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US-12616441-B2 - Fat fraction estimation from tissue non-linear response with ultrasound medical imaging

US12616441B2US 12616441 B2US12616441 B2US 12616441B2US-12616441-B2

Abstract

For tissue property (e.g., fat fraction) estimation, ultrasound is used to measure non-linearity response of tissue (e.g., liver tissue). The fat fraction is estimated from the measured non-linearity response. The estimated fat fraction may be more accurate due to estimation from the measured tissue non-linearity response. By combining with other ultrasound-based measurements, such as scatter, attenuation, and/or speed of sound, the ultrasound-based estimation of fat fraction may be even more accurate. Other tissue properties may be estimated from the tissue non-linearity response alone or in combination with other measurements.

Inventors

  • Yassin Labyed

Assignees

  • SIEMENS MEDICAL SOLUTIONS USA, INC.

Dates

Publication Date
20260505
Application Date
20240911

Claims (20)

  1. 1 . A method for fat fraction estimation with an ultrasound scanner, the method comprising: generating one or more measures of scattering and/or shear wave propagation in tissue from a scan of a patient by the ultrasound scanner; generating a measure of tissue non-linear response of the tissue from the scan of the patient by the ultrasound scanner, the measure of tissue non-linear response comprising separate values at two or more frequency bands; estimating, by a processor, the fat fraction of the tissue of the patient from (1) the one or more measures of scattering and/or shear wave propagation and (2) the separate values at the two or more frequency bands of the measure of tissue non-linear response; and outputting an ultrasound image including an indication of the fat fraction as estimated.
  2. 2 . The method of claim 1 wherein generating the measures from the scan comprises separate transmit and receive events for the one or more measures of scattering and/or shear wave propagation and the measure of tissue non-linear response.
  3. 3 . The method of claim 1 wherein generating the one or more measures of scattering and/or shear wave propagation comprises generating a measure of frequency-dependent acoustic attenuation coefficient, a frequency-dependent backscatter coefficient, sound speed, or combinations thereof.
  4. 4 . The method of claim 1 wherein generating the one or more measures of scattering and/or shear wave propagation comprises generating a shear wave speed.
  5. 5 . The method of claim 1 wherein generating the measure of the tissue non-linear response comprises transmitting ultrasound at different powers and characterizing a variation in responses to the transmitting as a function of the different powers.
  6. 6 . The method of claim 5 wherein characterizing comprises determining a non-linear coefficient of the responses as a function of the different powers.
  7. 7 . The method of claim 5 wherein characterizing comprises generating a curve or dataset of the responses as a function of the different powers.
  8. 8 . The method of claim 5 wherein transmitting the ultrasound at the different powers comprises transmitting the ultrasound at at least five different powers.
  9. 9 . The method of claim 1 wherein generating the measure of the tissue non-linear response comprises characterizing the responses as echoes in fundamental and harmonic frequencies of a frequency of the transmitted ultrasound.
  10. 10 . The method of claim 1 wherein estimating comprises estimating with a machine-learnt classifier.
  11. 11 . The method of claim 1 wherein estimating comprises estimating with a linear model.
  12. 12 . The method of claim 1 wherein estimating comprises estimating where the measure of tissue non-linear response comprises a non-linearity coefficient input to a fat fraction model.
  13. 13 . The method of claim 1 wherein estimating comprises estimating where the measure of tissue non-linear response comprises a curve provided to a fat fraction model.
  14. 14 . The method of claim 1 further comprising generating a measure of on-axis displacement of the tissue, and wherein estimating comprises estimating as a function of the measure of on-axis displacement.
  15. 15 . A system for fat fraction estimation, the system comprising: a transducer; a beamformer configured to transmit pulses at different powers in a patient and receive ultrasound data responsive to the pulses with the transducer; an image processor configured to determine a tissue non-linearity response from the ultrasound data, the tissue non-linearity response determined as a curve or dataset of the tissue non-linearity responses to change in transmit power without calculation of a non-linearity coefficient, and configured to estimate the fat fraction from the tissue non-linearity response with the curve or dataset input to a fat fraction model, which outputs the fat fraction; and a display configured to display the value of the fat fraction.
  16. 16 . The system of claim 15 wherein the image processor is configured to estimate the fat fraction with a machine-learnt classifier.
  17. 17 . The system of claim 15 wherein the image processor is configured to estimate the fat fraction from the tissue non-linearity response and a scatter parameter and/or shear wave parameter.
  18. 18 . The system of claim 17 wherein the image processor is configured to determine the tissue non-linearity response as a non-linearity coefficient.
  19. 19 . A method for tissue property estimation with an ultrasound system, the method comprising: determining, by the ultrasound system, a plurality of scattering parameters of tissue; determining, by the ultrasound system, a plurality of shear wave parameters of the tissue; determining, by the ultrasound system, a non-linearity response of the tissue, the non-linearity response comprising separate values at two or more frequency bands, the separate values comprising a curve or dataset of measures without calculation of a non-linearity coefficient; estimating the tissue property from the scattering parameters, the shear wave parameters, and the non-linearity response; and displaying the tissue property.
  20. 20 . The method of claim 19 wherein determining the non-linearity response comprises determining from acoustic echoes responsive to different transmit powers.

