DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on August 1, 2023, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendments
Applicant’s Preliminary Amendment to the abstract and the claims filed on August 1, 2023, has been entered and made of record.
Currently Pending Claim(s) 1-19
Independent Claim(s) 1 and 18
Amended Claim(s) 1 and 3-17
Newly Added Claim(s) 19
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1, 3, 6, 7, 11, 12, and 18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, the term “focusing quality criterion” is not clear. In the specification and dependent claims, focusing quality criterion can be different values such as phase properties or coherence. The Examiner interprets this term broadly and recommends that the Applicant explains what the focusing quality criterion is in claim 1 to overcome the rejections. Additionally, the term “evaluating the second subset” is not clear. The Examiner recommends explaining how the second subset is evaluated and what values are output from the evaluation.
Regarding claims 3 and 6, the phrase “phase properties” is unclear. Paragraphs 0034-0040 of the explain some information comprising the phase properties, but phase properties comprising “proportions of phase properties” is not clear and should be explained. Additionally in claim 6, there is insufficient antecedent basis for the limitation "the phase properties" in the claim.
Regarding claim 7, the claimed function is not clearly defined. The use of the function for calculating phase properties is not explained. The Examiner recommends explaining each of the variables x, y, and G, and how these variables each relate to the phase properties.
Regarding claim 12, there is insufficient antecedent basis for the limitation "focusing quality map" in the claim.
Regarding claim 18, see the rejection to claim 1 above.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 17 is rejected under 35 U.S.C. 101 because the claim is directed to non-statutory subject matter. Claim 17 not fall within at least one of the four categories of patent eligible subject matter because is claims “a computer program,” which could be interpreted as a signal. The Examiner recommends amending claim 17 as shown below:
“A non-transitory computer-readable medium comprising instructions, which when executed by a data processing system, cause the data processing system to carry out the method according to claim 1.”
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3 and 10-19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Huang et al. (US 2022/0280138 A1), hereafter Huang.
Regarding claim 1, Huang teaches a method of evaluating ultrasound data for the purpose of ultrasound attenuation estimation in a medium ([0004] “In accordance with the principles of the present invention, an ultrasound imaging system, method, and technique are described for more accurately estimating acoustic attenuation coefficients over an ultrasound image field.”), the medium being associated with ultrasound spatio-temporal signal data received from the medium in response to at least one ultrasound plane and/or diverging wave emitted into the medium ([0004] “Instead of acquiring echo signal data only in the plane of interest, echo signals for acoustic attenuation estimation are acquired from a plurality planes differing in elevation. Echo signal data is thus acquired adjacent to the primary plane of interest.” Also see Fig. 2 showing multiple ultrasound planes received in response to spatio-temporal signal data.), the method comprising:
beamforming a first subset of ultrasound spatio-temporal signal data to obtain a second subset of beamformed ultrasound data ([0014] “The echoes received by a contiguous group of transducer elements are beamformed by appropriately delaying them and then combining them.”),
determining a focusing quality criterion from the first subset, and evaluating the second subset as a function of the focusing quality criterion of the first subset (Huang teaches calculating an attenuation coefficient map for determining the amount of attenuation throughout a volume of human tissue being examined. [0021] “The different attenuation coefficient maps produced by the attenuation coefficient estimator are coupled to a confidence measure estimator 52, which produces spatially corresponding maps of estimated confidence (i.e., reliability,) either of a single attenuation coefficient map or of one attenuation coefficient map in relation to another. The attenuation coefficient maps and the results of the confidence estimations are coupled to an attenuation coefficient map compounder 54, which compounds (combines) the coefficient map values in the elevation dimension on a pixel-by-pixel basis, such as by weighted averaging, where the weighting is determined by the confidence estimations.” This attenuation coefficient map is calculated from the beamformed data, outputting an image, and the confidence measures are used for evaluating the attenuation coefficient maps. Huang teaches that the confidence values are coherence values derived from the pre-beamformed signal data (first subset) [0025] “Other methods or metrics for deriving confidence measures include texture analysis, flow measurement, tissue response to acoustic radiation force, and coherence in pre-beam-summed channel data.”).
