System and method for ultrasound attenuation coefficient estimation

Abstract

Systems and methods for improving ultrasonic attenuation coefficient estimation ("ACE") techniques are described herein. In one aspect, noise can be suppressed by using a signal-to-noise ratio ("SNR") as a quality control metric to limit the depth range of the ACE. In this way, the appropriate depth range for each frequency component can be adaptively changed given a sufficient SNR for the ACE. In another aspect, elevation focus normalization is used to reduce errors associated with diffraction effects when estimating the attenuation coefficient. In another aspect, a method for suppressing noise in the ACE technique is provided. Noise can be arbitrarily suppressed based on a noise field calculated from measurements obtained in the absence of ultrasonic transmission, based on a unique combination of consecutive image frames, based on a singular value decomposition ("SVD") clutter filter cutoff, or a combination thereof.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of U.S. Provisional Patent Application Serial No. 62 / 788,430, filed on January 4, 2019, and entitled “SYSTEM AND METHOD FOR ULTRASONIC ATTENUATION COEFFICIENT ESTIMATION,” which is incorporated herein by reference in its entirety. Background Art

[0003] Ultrasound attenuation coefficient estimation ("ACE") has potential value for many clinical applications, including differentiating tumors and quantifying fat content in the liver. For example, in a non-limiting example of fat content detection, accumulation of fat droplets in the liver can lead to steatosis, and in many cases, steatosis may progress to fibrosis, cirrhosis, liver failure, or hepatocellular carcinoma. Fat content increases ultrasound attenuation during ultrasound propagation in the liver; therefore, the ultrasound attenuation coefficient can be a useful parameter for quantifying liver fat. Compared to the gold standard, liver biopsy, ACE provides a noninvasive and reproducible measurement, which is important for both initial screening and follow-up examinations. Therefore, accurate ACE has clinical utility in the detection and assessment of fatty liver.

[0004] Examples of ultrasound ACE methods include the spectral shift method and the reference phantom method. The spectral shift method estimates that the center frequency shifts down along the depth. On the other hand, the reference phantom method uses a well-calibrated phantom to normalize all system-dependent effects, such as focusing, time gain compensation ("TGC"), and diffraction, and then estimates the decay rate of frequency with depth. Summary of the invention

[0005] The present disclosure overcomes the above disadvantages by providing a method for estimating ultrasonic attenuation coefficient data using an ultrasonic system. Signal power spectrum data and noise power spectrum data are provided to a computer system. Signal-to-noise ratio (SNR) data is calculated using the signal power spectrum data and the noise power spectrum data. The SNR data is used to estimate the frequency bandwidth, and the SNR data is used to estimate the ultrasonic depth setting. The frequency bandwidth and the ultrasonic depth setting are then used to acquire ultrasonic data, and the attenuation coefficient data is estimated from the ultrasonic data.

[0006] Another aspect of the present disclosure is to provide a method for generating elevation focus normalized data using an ultrasound system. First data is acquired using an ultrasound system. The first data is acquired using a first elevation focus setting and a first lateral focus setting. Second data is acquired using the ultrasound system. The second data is acquired under the first elevation focus setting and a second lateral focus setting, the second lateral focus setting being different from the first lateral focus setting. Then, normalized data for elevation focus is generated by normalizing the second data using the first data.

[0007] The above and other aspects and advantages of the present disclosure will become apparent from the following description. In the description, reference is made to the accompanying drawings which form a part of this specification, in which preferred embodiments are shown by way of illustration. However, this embodiment may not represent the full scope of the present invention, and therefore, reference is made to the claims and this document to interpret the scope of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 is a flow chart illustrating example method steps for calculating SNR and using the SNR as a quality metric for adaptively limiting the depth range of frequency components in ultrasound attenuation coefficient estimation.

[0009] Figure 2 is an example of the power spectrum (in the logarithmic domain to base 10) of the backscattered signal (red dashed curve) and the noise (blue dashed curve).

[0010] Figure 3 ] is an example of a frequency power decay curve (solid curve) measured along the depth at three example frequencies (ie 5 MHz, 6 MHz and 7 MHz). The corresponding noise power variation with depth is shown as a dashed curve.

[0011] Figure 4 is an example of a 2D SNR plot (log domain) as a function of frequency (y-axis) and depth (x-axis). The white dashed curve indicates the area where the SNR is greater than zero.

[0012] Figure 5 is a flow chart setting forth the steps of an example method of elevation focus normalization.

