Methods and systems for tracking organizational movement using covariance and anti-aliasing parametric tools

Abstract

A method for determining the time-dependent position of tissue in response to a stimulus may include: receiving ultrasound data reflected from the tissue; calculating a covariance matrix from the ultrasound data, from the depth group and over multiple pulse periods of the ultrasound data; and calculating one or more eigenvectors and associated eigenvalues ​​of the covariance matrix, wherein the one or more eigenvectors represent a first trace of the tissue's position. In one embodiment, the method includes determining a first trace of the tissue's position related to the principal eigenvector components of the one or more eigenvectors.

Description

Background Art

[0001] Cross-reference This application claims the benefit of U.S. Provisional Patent Application No. 63 / 500,833, filed May 8, 2023, which is hereby incorporated by reference in its entirety.

[0002] Tympanometry is a medical examination that enables determination of the mobility of a patient's eardrum in response to pressure changes (e.g., air flow). A healthy eardrum moves in response to pressure. Immobility may be due to fluid in the middle ear. Otitis media with effusion is characterized by the presence of fluid adjacent to the eardrum. Therefore, establishing a diagnosis of otitis media with effusion can be assisted by tympanometry.

Summary of the Invention

Means for Solving the Problems

[0003] Tympanometry using ultrasound can present both advantages and challenges. For example, ultrasound typically requires a coupling medium. However, it may be infeasible to fill the ear canal with a coupling medium. But air-coupled ultrasound can be difficult. For example, air can typically have an acoustic impedance with significant mismatch with the transducer and / or the material being measured. The above problems can potentially reduce the sensitivity of the measurement. Additionally, ultrasound measurements can characteristically have a low signal-to-noise during or near the application of air pressure stimulation. Therefore, determining the position of the eardrum during or temporally near the application of air pressure stimulation can be difficult.

[0004] The methods and systems disclosed herein address at least some of the problems identified above. Autoregressive and covariance analysis may be used separately or in combination to improve the extraction of tissue motion and position in this region by at least partially utilizing the periodicity of the received ultrasonic signals.

[0005] In one embodiment, the Specified herein discloses a method for determining the time-dependent location of tissue in response to a stimulus, the method comprising: (a) receiving ultrasound data derived from ultrasound waveforms reflected from the tissue; (b) calculating a covariance matrix from the ultrasound data from depth groups and for multiple pulse periods of the ultrasound data; (c) calculating one or more eigenvectors and associated eigenvalues ​​of the covariance matrix; and (d) identifying a first trace of the tissue location associated with the principal eigenvector components of one or more eigenvectors.

[0006] In some embodiments, a method for determining the time-dependent location of tissue in response to a stimulus further includes analyzing the frequency components of ultrasound data over multiple pulse periods. In some embodiments, a method for determining the time-dependent location of tissue in response to a stimulus further includes calculating a second trace of the tissue location based at least in part on the frequency components.

[0007] In some embodiments, a method for determining the time-dependent location of tissue in response to a stimulus further includes outputting an indication of the disease state of the tissue in response to a second trace of the tissue's location. In some embodiments, a method for determining the time-dependent location of tissue in response to a stimulus further includes outputting an indication of the health state of the tissue in response to a second trace of the tissue's location. In some embodiments, a method for determining the time-dependent location of tissue in response to a stimulus further includes outputting an indication of the undetermined state of the tissue in response to a second trace of the tissue's location.

[0008] In some embodiments, the multiple pulse periods are continuous. In some embodiments, the tissue is the eardrum.

[0009] In some embodiments, a method for determining the time-dependent location of tissue in response to a stimulus further includes outputting a display of the disease state of the tissue in response to a first trace of the tissue's location. In some embodiments, a method for determining the time-dependent location of tissue in response to a stimulus further includes outputting a display of the health state of the tissue in response to a first trace of the tissue's location. In some embodiments, a method for determining the time-dependent location of tissue in response to a stimulus further includes outputting a display of the undetermined state of the tissue in response to a first trace of the tissue's location.

[0010] In some embodiments, each pulse period of a plurality of pulse periods is associated with one associated covariance matrix from a plurality of associated covariance matrices. In some embodiments, the method for determining the time-dependent location of tissue in response to a stimulus further includes calculating a set of displacement vectors for the plurality of associated covariance matrices. In some embodiments, the plurality of associated covariance matrices are associated with pulse periods over consecutive and adjacent depths of ultrasound data. In some embodiments, the set of displacement vectors for the plurality of associated covariance matrices is calculated based on a target quality signal ratio. In some embodiments, the target quality signal ratio is a value between 0.00 and 1.00. In some embodiments, the method for determining the time-dependent location of tissue in response to a stimulus further includes stitching together segments of the set of displacement vectors to generate a time trace of the tissue location. In some embodiments, the time trace of the tissue location is the first trace.

[0011] In some embodiments, a method for determining the time-dependent location of tissue in response to a stimulus further includes analyzing one or more eigenvectors of a covariance matrix. In some embodiments, each of the one or more eigenvectors includes an orbital rotation related to the phase of the ultrasound data. In some embodiments, a method for determining the time-dependent location of tissue in response to a stimulus further includes applying a bias to each of the one or more eigenvectors such that the eigenvectors trace a circle around the origin in phase space. In some embodiments, a method for determining the time-dependent location of tissue in response to a stimulus further includes calculating a second trace of the tissue location using the resulting biased eigenvectors. In some embodiments, analyzing one or more eigenvectors further includes analyzing membrane motion information of one or more eigenvectors. In some embodiments, analyzing one or more eigenvectors further includes repeating the analysis in 10 ms steps within an adjustable 20 millisecond (ms) analysis window. In some embodiments, analyzing one or more eigenvectors further includes analyzing depth range values. In some embodiments, analyzing one or more eigenvectors further includes creating a set of principal eigenvectors at each 10 millisecond (ms) step. In some embodiments, analyzing one or more eigenvectors further includes creating a set of principal eigenvectors within each 20ms analysis window.

[0012] In some embodiments, the stimulus is pneumatic excitation. In some embodiments, the pneumatic excitation is an air jet.

[0013] In some embodiments, a method for determining the time-dependent location of tissue in response to a stimulus further includes analyzing the frequency components of ultrasound data over multiple pulse periods, and determining one or more regions in which the first trace includes non-physical tissue location or motion. In some embodiments, a method for determining the time-dependent location of tissue in response to a stimulus further includes analyzing the frequency components of ultrasound data over multiple pulse periods, and replacing the first trace with at least a portion of the second trace in one or more regions. In some embodiments, determining one or more regions in which the first trace includes non-physical tissue location or motion involves utilizing autoregression. In some embodiments, the autoregression is cubic autoregression. In some embodiments, the autoregression is higher than cubic autoregression.

[0014] In another aspect, the Specified herein discloses a method for improving the accuracy of determining the time-dependent location of tissue in response to a stimulus, the method comprising applying autoregression to a signal trace to generate a plurality of poles, using the features of the plurality of poles to eliminate or select one of the plurality of poles, and generating an improved signal trace of the tissue location from the remaining poles or the selected pole of the plurality of poles.

[0015] In some embodiments, autoregression is applied to one or more points in a signal trace. In some embodiments, the autoregression is cubic autoregression. In some embodiments, the autoregression is higher than cubic autoregression. In some embodiments, the dimension of the autoregression corresponds to the number of frequency peaks in the isolated signal trace. In some embodiments, the signal trace includes a covariance motion detection trace. In some embodiments, autoregression is applied to one or more eigenvectors of the covariance motion detection trace. In some embodiments, a method to improve the accuracy of determining the time-dependent location of tissue in response to a stimulus further includes estimating the mean frequency of the signal trace using angular values ​​between each of a plurality of poles and the center of the complex unit circle in the complex plane. In some embodiments, the angular values ​​are calculated by taking the inverse tangent obtained by dividing the real component of the pole by the imaginary component of the pole.

[0016] In some embodiments, a method for improving the accuracy of determining the time-dependent location of a stimulus-responsive tissue further includes estimating the frequency variance of a signal trace using radius values ​​between each of several poles and the center of the complex unit circle in the complex plane. In some embodiments, a method for improving the accuracy of determining the time-dependent location of a stimulus-responsive tissue further includes estimating the velocity represented by the signal trace using radius values ​​between each of several poles and the center of the complex unit circle in the complex plane.

[0017] In some embodiments, a method for improving the accuracy of determining the time-dependent location of tissue in response to a stimulus further includes plotting a velocity trace over the time of the signal trace by plotting angles between multiple poles and the center of the complex unit circle in the complex plane.

[0018] In some embodiments, a method for improving the accuracy of determining the time-dependent position of tissue in response to a stimulus further includes calculating one or more displacement values ​​between the position of a velocity trace and the position of a signal trace. In some embodiments, the determination of one or more displacement values ​​includes integrating the velocity of the velocity trace. In some embodiments, the features of the multiple poles include radius, angle, velocity, complex plane distance between the multiple poles, velocity change between the multiple poles, change in inertial velocity between one or more poles, or momentum of the multiple poles, or any combination thereof. In some embodiments, the multiple poles are segmented into one or more frequency groups based on frequency.

[0019] In some embodiments, generating an optimized signal trace further includes replacing multiple poles of the signal trace with multiple poles of different frequency groups. In some embodiments, multiple poles of the signal trace are replaced if the absolute error of one or more of the pole features exceeds a threshold. In some embodiments, the threshold is a value calculated by one or more of the pole selection algorithms in Table 3. In some embodiments, the poles are of opposite signs. In some embodiments, the threshold is between 0.1 and 5.0. In some cases, the threshold is about 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, or greater than about 5.0. In some embodiments, the threshold is π / 3 and the poles are of opposite signs.

[0020] In other embodiments, the Specified

[0021] In some embodiments, the processor is further configured to (i) analyze the frequency components of ultrasound data over multiple pulse periods, and (ii) calculate a second trace of tissue location based at least partially on the frequency components.

[0022] In some embodiments, the processor is further configured to output a disease status indication in response to a second trace of tissue location. In some embodiments, the processor is further configured to output a health status indication in response to a second trace of tissue location. In some embodiments, the processor is further configured to output an undetermined status indication in response to a second trace of tissue location.

[0023] In some embodiments, the system further comprises an airtight otoscope. In some embodiments, a processor is operably connected to the airtight otoscope.

[0024] In some embodiments, the system further comprises a capacitive micromachine ultrasonic transducer. In some embodiments, a processor is operably connected to the capacitive micromachine ultrasonic transducer. In some embodiments, the capacitive micromachine ultrasonic transducer is located within an otoscope.

[0025] In some embodiments, the multiple pulse periods are continuous. In some embodiments, the tissue is the eardrum. In some embodiments, the processor is further configured to output a disease state indication in response to a first trace of the tissue location. In some embodiments, the processor is further configured to output a health state indication in response to a first trace of the tissue location. In some embodiments, the processor is further configured to output an undetermined state indication in response to a first trace of the tissue location.