Regarding claim 2, Huang teaches the method according to claim 1, wherein the first subset is selected in a predefined beamforming process as a function of a first predefined spatial region in the medium, and the second subset comprises image data of the first predefined spatial region (Fig. 2 shows various planes, which are each spatial regions. Label 82 shows echo data for a plane, which is the first subset. Huang’s method teaches beamforming the echo data and applying confidence measures to produce an attenuation coefficient map for the plane, which involves beamforming the first subset to produce image data.).
Regarding claim 3, Huang teaches the method according to claim 1, wherein the focusing quality criterion comprises at least one of: a coherence property of the first subset, and phase properties of the first subset (Huang teaches that confidence measures can be derived using the coherence of the echo signals from the pre-beamformed data (first subset) [0025] “Other methods or metrics for deriving confidence measures include texture analysis, flow measurement, tissue response to acoustic radiation force, and coherence in pre-beam-summed channel data.”).
Regarding claim 10, Huang teaches the method according to claim 1, wherein at least one of:
Beamforming the first subset comprises: beamforming a plurality of first subsets of ultrasound spatio-temporal signal data to obtain a respective plurality of second subset of beamformed ultrasound data, wherein the plurality of first subsets is associated with different spatial regions in the medium ([0014] “The echoes received by a contiguous group of transducer elements are beamformed by appropriately delaying them and then combining them.” In Fig. 2, Huang teaches performing the method of computing acoustic attenuation coefficient maps for different planes in different spatial regions, differentiated by elevation. [0027] “Located in correspondence with this image plane are elevational different planes A, B, C, D and E from which echo signals are acquired for the computation of acoustic attenuation coefficient values in each plane.”), and
determining the focusing quality criterion comprises: determining for each of a plurality of first subsets a focusing quality criterion (The acoustic attenuation coefficient maps are evaluated using confidence measurements, which are focusing quality criterion. For example, confidence measurements are coherence values determined from the first subset(s). [0014] “Other methods or metrics for deriving confidence measures include… coherence in pre-beam-summed channel data.” Huang teaches determining confidence measurements for each of the plurality of planes. [0026] “The attenuation coefficient map compounder 54 produces a final attenuation coefficient map by compounding the elevationally different attenuation coefficient maps. During compounding, the coefficient values of an attenuation coefficient map with higher confidence factors and/or higher consistency with other maps will be given larger weights in the combining process.”).
Regarding claim 11, Huang teaches the method according to claim 1, wherein evaluating the second subset comprises:
evaluating a plurality of second subsets as a function of the focusing quality criteria of the respective first subsets ([0004] “Instead of acquiring echo signal data only in the plane of interest, echo signals for acoustic attenuation estimation are acquired from a plurality planes differing in elevation. Echo signal data is thus acquired adjacent to the primary plane of interest.” The acoustic attenuation coefficient maps are evaluated using confidence measurements, which are focusing quality criterion. For example, confidence measurements are coherence values determined from the first subset(s). [0014] “Other methods or metrics for deriving confidence measures include… coherence in pre-beam-summed channel data.” Huang teaches determining confidence measurements for each of the plurality of planes. [0026] “The attenuation coefficient map compounder 54 produces a final attenuation coefficient map by compounding the elevationally different attenuation coefficient maps. During compounding, the coefficient values of an attenuation coefficient map with higher confidence factors and/or higher consistency with other maps will be given larger weights in the combining process.”).