[0013] Figure 6 is an example of the singular value distribution after acquiring 50 frames of B-mode images. The red dots represent non-limiting examples of cutoff order selection.

[0014] Figure 7 is an example of the power spectrum of the backscattered signal without (blue curve) and with (red curve) the SVD clutter filter.

[0015] Figure 8 is a block diagram of an example ultrasound system that may implement the methods described in the present disclosure. DETAILED DESCRIPTION

[0016] Described herein are systems and methods for improving ultrasonic attenuation coefficient estimation ("ACE") techniques. In one aspect, noise can be suppressed by using a signal-to-noise ratio ("SNR") as a quality control metric to limit the depth range of the ACE. In this way, the appropriate depth range for each frequency component can be adaptively changed given a sufficient SNR for the ACE. In another aspect, lateral focus reduction and elevation focus normalization can be used to reduce errors associated with diffraction effects when estimating the attenuation coefficient. In another aspect, a method for suppressing noise in the ACE technique is provided. Noise can be arbitrarily suppressed based on a noise field calculated from measurements obtained in the absence of ultrasonic transmission, based on a unique combination of consecutive image frames, based on a singular value decomposition ("SVD") clutter filter cutoff, or a combination thereof.

[0017] In ultrasound imaging, the spectrum of the backscattered ultrasound signal of ACE can be modeled as the sum of the spectrum of the pure signal and the noise:

[0018] Y(f)=S(f)+N(f) (1);

[0019] Among them, Y(f) is the measured spectrum, S(f) is the pure signal spectrum, and N(f) is the noise spectrum. Then, the frequency power spectrum of the backscattered signal can be calculated as:

[0020] |Y(f)| 2 =Y(f)Y(f) * =(S(f)+N(f))(S(f)+N(f)) * =S(f)S(f) * +N(f)N(f) * +S(f)N(f) * +S(f) * N(f) (2);

[0021] Where * represents the complex conjugate. Since the noise and pure signal are uncorrelated, S(f)N(f) * and S(f) * The expected value of the term N(f) is zero. Therefore, equation (2) can be simplified to:

[0022] |Y(f)| 2 =S(f)S(f) * +N(f)N(f) * =|S(f)| 2 +|N(f)| 2 (3).

[0023] If the noise power spectrum |N(f)| can be measured 2 , then the pure signal power spectrum can be estimated as:

[0024] |S(f)| 2 =|Y(f)| 2 -|N(f)| 2 (4).

[0025] As mentioned above, in some aspects of the present disclosure, a method for ACE is provided, wherein SNR is used as a quality metric for adaptively limiting the depth range of frequency components in ACE. Figure 1 , a flow chart illustrating steps of an example method for calculating SNR and using SNR as a quality metric for adaptively limiting the depth range of frequency components in ultrasound attenuation coefficient estimation.

[0026] The method includes providing signal power spectrum data to a computer system, as shown in step 102. The signal power spectrum data may include a measured signal power spectrum, |Y(f)| 2 , or the estimated signal power spectrum, |S(f)| 2 The signal power spectrum data may be provided to the computer system by accessing or retrieving the signal power spectrum data from a memory or other data storage device or medium. Additionally or alternatively, the signal power spectrum data may be acquired by an ultrasound system and provided to the computer system, wherein the computer system may constitute a part of the ultrasound system.

[0027] The method also includes providing noise power spectrum data, |N(f)|, to a computer system. 2 , as shown in step 104. Like the signal power spectrum data, the noise power spectrum data can be provided to the computer system by accessing or retrieving the noise power spectrum data from a memory or other data storage device or medium, or acquiring the noise power spectrum data through an ultrasound system.

[0028] By using the signal power spectrum data and the noise power spectrum data, one or more SNR values ​​are calculated, as shown in step 106. The one or more SNR values ​​indicate the amount of noise contained in the noise power spectrum. The one or more SNR values ​​are calculated as a frequency function. As an example, the SNR value can be calculated as:

[0029] or

[0030] As another example, the SNR value may be calculated as SNR (log domain).

[0031] SNR dB (f) = 10log|Y(f)| 2 -10log|N(f)| 2 or

[0032] SNR dB(f) = 10log|S(f)| 2 -10log|N(f)| 2 (6).

[0033] By using one or more SNR values, a frequency bandwidth for a subsequent ACE technique is determined, as shown in step 108. As an example, determining the frequency bandwidth may include calculating or otherwise determining an upper bound and a lower bound for the frequency bandwidth. For example, a threshold may be applied to define the minimum and maximum boundaries of the frequency bandwidth. As an example, the following SNR thresholds may be used:

[0034] SNR(f)≥1 or SNR dB (f)≥0 (7).