[0026] In some embodiments, each pulse period of a plurality of pulse periods is associated with one associated covariance matrix from a plurality of associated covariance matrices. In some embodiments, the system further includes calculating a set of displacement vectors of the plurality of associated covariance matrices. In some embodiments, the plurality of associated covariance matrices are associated with a pulse period over successive adjacent depths of ultrasonic data. In some embodiments, the set of displacement vectors of the plurality of associated covariance matrices is calculated based on a target quality signal ratio. In some embodiments, the target quality signal ratio is a value between 0.00 and 1.00. In some embodiments, the processor is further configured to stitch segments of the set of displacement vectors to generate a time trace of the position of the tissue. In some embodiments, the time trace of the position of the tissue is a first trace.

[0027] In some embodiments, a processor of the system is further configured to: (i) analyze one or more eigenvectors of a covariance matrix (each of the one or more eigenvectors includes an orbital rotation associated with the phase of ultrasonic data), (ii) apply a bias to each of the one or more eigenvectors such that the eigenvectors orbit the origin in the phase space, and (iii) use the resulting biased eigenvectors to calculate a second trace of the position of the tissue. In some embodiments, the processor is further configured to analyze the one or more eigenvectors by analyzing membrane motion information of the one or more eigenvectors.

[0028] In some embodiments, the processor is further configured to repeat the analysis in 10 ms steps within an adjustable 20 ms analysis window. In some embodiments, the processor is further configured to analyze one or more eigenvectors by analyzing the depth range values of the one or more eigenvectors. In some embodiments, the processor is further configured to analyze one or more eigenvectors by generating a set of dominant eigenvectors every 10 ms steps. In some embodiments, the processor is further configured to analyze one or more eigenvectors by generating a set of dominant eigenvectors within each 20 ms analysis window.

[0029] In some embodiments, the stimulus is pneumatic excitation. In some embodiments, the pneumatic excitation is an air jet.

[0030] In some embodiments, the processor is further configured to (i) identify one or more regions where the first trace includes the position or movement of non-physical tissue, and (ii) replace the first trace with at least a portion of the second trace in the one or more regions. In some embodiments, the processor is further configured to analyze the frequency components of the complex demodulation of the ultrasonic data by applying autoregression. In some embodiments, the autoregression is a third-order autoregression. In some embodiments, the autoregression is higher than a third-order autoregression.

[0031] In another aspect, a system for improving the accuracy of determining the time-dependent position of tissue in response to a stimulus is disclosed herein, the system including a processor storing executable instructions that, when executed: apply autoregression to a signal trace to generate a plurality of poles, use the characteristics of the plurality of poles to remove or select one of the plurality of poles, and generate an improved signal trace of the position of the tissue from the remaining or selected poles of the plurality of poles.

[0032] In some embodiments, the processor is further configured to apply autoregression to one or more points in a signal trace. In some embodiments, the autoregression is cubic autoregression. In some embodiments, the autoregression is higher than cubic autoregression. In some embodiments, the dimension of the autoregression corresponds to the number of frequency peaks in the isolated signal trace. In some embodiments, the signal trace includes a covariance motion detection trace. In some embodiments, the autoregression is applied to one or more eigenvectors of the covariance motion detection trace.

[0033] In some embodiments, the system's processor is further configured to estimate the average frequency of the signal trace using the angle values ​​between each of the poles and the center of the complex unit circle in the complex plane. In some embodiments, the processor is further configured to calculate the angle values ​​by taking the inverse tangent obtained by dividing the real component of the pole by the imaginary component of the pole. In some embodiments, the processor is further configured to estimate the variance of the signal trace using the radius values ​​between each of the poles and the center of the complex unit circle in the complex plane. In some embodiments, the processor is further configured to estimate the velocity of the signal trace frequency using the radius values ​​between each of the poles and the center of the complex unit circle in the complex plane. In some embodiments, the processor is further configured to plot a velocity trace over time of the signal trace by plotting the angles between the poles and the center of the complex unit circle in the complex plane. In some embodiments, the processor is further configured to calculate one or more displacement values ​​between the position of the velocity trace and the position of the signal trace. In some embodiments, the processor is further configured to calculate one or more displacement values ​​by integrating the velocity of the velocity trace.

[0034] In some embodiments of the system, the features of multiple poles include radius, angle, velocity, complex plane distance between multiple poles, velocity change between multiple poles, change in inertial velocity between one or more poles, or momentum of multiple poles, or any combination thereof. In some embodiments, the processor is further configured to segment the multiple poles into one or more frequency groups based on frequency. In some embodiments, the processor is further configured to generate an optimized signal trace by replacing multiple poles in a signal trace with multiple poles from different frequency groups. In some embodiments, the processor is further configured to replace multiple poles in a signal trace if one or more absolute errors between the features of multiple poles exceed a threshold. In some embodiments, the threshold is a value calculated by one or more of the pole selection algorithms in Table 3. In some embodiments, the poles are of opposite signs. In some embodiments, the threshold is between 0.1 and 5.0. In some cases, the threshold is about 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, or greater than about 5.0. In some embodiments, the threshold is π / 3 and the poles are of opposite signs.

[0035] In another aspect, the Specified Information Discloses a system for improving the determination of time-dependent position, the system comprising: an analysis of covariance model including executable instructions, the instructions configured to: (a) derive one or more covariance matrices from ultrasound data, (b) compute one or more eigenvectors of one or more covariance matrices, and (c) generate a trace from one or more eigenvectors; one or more autoregressives configured to generate multiple poles; an antialiasing model including executable instructions, the instructions configured to select one or more regions of a trace that deviate from a phase track when executed by a processor; and a signal trace model including executable instructions, the instructions configured to: When executed by: (d) a signal trace model configured to eliminate one or more poles in one or more regions of a trace selected by the anti-aliasing model (wherein the signal trace model is configured to eliminate one or more poles on at least partially the effect of one or more features of one or more poles); and (e) a signal trace model configured to select one or more replacement poles of one or more alternative signal traces to replace the one or more poles eliminated in (d) (wherein the signal trace model is configured to select one or more replacement poles on at least partially the effect of one or more features of one or more replacement poles); and an output generator configured to produce an improved signal trace of tissue locations from one or more replacement poles output by the anti-aliasing model.

[0036] In some embodiments, one or more features include the radius from one or more poles to the center of the complex unit circle, the angle between one or more poles and the center of the complex unit circle, the angular velocity between one or more poles, the distance in the complex plane between one or more poles, the change in velocity between one or more poles, the change in inertial velocity between one or more poles, or the momentum of one or more poles, or any combination thereof.

[0037] Further aspects and advantages of the present disclosure will be readily apparent to those skilled in the art from the following detailed description, which illustrates and describes only exemplary embodiments of the present disclosure. As will be understood, other and different embodiments are possible, and some of their details can be modified in various obvious ways without departing from the present disclosure. Accordingly, the drawings and description are intended to be illustrative and not restrictive.

[0038] Reference All publications, patents, and patent applications referenced herein are invoked by reference to the same extent as if each individual publication, patent, or patent application were specifically and individually indicated as being invoked by reference herein. This Specification is intended to supersede and / or take precedence over any publications, patents, or patent applications invoked by reference to the extent that they conflict with any disclosures contained herein.

[0039] Novel features of the present invention are shown in particular in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by referring to the following embodiments for carrying out the invention, which describe exemplary embodiments in which the principles of the present invention are utilized, and to the appended drawings (also referred to herein as "Figure" and "FIG."). [Brief explanation of the drawing]

[0040] [Figure 1] A schematic diagram of pulsed ultrasonic wave propagation in tone bursts is shown.

[0041] [Figure 2]The following panels illustrate an example of ultrasonic data spectral analysis of arterial blood flow. Panel (A) shows an example of echo amplitude from a moving target (blood cells) against depth on the vertical axis and time on the horizontal axis. Panel (B) shows a spectral analysis of ultrasonic data of blood flow in the proximal right middle cerebral artery (RMCA), with blood flow velocity in cm / second on the right vertical axis and a total time of 4 seconds on the horizontal axis. Panel (C) shows the intersection of ultrasonic beams with tissue simultaneously with the right middle cerebral artery (RMCA), right anterior cerebral artery (RACA), and left anterior cerebral artery (LACA).

[0042] [Figure 3] This panel shows high-frequency ultrasonic data from the eardrum. Panel (A) shows high-frequency (RF) echo data from the eardrum (human), where a vertical depth of 0 mm corresponds to 8.5 mm from the transducer, and the TM signal is at a depth of approximately 13-15 mm. Panel (B) shows a magnified version of Panel (A) within the area indicated by the arrow. Panel (C) shows a magnified version of Panel (B) within the area indicated by the arrow.

[0043] [Figure 4] An enlarged version of panel (C) in Figure 3 is shown, with three areas highlighted and three horizontal lines drawn, each line being approximately 20 ms long.

[0044] [Figure 5] The deconstruction of regions 1, 2, and 3 in Figure 4 into 80 overlapping phase track groups is shown in panels (a), (b), and (c), and the amplitudes of the signals in regions 1, 2, and 3 in Figure 4 are shown in panels (d), (e), and (f).

[0045] [Figure 6] This paper presents examples of unpacking and repairing the eardrum, as well as examples of non-physical phase behavior that may occur when tracking the eardrum.

[0046] [Figure 7] This shows one practical example of discontinuities generated in covariance techniques using blood flow data.

[0047] [Figure 8] A set of vectors D containing the positional changes of panel (a) during each covariance calculation interval is shown, the curve of panel (a) is shown at a magnification, the mutual fit of panel (b) within its overlapping region is shown, the segment of panel (b) stitched together from edge to edge is shown, and the motion of the tympanic membrane in panel (c) is revealed.

[0048] [Figure 9] For example, the phase profile plotted onto the background of the original RF ultrasound data is shown.

[0049] [Figure 10] An example of a quality measurement method M calculated over a 20ms wide window with a 10ms increment interval (50% overlap) for a total duration of 1 second is shown.

[0050] [Figure 11] Figure 9 shows an expanded example of a covariance trace following the RF phase track of a TM.

[0051] [Figure 12] Except for two points (approximately 0.18 seconds and 0.62 seconds), the covariance trace remains faithful to the phase track RF data.

[0052] [Figure 13] An example of a second-order AR model for estimating the frequency spectrum of a signal is shown.

[0053] [Figure 14] An example of a first-order AR model for estimating the frequency spectrum of a signal is shown.

[0054] [Figure 15] First, we show the three poles that result when the cubic AR model is applied to the first eigenvector of the covariance shown in Figures 11 and 12.

[0055] [Figure 16] The plot of the pole angle over time in both velocity and radians is shown. The pole angle is calculated by taking the inverse tangent obtained by dividing the real component of a given pole by the imaginary component of the given pole.

[0056] [Figure 17] The plots of pole velocity and position for all pulse periods are shown. After calculating the velocity for all pulse periods, the displacement of TM can be obtained by integrating the velocity over time.

[0057] [Figure 18] This shows a comparison of covariance methods at pole 1 of a third-order AR model.

[0058] [Figure 19] This provides a visual overview of the process for adjusting the covariance trace based on AR analysis.