Regarding claim 12, Huang teaches the method according to claim 1, further comprising:
evaluating an area in the focusing quality map comprising a plurality of adjacent second subsets as a function of a difference between the focusing quality criteria of the respective first subsets (See the 35 USC 112(b) rejection for this claim. The “focusing quality map” is introduced here without antecedent basis and is not clear. Huang teaches determining an attenuation coefficient map and evaluating it using confidence measures—which are focusing quality criteria. The 3D map is constructed by creating 2D maps at different elevations and compounding them. Evaluation of attenuation values using confidence measures can occur on each plane. [0026] “The attenuation coefficient map compounder 54 produces a final attenuation coefficient map by compounding the elevationally different attenuation coefficient maps. During compounding, the coefficient values of an attenuation coefficient map with higher confidence factors and/or higher consistency with other maps will be given larger weights in the combining process.”).
Regarding claim 13, Huang teaches the method according to claim 1, wherein determining a focusing quality criterion comprises: determining the focusing quality criterion from the first subset and additionally from at least one further first subset associated with a neighboring spatial region with respect to the first predefined spatial region (Huang teaches determining confidence measures for each plane. Fig. 2 and Fig. 2(a) show multiple adjacent planes of echo data that are used for determining confidence values. Thus, focusing quality criterion is solved for more than one neighboring spatial region. Additionally, Huang teaches that confidence measures are coherence measures from pre-beamformed data; thus, confidence measures are focusing quality criterion. [0025] “Other methods or metrics for deriving confidence measures include texture analysis, flow measurement, tissue response to acoustic radiation force, and coherence in pre-beam-summed channel data.”).
Regarding claim 14, Huang teaches the method according to claim 1, further comprising:
determining a speckle statistic of the second set, wherein the second set optionally comprises IQ beamformed ultrasound data ([0016] “The coherent echo signals undergo signal processing by a signal processor 26, which includes filtering by a digital filter and noise or speckle reduction as by spatial or frequency compounding. The filtered echo signals are coupled to a quadrature bandpass filter (QBP) 28. The QBP performs three functions: band limiting the RF echo signal data, producing in-phase and quadrature pairs (I and Q) of echo signal data, and decimating the digital sample rate.”).
Regarding claim 15, Huang teaches a method of estimating an ultrasound attenuation property in a medium, comprising: the method according to claim 1, selecting at least one evaluated second subset as a function of the respective focusing quality criterion, and estimating the ultrasound attenuation property by applying a predefined attenuation estimation method to the selected second subset (In Fig. 2 and Fig. 2a, Huang shows multiple subsets of echo data, confidence data, and attenuation coefficients. Huang teaches a method for determining the attenuation properties for each subset and then compounding them into an attenuation coefficient map. Thus, at least one selected subset is evaluated for determining attenuation properties. [0021] “The different attenuation coefficient maps produced by the attenuation coefficient estimator are coupled to a confidence measure estimator 52, which produces spatially corresponding maps of estimated confidence (i.e., reliability,) either of a single attenuation coefficient map or of one attenuation coefficient map in relation to another. The attenuation coefficient maps and the results of the confidence estimations are coupled to an attenuation coefficient map compounder 54, which compounds (combines) the coefficient map values in the elevation dimension on a pixel-by-pixel basis, such as by weighted averaging, where the weighting is determined by the confidence estimations.” Also see 0022, which describes some predefined methods for calculating attenuation.).
Regarding claim 16, Huang teaches A method of estimating an ultrasound backscattering coefficient in a medium, comprising: the method according to claim 1, selecting at least one evaluated second subset as a function of the respective focusing quality criterion and/or as a function of the respective ultrasound attenuation property, and estimating the ultrasound backscattering coefficient by applying a predefined backscattering estimation method to the selected second subset (Huang teaches determining attenuation coefficients from multiple subsets and evaluating them with the confidence measures—which are focusing criterion. Calculating attenuation coefficients requires determining backscattering. [0019] “The attenuation coefficient estimator is capable of producing an attenuation coefficient map from echo signals acquired from a scan plane and, in accordance with the present invention, a plurality of attenuation coefficient maps from a plurality of scan planes separated in the elevation dimension, as described more fully in the subsequent drawings and description. The attenuation coefficient estimator 50 operates on the tissue values of I, Q data prior to detection of pixel values of a B mode image, and processes the tissue values in conjunction with a map of reference values, such as attenuation coefficient measurements made of a homogeneous tissue phantom, a theoretical model of attenuation coefficients, or a numerical simulation of attenuation coefficients.” Additionally, see Fig. 2 showing how the data is divided into different subsets. Attenuation coefficient maps are calculated for each layer and evaluated with confidence maps. Also see 0022, which describes some possible predefined methods for calculating attenuation.).
