Doppler ultrasound has been used for many years to measure the flow rate of fluids. These fluids can reflect sound energy and shift the frequency of this sound in a direction and by an amount proportional to the direction and velocity of movement of the fluid relative to an ultrasound transducer. A detailed explanation of Doppler ultrasound techniques can be found in a number of publications, including P. T. Wells and R. Skidmore, "Doppler Developments in the Last Quinquenium," Ultrasound in Medicine and Biology, 11:p.613:623, 1985.
When a reflected frequency modulated ultrasound signal is received, it is typically converted to a lower frequency, e.g. d.c., to remove the carrier and provide an audio baseband signal. The baseband signal is then digitized. A spectrum analyzer is used to obtain the Fourier transform of the digitized baseband signal for producing a spectrum of the power at each frequency therein. The spectrum, which contains both positive and negative frequency components corresponding to changes in the velocity of the fluid flow in a forward and a reverse direction, is then used to distinguish flow towards the transducer from flow away from the transducer.
Referring to FIGS. 1 and 2, there is shown, respectively, a typical power spectrum output from a spectrum analyzer for cases of forward fluid flow (flow toward the transducer) and reverse fluid flow (flow away from the transducer). Forward flow corresponds to signal energy in the positive frequency portion of the power spectrum; i.e., the signal to the right of the vertical center line in FIGS. 1 and 2. Similarly, reverse flow is represented by the negative frequency portion of the power spectrum--the signal to the left of the center line in FIGS. 1 and 2. The amplitude of the power spectrum at each frequency corresponds to the percentage of the total volume of fluid flowing at a particular velocity.
For example, as shown in FIG. 1, the highest percentage of the total volume of fluid is flowing toward the transducer at a velocity corresponding to a frequency f2 and an approximately equal percentage of the total volume of fluid is flowing toward the transducer at a velocity corresponding to the frequency f1. Frequencies f3 and f4 both represent reasonable estimates of the edge dividing Doppler signal from background noise. The statistical nature of the background noise makes the exact position of the edge somewhat ambiguous. As will be seen, the subject invention exploits the statistical nature of the edge in order to improve its estimation. In FIG. 2, the highest percentage of the total volume of fluid is flowing away from the transducer at a velocity corresponding to the frequency f5. The edge between signal and noise in the reverse direction is represented by frequency f6.
The spectrum to the left of the centerline and to the right of f3 (or f4, if f4 is considered the edge) in FIG. 1 is mainly noise. The spectrum to the right of the centerline and to the left of f6 in FIG. 2 is mainly noise.
Spectral distributions such as FIG. 1 or FIG. 2 represent the Doppler signal, and hence the flow of the fluid, during a brief (approximately 10 ms) interval. A Doppler ultrasound apparatus shows the dynamic behavior of the fluid by displaying many, perhaps 100, such spectral distributions, called "frames", every second. FIG. 3 shows a curve of the most likely positions of the edge frequency as a function of time. Time proceeds along the horizontal (x) axis. Doppler frequency (and hence velocity of the fluid) proceeds along the vertical (y) axis. In the preferred embodiment, the Doppler ultrasound apparatus uses a color monitor to display information. The percentage of the total volume of fluid flowing at a particular velocity is represented by different colors of the display at that particular time and velocity. Brighter color represent relatively larger volume of flow at that velocity. FIG. 3 shows the edge rising and falling, in this case, with the cardiac cycle. As the heart contracts, the moves faster and the Doppler frequency shift increases. As the heart relaxes, the fluid moves more slowly and the Doppler frequency shift decreases.
It can be seen from FIGS. 1 and 2 that the exact positions of the edges are somewhat ambiguous as a result of the statistical nature of the background noise. Further, in typical situations, it is necessary to determine the edges of many spectral distributions in a short time. Thus, it is desirable to have a method and an apparatus which can estimate edges accurately and efficiently.
The power spectrum output from a spectrum analyzer is typically divided into frequency bins. A method that is currently used in a Doppler ultrasound apparatus starts at the highest frequency bin and iterates downward. The edge is the first bin that is both (1) greater than a threshold parameter "noise" and (2) that bin, plus the next n (where n could be 3), add to more than m (where m could be 4) times the noise parameter. An algorithm, in pseudo code form, using this method is shown below: ##STR1##
There is a similar method which finds negative edges by looking from the most negative bin toward the baseline in the same way as described above.
A similar algorithm was also described in T. D'Alessio "`Objective` Algorithm for Maximum Frequency Estimators in Doppler Spectral Analyzers," Medical and Biological Engineering and Computing, 23:p.63:68, 1985). Both algorithms (i.e., the algorithm described above in pseudo code form and D'Alessio's algorithm) start at the highest frequency and move a small "window" downward. The first algorithm predicts that the "edge" has been found when the average signal in the window is higher then the threshold parameter noise. D'Alessio predicts that the "edge" has been found when the values of r out of m in the window are higher than a threshold parameter.
D'Alessio, in the same paper, proposes a method for determining the threshold parameter. The threshold is defined to be an arbitrarily chosen number of dB below the maximum signal strength. D'Alessio simply asserts that r,m set to (2,2) works well. However, D'Alessio's method does not seem to be based on sound statistical principles.