Surgery – Diagnostic testing – Detecting nuclear – electromagnetic – or ultrasonic radiation
Reexamination Certificate
2001-06-22
2004-02-10
Jaworski, Francis J. (Department: 3737)
Surgery
Diagnostic testing
Detecting nuclear, electromagnetic, or ultrasonic radiation
C600S465000
Reexamination Certificate
active
06689064
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The invention is directed toward medical imaging systems, and more particularly toward minimizing unwanted clutter signals in an estimation of blood velocity while maintaining sensitivity to low velocity blood flow.
2. Description of the Prior Art
Diagnostic ultrasound equipment transmits sound energy into the human body and receives signals reflecting off tissue and organs such as the heart, liver, kidney, etc. These sound waves also reflect off blood cells that move through vessels and capillaries in tissue. Signals received by ultrasound devices are the a vector sum of waves reflecting off tissue components, e.g., heart wall, vessel wall, etc., and waves reflecting off blood cells.
Blood flow patterns are obtained from Doppler shifts or shifts in time domain cross correlation functions, due to blood cell motion, of reflected sound waves and displayed in a two-dimensional format known as Color Flow Imaging or Color Velocity Imaging. Generally, the amplitudes of reflected components for structures such as the heart or vessel walls have lower absolute velocities and are 20 dB to 40 dB (10-100 times) larger than reflected components due to blood cells. Algorithms that estimate blood velocities must account for effects due to clutter, that is, signal components from “stationary” or slowly moving structures such as the heart, liver, etc.
Prior art devices used fixed-frequency, fixed-order filtering techniques to remove or reduce the impact of clutter in velocity estimation. Also, prior art devices have used adaptive techniques such as null steering (see U.S. Pat. Nos. 4,016,528 and 5,197,477) to reduce or eliminate effects due to clutter. Still others propose parametric techniques to account for clutter (U.S. Pat. No. 5,228,009).
Generally, filter-based techniques require less computation than parametric techniques. However, filter-based techniques suffer from fixing the order of the filter. Filter-based techniques must assume clutter is sufficiently narrow band for fixed order filtering, otherwise the filter order must be over-specified to account for worst case clutter conditions.
SUMMARY OF THE INVENTION
It is an object of the present invention to provide a filtering technique that minimizes unwanted clutter signals in the estimate of blood velocity while maintaining sensitivity to low velocity blood flow.
It is another object of the present invention to provide an autoregressive parametric technique that dynamically and adaptively modifies filter coefficients and filter order, providing optimum filtering in the presence of low level or high level clutter.
This invention addresses clutter reduction in two ways. Firstly, it modifies the order of the clutter filter based on the power and mean frequency of the ultrasound blood plus clutter signal. Secondly, it modifies clutter filter coefficients based on concepts set forth in linear prediction and adaptive lattice filtering.
Accordingly, the present invention provides a system and method for implementing a clutter filter based on concepts in the field of linear prediction. The filter increases the order of the filter and determines filter coefficients based on the input signal and user definable control inputs. The filter coefficients are determined such that the filter zeros correspond to partial correlation coefficients, or are on the unit circle at the frequency of the partial correlation coefficients, or are selected from a predefined table of filter coefficients. Dynamic and adaptive modification of filter order and filter coefficients allows fine tuning the clutter filter based on a particular application or tissue type.
The method acquires clutter and blood data. Next, the signal strength and frequency of the data are estimated. Through an iterative process, the signal strength and frequency are each determined to be within one of several ranges, and the data is processed according to those signal strength and frequency ranges. The data may be passed unaltered, filtered according to a reflection coefficient or filtered according to a predefined filter coefficient.
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Clark David W
Hager Richard A
Thiele Karl E
Witt Jerome F
Jaworski Francis J.
Jung William C
Vodopia John
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