[0059] [Figure 20] This shows an exemplary implementation of an anti-aliasing (hybrid) algorithm.

[0060] [Figure 21] This shows a histogram of the absolute values ​​of the error between the AR velocity and the covariance velocity.

[0061] [Figure 22] The result of the anti-aliasing process is shown.

[0062] [Figure 23A] Pole 1 trace shows a region exhibiting discontinuity over three different pulse duration ranges, 251–500, 751–1000, and 1001–1250, respectively. [Figure 23B] Pole 1 trace shows a region exhibiting discontinuity over three different pulse duration ranges, 251–500, 751–1000, and 1001–1250, respectively. [Figure 23C] Pole 1 trace shows a region exhibiting discontinuity over three different pulse duration ranges, 251–500, 751–1000, and 1001–1250, respectively.

[0063] [Figure 24A] Five examples of implementing the above AR pole selection algorithm are shown below. [Figure 24B] Five examples of implementing the above AR pole selection algorithm are shown below. [Figure 24C] Five examples of implementing the above AR pole selection algorithm are shown below. [Figure 24D] Five examples of implementing the above AR pole selection algorithm are shown below. [Figure 24E] Five examples of implementing the above AR pole selection algorithm are shown below.

[0064] [Figure 25] This describes a computer system programmed or otherwise configured to carry out the methods provided herein.

[0065] [Figure 26] This is a flowchart illustrating an exemplary method for determining the time-dependent location of tissue in response to a stimulus, according to several embodiments.

[0066] [Figure 27] This is a flowchart illustrating exemplary methods for improving the determination of the time-dependent location of tissue in response to a stimulus, according to several embodiments.

[0067] [Figure 28] This is a flowchart illustrating an exemplary method for improving time-dependent position determination according to several embodiments.

[0068] [Figure 29A] A and B show side and front cross-sectional views, respectively, of an otoscope placed inside the ear, according to several embodiments. [Figure 29B]A and B show side and front cross-sectional views, respectively, of an otoscope placed inside the ear, according to several embodiments.

[0069] [Figure 30A] In some embodiments, A shows a side cross-sectional view of the microscope, and B shows a front cross-sectional view of the tip of the microscope. [Figure 30B] In some embodiments, A shows a side cross-sectional view of the microscope, and B shows a front cross-sectional view of the tip of the microscope.

[0070] [Figure 31] This is a flowchart illustrating an exemplary method for determining the time-dependent location of tissue in response to stimulation, incorporating an otoscope, according to several embodiments. [Modes for carrying out the invention]

[0071] While various embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided merely as examples. Those skilled in the art will also be able to conceive of numerous variations, changes, and substitutions without departing from the present invention. It should be understood that various substitutes may be used for the embodiments of the present invention described herein.

[0072] This specification discloses a method and system for air-coupled ultrasound of the tympanic membrane. The method and system disclosed herein can improve the determination of the position of the tympanic membrane during or near the application of pneumatic stimulation. Transducers and ultrasound systems are disclosed herein. The ultrasound signal may be measured from the medial end of the external auditory canal. Ultrasound data may improve the diagnosis of ear infections and systematize the collection and analysis of diagnostic information for evaluating ear infections.

[0073] The methods and systems disclosed herein offer at least some of the following advantages: If tissue is the target and not a confounding factor of the signal of interest, tissue clutter removal is absent or minimal. The approach to the target is through air within the external auditory canal and does not reach the target by the use of ultrasonic coupling gel and its movement through intervening tissue. The pulsed ultrasonic transmitter / sensor may be a capacitive micromachine ultrasonic transducer (CMUT).

[0074] The methods and systems disclosed herein may be used in combination with devices and methods for characterizing ductile film properties, surface properties, and subsurface properties, such as those described in, for example, the jointly owned U.S. Patent No. 7,771,356 and U.S. Patent Publications 2019 / 0365292, 2018 / 0310917, and 2017 / 0014053, each of which is incorporated by reference in whole. Furthermore, the machine learning methods and systems disclosed in the jointly owned U.S. Patent Publication 2020 / 0286227 may also be used in combination with the methods and systems disclosed herein, which is incorporated by reference in whole.

[0075] The methods and systems disclosed herein may be used in combination, for example, with the air-coupled capacitive micromachine ultrasonic transducer and the methods of use and manufacture thereof disclosed in co-owned U.S. Patent Publication No. 2021 / 0145406, which is incorporated by reference.

[0076] The methods and systems disclosed herein can be used in conjunction with exemplary devices and methods for transmitting optical illumination, such as those described in, for example, the jointly owned U.S. Patent Publication No. 2020 / 0107813, which are incorporated by reference in their entirety.

[0077] By using the methods and systems disclosed herein, various diagnostic information can be provided by characterizing numerous biological tissues. Biological tissues may include the organs of a patient. Microscopes may be placed in cavities of the body to characterize patient tissues. Patient organs or cavities of the body may include, to name a few, muscles, tendons, ligaments, mouth, tongue, pharynx, esophagus, stomach, intestines, anus, liver, gallbladder, pancreas, nose, larynx, trachea, lungs, kidneys, bladder, urethra, uterus, vagina, ovaries, testes, prostate, heart, arteries, veins, spleen, glands, brain, spinal cord, nerves, and so on.

[0078] Methods and systems disclosed herein may be used to characterize the tympanic membrane. For example, the membrane may be characterized to determine an ear condition, such as acute otitis media (AOM). Characterizing an ear exhibiting AOM may include detecting the presence of exudate and characterizing the type of exudate as serous, mucous, purulent, or a combination thereof. In AOM, middle ear exudate (MEE) may be induced by infectious agents and may be thin or serous in viral infections and thicker and purulent in bacterial infections. Thus, determining various properties of the fluid adjacent to the tympanic membrane may provide information that can be used to characterize the membrane.

[0079] Whenever the terms "at least," "greater than," or "greater than or equal to" precede the first number in a set of two or more numbers, the terms "at least," "greater than," or "greater than or equal to" apply to each number in that set. For example, 1, 2, or 3 or more are equivalent to 1 or more, 2 or more, or 3 or more.

[0080] Whenever the terms "no more than," "less than," or "less than or equal to" precede the first number in a set of two or more numbers, the terms "no more than," "less than," or "less than or equal to" apply to each number in that set. For example, 3, 2, or 1 or less are equivalent to 3 or less, 2 or less, and 1 or less.

[0081] Some embodiments of the invention described herein involve numerical ranges. Where a range exists, it includes its endpoints. Furthermore, all subranges and values ​​within that range exist as if they were explicitly written out. The terms “about” or “approximately” mean that a particular value is within an acceptable margin of error as determined by those skilled in the art, which depends in part on the method of measuring or determining the value, e.g., the limits of the measuring system. For example, “about” may mean a standard deviation of 1 or more than 1, according to the practice in the art. Alternatively, “about” may mean a range of up to 20%, up to 10%, up to 5%, or up to 1% of a given value. Unless otherwise stated, where a particular value is described in this application and claims, the term “about” can be considered to mean that a particular value is within an acceptable margin of error.

[0082] Examples of ultrasound related to blood flow Ultrasound can be used in medical applications to understand hemodynamics and / or to investigate pathological vascular conditions that deviate blood flow behavior from expected areas with normal limitations. One example is the detection of higher-than-normal arterial blood flow velocity. Higher-than-normal flow rates may indicate vascular stenosis due to lesions that reduce the effective area of ​​blood flow. Increased intravascular velocity can be a warning sign of an impending crisis, such as a heart attack or stroke. Furthermore, higher intravascular velocity may be an indicator supporting patient management for diagnosis and treatment.

[0083] In an exemplary approach to processing pulsed ultrasound measurements of blood flow signals, complex demodulation of the RF echo from each pulse period may be performed so that the reflected signal characteristics can be observed as a “baseband” signal centered on DC. In this example, the frequency components across the set of pulse periods may be proportional to the blood flow velocity.

[0084] A visual summary of signal processing for pulsed ultrasound of this nature is shown in Figures 1 and 2. Figure 1 schematically illustrates the physical propagation of a single ultrasound pulse within tissue, with the returned echo representing reflections at any given time from a resolution cell or gate centered on a specific echo depth. The width of the resolution gate is cΔt / 2, where Δt is the duration of the pulse burst emitted from the transducer. This resolution parameter relates to the depth range of the scatterer that contributes to the echo amplitude received by the transducer at any given time after the pulse burst has left the transducer.

[0085] Figure 1 shows a schematic diagram of pulsed ultrasound propagation in a tone burst. A tone burst is a component of pulsed ultrasound. As shown in the figure, the rising edge is at time T0, the falling edge begins at T1, and the duration is Δt = T1 - T0. The solid and dashed lines with positive slopes represent the rising and falling edges of the ultrasound tone burst at any given fixed time on the horizontal axis, respectively. These lines show that, considering round trips, the resolution of the tone burst, called the sample volume size of the ultrasound data, is cΔt / 2. Therefore, at time T2, echoes arrive from a depth range that extends to this sample volume size.

[0086] The activity described in this image repeats with each pulse period of the ultrasound dataset and is represented as one vertical line out of thousands in each motion-versus-time (M-mode) display.

[0087] Panel (a) in Figure 2 shows an example of echo amplitude from a moving target (blood cells) against depth on the vertical axis and time on the horizontal axis. The vertical axis represents a depth range of 25–85 mm, and the horizontal axis represents a duration of 4 seconds, showing power M-mode ultrasound analysis of blood flowing through the midcerebral and anterior cerebral arteries supplying the brain. Panel (b) in Figure 2 shows the ultrasound spectral analysis of blood flow in the proximal RMCA (of panel (a)). In this example, there is a high-pass filter with a cutoff of approximately 7 cm / second in the processing chain. This filter removed reflections from brain tissue approximately 1000 times the size of the blood flow signal. Without this removal of "tissue clutter," the construction of panel (a) would be impossible, as brain tissue signals could account for the majority of the analysis of the average blood flow velocity at a given time. Panel (C) in Figure 2 shows the crossing of ultrasound beams with tissue simultaneously with the right middle cerebral artery (RMCA), right anterior cerebral artery (RACA), and left anterior cerebral artery (LACA). These crossover regions show echoes from blood flow, as shown in panel (a) of Figure 2. The blood velocity in panel (a) is calculated using autocorrelation techniques and differs from the position calculations in the following explanation, which use covariance and autoregression.

[0088] Ultrasound measurement of the eardrum Figure 3 shows ultrasonic radio frequency data from the tympanic membrane. Panel (A) of Figure 3 shows radio frequency (RF) echo data from the tympanic membrane (human), where a depth of 0 mm on the vertical scale corresponds to 8.5 mm from the transducer, and the TM signal is at a depth of approximately 13-15 mm. The ultrasonic pulse has 21 cycles. Note that the bottom of the image is closest to the transducer. The image between the two horizontal lines in panel (A) of Figure 3 is magnified to generate panel (B) of Figure 3, and the image between the two vertical lines in panel (B) is further magnified to generate panel (C) of Figure 3. The absence of RF data in the vertical region near 80 ms in panel (C) of Figure 3 is due to a decrease in reflected energy from the TM. This decrease in energy makes tracking the TM position more difficult. Therefore, because the signal in this region is reduced, it may be useful to take all reflected data into account when determining the TM position. The covariance and autoregressive methods described herein address this signal region at least partially.