[0035] Using the determined frequency bandwidth, a depth setting can be determined for the ACE, as shown in step 110. For example, the depth setting can be determined by analyzing a frequency decay curve and a corresponding noise curve. As another example, the frequency bandwidth and depth setting can be determined by analyzing an SNR graph. In these examples, an SNR graph (e.g., a two-dimensional SNR graph) can be generated from the SNR values. This SNR graph shows that the SNR value is a function of frequency and depth.

[0036] Ultrasound data may then be acquired by operating the ultrasound system using the determined frequency bandwidth and depth settings, as shown in step 112. As shown in decision block 114, this process may be repeated so that the acquired ultrasound data is processed according to the above method to adaptively update the frequency bandwidth, depth settings, or both. For example, signal power spectrum data and noise power spectrum data are provided by calculating, estimating, extracting, or otherwise determining these data from the acquired ultrasound data, and these signal power spectrum data and noise power spectrum data are processed as described above to estimate new frequency bandwidth and depth setting parameters. The ultrasound attenuation coefficient may then be estimated from the acquired ultrasound data, as shown in step 116. Using the estimated attenuation coefficient, one or more attenuation coefficient maps may be generated for display to a user, storage for later use or processing, or both.

[0037] Figure 2 Examples of signal power spectrum data and noise power spectrum data are shown. Specifically, Figure 2 An example of the power spectrum (logarithmic base 10 domain) of the backscattered signal (dashed red curve) and the noise (dashed blue curve) at a depth of 6 cm is shown. The solid red curve shows the pure signal power spectrum (logarithmic base 10 domain). Figure 2 In the example shown, the SNR threshold in equation (7) can be used to limit the measured signal power spectrum |Y(f)| 2 The frequency bandwidth is about 3-7MHz, and the estimated signal power spectrum |S(f)|2 The frequency bandwidth is 3-6MHz.

[0038] In practice, the measured ultrasound signal power spectrum usually varies along depth. Figure 3 An example of a frequency power decay curve measured along the depth at three example frequencies (5 MHz, 6 MHz and 7 MHz) is shown. In addition, the corresponding noise power variation with depth is shown as a dashed curve. Due to time gain compensation, the noise power increases with depth. Figure 3 In , by comparing the frequency decay curves and their corresponding noise curves, the frequency-depth dependent SNR at each frequency and each depth can be calculated. Therefore, the SNR can also be used to adaptively limit the appropriate depth range of each frequency component and provide sufficient SNR for ACE.

[0039] exist Figure 3 In the example of , using the SNR threshold in equation (7), ACE can rely on the following depth ranges. For 5MHz, the entire depth region in the figure can be used for ACE. For 6MHz, the depth range of ACE can be roughly selected from 0.5cm to 7cm. And for 7MHz, the depth range of ACE can be roughly selected from 0.5cm to 6cm.

[0040] Figure 4 An example of a two-dimensional plot of frequency-depth dependent SNR (in the logarithmic domain) is shown. In this example, the Y-axis is frequency, f, and the X-axis is depth, z. As described above, the SNR can be used to determine the effective depth range at each frequency, or to determine the effective frequency bandwidth at certain depths, or to determine both for ACE. In a non-limiting example, regions where the SNR is greater than zero are selected as frequency bandwidths and depth regions for more reliable ACEs, such as Figure 4 Indicated by the white dashed curve in .

[0041] As previously described, in some aspects of the present disclosure, a method for ACE is provided in which elevation focus normalization is used to reduce diffraction effects that would otherwise reduce the accuracy of ACE. In some ultrasonic ACE methods, data from two close frequencies can be normalized by taking the power ratio between the two frequencies. This normalization can be performed under the assumption that the diffraction effects of the two close frequencies are equal. However, this assumption is not accurate in all cases. For example, this assumption is inaccurate for applications with strong focus and large f-numbers.

[0042] More commonly, the beam diffraction pattern changes rapidly along the depth at different frequencies and cannot be completely eliminated. In these cases, the residual diffraction effect will affect the accuracy of ACE. As an example, this problem can be solved by placing the focus position outside the depth range. In general, the lateral focus can also be adjusted to achieve this solution. As another example, multiple ultrasound image frames can be acquired with lateral focus set at different depth positions. Then, the multiple ultrasound frames can be averaged, or the power spectra acquired from these frames can be averaged to reduce the lateral focusing effect. However, for many clinical transducers, the elevation focus is typically fixed, which makes the depth range selection in these systems inflexible. Therefore, the systems and methods disclosed herein can provide an effective method to normalize the non-adjustable elevation focusing effect so that the depth range of ACE can be selected more flexibly.

