MAP over CBFV can be studied by recording a series of times as input and output of the system. This has been done in a range of frequencies, and it is attained by employing a Fast Fourier Transformation (FFT) [49]. Typically, oscillation fre­quencies are separated into three bands: low (<0.07 Hz), intermediate (0.07–0.3Hz), and high (>0.3Hz), although the limits vary among authors [47]. The behavior of the system interprets cerebral autoregulation as a high-pass lter based on three parameters: coherence, gain, and phase shift [48]. At high frequen­cies, (1) the oscillations of CBFV follow those of ABP, so coherence (which is simply a correlation coefcient within dened frequency range) is high; (2) these parameters oscillate almost synchronously, so the phase lag between them is near zero degrees (low phase shift); and (3) the ABP oscillations are transmitted undampened to the output (CBFV), so gain (amplitude) is usually >1. The oppo­site occurs toward the low-frequency band, with low coherence and gain and higher phase shift values. That is the way cerebral autoregulation is believed to exert its effect in the lower band of frequencies. This type of response is expected from a physiologic point of view, as vascular tone takes a few seconds to adjust to changes in ABP.To apply transform function analysis, researchers have proposed using CBFV and ABP time series monitoring for at least 10minutes. Module of coherence varies between 0 and 1, from good to impaired cerebral autoregulation. Phase shift varies between 0 and 90 degrees, from nonexistent to effective cere­bral autoregulation. Lower gain means adequate cerebral autoregulation and higher gain means poor cerebral autoregulation.

The TCD waveform of ow is represented by blood ow velocity over time dur­ing the cardiac cycle. The sonographer must recognize a waveform pattern visually by its appearance on the screen as well as by the sound of the ow signal. Sonographers must annotate the machine settings, including (1) transducer and sample volume (gate) positioning, (2) ow direction, (3) angle of insonation, (4) scale settings, and (5) sweep speed. Then, the different components of the cardiac cycle are identied, namely, (1) beginning of systole, (2) peak velocity during sys­tole, (3) dicrotic notch (signifying the closure of the aortic valve), (4) end-diastolic velocity, and (5) shape and magnitude of ow deceleration during the cardiac cycle.

If the gain settings are correct, the envelope outlines the waveform shape after the peak systolic rise, the ow deceleration, and the proportion of the end-diastolic ow component. These computer-based pattern recognition approaches have been developed to analyze TCD signals by detecting the boundary edge (envelope or fol­lower) of spectral waveform using the Sobel edge detection algorithm [54].

arterial compliance lead to high intra-individual variation in peak systolic ow in the MCA (91.0±16.9cm/s), peak diastolic ow (44.3±9.5cm/s), and average mean velocity (57.7±11.5cm/s) [63] and can result in diagnostic errors. It is dif­cult with this approach to objectively classify in quantitative terms those spectral features that are associated with varying degrees of stenosis in the intracranial arter­ies. The use of a more quantitative and objective approach to analyzing Doppler spectral waveforms and classifying the varying degree of stenosis in the intracranial arteries is thus highly desirable.

(i.e., terminal ICA, proximal M1, or adjacent segments with different patency). The highest velocities in waveform are often considered to be the site of maximum vaso­spasm (Fig.17.2). However, a mirror artifact and/or hyperemia must be excluded by using the LR.The signal-to-noise ratio appears to be optimized (i.e., no noise in the background).

directed to a vascular bed with a lower resistance. This can happen in patients with TBI because different brain areas may sustain various degrees of disturbed auto­regulation or unequal distribution of ICP and mass effect.