[0089] Figure 4 shows an enlarged version of panel (C) of Figure 3, with three horizontal lines drawn, each approximately 20 ms long. The three sets are placed in different non-overlapping regions of the TM echo reflections. Each set of 10 lines is an exemplary instance for sampling complex RF echoes (via Hilbert transform) and determining the covariance matrix from the sum cross product of each of the 10 complex echo vectors.

[0090] Figure 5 shows the deconstruction of regions 1, 2, and 3 of Figure 4, divided into 80 overlapping phase track sets in panels (a), (b), and (c), respectively. Each track contains a value from each M pulse period (e.g., M=100 at a pulse repetition frequency of 5 kHz, or a time span of 100 / 5000=20 ms). In these 80 track sets, there is one track for each gate depth or a / d sample increment.

[0091] Panels (a), (b), and (c) in Figure 5 have 80 phase tracks drawn overlapping each other, demonstrating that the RF echoes, which are the source of this data, are highly consistent in their phase.

[0092] Panels (d), (e), and (f) of Figure 5 show the signal amplitudes for regions 1, 2, and 3 of Figure 4. Panel (b) of Figure 5 shows jaggedness specific to phase propagation. This is further emphasized by noting the signal energy levels in panels (d), (e), and (f) of Figure 5. This energy has a large drop of 15–20 dB in the jagged region of panel (b) of Figure 5. This jaggedness in the phase spectrum can cause problems when extracting the kinematics of the scattering target (in this case, the tympanic membrane) using the unwrapped phase from the Hilbert transform without other corrections.

[0093] A method for determining the time-dependent location of tissue in response to a stimulus. Disclosed herein are methods and systems for determining the time-dependent location of tissue in response to a stimulus. The tissue may be biological tissue. The tissue may be plant, food, animal, or human. The tissue may be human tissue such as an organ. The tissue may be a smooth tissue organ or a musculoskeletal organ. The tissue may be, for example, the heart, liver, spleen, ear organs, gallbladder, pancreas, kidney, bladder, uterus or ovary, prostate, thyroid and parathyroid glands, blood vessels, eye organs, appendix, stomach and intestines, or any organ. The tissue may be the eardrum. The stimulus may be a physical stimulus. The stimulus may be a pressure stimulus, electrical stimulus, electromagnetic stimulus, physical contact stimulus, fluid injection stimulus, or sensory stimulus, or another type of stimulus. The stimulus may be a pressure change stimulus.

[0094] In some cases, the method may include receiving ultrasonic data. In other embodiments, the method may include receiving another type of electromagnetic data. The ultrasonic data can be derived from ultrasonic waveforms reflected from tissue. In other embodiments, the ultrasonic data can be derived from one or more fragments of ultrasonic waveforms reflected from tissue. The method may include calculating a covariance matrix from the ultrasonic data. The covariance data can be a function of depth over multiple pulse periods of ultrasonic data. The covariance data may be a function of depth over approximately 2 pulse periods, 3 pulse periods, 4 pulse periods, 5 pulse periods, approximately 10 pulse periods, approximately 15 pulse periods, approximately 20 pulse periods, approximately 25 pulse periods, approximately 50 pulse periods, approximately 100 pulse periods, approximately 200 pulse periods, approximately 300 pulse periods, approximately 400 pulse periods, approximately 500 pulse periods, approximately 1000 pulse periods, approximately 2000 pulse periods, approximately 3000 pulse periods, approximately 4000 pulse periods, approximately 5000 pulse periods, or more than approximately 5000 pulse periods. In some cases, the number of pulse periods may be 4500 to 5000 pulse periods. In some cases, the number of pulse periods may be 4800 pulse periods. The method may further include calculating one or more eigenvectors and associated eigenvalues ​​of the covariance matrix.In some cases, the method involves 1 eigenvector, 2 eigenvectors, 3 eigenvectors, 4 eigenvectors, 5 eigenvectors, 6 eigenvectors, 7 eigenvectors, 8 eigenvectors, 9 eigenvectors, 10 eigenvectors, 11 eigenvectors, 12 eigenvectors, 13 eigenvectors, 14 eigenvectors, 15 eigenvectors, 16 eigenvectors, 17 eigenvectors, 18 eigenvectors, 19 eigenvectors, 20 eigenvectors, 21 eigenvectors, 22 eigenvectors, 23 eigenvectors, 24 eigenvectors, 25 eigenvectors, 26 eigenvectors The method may include calculating eigenvectors 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, or more than 50 eigenvectors. The method may further include determining a first trace of the tissue location related to the principal eigenvector components of one or more eigenvectors.

[0095] In some cases, the method may further include determining the time-dependent location of tissue in response to stimulation by analyzing the frequency components of ultrasound data over multiple pulse periods. In some cases, the frequency components may be traces. In some cases, the method may further include calculating a second trace of tissue location based at least in part on the frequency components. In some cases, the frequency components may be frequency measurements. In some cases, the frequency components may be changes in frequency or the rate of frequency.

[0096] In some cases, the method may further include outputting a display of the disease state of the tissue. In some cases, the display may be in response to a trace. In some cases, the display may be in response to a second trace of the tissue's location. In other embodiments, the method may further include outputting a display of the health state of the tissue. In some cases, the display may be in response to a second trace of the tissue's location. In other embodiments, the method may further include outputting a display of the undetermined state of the tissue. In some cases, the display may be in response to a second trace of the tissue's location. In some cases, the display may be in response to a first trace of the tissue. In some cases, the display may be in response to a third trace of the tissue.

[0097] In some cases, the pulse periods may be continuous. In other cases, the pulse periods may be discontinuous. In some cases, the tissue may be an organ of the ear, such as the eardrum, ear bones, Eustachian tube, cochlea, oval window, or another organ of the ear. In some cases, the tissue may be another human organ, such as the heart, liver, kidneys, stomach, eye, brain, or another organ.

[0098] In some cases, the method may further include outputting a display of the disease state of the tissue. In some cases, the display may be in response to a trace. In some cases, the display may be in response to a first trace of the tissue's location. In other embodiments, the method may further include outputting a display of the health state of the tissue. In some cases, the display may be in response to a first trace of the tissue's location. In other embodiments, the method may further include outputting a display of the undetermined state of the tissue. In some cases, the display may be in response to a first trace of the tissue's location. In some cases, the display may be in response to a second trace of the tissue. In some cases, the display may be in response to a third trace of the tissue.

[0099] In some cases, each pulse period of multiple pulse periods can be associated with one associated covariance matrix from multiple associated covariance matrices. In some cases, the multiple covariance matrices can be 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, It may include the variance matrix, the covariance matrix of 27, the covariance matrix of 28, the covariance matrix of 29, the covariance matrix of 30, the covariance matrix of 31, the covariance matrix of 32, the covariance matrix of 33, the covariance matrix of 34, the covariance matrix of 35, the covariance matrix of 36, the covariance matrix of 37, the covariance matrix of 38, the covariance matrix of 39, the covariance matrix of 40, the covariance matrix of 41, the covariance matrix of 42, the covariance matrix of 43, the covariance matrix of 44, the covariance matrix of 45, the covariance matrix of 46, the covariance matrix of 47, the covariance matrix of 48, the covariance matrix of 49, the covariance matrix of 50, or more than 50 covariance matrices.

[0100] In some cases, the method may further involve calculating a set of displacement vectors for multiple related covariance matrices. In some cases, multiple related covariance matrices may be associated with pulse periods over consecutive and adjacent depths of ultrasound data. In some cases, analysis of the covariance matrix over the entire phase track can reveal the principal components of tympanic membrane motion. This technique may be advantageous in helping to rescue low-energy, jagged ultrasound signals, as shown in panel (b) of Figure 5. The covariance matrix may be constructed by acquiring Hilbert-transformed RF ultrasound samples within a region of ultrasound data. The region may be a clear echo region. The region may also coincide with the length of the pulse duration of the ultrasound data. The covariance matrix can be analyzed by Hilbert transform.

[0101] The analytical pulse echo signal resulting from the Hilbert transform of a single pulse period, denoted by "k" for the kth pulse period, can often be expressed as follows:

number

number

number

number

[0102] The completed covariance matrix is ​​the sum of adjacent depth groups m={m0,m1,m2,...,mR}.

number

number

number

[0103] The covariance matrix resulting from a series of contributions across a continuous depth range spanning a specific tympanic membrane echo can be expressed as follows:

number

number

number

number

number

number

[0104] This allows demodulation to be avoided. This is because the carrier frequency at the start of the description of K is effectively subtracted by multiplying it by a second exponent that is conjugated to one exponential function.

[0105] The covariance matrix may be calculated based on the mathematical description above. Using the resulting matrix, the eigenvectors and eigenvalues ​​can be expressed as follows:

number

number

number

[0106] In some cases, the displacement vectors of multiple related covariance matrices are calculated based on the target quality signal ratio. The target quality signal ratio can be a value between 0.00 and 1.00. In some cases, the target quality signal ratio may be 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.20, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.30, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.40, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0 0.50, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0. It could be 76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, or 1.00.

[0107] In some cases, the method may further include stitching together segments of a set of displacement vectors to generate a time trace of the tissue's location. In some cases, the time trace may be a first trace. In some cases, the time trace may be a second trace. In some cases, the time trace may be a third trace.

[0108] In some cases, a method for determining the time-dependent position of tissue in response to a stimulus may further involve analyzing one or more eigenvectors of the covariance matrix. In some cases, each of the one or more eigenvectors may involve orbital rotations related to the phase of the ultrasound data. Note that there are many ways to represent the eigenvectors and eigenvalues ​​of the covariance operator. In some cases, membrane motion may typically be contained within a first eigenvector. In some cases, membrane motion may typically be contained within a second eigenvector. In some cases, membrane motion may typically be contained within a third eigenvector. In some cases, membrane motion may typically be contained within different eigenvectors.

[0109] In some cases, as shown in panels (a), (b), and (c) of Figure 5, the eigenvectors may have similar phase-tracking behavior over many depths. In some cases, the overall signal energy may be clustered in a very small number of eigenvectors.

number

number

[0110] M can be evaluated at various depths along the ultrasonic beam over the number of pulse periods included in the covariance calculation. When no target is present, M can be a value between 0 and 1. In some cases, M may have a higher variance than when a target is present. These aspects of quality ratio behavior are shown, for example, in Figure 10.

[0111] Physical models of tympanic membrane motion can exhibit a continuous cycle of phase without jumps or discontinuities. Panels (a), (b), and (c) of Figure 6 show examples of non-physical phase behavior that may occur when tracking the tympanic membrane, along with an example of unpacking and correcting the tympanic membrane.

