zenduck.me: Moving Target Indicator an overview

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11.7.4 Implementations of Color Flow Imaging

All of the estimators described have qualities of being fast, robust, and efficient; consequently, they have been implemented in hardware and digital signal processors (DSPs). The initial signal processing is similar to that used to create I and Q paths for PW Doppler, except that wall filters follow analog-to-digital (A/D) conversion. The wall filters can be feedback recursive filters of the moving target indicator (Magnin, 1987) or the delay line canceller type (Evans, 1993). After filtering, the signals enter the mean frequency estimator and turbulence estimators. The results of these calculations then enter a display encoder and digital scan conversion. The time domain method differs substantially from the phase-based methods in that quadrature sampling is not necessary, so that after wall filtering, the signals are processed by a cross-correlator.

All methods undergo a color mapping scheme that can vary among manufacturers, so only the basic concepts can be dealt with here (Magnin, 1987). A color image is overlaid on a standard gray-scale image. The colors chosen are not the actual colors of blood but represent blood flow velocity and direction. Colors are assigned to the direction of flow relative to the transducer; for example, with red for flow toward the transducer and blue for flow away from it. Green, another primary color, can be added to indicate turbulence. Either the hue of the color is increased as the velocity increases or, alternatively, the intensity is increased. In Figure 11.15, the red/blue scheme is shown with increasing intensity.

Frame rate is always at a premium, and it depends on the total number of vectors or transmit events and the scan depths. In multiple modes, and color flow always includes a gray-scale B-scan image, it is possible to have not only differently shaped pulses but also different scan depths (Szabo, Melton, & Hempstead, 1988). To catch fast-moving flow, frame rate can be increased by giving the user the option of reducing the size of the region of interest for CFI (as shown in the inset of Figure 11.13). Figure 11.15 shows a triplex image CFI and a gray-scale and PW Doppler. The ability to display several modes at once (Barber et al., 1974) is very useful clinically, especially for the placement of Doppler lines, but this also increases frame rate and both processing and line-sequencing complexity.

While there is no doubt of CFI’s usefulness, its limitations must also be kept in mind. First, mean blood flow velocity is estimated on the basis of a few time samples; therefore, the values obtained will not be as accurate as PW and CW Doppler measurements based on longer dwell times (length of time during which a transducer is held at the same position), many more sample points, and more precise FFT algorithms. Second, the velocity values derived from CFI have an implicit cosine θ variation with no correction for this part of the Doppler effect. As an example, consider a sector scan in which the middle vector line is perpendicular to a vessel with blood flowing left to right. Under certain conditions, a CFI of this situation will display blood as flowing left to right on the left side of the image, as stopping at dead center (cos 90°=0), and as reversing flow on the right half of the image. This kind of geometry is avoided in clinical practice, with the flow vectors always at some angle to the vessel. A third cautionary observation is that aliasing can occur (the mapping of high velocities into lower ones); these situations are often unusual enough to be noticed. Fourth, changes in flow velocity can occur out of the imaging plane and be mapped into the field of view. Fifth, “flash artifacts” can occur (the incorrect mapping of moving blood onto tissue regions). This effect may be caused by tissue movement or by an inappropriate setting or limitation of the wall filter.