Regarding claim 17, Huang teaches a computer program comprising computer-readable instructions which when executed by a data processing system cause the data processing system to carry out the method according to claim 1 ([0001] “This invention relates to ultrasound imaging systems and, in particular, to the imaging of acoustic attenuation coefficient maps with 1.75D and 2D array transducers.”).
Regarding claim 18, Huang teaches a system for evaluating ultrasound data for the purpose of ultrasound attenuation estimation in a medium ([0001] “This invention relates to ultrasound imaging systems and, in particular, to the imaging of acoustic attenuation coefficient maps with 1.75D and 2D array transducers.”), the medium being associated with ultrasound spatio- temporal signal data received from the medium in response to at least one ultrasound plane and/or diverging wave emitted into the medium ([0004] “Instead of acquiring echo signal data only in the plane of interest, echo signals for acoustic attenuation estimation are acquired from a plurality planes differing in elevation. Echo signal data is thus acquired adjacent to the primary plane of interest.” Also see Fig. 2 showing multiple ultrasound planes received in response to spatio-temporal signal data.),
wherein the system comprises a processing unit (See processors labeled 26, 30, 34, 44, and 48, for performing Huang’s methods.) configured to:
beamform a first subset of ultrasound spatio-temporal signal data to obtain a second subset of beamformed ultrasound data ([0014] “The echoes received by a contiguous group of transducer elements are beamformed by appropriately delaying them and then combining them.”),
determine a focusing quality criterion from the first subset, and evaluate the second subset as a function of the focusing quality criterion of the first subset (Huang teaches calculating an attenuation coefficient map for determining the amount of attenuation throughout a volume of human tissue being examined. [0021] “The different attenuation coefficient maps produced by the attenuation coefficient estimator are coupled to a confidence measure estimator 52, which produces spatially corresponding maps of estimated confidence (i.e., reliability,) either of a single attenuation coefficient map or of one attenuation coefficient map in relation to another. The attenuation coefficient maps and the results of the confidence estimations are coupled to an attenuation coefficient map compounder 54, which compounds (combines) the coefficient map values in the elevation dimension on a pixel-by-pixel basis, such as by weighted averaging, where the weighting is determined by the confidence estimations.” This attenuation coefficient map is calculated from the beamformed data, outputting an image, and the confidence measures are used for evaluating the attenuation coefficient maps. Huang teaches that the confidence values are coherence values derived from the pre-beamformed signal data (first subset) [0025] “Other methods or metrics for deriving confidence measures include texture analysis, flow measurement, tissue response to acoustic radiation force, and coherence in pre-beam-summed channel data.”).
Regarding claim 19, Huang teaches the method according to claim 11, wherein evaluating the second subset comprises generating a focusing quality map based on the evaluated plurality of second subsets (Huang teaches methods for determining confidence measurements, which are focusing quality criterion. For example, confidence measures can be coherence values. [0025] “Other methods or metrics for deriving confidence measures include… coherence in pre-beam-summed channel data.” In Fig. 2 and Fig. 2a, Huang shows multiple subsets of echo data, confidence measurements, and attenuation coefficients. The attenuation coefficient maps are weighted by confidence measurement maps. [0021] “…confidence measure estimator 52, which produces spatially corresponding maps of estimated confidence… The attenuation coefficient maps and the results of the confidence estimations are coupled to an attenuation coefficient map compounder 54, which compounds (combines) the coefficient map values in the elevation dimension on a pixel-by-pixel basis, such as by weighted averaging, where the weighting is determined by the confidence estimations.”
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Huang (US 2022/0280138 A1) and further in view of Gong et al. (US 2022/0091243 A1), hereafter Gong.