[0112] Panel (a) of Figure 6 shows an example of eigenvectors of covariance from region 2 of Figure 4. In some cases, the eigenvectors may indicate, for example, a decrease in amplitude. The observed decrease may occur because the reflection intensity from the tympanic membrane is lower than the reflection from nearby tissue (such as the tympanic umbilicus) which is stationary (not moving) by comparison. Panel (a) of Figure 6 shows one example of a path trajectory cycle to which a bias can be applied. Panel (b) of Figure 6 shows eigenvectors similar to those in Panel (a) of Figure 6, but includes a pre-inserted bias, for example, so that all trajectories of the signal are around the origin of the complex plane. Panel (c) of Figure 6 shows, for example, the cumulative phase changes of the path in Panel (a) of Figure 6 as Trace 1, and the cumulative phase changes of the path in Panel (b) of Figure 6 as Trace 2. The difference between Trace 2 and Trace 1 can be seen, for example, in the dashed Trace 3. For example, there may be a difference of 2π due to bias correction from Panel (a) of Figure 6 to Panel (b) of Figure 6.

[0113] In some cases, methods for determining the time-dependent location of tissue in response to a stimulus may further involve applying a bias to one or more eigenvectors so that the eigenvectors trace circles around the origin in phase space. Applying a correction bias to the data, as shown in panel (b) of Figure 6, may be one solution to the problem of non-physical phase extraction, but it may introduce its own artifacts into the data.

[0114] Figure 7 shows, for example, that an ultrasound signal can be a linear sum of phasors in the complex plane. As illustrated, if the phasor trajectory centers are biased away from the origin in the complex plane, the motion associated with a particular phasor may become ambiguous. Panel (a) of Figure 7 shows phasor 2, which may be the signal of interest, and which may orbit away from the origin due to bias from phasor 1. Panel (b) of Figure 7 shows that phasor 2, which may be the signal of interest, can orbit the origin in the complex plane. Panel (c) of Figure 7 shows an example of the two phasor behaviors in panel (a) of Figure 7 using ultrasound signal data from brain tissue. In some cases, phasor 1 may be a slow-moving phasor, while particles in the blood flow of the middle cerebral artery are phasor 2, which may be a fast-moving phasor. Panel (d) of Figure 7 shows the same brain / specific data example as Panel (b) of Figure 7, except that slow brain phasors can be removed using a high-pass filter (clutter filter), leaving only the embolic signal as a residual phasor orbiting the complex origin. Panel (e) of Figure 7 shows an exemplary calculation of embolic position based on data of type bias in Panel (a) of Figure 7, which may not accurately represent the movement of the embolic in the bloodstream. Panel (f) of Figure 7 may show a calculation of embolic position based on data of features in Panel (b) and Panel (d) of Figure 7, for example, which can improve the accuracy when calculating embolic movement.

[0115] As shown in panel (e) of Figure 5, in some examples, a noise-versus-signal dip may exist in a region near the target. The target may be, for example, the eardrum. Analysis of covariance across the same region including the target may, in some cases, reduce broadband noise and better reveal the motion path and dynamics. In some cases, the noise-process covariance (data without a target) will use more eigenvectors to describe the process than the target covariance (data with a target). Analysis of covariance when a target is present may, in some cases, result in one or one-order-of-magnitude components to describe the dynamics of the target. The principal eigenvector (with the largest eigenvalue) may, in some cases, describe the motion of the target with contributions from all a / d samples within the target echo. In some cases, broadband noise may tend to be analyzable separately from the signal.

[0116] In some cases, a method for determining the time-dependent position of tissue in response to a stimulus may further include calculating a second trace of the tissue's position using the resulting biased eigenvectors. In some cases, analyzing one or more eigenvectors may further include analyzing membrane motion information of one or more eigenvectors. In some cases, analyzing one or more eigenvectors may further include repeating the analysis in 10-millisecond (ms) steps. In some cases, the 10-ms step may be within an adjustable 20-ms analysis window. In some cases, analyzing one or more eigenvectors may further include analyzing depth range values. In some cases, analyzing one or more eigenvectors may further include creating a set of principal eigenvectors at each 10-millisecond (ms) step. In some cases, analyzing one or more eigenvectors may further include creating a set of principal eigenvectors within each 20-ms step.

[0117] In some examples, the calculation of the principal eigenvectors for covariance analysis over the depth and time ranges of the signal may be repeated, as shown in panel (A) of Figure 3. In the example shown in panel (A) of Figure 3, in some cases the data stream may be extended for a period of 1 second during tympanic membrane data acquisition. This may be data for which covariance analysis is performed, for example, about 5000 pulse periods. In some cases, the analysis may be repeated in 10 ms steps. In some cases, the analysis may be further repeated over a 20 ms window from the early edge of the signal to the latest edge of the signal. In some cases, the analysis may be further repeated for the M1 value which is the center of the tympanic membrane silhouette. For example, the top 80 M1 values ​​at each time position in Figure 10 are related to the silhouettes in panel (A) and panel (B) of Figure 3. In some cases the stimulus may be an excitation. In some cases the excitation may be a physical excitation, an electrical excitation, or an electromagnetic excitation. In some cases the stimulus may be a pneumatic excitation. In some cases the pneumatic excitation may be an air jet.

[0118] For example, the set of principal eigenvectors that can be obtained for each window position, with each window incrementing by 10ms over a time range of 1 second, may be expressed as follows:

number

[0119] The membrane motion information contained in the eigenvectors can be obtained by the following equation.

number

number

number

number

[0120] Similar to the set of eigenvectors described above, there is a set of displacement vectors, which can be expressed by the following equation.

number

[0121] Panel (a) of Figure 8 shows an example of a set of vectors D containing positional changes between each covariance calculation time interval. These individual segments can represent relative positional changes made by the tympanic membrane. In some cases, they may be “stitched” together with adjacent segments. Panel (b) of Figure 8 shows, for example, a magnified view of these curves, illustrating the mutual fitting in overlapping regions. Panel (c) of Figure 8 shows an example of segments that have been stitched together end to end to reveal tympanic membrane motion.

[0122] Figure 9 shows, for example, a phase profile plotted onto a background of the original RF ultrasound data.

[0123] Figure 10 shows an example of a quality metric M calculated over a 20ms wide window with 10ms increments for a total duration of 1 second. The noise background may be close to a level of 0.2, compared to a “target” close to 1.0, which occasionally dips over a time span and depth span equal to the ultrasonic sample volume size. The upper path can be the position of the maximum value of M over depth, every 10ms increment.

[0124] A system for determining the time-dependent location of tissue in response to stimuli. Disclosed herein is a system for determining the time-dependent location of tissue in response to a stimulus. The tissue may be biological tissue. The tissue may be plant, food, animal, or human. The tissue may be human tissue such as an organ. The tissue may be a smooth tissue organ or a musculoskeletal organ. The tissue may be, for example, the heart, liver, spleen, ear organs, gallbladder, pancreas, kidney, bladder, uterus or ovary, prostate, thyroid and parathyroid glands, blood vessels, eye organs, appendix, stomach and intestines, or any organ. The tissue may be the eardrum. The stimulus may be a physical stimulus. The stimulus may be a pressure stimulus, electrical stimulus, electromagnetic stimulus, physical contact stimulus, fluid injection stimulus, or sensory stimulus, or another type of stimulus. The stimulus may be a pressure change stimulus.

[0125] The system may include a processor that stores executable instructions configured to receive ultrasonic data at runtime. In other embodiments, the processor is configured to receive another type of electromagnetic data. The ultrasonic data can be derived from ultrasonic waveforms reflected from tissue. In other embodiments, the ultrasonic data can be derived from one or more fragments of ultrasonic waveforms reflected from tissue. The processor may be configured to calculate a covariance matrix from the ultrasonic data. The covariance data can be a function of depth over multiple pulse periods of ultrasonic data. The covariance data may be a function of depth over approximately 2 pulse periods, 3 pulse periods, 4 pulse periods, 5 pulse periods, approximately 10 pulse periods, approximately 15 pulse periods, approximately 20 pulse periods, approximately 25 pulse periods, approximately 50 pulse periods, approximately 100 pulse periods, approximately 200 pulse periods, approximately 300 pulse periods, approximately 400 pulse periods, approximately 500 pulse periods, approximately 1000 pulse periods, approximately 2000 pulse periods, approximately 3000 pulse periods, approximately 4000 pulse periods, approximately 5000 pulse periods, or more than approximately 5000 pulse periods. In some cases, the number of pulse periods may be 4500 to 5000 pulse periods. In some cases, the number of pulse periods may be 4800 pulse periods. The processor may be configured to compute one or more eigenvectors of the covariance matrix and their associated eigenvalues.In some cases, the processor may process 1 eigenvector, 2 eigenvectors, 3 eigenvectors, 4 eigenvectors, 5 eigenvectors, 6 eigenvectors, 7 eigenvectors, 8 eigenvectors, 9 eigenvectors, 10 eigenvectors, 11 eigenvectors, 12 eigenvectors, 13 eigenvectors, 14 eigenvectors, 15 eigenvectors, 16 eigenvectors, 17 eigenvectors, 18 eigenvectors, 19 eigenvectors, 20 eigenvectors, 21 eigenvectors, 22 eigenvectors, 23 eigenvectors, 24 eigenvectors, 25 eigenvectors, and 26 eigenvectors. The processor may be configured to compute 27 eigenvectors, 28 eigenvectors, 29 eigenvectors, 30 eigenvectors, 31 eigenvectors, 32 eigenvectors, 33 eigenvectors, 34 eigenvectors, 35 eigenvectors, 36 eigenvectors, 37 eigenvectors, 38 eigenvectors, 39 eigenvectors, 40 eigenvectors, 41 eigenvectors, 42 eigenvectors, 43 eigenvectors, 44 eigenvectors, 45 eigenvectors, 46 eigenvectors, 47 eigenvectors, 48 ​​eigenvectors, 49 eigenvectors, 50 eigenvectors, or more than 50 eigenvectors. The processor may be further configured to determine a first trace of the tissue's location related to the principal eigenvector components of one or more eigenvectors.

[0126] In some cases, the system may further include a processor configured to determine the time-dependent location of tissue in response to a stimulus by analyzing the frequency components of ultrasound data over multiple pulse periods. In some cases, the frequency components may be traces. In some cases, the processor may be further configured to calculate a second trace of the tissue location based at least in part on the frequency components. In some cases, the frequency components may be measurements of frequency. In some cases, the frequency components may be changes in frequency or rates of frequency.

[0127] In some cases, the system may further include a processor configured to output a display of the disease state of the tissue. In some cases, the display may be in response to a trace. In some cases, the display may be in response to a second trace of the tissue's location. In other embodiments, the system may further include a processor configured to output a display of the health state of the tissue. In some cases, the display may be in response to a second trace of the tissue's location. In other embodiments, the system may further include a processor configured to output a display of the undetermined state of the tissue. In some cases, the display may be in response to a second trace of the tissue's location. In some cases, the display may be in response to a first trace of the tissue. In some cases, the display may be in response to a third trace of the tissue.

[0128] In some cases, the pulse periods may be continuous. In other cases, the pulse periods may be discontinuous. In some cases, the tissue may be an organ of the ear, such as the eardrum, ear bones, Eustachian tube, cochlea, oval window, or another organ of the ear. In some cases, the tissue may be another human organ, such as the heart, liver, kidneys, stomach, eye, brain, or another organ.