[0043] Reference now Figure 5 , the flowchart shows the steps of an example method for normalizing the elevation focus. The method includes acquiring first ultrasound data, as shown in step 502. For example, the first ultrasound data may be a first frame of B-mode data. In particular, the first ultrasound data is acquired using a lateral focus that matches the elevation focus. In addition, the same lateral f-number and elevation f-number should be used to acquire the first ultrasound data. Taking the lateral focus and the elevation focus as 6 cm as an example, it can be assumed that

[0044] D 仰角 =D 侧向，6cm (8);

[0045] Among them, D 仰角 and D 侧向，6cm These are the diffraction patterns of elevation focusing and lateral focusing at 6 cm, respectively. The diffraction pattern is a function of frequency f and depth z. The total diffraction pattern in the backscattered power spectrum is the product of the two focusing effects and can be expressed as:

[0046]

[0047] This method includes acquiring second ultrasound data, as shown in step 504. For example, the second ultrasound data can be a second frame of B-mode data. In particular, the second ultrasound data is acquired using the same transducer and acquisition parameter settings as the first ultrasound data, except that a different lateral focus position is set for the second ultrasound data. Typically, the lateral focus of the second frame can be set outside the ACE depth range to reduce the effect of diffraction. For example, for an ACE depth range of 5-8 cm depth, a lateral focus of 4 cm depth can be used. Therefore, in some cases, the ACE depth range can be selected using the above method, and then the acquisition parameters for the second ultrasound data are selected here. The total diffraction of the second ultrasound data is:

[0048] D总_2 =D 仰角 D 侧向，4cm (10).

[0049] In practice, the first ultrasonic data and the second ultrasonic data may be obtained in any order (i.e., the first ultrasonic data need not be obtained before the second ultrasonic data). Then, as shown in step 506, the elevation focusing effect may be normalized. For example, the elevation focusing effect may be normalized as:

[0050]

[0051] By using this normalization, the diffraction pattern has a squared lateral focusing effect at depths outside the depth range (such as 4 cm in this example), and the elevation focusing effect is canceled. Because the lateral focusing is outside the ACE depth range, its impact on ACE accuracy is minimal. Those skilled in the art will recognize that the elevation focusing normalization can be applied to applications other than ACE, such as other tissue property estimation, including backscatter coefficient, effective scattering diameter, and effective acoustic concentration.

[0052] In addition to the above methods, it is also possible to reduce the bias caused by noise that contaminates the ACE signal. Such methods may be referred to as "noise suppression" or "noise reduction." Described herein are some example methods for effectively measuring and suppressing noise in backscattered signal power spectrum data to obtain more accurate ACE.

[0053] As an example, the noise field can be experimentally measured in the same ultrasound system configuration as the ACE signal acquisition by turning off the transmission and reception of the transducer. This method directly measures the background noise of the ultrasound system as an estimate of the noise deviation. The noise power spectrum can then be subtracted from the backscattered signal power spectrum to obtain the pure signal power spectrum using equation: (4). Figure 2 An example of the measured signal and noise power spectra (in the logarithmic domain) is shown (dashed red curve: measured signal; dashed blue curve: measured noise). The pure signal power spectrum can then be obtained by subtracting the noise power from the backscattered signal power at each frequency in the spectrum, |S(f)| 2 , as shown in equation: (4). The solid red curve shows the pure signal power spectrum after noise reduction (in the logarithmic domain). The influence of noise on the ACE power spectrum is effectively suppressed.

[0054] As another example, the noise deviation can be measured by acquiring two frames of ultrasound B-mode data consecutively, such as:

[0055] Y1(f)=S1(f)+N1(f) (12);

[0056] Y2(f)=S2(f)+N2(f) (13).

[0057] If the interleaving time interval between two ultrasound frames is minimal and the imaging object is relatively stationary, then it can be inferred that S1(f) = S2(f). The sum of equations (12) and (13) then yields:

[0058] Y1(f)+Y2(f)=2S(f)+N1(f)+N2(f) (14);

[0059] Subtracting equations (12) and (13) yields:

[0060] Y1(f)-Y2(f)=N1(f)-N2(f) (15).