Description

BACKGROUND The present embodiments relate to ultrasound imaging. A tissue property, such as liver fat fraction, is measured using ultrasound. Nonalcoholic fatty liver disease (NAFLD) is the most common liver disease in American adults and children. NAFLD is characterized by excess hepatic fat accumulation as well as hepatic fibrosis. Fat fraction may be measured as an indicator of NAFLD. Fat fraction in the liver or other tissues, such as breast tissue, and/or other tissue properties (e.g., degree of fibrosis) provide diagnostically useful information. Magnetic resonance imaging (MRI) accurately measures the proton density fat fraction (PDFF) as a biomarker of hepatic fat content. However, MRI is not widely available and expensive. Ultrasound imaging is more readily available and less expensive. An ultrasound-based technique to quantify liver fat may advance clinical care. In one approach, ultrasound-based shear wave imaging is used to estimate fat fraction. This approach may not fully address the complexities of tissue. In another approach, ultrasound-based scatter and/or shear wave are used to estimate fat fraction. For example, the attenuation coefficient and backscatter coefficient are measured with ultrasound and used to estimate the fat fraction (ultrasound-derived fat fraction). While accurate, the accuracy of fat fraction estimation using ultrasound may be improved. SUMMARY By way of introduction, the preferred embodiments described below include methods, instructions, and systems for tissue property (e.g., fat fraction) estimation with ultrasound. Ultrasound is used to measure non-linearity response of tissue (e.g., liver tissue). The fat fraction is estimated from the measured non-linearity response. The estimated fat fraction may be more accurate due to estimation from the measured tissue non-linearity response. By combining with other ultrasound-based measurements, such as scatter, attenuation, and/or speed of sound, the ultrasound-based estimation of fat fraction may be even more accurate. Other tissue properties may be estimated from the tissue non-linearity response alone or in combination with other measurements. In a first aspect, a method is provided for fat fraction estimation with an ultrasound scanner. One or more measures of scattering and/or shear wave propagation in tissue are generated from a scan of a patient by the ultrasound scanner. A measure of tissue non-linear response of the tissue is generated from the scan of the patient by the ultrasound scanner. A processor estimates the fat fraction of the tissue of the patient from (1) the one or more measures of scattering and/or shear wave propagation and (2) the measure of tissue non-linear response. An ultrasound image, including an indication of the fat fraction as estimated, is displayed. In a second aspect, a system is provided for fat fraction estimation. A beamformer is configured to transmit pulses at different powers in a patient and receive ultrasound data responsive to the pulses with a transducer. An image processor is configured to determine a tissue non-linearity response from the ultrasound data and configured to estimate the fat fraction from the tissue non-linearity response. A display is configured to display the value of the fat fraction. In a third aspect, a method is provided for tissue property estimation with an ultrasound system. The ultrasound system determines a plurality of scattering parameters of tissue, a plurality of shear wave parameters of the tissue, and a non-linearity response of the tissue. The tissue property is estimated from the scattering parameters, the shear wave parameters, and the non-linearity response. The tissue property is displayed. The Illustrative Embodiments listed below summarize other features or aspects. Any one or more of the aspects described above or in the Illustrative Embodiments may be used alone or in combination with other of the Illustrative Embodiments, features, or aspects. Any aspects or features of one of method, system, or computer readable media may be used in the others of method, system, or computer readable media. These and other aspects, features and advantages will become apparent from the following detailed description of preferred embodiments, which is to be read in connection with the accompanying drawings. The present invention is defined by the following claims, and nothing in this section should be taken as a limitation on those claims. Further aspects and advantages of the invention are discussed below in conjunction with the preferred embodiments and may be later claimed independently or in combination. BRIEF DESCRIPTION OF THE DRAWINGS The components and the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the invention. Moreover, in the figures, like reference numerals designate corresponding parts throughout the different views. FIG. 1 is a flow chart diagram of one embodiment of a method for e