[0129] In some cases, the system may further include a processor configured to output a display of the disease state of the tissue. In some cases, the display may be in response to a trace. In some cases, the display may be in response to a first trace of the tissue's location. In other embodiments, the system may further include a processor configured to output a display of the health state of the tissue. In some cases, the display may be in response to a first trace of the tissue's location. In other embodiments, the system may further include a processor configured to output a display of the undetermined state of the tissue. In some cases, the display may be in response to a first trace of the tissue's location. In some cases, the display may be in response to a second trace of the tissue. In some cases, the display may be in response to a third trace of the tissue.

[0130] In some cases, each pulse period of multiple pulse periods can be associated with one associated covariance matrix from multiple associated covariance matrices. In some cases, the multiple covariance matrices can be 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, It may include the variance matrix, the covariance matrix of 27, the covariance matrix of 28, the covariance matrix of 29, the covariance matrix of 30, the covariance matrix of 31, the covariance matrix of 32, the covariance matrix of 33, the covariance matrix of 34, the covariance matrix of 35, the covariance matrix of 36, the covariance matrix of 37, the covariance matrix of 38, the covariance matrix of 39, the covariance matrix of 40, the covariance matrix of 41, the covariance matrix of 42, the covariance matrix of 43, the covariance matrix of 44, the covariance matrix of 45, the covariance matrix of 46, the covariance matrix of 47, the covariance matrix of 48, the covariance matrix of 49, the covariance matrix of 50, or more than 50 covariance matrices.

[0131] In some cases, the system may further include a processor configured to compute a set of displacement vectors for multiple related covariance matrices. In some cases, multiple related covariance matrices may be associated with pulse periods over consecutive and adjacent depths of ultrasound data. In some cases, analysis of the covariance matrix over the entire phase track can reveal the principal components of tympanic membrane motion. This technique may be advantageous in helping to rescue low-energy, jagged ultrasound signals, as shown in panel (b) of Figure 5. The covariance matrix may be constructed by acquiring Hilbert-transformed RF ultrasound samples within a region of ultrasound data. The region may be a clear echo region. The region may also coincide with the length of the pulse duration of the ultrasound data. The covariance matrix can be analyzed by Hilbert transform.

[0132] The analytical pulse echo signal resulting from the Hilbert transform of a single pulse period, denoted by "k" for the kth pulse period, can often be expressed as follows:

number

number

number

number

[0133] The completed covariance matrix is ​​the sum of adjacent depth groups m={m0,m1,m2,...,mR}.

number

number

number

[0134] The covariance matrix resulting from a series of contributions across a continuous depth range spanning a specific tympanic membrane echo can be expressed as follows:

number

number

number

number

number

number

[0135] This allows demodulation to be avoided. This is because the carrier frequency at the start of the description of K is effectively subtracted by multiplying it by a second exponent that is conjugated to one exponential function.

[0136] The covariance matrix may be calculated based on the mathematical description above. Using the resulting matrix, the eigenvectors and eigenvalues ​​can be expressed as follows:

number

number

number

[0137] In some cases, the displacement vectors of multiple related covariance matrices are calculated based on the target quality signal ratio. The target quality signal ratio can be a value between 0.00 and 1.00. In some cases, the target quality signal ratio may be 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.20, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.30, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.40, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0 0.50, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.60, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0. It could be 76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, or 1.00.

[0138] In some cases, the system may further include a processor configured to stitch together segments of a set of displacement vectors to generate a time trace of the tissue's location. In some cases, the time trace may be a first trace. In some cases, the time trace may be a second trace. In some cases, the time trace may be a third trace.

[0139] In some cases, a system for determining the time-dependent position of tissue in response to a stimulus may further include a processor configured to analyze one or more eigenvectors of the covariance matrix. In some cases, each of the one or more eigenvectors may include orbital rotations related to the phase of the ultrasound data. Note that there are many ways to represent the eigenvectors and eigenvalues ​​of the covariance operator. In some cases, membrane motion may typically be contained within a first eigenvector. In some cases, membrane motion may typically be contained within a second eigenvector. In some cases, membrane motion may typically be contained within a third eigenvector. In some cases, membrane motion may typically be contained within different eigenvectors.

[0140] In some cases, as shown in panels (a), (b), and (c) of Figure 5, the eigenvectors may have similar phase-tracking behavior over many depths. In some cases, the overall signal energy may be clustered in a very small number of eigenvectors.

number

number

[0141] M can be evaluated at various depths along the ultrasonic beam over the number of pulse periods included in the covariance calculation. When no target is present, M can be a value between 0 and 1. In some cases, M may have a higher variance than when a target is present. These aspects of quality ratio behavior are shown, for example, in Figure 10.

[0142] Physical models of tympanic membrane motion can exhibit a continuous cycle of phase without jumps or discontinuities. Panels (a), (b), and (c) of Figure 6 show examples of non-physical phase behavior that may occur when tracking the tympanic membrane, along with an example of unpacking and correcting the tympanic membrane.

[0143] Panel (a) of Figure 6 shows an example of eigenvectors of covariance from region 2 of Figure 4. In some cases, the eigenvectors may indicate, for example, a decrease in amplitude. The observed decrease may occur because the reflection intensity from the tympanic membrane is lower than the reflection from nearby tissue (such as the tympanic umbilicus) which is stationary (not moving) by comparison. Panel (a) of Figure 6 shows one example of a path trajectory cycle to which a bias can be applied. Panel (b) of Figure 6 shows eigenvectors similar to those in Panel (a) of Figure 6, but includes a pre-inserted bias, for example, so that all trajectories of the signal are around the origin of the complex plane. Panel (c) of Figure 6 shows, for example, the cumulative phase changes of the path in Panel (a) of Figure 6 as Trace 1, and the cumulative phase changes of the path in Panel (b) of Figure 6 as Trace 2. The difference between Trace 2 and Trace 1 can be seen, for example, in the dashed Trace 3. For example, there may be a difference of 2π due to bias correction from Panel (a) of Figure 6 to Panel (b) of Figure 6.

[0144] In some cases, a system for determining the time-dependent location of tissue in response to a stimulus may further include a processor configured to bias one or more eigenvectors so that the eigenvectors trace circles around an origin in phase space. As shown in panel (b) of Figure 6, applying a correction bias to the data may be one solution to the problem of non-physical phase extraction, but it may introduce its own artifacts into the data.

[0145] Figure 7 shows, for example, that an ultrasound signal can be a linear sum of phasors in the complex plane. As illustrated, if the phasor trajectory centers are biased away from the origin in the complex plane, the motion associated with a particular phasor may become ambiguous. Panel (a) of Figure 7 shows phasor 2, which may be the signal of interest, and which may orbit away from the origin due to bias from phasor 1. Panel (b) of Figure 7 shows that phasor 2, which may be the signal of interest, can orbit the origin in the complex plane. Panel (c) of Figure 7 shows an example of the two phasor behaviors in panel (a) of Figure 7 using ultrasound signal data from brain tissue. In some cases, phasor 1 may be a slow-moving phasor, while particles in the blood flow of the middle cerebral artery are phasor 2, which may be a fast-moving phasor. Panel (d) of Figure 7 shows the same brain / specific data example as Panel (b) of Figure 7, except that slow brain phasors can be removed using a high-pass filter (clutter filter), leaving only the embolic signal as a residual phasor orbiting the complex origin. Panel (e) of Figure 7 shows an exemplary calculation of embolic position based on data of type bias in Panel (a) of Figure 7, which may not accurately represent the movement of the embolic in the bloodstream. Panel (f) of Figure 7 may show a calculation of embolic position based on data of features in Panel (b) and Panel (d) of Figure 7, for example, which can improve the accuracy when calculating embolic movement.

[0146] As shown in panel (e) of Figure 5, in some examples, a noise-versus-signal dip may exist in a region near the target. The target may be, for example, the eardrum. Analysis of covariance across the same region including the target may, in some cases, reduce broadband noise and better reveal the motion path and dynamics. In some cases, the noise-process covariance (data without a target) will use more eigenvectors to describe the process than the target covariance (data with a target). Analysis of covariance when a target is present may, in some cases, result in one or one-order-of-magnitude components to describe the dynamics of the target. The principal eigenvector (with the largest eigenvalue) may, in some cases, describe the motion of the target with contributions from all a / d samples within the target echo. In some cases, broadband noise may tend to be analyzable separately from the signal.

[0147] In some cases, a system for determining the time-dependent location of tissue in response to a stimulus may further include a processor configured to compute a second trace of the tissue's location using the resulting biased eigenvectors. In some cases, analyzing one or more eigenvectors may further include analyzing membrane motion information of one or more eigenvectors. In some cases, analyzing one or more eigenvectors may further include repeating the analysis in 10-millisecond (ms) steps. In some cases, the 10-ms step may be within an adjustable 20-ms analysis window. In some cases, analyzing one or more eigenvectors may further include analyzing depth range values. In some cases, analyzing one or more eigenvectors may further include creating a set of principal eigenvectors at each 10-millisecond (ms) step. In some cases, analyzing one or more eigenvectors may further include creating a set of principal eigenvectors within each 20-ms step.

[0148] In some examples, the calculation of the principal eigenvectors for covariance analysis over the depth and time ranges of the signal may be repeated, as shown in panel (A) of Figure 3. In the example shown in panel (A) of Figure 3, in some cases the data stream may be extended for a period of 1 second during tympanic membrane data acquisition. This may be data for which covariance analysis is performed, for example, about 5000 pulse periods. In some cases, the analysis may be repeated in 10 ms steps. In some cases, the analysis may be further repeated over a 20 ms window from the early edge of the signal to the latest edge of the signal. In some cases, the analysis may be further repeated for the M1 value which is the center of the tympanic membrane silhouette. For example, the top 80 M1 values ​​at each time position in Figure 10 are related to the silhouettes in panel (A) and panel (B) of Figure 3. In some cases the stimulus may be an excitation. In some cases the excitation may be a physical excitation, an electrical excitation, or an electromagnetic excitation. In some cases the stimulus may be a pneumatic excitation. In some cases the pneumatic excitation may be an air jet.

[0149] For example, the set of principal eigenvectors that can be obtained for each window position, with each window incrementing by 10ms over a time range of 1 second, may be expressed as follows:

number

[0150] The membrane motion information contained in the eigenvectors can be obtained by the following equation.

number

number

number

number

[0151] Similar to the set of eigenvectors described above, there is a set of displacement vectors, which can be expressed by the following equation.

number

[0152] Panel (a) of Figure 8 shows an example of a set of vectors D containing positional changes between each covariance calculation time interval. These individual segments can represent relative positional changes made by the tympanic membrane. In some cases, they may be “stitched” together with adjacent segments. Panel (b) of Figure 8 shows, for example, a magnified view of these curves, illustrating the mutual fitting in overlapping regions. Panel (c) of Figure 8 shows an example of segments that have been stitched together end to end to reveal tympanic membrane motion.