[0061] The power spectrum of equations (14) and (15) can be calculated as:

[0062] |Y1(f)+Y2(f)| 2 =4|S(f)| 2 +|N1(f)| 2 +|N2(f)| 2 =4|S(f)| 2 +2|N(f)| 2 (16);

[0063] and

[0064] |Y1(f)-Y2(f)| 2 =|N1(f)| 2 +|-N2(f)| 2 =2|N(f)| 2 (17).

[0065] Equations (16) and (17) contain the same amount of noise power, so the pure signal power spectrum can be estimated as:

[0066]

[0067] As yet another example, clutter filters, such as those used for ultrasound Doppler imaging (eg, singular value decomposition ("SVD") clutter filters) may be used to suppress noise in ACE. For example, multiple B-mode images may be acquired in succession for SVD processing. Figure 6 An example of the singular value distribution after acquiring 50 frames of B-mode images is shown. In general, high-order singular values ​​mainly correspond to noise.

[0068] You can choose the cutoff order ( Figure 6 ) in order to filter out signals corresponding to singular value orders exceeding a given cutoff order before ACE analysis. Figure 7An example of the power spectrum of the backscattered signal without (blue curve) and with (red curve) the SVD clutter filter is shown. The noise in the measured ACE power spectrum has been suppressed by the clutter filter. An example of the SVD clutter filter is described in co-pending International Patent Application No. PCT / US2017 / 016190, which is incorporated herein by reference in its entirety.

[0069] Another example of "noise" is speckle noise due to constructive and destructive interference of backscattered signals. Speckle noise causes oscillations on the normalized spectrum, which undermines the accuracy of ACE. In order to improve the estimation accuracy of ACE, the effect of speckle noise can be reduced by averaging the power spectra of multiple A-lines. As an example, A-lines can be selected laterally from an ultrasound frame within a relatively uniform area. In another example, multiple ultrasound frames corresponding to different areas (and therefore different scattering) can be obtained while the patient is breathing freely and the target anatomical structure (such as the liver) moves. Then, A-line averaging can be performed using A-lines from different lateral positions within the same frame, or A-lines from different ultrasound frames, or both.

[0070] Figure 8 An example of an ultrasound system 800 that can implement the methods described in the present disclosure is shown. The ultrasound system 800 includes a transducer array 802, which includes a plurality of individually driven transducer elements 804. The transducer array 802 may include any suitable ultrasound transducer array, including a linear array, a curved array, a phased array, etc. Similarly, the transducer array 802 may include a 1D transducer, a 1.5D transducer, a 1.75D transducer, a 2D transducer, a 3D transducer, etc.

[0071] When excited by the transmitter 806, a given transducer element 804 generates a burst of ultrasonic energy. Ultrasonic energy (e.g., echoes) reflected from the object or subject under study back to the transducer array 802 is converted into electrical signals (e.g., echo signals) by each transducer element 804 and can be applied to a receiver 808 separately through a set of switches 810. The transmitter 806, receiver 808, and switches 810 operate under the control of a controller 412, which may include one or more processors. As an example, the controller 812 may include a computer system.

[0072] The transmitter 806 can be programmed to transmit unfocused or focused ultrasound waves. In some configurations, the transmitter 806 can also be programmed to transmit diverging waves, spherical waves, cylindrical waves, plane waves, or a combination thereof. In addition, the transmitter 806 can be programmed to transmit spatially or temporally encoded pulses.

[0073] Receiver 808 may be programmed to perform an appropriate detection sequence for the imaging task at hand. In some embodiments, the detection sequence may include one or more of progressive scanning, compound plane wave imaging, synthetic aperture imaging, and compound diverging beam imaging.

[0074] In some configurations, the transmitter 806 and the receiver 808 may be programmed to achieve a high frame rate. For example, a frame rate associated with an acquisition pulse repetition frequency ("PRF") of at least 100 Hz may be achieved. In some configurations, the ultrasound system 800 may sample and store at least one hundred sets of echo signals in the time direction.

[0075] The controller 812 can be programmed to design an acquisition sequence using the techniques described in the present disclosure or other methods known in the art. In some embodiments, the controller 812 receives user input that defines various factors used in the design of the acquisition sequence. In some examples, ultrasound data can be acquired using plane wave ultrasound acquisition. In other examples, ultrasound data can be acquired using an acquisition scheme that implements one or more focused ultrasound beams. Other examples of acquiring ultrasound data can also be used according to the knowledge of those skilled in the art.

