Classification Preprocessing in Medical Ultrasound Shear Wave Imaging

• US 20130066204A1
• Filed: 09/09/2011
• Published: 03/14/2013
• Est. Priority Date: 09/09/2011
• Status: Active Grant

• Current Assignee: Siemens Medical Solutions USA Incorporated (Siemens AG)
• Original Assignee: Siemens Medical Solutions USA Incorporated (Siemens AG)
• Inventors: Fan, Liexiang, Freiburger, Paul, Benson, John
• Granted Patent: US 10,338,203 B2
• Time in Patent Office: Days
• US Class Current: 600/438
• CPC Class Codes: A61B 8/0858 involving measuring tissue layers, e.g. skin, interfaces
• CPC Class Codes: A61B 8/14 Echo-tomography
• CPC Class Codes: A61B 8/485 involving measuring strain or elastic properties
• CPC Class Codes: A61B 8/5207 involving processing of raw data to produce diagnostic data, e.g. for generating an image reconstruction G06T11/00
• CPC Class Codes: A61B 8/5223 for extracting a diagnostic or physiological parameter from medical diagnostic data for algorithms to analyse biomedical images G06T7/0012
• CPC Class Codes: G01S 7/52022 using a sequence of pulses, at least one pulse manipulating the transmissivity or reflexivity of the medium
• CPC Class Codes: G01S 7/52036 using analysis of echo signal for target characterisation
• CPC Class Codes: G03B 42/06 using ultrasonic, sonic or infrasonic waves
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

Classification preprocessing is provided for medical ultrasound shear wave imaging. In response to stress, the displacement at one or more locations in a patient is measured. The displacement over time is a curve representing a shift in location. One or more characteristics of the curve, such as signal-to-noise ratio and maximum displacement, are used to classify the location. The location is classified as fluid or fluid tissue, solid tissue, or non-determinative. Subsequent shear imaging may provide shear information for locations of solid tissue and not at other locations.

Claims

1. A method for classification preprocessing to be used in medical ultrasound shear wave imaging, the method comprising:

measuring, with ultrasound, displacement over time at a first location within a patient in response to an impulse excitation;

determining a maximum of the displacement over time;

determining a signal-to-noise ratio of the displacement over time; and

classifying, with a processor, the first location as a first one of a plurality types of tissue, the classifying being a function of the maximum of the displacement over time and the signal-to-noise ratio of the displacement over time.

2. The method of claim 1 further comprising:

transmitting an acoustic excitation into a patient, the impulse excitation comprising the acoustic excitation;

wherein measuring the displacement comprises repetitively scanning the first location with the ultrasound.

3. The method of claim 1 wherein measuring the displacement comprises transmitting ultrasound to the tissue and receiving reflections from the transmitting, the transmitting the ultrasound and receiving being performed multiple times, and detecting the displacement from the reflections from the multiple receiving.

4. The method of claim 1 further comprising:

repeating the classifying for a plurality of locations;

performing shear wave imaging of the patient including the first location and the plurality of locations; and

spatially masking the shear wave imaging as a function of the classifying for the first location and the plurality of locations.

5. The method of claim 1 wherein measuring the displacement over time comprises measuring the displacement as the tissue relaxes after the impulse excitation.

6. The method of claim 1 wherein determining the signal-to-noise ratio comprises:

filtering the displacement over time;

subtracting the filtered displacement over time from the displacement over time;

calculating a root mean square of a result of the subtracting; and

dividing an integral of the filtered displacement by the root mean square, a result of the dividing comprising the signal-to-noise ratio.

7. The method of claim 1 wherein classifying comprises classifying the first one as one of a group consisting of fluid, solid tissue, and not determinable.

8. The method of claim 1 wherein classifying comprises classifying the first one as fluid tissue or solid tissue.

9. The method of claim 1 wherein classifying comprises classifying with fuzzy logic or a probability function.

10. The method of claim 1 further comprising

determining a signal-to-noise ratio of an ultrasound signal;

and

determining a de-correlation coefficient of the ultrasound signal over time;

wherein classifying comprises classifying as a function of the maximum of the displacement over time, the signal-to-noise ratio of the displacement over time, the signal-to-noise ratio of the ultrasound signal, and the de-correlation coefficient of the ultrasound signal over time.

11. In a non-transitory computer readable storage medium having stored therein data representing instructions executable by a programmed processor for classification preprocessing in medical ultrasound shear wave imaging, the storage medium comprising instructions for:

determining a displacement profile of response in a patient;

calculating a first characteristic of the displacement profile;

distinguishing fluid and fluid tissue from solid tissue based, at least in part, on the first characteristic; and

masking out shear wave information from an image for fluid or fluid tissue.

12. The non-transitory computer readable storage medium of claim 11 wherein determining the displacement profile comprises receiving data representing a region of the patient at different times and correlating the data.

13. The non-transitory computer readable storage medium of claim 11 wherein determining the displacement profile comprise determining a magnitude of displacement as a function of time after an impulse causing displacement of tissue in the patient.

14. The non-transitory computer readable storage medium of claim 11 wherein calculating comprises calculating a signal-to-noise ratio of the displacement profile.

15. The non-transitory computer readable storage medium of claim 11 wherein calculating comprises calculating a maximum displacement from the displacement profile.

16. The non-transitory computer readable storage medium of claim 11 wherein distinguishing comprises classifying as a function of the first characteristic.

17. The non-transitory computer readable storage medium of claim 11 wherein distinguishing comprises classifying between (a) the fluid and the fluid tissue, (b) solid tissue, and (c) another class.

18. The non-transitory computer readable storage medium of claim 11 wherein masking comprises displaying the image representing shear wave information for solid tissue regions and not for fluid or fluid tissue regions.

19. A system for classification preprocessing in medical ultrasound shear wave imaging, the system comprising:

a transducer configured to transmit an acoustic impulse excitation into a patient and configured to scan with ultrasound a region of the patient;

a receive beamformer configured to generate data representing the region at different times after the acoustic impulse excitation, the data generated from the scan with ultrasound;

a processor configured to estimate tissue displacement induced by the acoustic impulse excitation, derive at least one parameter describing a characteristic of a profile of the tissue displacement, and classify tissue in the region based on at least the one parameter; and

a display configured to display an image representing shear for first locations, the first locations determined as a function of the classification of the tissue.

20. The system of claim 19 wherein the processor is configured to derive the one parameter as a signal-to-noise ratio, is configured to derive another parameter as a maximum displacement of the profile, and is configured to classify based on the signal-to-noise ratio and the maximum displacement.

21. The system of claim 19 wherein the processor is configured to derive the one parameter as a signal-to-noise ratio, is configured to derive another parameter as a maximum displacement of the profile, is configured to compute a statistic of the data in time, space, or time and space, and is configured to classify based on the statistic, signal-to-noise ratio and the maximum displacement.