[0153] Figure 9 shows, for example, a phase profile plotted onto a background of the original RF ultrasound data.

[0154] Figure 10 shows an example of a quality metric M calculated over a 20ms wide window with 10ms increments for a total duration of 1 second. The noise background may be close to a level of 0.2, compared to a “target” close to 1.0, which occasionally dips over a time span and depth span equal to the ultrasonic sample volume size. The upper path can be the position of the maximum value of M over depth, every 10ms increment.

[0155] Methods of autoregression Further correction of unwrapped phases may be beneficial. For example, near pressure transitions, the phase may exhibit a singularity, such as a jump in the phase suggesting a non-physical, rapid movement of the eardrum. Such singularities are referred to as inaccuracies in the resulting motion detection, or "aliases." Often, these aliases are incorrect. Optionally, these non-physical motions may be addressed using the anti-aliasing methods and systems described below.

[0156] Figure 11 shows an enlarged example of a covariance trace following the RF phase track of a TM similar to that in Figure 9.

[0157] Figure 12 shows that the covariance trace remains faithful to the phase track RF data, except for two locations (at approximately 0.18 seconds and 0.62 seconds). These two covariance misses occur during the pressure transition region, where the signal energy is lowest (indicated by triangles). As shown in Figure 12, the covariance can "hop" from one phase track to another, jumping off the correct path. This can occur during the pressure transition region where the signal energy is lowest.

[0158] Processing ultrasound data may involve analyzing the frequency components of the complex demodulation of the RF echo over the pulse duration. Since the frequency of the slow-time ultrasound shift can be proportional to the velocity, frequency estimation techniques can be applied over slow time to track the velocity of the reflector over time. Autoregressive (AR) models are techniques that can be used to estimate the frequency spectrum of a signal.

[0159] In some cases, a method for determining the time-dependent location of tissue in response to a stimulus may further include analyzing the frequency components of ultrasound data over multiple pulse periods. In some cases, the method may further include determining one or more regions in which the first trace contains non-physical tissue location or movement. In some cases, a method for determining the time-dependent location of tissue in response to a stimulus may further include analyzing the frequency components of ultrasound data over multiple pulse periods. In some cases, the method may further include replacing the first trace with at least a portion of the second trace in one or more regions. In some cases, determining one or more regions in which the first trace may contain non-physical tissue location or movement may involve utilizing autoregression. In some cases, the autoregression may be cubic autoregression. In some cases, the autoregression may be higher than cubic autoregression. In some cases, the autoregression may be linear autoregression. In some cases, the autoregression may be quadratic autoregression. In some cases, the autoregression may be quartic autoregression. In some cases, the autoregression may be quintic autoregression. In some cases, autoregression can be higher than quintic autoregression.

[0160] A method disclosed herein for improving the accuracy of determining the time-dependent location of a stimulus-responsive tissue may include generating a frequency spectrum of a signal trace. The method may further include applying autoregression to generate one or more poles. The number of poles generated may be one, two, three, or more than three. The method may further include using the features of one or more poles to eliminate or select one of the one or more poles. The number of poles eliminated or selected may be one, two, three, or more than three. The method may further include generating an improved signal trace of the tissue location from the remaining or selected poles.

[0161] Figure 13 shows an example of a second-order AR model for estimating the frequency spectrum of a signal. The left panel shows an artificial signal created by summing a 3Hz sine wave and a 5Hz sine wave. The center panel shows the frequency spectrum of the signal estimated using the illustrated equation. The center panel shows the estimated frequency spectrum, which, as expected, has peaks at 3Hz and 5Hz. The coefficients a(1) and a(2) in the equation are AR coefficients solved using the Berg algorithm. The right panel shows that the poles of the center equation contain information about the frequency estimates. The angle of each pole on the complex unit circle can correspond to the peaks of the frequency spectrum, and + / -π is equal to the + / - Nyquist frequency. Radius of each pole

number

number

[0162] For the AR model, see the method of Young Bok Ahn and Song Bai Park, “Estimation of mean frequency and variance of ultrasonic Doppler signal by using second-order autoregression,” in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 38, no. 3, pp. 172-182, May 1991, doi: 10.1109 / 58.79600 (incorporated herein by reference for all purposes).

[0163] In some examples, the frequency components of a signal can be estimated using an AR model. The dimension of the AR model may determine the number of different frequency peaks that the AR model can separate. In Figure 13, for example, the input signal has two frequencies that can be separated, and therefore the second-order AR model returned two frequency peaks.

[0164] Figure 14 shows an example of estimating the frequency spectrum of a signal using a first-order AR model. Since a first-order AR model can return one frequency peak in the spectral estimate, if the input signal contains two frequencies, the AR model can return the average of these two frequencies.

[0165] The AR model can be implemented using the method described in C. Kasai, K. Namekawa, A. Koyano and R. Omoto, “Real-Time Two-Dimensional Blood Flow Imaging Using an Autocorrelation Technique,” ​​in IEEE Transactions on Sonics and Ultrasonics, vol. 32, no. 3, pp. 458-464, May 1985, doi: 10.1109 / T-SU.1985.31615 (which is incorporated herein by reference for all purposes).

[0166] Figures 13 and 14 illustrate, for example, the application of AR frequency estimation to a simple artificial signal, but this technique may also be applied to slow-time ultrasonic signals to estimate the velocity of the TM.

[0167] The AR processing techniques described herein are not limited to first-order or second-order models. In some cases, autoregression may be applied to one or more points in a signal trace. In some cases, autoregression may be applied to two points, three points, four points, five points, or more than five points in a signal trace. In some cases, autoregression may be cubic autoregression. In some cases, autoregression may be higher than cubic autoregression. In some cases, the dimension of autoregression may correspond to the number of frequency peaks in the isolated signal trace. In some cases, the signal trace may include a covariance motion detection trace. In some cases, autoregression may be applied to one or more eigenvectors of the covariance motion detection trace. In some cases, autoregression may be applied to two eigenvectors, three eigenvectors, four eigenvectors, five eigenvectors, or more than five eigenvectors of the covariance motion detection trace. In some cases, a method to improve the accuracy of determining the time-dependent location of tissue in response to a stimulus may further include estimating the mean frequency of the signal trace. In some cases, estimating the average frequency of a signal trace may involve using the angular value between each of one or more poles and the center of the complex unit circle in the complex plane. In some cases, the angular value may be calculated by taking the inverse tangent obtained by dividing the real component of the pole by the imaginary component of the pole.

[0168] In some cases, methods to improve the accuracy of determining the time-dependent location of a stimulus-responsive tissue may include estimating the frequency variance of the signal trace. The variance can be estimated using the radius value between each of one or more poles and the center of the complex unit circle in the complex plane. In some cases, methods to improve the accuracy of determining the time-dependent location of a stimulus-responsive tissue may further include estimating velocity. In some cases, velocity can be represented by the signal trace using the radius value between each of one or more poles and the center of the complex unit circle in the complex plane. In some cases, the number of poles may be one, two, three, four, five, or more than five.

[0169] In some cases, methods to improve the accuracy of determining the time-dependent location of tissue in response to a stimulus may further include plotting a velocity trace over the time of the signal trace. In some cases, a velocity trace can be plotted over the time of the signal trace by plotting the angles between one or more poles and the center of the complex unit circle in the complex plane.

[0170] Using the radii of each pole, the variance of the estimated frequency spectrum can be determined, for example, using the following equation, where T is the pulse period and r is the pole radius.

number

[0171] Figure 15 shows the poles of the spectral estimates obtained when, for example, a 12-point, third-order AR model is applied to the first eigenvector of the covariance. Pole 1 can mainly estimate the low-frequency velocity of TM, while poles 2 and 3 can mainly estimate the velocity from the high-frequency noise.

[0172] The poles shown in Figure 15 can be found by solving the square root of the denominator of the third-order estimate of the power spectral density.

number

[0173] To determine the square root ("pole") of the denominator, we can find the zeros of the cubic polynomial in the denominator of the above equation using the cubic equation shown below. These zeros of the cubic polynomial are the poles of the cubic AR model, and these three poles provide the frequency estimates used for processing ultrasonic data.

number

[0174] Pole 1 of the cubic AR model corresponds to the square root of 1 in the above cubic equation. Poles 2 and 3 are therefore "angle conjugates" of pole 1, just as square roots 2 and 3 are combinations of conjugates of square root 1.

[0175] As shown in Figure 15, applying the 12-point cubic AR model to the first eigenvector of the covariance can yield three poles.

[0176] As shown in Figure 16, the velocity over time can be determined by plotting the angle of a given pole for all pulse periods. This angle can be converted to velocity using the formula above. If the displacement is obtained by integrating the velocity estimated by pole 1 over time (Figure 17), the integral can be compared with the position of pole 1 to trace a covariance position trace.

[0177] In some cases, methods to improve the accuracy of determining the time-dependent position of tissue in response to a stimulus may further include calculating one or more displacement values ​​between the position of the velocity trace and the position of the signal trace. In some cases, determining one or more displacement values ​​may include integrating the velocity of the velocity trace. In some cases, the features of one or more poles may include radius, angle, velocity, complex plane distance between one or more poles, velocity change between one or more poles, change in inertial velocity between one or more poles, or momentum of one or more poles, or any combination thereof. In some cases, one or more poles may be segmented into one or more frequency groups based on frequency. In some cases, one or more poles may be segmented into two frequency groups. In some cases, one or more poles may be segmented into three frequency groups. In some cases, one or more poles may be segmented into more than three frequency groups.

[0178] Table 1 below shows the ultrasonic shift sequence data.

number

[0179] FIG. 16 shows an exemplary plot of pole angle over time in both speed and radians. The pole angle can be calculated by taking the arctangent of the real component of a given pole divided by the imaginary component of the given pole. This angle can be plotted over time and then converted to speed using the above equation.

[0180] FIG. 17 shows plots of pole speed and position for all pulse periods. After calculating the speed for all pulse periods, the speed can be integrated over time to obtain the displacement of the TM.

[0181] FIG. 18 shows a comparison of the covariance method for pole 1 of the third-order AR model.

[0182] In some cases, generating an optimized signal trace may further involve replacing one or more poles of the signal trace with one or more poles of different frequency groups. In some cases, one or more poles of the signal trace may be replaced if the absolute error between one or more features of one or more poles exceeds a threshold. In some embodiments, the threshold is a value calculated by one or more of the pole selection algorithms in Table 3. In some embodiments, the poles are of opposite signs. In some embodiments, the threshold is between 0.1 and 5.0. In some cases, the threshold is about 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, or greater than about 5.0. In some cases, one or more poles of the signal trace may be replaced if the absolute error between one or more features of one or more poles exceeds a threshold. In some embodiments, the threshold is a value calculated by one or more of the pole selection algorithms in Table 3. In some embodiments, the poles are of opposite signs. In some embodiments, the threshold is between 0.1 and 5.0. In some cases, the threshold can be approximately 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, or greater than approximately 5.0. In some cases, the threshold can be π / 3, and the poles can be of opposite signs. In some cases, the threshold can be π / 2, π / 3, π / 4, π / 5, π / 6, π / 7, π / 8, π / 9, π / 10, or less than π / 10. In some cases, the poles can be of the same sign.