[0076] Scanning may be performed by setting switches 810 to their transmit positions, thereby directing transmitters 806 to be temporarily turned on to excite transducer elements 804 during a single transmit event according to an acquisition sequence. Switches 810 may then be set to their receive positions and subsequent echo signals generated by transducer elements 804 in response to one or more detected echoes are measured and applied to receiver 808. The individual echo signals from transducer elements 804 may be combined in receiver 808 to produce a single echo signal.

[0077] The echo signal is transmitted to a processing unit 814 to process the echo signal or an image generated from the echo signal, and the processing unit 814 can be implemented by a hardware processor and a memory. As an example, the processing unit 814 can collect ultrasound data using acquisition parameters (e.g., frequency bandwidth and depth), which are determined by the method described in the present disclosure. As another example, the processing unit 814 can estimate the attenuation coefficient value and generate an attenuation coefficient map from the data collected using the method described in the present disclosure. As another example, the processing unit 814 can generate elevation focus normalized data using the method described in the present disclosure. The processing unit 814 can also generate noise reduction or noise suppression data using the method described in the present disclosure. The image generated by the echo signal of the processing unit 814 can be displayed on the display system 816.

[0078] This disclosure has described one or more preferred embodiments, and it should be understood that many equivalents, alternatives, variations and modifications besides those explicitly stated are possible and within the scope of the invention.

Claims

1. A method for estimating ultrasonic attenuation coefficient data using an ultrasonic system, the method comprising: (a) providing signal power spectrum data to a computer system; (b) providing noise power spectrum data to the computer system; (c) calculating signal-to-noise ratio (SNR) data using the signal power spectrum data and the noise power spectrum data; (d) estimating at least one of a frequency bandwidth or an ultrasound depth setting using the SNR data; (e) acquiring ultrasound data using at least one of the frequency bandwidth or the ultrasound depth setting; (f) generating backscatter signal power spectrum data according to the collected ultrasound data; (g) generating pure signal power spectrum data by subtracting the noise power spectrum data from the backscattered signal power spectrum data; (h) using the pure signal power spectrum data to estimate attenuation coefficient data, wherein noise in the attenuation coefficient data is suppressed based on using at least one of the frequency bandwidth or the ultrasound depth setting when acquiring the ultrasound data, wherein the noise in the attenuation coefficient data is additionally suppressed by using the pure signal power spectrum data in the attenuation coefficient data estimation. 2 . The method of claim 1 , wherein estimating the frequency bandwidth comprises estimating an upper bound of the frequency bandwidth and estimating a lower bound of the frequency bandwidth based on the SNR data.

3. The method of claim 2, wherein the upper bound of the frequency bandwidth and the lower bound of the frequency bandwidth are estimated based on thresholding the SNR data using at least one threshold.

4. The method of claim 3, wherein an upper bound of the frequency bandwidth is estimated based on thresholding the SNR data with a first threshold, and a lower bound of the frequency bandwidth is estimated based on thresholding the SNR data with a second threshold.

5. The method of claim 1, wherein the ultrasound depth setting is estimated by analyzing a frequency decay curve as a function of the SNR data. 6 . The method of claim 1 , wherein estimating the frequency bandwidth and estimating the ultrasound depth setting together comprise generating an SNR map from the SNR data and processing the SNR map to select the frequency bandwidth and the ultrasound depth setting.

7. The method according to claim 1, wherein the signal power spectrum data comprises measured signal power spectrum data, |Y(f)| 2 .

8. The method of claim 1, wherein the signal power spectrum data comprises estimated signal power spectrum data, |S(f)| 2 .

9. The method according to claim 1, wherein the attenuation coefficient data comprises an attenuation coefficient map, which intuitively describes the spatial distribution of attenuation coefficient values ​​in a region where the ultrasound data is acquired.

10. A method for estimating ultrasonic attenuation coefficient data using an ultrasonic system, the method comprising: (a) providing signal power spectrum data to a computer system; (b) providing noise power spectrum data to the computer system; (c) calculating signal-to-noise ratio (SNR) data using the signal power spectrum data and the noise power spectrum data; (d) estimating at least one of a frequency bandwidth or an ultrasound depth setting using the SNR data; (e) acquiring ultrasound data using at least one of the frequency bandwidth or the ultrasound depth setting; and (f) estimating attenuation coefficient data from the ultrasound data; Further comprising updating the frequency bandwidth and the ultrasound depth setting by estimating signal power spectrum data and noise power spectrum data from the acquired ultrasound data and repeating steps (c)-(d) using the estimated signal power spectrum data and noise power spectrum data.

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