzed in real time and appropriately adjusted. Automated edge detection algo- rithms commonly track the papillary muscle or trabeculae as the endocardial border (Fig. 5.9) or foreshorten the apex by tracking vague echoes in the apical cavity which are related to beam width and do not represent the true endocardial border (Fig. 5.11). Intravenous contrast for leŠ ventricular opaciƒcation is also a valuable technique for enhancing endocardial border deƒnition. It has been recommended that if two or more ventricular segments are poorly visualized, there is incremental yield of intravenous con- trast for leŠ ventricular opaciƒcation both for regional wall motion assessment and for reproducibility of volume determination. Intra- venous contrast can be employed either with two-dimensional or with three-dimensional imaging and, as discussed in Chapter 3, requires attention to detail with respect to mechanical index and other technical factors of imaging. Assessment of Left Ventricular Function With Three-Dimensional Echocardiography A three-dimensional echocardiographic dataset which potentially includes all four cardiac chambers can be acquired through a number

FIGURE 5.11. Apical four-chamber view recorded in the same patient depicted in Figure 5.10. This figure represents the first approximation of the ventricular boundary by the automated edge detection algorithm. Note that the automatically detected boundary ( dotted lines ) has foreshortened the left ventricle and located the apex well short of its true location ( arrows ). At the lower right is an expanded view of the same image. The downward-pointing arrows denote the location of the epicardial boundary of the apex and the double-headed arrow the distance between the automatically detected boundary and the endocardium of the apex. In this instance the erroneous boundary detection was related to vague echoes in the apex due to beam width artifact. Also note that along the lateral wall the detection algorithm has identified the endocar- dial border at the tip of the papillary muscle. This has resulted in an overestimation of ejection fraction and an underestimation of ventricular volume as noted at the upper right. The image in Figure 5.10 was recorded after a single manual manipulation of the boundary. be calculated continuously through the cardiac cycle and graphi- cally displayed over time. Stroke volume and ejection fraction can be calculated from the maximal and minimal volumes. An additional methodology for tracking the myocardium is “speckle tracking.” is technique relies on creating an “acoustic signature” of multiple regions of interest within the myocardium (Fig. 5.13). e acoustic signature of any given region remains sta- ble throughout the contraction sequence and therefore the region can be tracked over the cardiac cycle. Most currently available echo- cardiographic platforms provide speckle tracking in two dimen- sions. Accurate high frame rate three-dimensional speckle tracking is still in development. e speckle tracking technology provides a

FIGURE 5.12. Composite image derived from a real-time three-dimensional full-volume dataset. The image at the far lower right is the cropped three-dimensional volume set in which the details of the left ventricular cavity are identified. The images at the left include apical four-chamber (4C), apical long-axis (ALx) and two-chamber (2C) views. At the lower right quadrant is a fitted model of the left ventricle and left atrium derived from acoustic boundary determination. The table at the upper right outlines end-diastolic and end-systolic volumes, ejection fraction, and left atrial area.

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