Regarding claim 4, Huang teaches the method according to claim 1 but fails to teach determining a signal-to-noise ratio parameter from the first subset, and evaluating the second subset as a function of the signal-to-noise ratio parameter of the first subset.
However, Gong teaches determining a signal-to-noise ratio parameter from the first subset, and evaluating the second subset as a function of the signal-to-noise ratio parameter of the first subset (See Fig. 1. Gong teaches determining the signal-to-noise ratio values from signal data, then acquiring ultrasound data, and then evaluating the ultrasound data with the signal-to-noise ratio values for estimating attenuation coefficients. [0004] “Signal-to-noise ratio (SNR) data are computed using the signal power spectrum data and the noise power spectrum data. A frequency bandwidth is estimated using the SNR data and an ultrasound depth setting is estimated using the SNR data. Ultrasound data are then acquired using the frequency bandwidth and the ultrasound depth setting, and attenuation coefficient data are estimated from the ultrasound data.” [0020] “Referring now to FIG. 1, a flowchart is illustrated as setting forth the steps of an example method for computing SNR and using the SNR as a quality metric for adaptively restricting the depth range for frequency components in ultrasound attenuation coefficient estimation.”).
Huang and Gong are analogous in the art to the claimed invention, because both teach methods for ultrasound attenuation coefficient estimation. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Huang’s invention by considering the signal-to-noise ratio when determining attenuation coefficient maps. This modification would allow for the signal-to-noise ratio to be used for restricting depth ranges ([Abstract] “In one aspect, noise can be suppressed by using signal-to-noise ratio (“SNR”) as a quality control metric for restricting depth ranges for ACE. In this way, the proper depth range for each frequency component can be adaptively changed with sufficient SNR for ACE.” [0020] “As noted, in some aspects of the present disclosure a method is provided for ACE in which SNR is used as a quality metric for adaptively restricting the depth range for frequency components in ACE. Referring now to FIG. 1, a flowchart is illustrated as setting forth the steps of an example method for computing SNR and using the SNR as a quality metric for adaptively restricting the depth range for frequency components in ultrasound attenuation coefficient estimation.”).
Claims 5-6 and 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Huang (US 2022/0280138 A1) and further in view of Rigby (US 5,910,115 A).
Regarding claim 5, Huang teaches the method according to claim 1. Huang teaches wherein the focusing quality criterion is coherence from pre-beam-summed channel data; see 0025. However, Huang does not specifically teach calculating a ratio. More specifically, Huang fails to teach wherein the focusing quality criterion comprises at least one of a B-mode ratio and a coherence ratio determined by at least one of: normalizing the beamforming process by an incoherent energy arising from the first predefined spatial region, and determining a coherent sum and a non-coherent sum of the first subset and setting them in a ratio.
However, Rigby teaches wherein the focusing quality criterion comprises at least one of a B-mode ratio and a coherence ratio determined by at least one of: normalizing the beamforming process by an incoherent energy arising from the first predefined spatial region, and determining a coherent sum and a non-coherent sum of the first subset and setting them in a ratio ([Col. 2, lines 28-34] “In accordance with the method of the invention, a quantity, called the coherence factor, is calculated for each pixel in the image. The coherence factor is defined to be the ratio of two quantities: the amplitude of the receive signals Summed coherently and the amplitude of the receive signals Summed incoherently. The coherence data is stored in buffer memory and is optionally spatially filtered and mapped.” [Col. 4, lines 47-53] “Detection processor 52 calculates and applies a coherence factor C in accordance with the present invention. The coherence factor is calculated for each pixel in the image and is defined to be the ratio of two quantities: the amplitude of the sum of the receive signals and the sum of the amplitudes of the receive signals.”).
