Surgery – Diagnostic testing – Detecting nuclear – electromagnetic – or ultrasonic radiation
Reexamination Certificate
2002-03-19
2003-12-16
Jaworski, Francis J. (Department: 3737)
Surgery
Diagnostic testing
Detecting nuclear, electromagnetic, or ultrasonic radiation
Reexamination Certificate
active
06663567
ABSTRACT:
CROSS-REFERENCE TO RELATED APPLICATIONS
Not Applicable.
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to ultrasonic imaging, and more particularly, to a method and apparatus for improving and enhancing Doppler ultrasonic images.
2. Description of the Background Art
Ultrasonic imaging is frequently used for a variety of diagnostic procedures because it is non-invasive, low cost, and has a fast response time. These qualities are especially valuable in medical fields where an added benefit is reducing or eliminating a patient's exposure to radiation. Typically, ultrasonic imaging is accomplished by first generating and directing an ultrasonic wave into a media under investigation, then observing any resulting waves that are reflected back from dissimilar tissues and tissue boundaries within the media under investigation. The resulting waves are received as signals. These received signals are then post-processed and imaged on a screen by plotting a spot whose intensity is proportional to the amplitude of a reflected wave from a given location. The location of a particular spot in an image is based upon a known transmission and re-radiation rate after an ultrasonic wave is pulsed into the media under investigation.
Color Doppler imaging is a version of ultrasonic imaging used in medical ultrasonic systems. Color Doppler imaging generates two-dimensional, color-mapped images for displaying a Doppler velocity (or energy, or both) of a patient's blood flow. Typically, a color image is derived from Doppler parameter data and is overlaid on a corresponding two-dimensional tissue image. This overlay allows the user to simultaneously view blood flow dynamics and underlying tissue structures.
Doppler parameter data can be detected and extracted from reflected ultrasonic pulses by practicing the following steps: 1) transmitting multiple ultrasonic pulses via a transducer to a media under investigation; 2) receiving any returned signals responsive to the transmitted multiple ultrasonic pulses; 3) generating, through a beamformer, a two-dimensional complex image with preserved phase information for each of the received ultrasonic pulses; 4) applying clutter filtering across consecutively generated complex images to remove signals from any stationary objects; and, 5) calculating Doppler parameter data at each sample point, through auto-correlation methods or other means, from consecutive complex images corresponding to the multiple ultrasonic pulses.
Once generated, the Doppler parameter data are normally post-processed. Post-processing prepares the raw Doppler parameter data for viewing on a final display device. Post-processing of Doppler parameter data typically includes:
using one or more thresholds to exclude any image samples from Doppler parameter data that correspond to any random noise or residual clutter;
optionally applying some image processing techniques (e.g., smoothing, noise reduction, etc.) to the Doppler parameter data; and,
color mapping and scan conversion of the Doppler parameter data.
Prior art ultrasonic systems use one or more threshold values to sort, or classify, Doppler parameter data thereby separating image samples of moving targets of interest (e.g., blood flow) from the image samples of residual clutter or noise. For instance, a lower limit of a Doppler energy threshold, E
T
, can be set to exclude any image samples having a Doppler energy smaller than E
T
from an image of Doppler energy. Similarly, a lower limit of a Doppler velocity threshold, V
T
, can be established to remove any image samples whose absolute value of Doppler velocity is less than V
T
from an image of Doppler velocity. Other types of thresholds may also be applied to other types of Doppler parameter data. Further, various threshold types can be combined to identify any image samples that represent only a desired moving target, such as blood flow, in an image of Doppler velocity.
One of the shortcomings of this method is that image samples of two classes of Doppler parameter data (e.g., blood flow and clutter
oise) often have parameter space overlaps. Therefore, misclassifying a number of samples in each class is common. This misclassification results in either black holes (i.e., apparent stationary regions) in a blood flow region, or random Doppler noise (i.e., apparent moving targets of interest) in stationary tissue regions.
SUMMARY OF THE INVENTION
An embodiment of the invention is a system for Doppler ultrasonic imaging that includes a comparator for comparing at least one Doppler parameter input with a threshold value and outputting a result. The result is used as a first mask. The comparator has at least one threshold value associated with each Doppler parameter input. A spatial filter is coupled to the comparator for producing a second mask. A classification operator is coupled to the spatial filter for generating a third mask. The classification operator is capable of effectively comparing a value of a given pixel to a value of any nearby neighboring pixels and reclassifying any given non-majority pixel to a majority value of the nearby neighboring pixels. An embodiment of the invention further includes a multi-parameter generator for outputting at least one Doppler parameter. The multi-parameter generator has a functional relationship based on at least one Doppler parameter input and at least one of the masks. Furthermore, a second spatial filter is coupled to receive the Doppler parameter input.
A further embodiment of the invention is a method for Doppler ultrasonic image processing that includes comparing at least one Doppler parameter input with an associated threshold value and outputting a result based on the comparison. A first mask is generated using the result. A second mask is then generated by filtering the first mask with a spatial filter. The method classifies the second mask. A third mask is generated by classifying said second mask. The classifying step includes effectively comparing a value of a given pixel to a value of any nearby neighboring pixels and reclassifying any given non-majority pixel to a majority value of the nearby neighboring pixels. A multivariable function is used to output at least one Doppler parameter. The multivariable function is chosen to include a functionality based on at least one Doppler parameter input and at least one of the masks.
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Ji Ting-Lan
McLaughlin Glen
Carr & Ferrell LLP
Jaworski Francis J.
Zonare Medical Systems, Inc.
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