Huang and Rigby are analogous in the art, because both teach methods of determining coherence from the pre-beamformed signal data and using the coherence to evaluate the beamformed image data. In 0021, Huang teaches weighting the pixels of attenuation coefficient map (which is ultrasound image data) by confidence values (which are coherence values, see the last sentence in 0025). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Huang’s method by determining the coherence as a ratio of the coherent sum and non-coherent sum of the pre-beamformed signal data. This modification provides a coherence factor for filtering an ultrasound image and would allow for increasing contrast of specific tissue types and suppressing speckle noise ([Col. 2, lines 18-21] “The filter increases contrast between tissue types by distinguishing them on the basis of the degree of coherence of the receive ultrasound signals.”).
Regarding claim 6, Huang and Rigby teach the method according to claim 5. Huang further teaches wherein the phase properties comprise at least one of: signs, phases, a sign proportion, phase intervals, phase evolution states, and proportions of phase properties ([0013] “Among the transmit characteristics controlled by the transmit controller are the number, spacing, amplitude, phase, frequency, polarity, and diversity of transmit waveforms.” Huang teaches an ultrasound system capable of controlling the characteristics of transmitted waves.).
Regarding claim 8, Huang teaches the method according to claim 1, but Huang fails to teach wherein evaluating the second subset comprises comparing the focusing quality criterion of the respective first subset with a predefined range.
However, Rigby teaches wherein evaluating the second subset comprises comparing the focusing quality criterion of the respective first subset with a predefined range (Rigby teaches using coherence of the received signals to determine a coherence factor that can be used to filter pixels in the ultrasound image. Thus, the coherence factor is focusing quality criterion of the first subset, and it is used for filtering and/or evaluating the second subset. Filtering involves determining if the factor is within a predefined range. [Col. 2, lines 28-46] “In accordance with the method of the invention, a quantity, called the coherence factor, is calculated for each pixel in the image. The coherence factor is defined to be the ratio of two quantities: the amplitude of the receive signals summed coherently and the amplitude of the receive signals summed incoherently. The coherence data is stored in buffer memory and is optionally spatially filtered and mapped. The amplitude data is concurrently acquired and stored in buffer memory. The system of the invention can be selectively operated to display the coherence information alone, the amplitude information alone, or a combination of the coherence and amplitude information. In accordance with the preferred embodiment, this combination consists of multiplying, Sample by Sample, the receive beam formed amplitude by the coherence factor, and then displaying the modified amplitude conventionally, i.e., by log-compressing and Scan converting.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Huang’s invention by applying a filter to the attenuation coefficient map using the coherence. This modification would allow for the coherence to better distinguish different types of tissue and provide suppression of speckle noise ([Col. 2, lines 18-21] “The filter increases contrast between tissue types by distinguishing them on the basis of the degree of coherence of the receive ultrasound signals.”) Additionally, in 0021, Huang already teaches weighting the pixels of attenuation coefficient map (which is ultrasound image data) by confidence values (which are coherence values, see the last sentence in 0025), so this modification is similar to Huang’s method.
Regarding claim 9, Huang and Rigby teach the method according to claim 8. Rigby further teaches wherein at least one of: the second subset is evaluated to be useable for the purpose of ultrasound attenuation estimation in a medium, when the focusing quality criterion of the respective first subset is within the predefined range, and the second subset is evaluated to be not useable for the purpose of ultrasound attenuation estimation in a medium, when the focusing quality criterion of the respective first subset is outside the predefined range ([Col. 5, line 67 – Col. 6, line 3] “The filtering and Scaling operations are performed in buffer 60 by applying a two-dimensional filter 62 and a coherence map 64. The filtered and scaled coherence factor data is indicated by output C in FIG. 4.” Rigby teaches filtering the ultrasound image pixels (second subset) by the coherence factor (focusing quality criterion). Pixel data that falls outside of the filter range can be adjusted or filtered out completely based on a predefined quality threshold or range. [Col. 5, lines 42-48] “For example, the alternate mapping M1 shown in FIG. 5 will zero out the data (C=0) when the coherence factor C falls below a predetermined threshold. Similarly alternate mapping M2 zeros out the data at another threshold. This can be useful in cases where the primary diagnostic concern is identifying blood vessels in an image.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Huang’s invention by applying a filter to the attenuation coefficient map using the coherence. This modification would allow for the coherence to better distinguish different types of tissue and provide suppression of speckle noise ([Col. 2, lines 18-21] “The filter increases contrast between tissue types by distinguishing them on the basis of the degree of coherence of the receive ultrasound signals.”, and filtering out data based on the coherence can allow for specific tissue types to be analyzed ([Col. 5, lines 42-48] “For example, the alternate mapping M1 shown in FIG. 5 will zero out the data (C=0) when the coherence factor C falls below a predetermined threshold. Similarly alternate mapping M2 zeros out the data at another threshold. This can be useful in cases where the primary diagnostic concern is identifying blood vessels in an image.”).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Huang (US 2022/0280138 A1), and further in view of Tsui et al. (Effects of Fatty Infiltration of the Liver on the Shannon Entropy of Ultrasound Backscattered Signals. Entropy 2016, 18, 341.), hereafter Tsui.
Regarding claim 7, Huang teaches the method according to claim 3, but fails to teach the specifics of analyzing phase properties. Specifically, Huang fails to teach wherein the phase properties are determined by using a predefined function G, the function G being at least one of: a function G(x) that is symmetric about the axis x = 50%, configured to maximize at x=50% and minimize at x=0% and x=100% or to minimize at x=50% and maximize at x=0% and x=100%, a bell-shaped function, a normal distribution function, and a Shannon entropy function.
However, Tsui teaches wherein the phase properties are determined by using a predefined function G, the function G being at least one of: a function G(x) that is symmetric about the axis x = 50%, configured to maximize at x=50% and minimize at x=0% and x=100% or to minimize at x=50% and maximize at x=0% and x=100%, a bell-shaped function, a normal distribution function, and a Shannon entropy function (Tsui teaches modeling the phase properties using distributions. [Section 1, par. 5] “The second method uses statistical distributions to model the echo amplitude distribution for a more accurate statistical analysis. This concept has been realized using statistical models, such as K 26 and Nakagami distributions 27,28, indicating that the echo amplitude distribution varies from pre-Rayleigh to Rayleigh with the formation of fatty liver.” [Section 2.1, par. 1] “Shannon defined entropy as a measure of information [34]. For an ultrasound backscattered signal f(t), Shannon entropy is defined as the negative of the logarithm of the echo amplitude distribution w(y) [36]:”).
Huang and Tsui are analogous in the art to the claimed invention, because both teach methods of evaluating ultrasound data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Huang’s invention by analyzing the RF data using a distribution for obtaining a focusing quality criterion. This modification would use well-known and established methods in ultrasound for determining changes in ultrasound signals (Tsui shows that analyzing the statistical properties using distributions, such as a Shannon entropy function, is well known in the art. [Introduction, par. 6] “Recall that Shannon established information theory and defined entropy as a measure of information uncertainty [34]. Hughes pioneered using Shannon entropy for analyzing ultrasound signals, indicating that entropy is able to quantitatively describe changes in the microstructures of scattering media [35,36,37,38,39]. According to the above literature review, we assume that entropy (the signal uncertainty) may be used as a non-model-based approach for visualizing changes in the statistical properties of ultrasound signals induced by fatty infiltration in the liver.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chen et al. (US 12,216,233 B2) teaches methods for ultrasound attenuation coefficient estimation caused by signal interferences and/or non-uniform tissue structures.
Kanayama (US 10,932,750 B2) teaches methods for estimating attenuation constants in a region-of-interest of an ultrasound.
Shen et al. (Adaptive optimization of ultrasound beamforming sound velocity using sub-aperture differential phase gradient. Ultrasonics. Volume 79. Pages 52-59.) teaches methods for using the differential phase gradient of channel data for estimating the optimal sound velocity for beamforming.
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/Eric Shoemaker/
Patent Examiner
/JENNIFER MEHMOOD/Supervisory Patent Examiner, Art Unit 2664