[0183] A system for monitoring tissue movement in response to stimuli. In some cases, what is disclosed herein may be a system including a processor configured to apply autoregression to one or more points of a signal trace. In some cases, autoregression may be applied to two points, three points, four points, five points, or more than five points of a signal trace. In some cases, autoregression may be cubic autoregression. In some cases, autoregression may be higher than cubic autoregression. In some cases, the dimension of autoregression may correspond to the number of frequency peaks of the isolated signal trace. In some cases, the signal trace may include a covariance motion detection trace. In some cases, autoregression may be applied to one or more eigenvectors of the covariance motion detection trace. In some cases, autoregression may be applied to two eigenvectors, three eigenvectors, four eigenvectors, five eigenvectors, or more than five eigenvectors of the covariance motion detection trace. In some cases, a system including a processor configured to improve the accuracy of determining the time-dependent location of tissue in response to a stimulus may further include estimating the mean frequency of the signal trace. In some cases, estimating the average frequency of a signal trace may involve using the angular value between each of one or more poles and the center of the complex unit circle in the complex plane. In some cases, the angular value may be calculated by taking the inverse tangent obtained by dividing the real component of the pole by the imaginary component of the pole.

[0184] In some cases, a system including a processor configured to improve the accuracy of determining the time-dependent location of a stimulus-responsive tissue may further include a processor configured to estimate the frequency variance of a signal trace. The variance can be estimated by the processor using the radius value between each of one or more poles and the center of the complex unit circle in the complex plane. In some cases, a system including a processor configured to improve the accuracy of determining the time-dependent location of a stimulus-responsive tissue may further include a processor for estimating velocity. In some cases, velocity can be represented by the signal trace using the radius value between each of one or more poles and the center of the complex unit circle in the complex plane. In some cases, the number of poles may be one, two, three, four, five, or more than five.

[0185] In some cases, a system including a processor configured to improve the accuracy of time-dependent position may include a processor configured to determine the velocity of tissue in response to a stimulus. Velocity determination may further include plotting a velocity trace over the time of the signal trace. In some cases, the velocity trace can be plotted over the time of the signal trace by plotting angles between one or more poles and the center of the complex unit circle in the complex plane.

[0186] Using the radii of each pole, the variance of the estimated frequency spectrum can be determined, for example, using the following equation, where T is the pulse period and r is the pole radius.

number

[0187] Figure 15 shows the poles of the spectral estimates obtained when, for example, a 12-point, third-order AR model is applied to the first eigenvector of the covariance. Pole 1 can mainly estimate the low-frequency velocity of TM, while poles 2 and 3 can mainly estimate the velocity from the high-frequency noise.

[0188] The poles shown in Figure 15 can be found by solving the square root of the denominator of the third-order estimate of the power spectral density.

number

[0189] To determine the square root ("pole") of the denominator, we can find the zeros of the cubic polynomial in the denominator of the above equation using the cubic equation shown below. These zeros of the cubic polynomial are the poles of the cubic AR model, and these three poles provide the frequency estimates used for processing ultrasonic data.

number

[0190] Pole 1 of the cubic AR model corresponds to the square root of 1 in the above cubic equation. Poles 2 and 3 are therefore "angle conjugates" of pole 1, just as square roots 2 and 3 are combinations of conjugates of square root 1.

[0191] As shown in Figure 15, applying the 12-point cubic AR model to the first eigenvector of the covariance can yield three poles.

[0192] As shown in Figure 16, the velocity over time can be determined by plotting the angle of a given pole for all pulse periods. This angle can be converted to velocity using the formula above. If the displacement is obtained by integrating the velocity estimated by pole 1 over time (Figure 17), the integral can be compared with the position of pole 1 to trace a covariance position trace.

[0193] In some cases, a system including a processor configured to improve the accuracy of determining the time-dependent position of tissue in response to a stimulus may include a processor configured to calculate one or more displacement values ​​between the position of a velocity trace and the position of a signal trace. In some cases, the determination of one or more displacement values ​​may include integrating the velocity of the velocity trace. In some cases, the features of one or more poles may include radius, angle, velocity, complex plane distance between one or more poles, velocity change between one or more poles, change in inertial velocity between one or more poles, or momentum of one or more poles, or any combination thereof. In some cases, one or more poles may be segmented into one or more frequency groups based on frequency. In some cases, one or more poles may be segmented into two frequency groups. In some cases, one or more poles may be segmented into three frequency groups. In some cases, one or more poles may be segmented into more than three frequency groups.

[0194] In some cases, generating an optimized signal trace may further involve replacing one or more poles of the signal trace with one or more poles of different frequency groups. In some cases, one or more poles of the signal trace may be replaced if the absolute error between one or more features of one or more poles exceeds a threshold. In some embodiments, the threshold is a value calculated by one or more of the pole selection algorithms in Table 3. In some embodiments, the poles are of opposite signs. In some embodiments, the threshold is between 0.1 and 5.0. In some cases, the threshold is about 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, or greater than about 5.0. In some cases, one or more poles of the signal trace may be replaced if the absolute error between one or more features of one or more poles exceeds a threshold. In some cases, the threshold is π / 3, and the poles may be of opposite signs. In some cases, the threshold can be π / 2, π / 3, π / 4, π / 5, π / 6, π / 7, π / 8, π / 9, π / 10, or less than π / 10. In some cases, the poles can have the same sign.

[0195] Hybrid Method and System A hybrid approach that combines the fidelity of covariance and the ability to reliably track a phase track during pressure transitions in AR estimation can be advantageous. A hybrid method or system can combine both a covariance processing algorithm and an autoregressive processing algorithm into one method.

[0196] In some cases, the AR trace can be used to adjust the covariance. This adjustment may be applied to regions where the covariance does not follow a "physical" phase track, i.e., a track consistent with a physical understanding of membrane motion.

[0197] FIG. 19 provides an exemplary outline of a process for adjusting a covariance trace based on AR analysis. As shown, the covariance trace may transition from one phase track to another. Regions where the covariance misses a pressure transition can be missed regions. A missed region can be replaced with a section of the AR trace to create an anti-aliasing trace. This anti-aliasing trace can represent a combination of covariance and AR processing.

[0198] FIG. 20 shows an exemplary implementation of an anti-aliasing (hybrid) algorithm. Three different TM displacement traces are shown (top). The third-order AR trace and the covariance trace can be combined to form an anti-aliasing trace. The speed trace of the AR processing and the covariance are shown in the central panel. The absolute error between the AR speed and the covariance speed is shown, for example, in the lower panel. If the difference in the absolute error between the AR trace and the covariance trace is greater than π / 3 and the speeds have opposite signs, the covariance displacement trace can be fixed up with the AR trace.

[0199] FIG. 21 shows an exemplary histogram of the absolute value of the error between the AR speed and the covariance speed.

[0200] Figure 22 shows an exemplary result of anti-aliasing processing. AR traces and covariance traces can be combined to form an anti-aliased trace. This hybrid trace is faithful to the RF phase tracking data and may not miss pressure transitions.

[0201] The above method may be performed in close proximity to the covariance calculation in time, or as a post-processing routine.

[0202] A system for improving the determination of time-dependent position is disclosed herein. The system may include an analysis of covariance model. The analysis of covariance model may include a executable instruction which, when executed by a processor, can be configured to derive one or more covariance matrices from ultrasound data. The processor may be configured to derive one, two, three, four, five, or more than five covariance matrices from ultrasound data. The processor may be further configured to compute one or more eigenvectors of one or more covariance matrices. The processor may be configured to compute one, two, three, four, five, or more than five eigenvectors of one or more covariance matrices. The processor may be further configured to generate traces from one or more eigenvectors. The processor may be configured to generate traces from one, two, three, four, five, or more than five eigenvectors. The system may further include one or more autoregressives configured to generate one or more poles. The autoregressives may be configured to generate one, two, three, or more than three poles. The system may further include an anti-aliasing model that includes executable instructions, which, when executed by the processor, may be configured to select one or more regions of a trace that deviate from the phase track. The processor may be configured to select one, two, three, four, five, or more regions of a trace that deviates from the phase track. The system may also include a signal trace model that includes executable instructions, which, when executed by the processor, may be further configured to eliminate one or more poles in one or more regions selected by the anti-aliasing model. The signal trace model may be configured to eliminate one or more poles based at least in part on the effect of one or more features of one or more poles. The signal trace model may be further configured to select one or more replacement poles of one or more alternative signal traces to replace the one or more poles that have been removed.In some cases, the signal trace model may be configured to select one or more exchange poles based at least partially on the effect of one or more features of one or more exchange poles. In some cases, the signal trace model may be configured to select one, two, three, or more than three exchange poles. In some cases, the system may further include an output generator configured to produce an improved signal trace of tissue locations from one or more exchange poles output by the anti-aliasing model.

[0203] In some cases, one or more features may include the radius from one or more poles to the center of the complex unit circle, the angle between one or more poles and the center of the complex unit circle, the angular velocity between one or more poles, the distance in the complex plane between one or more poles, the change in velocity between one or more poles, the change in inertial velocity between one or more poles, or the momentum of one or more poles, or any combination thereof.

[0204] The Choice of the Extremes In some cases, selecting the first pole (referred to as "pole number 1" herein) in autoregression may not be advantageous. However, this may not be the pole that returns the smallest absolute error. This specification discloses an improved method for pole selection in autoregression. For example, Figure 17 shows exemplary velocity and position traces resulting from always selecting pole 1 in an AR model. In some cases, there may be discontinuities resulting in the velocity and position traces (Figures 23A-C).

[0205] Figures 23A, 23B, and 23C show regions where the trace of pole 1 exhibits discontinuity over three different pulse duration ranges, 251–500, 751–1000, and 1001–1250, respectively.

[0206] Figure 23A (left) shows an exemplary pole 1 position trace plotted on the RF phase track and a pole 1 position trace with discontinuous poles removed. Figure 23A (right) shows exemplary autoregressive poles 1, 2, and 3 plotted on the complex unit circle. For example, if pole 1 is discontinuous, a different pole can be selected.

[0207] The decision to select pole 1, pole 2, or pole 3 may be made using one or more of the classifiers listed in Tables 2 and 3 below. [Table 2-1] [Table 2-2] [Table 2-3] [Table 3-1] [Table 3-2] [Table 3-3] [Table 3-4] [Table 3-5]

[0208] Figures 24A, 24B, 24C, 24D, and 24E show five examples of the implementation of the AR pole selection algorithm described above.

[0209] Computer system This disclosure provides a computer system programmed to implement the method of this disclosure. Figure 25 shows a computer system 2501 programmed, or otherwise configured, to perform the method for determining the time-dependent location of tissue disclosed herein. The computer system 2501 can control various aspects of the otoscope and system for determining the time-dependent location of tissue of this disclosure, such as transmitting stimuli, transmitting and / or receiving ultrasound signals, and performing various data analysis operations and suboperations. The computer system 2501 may be a computer system located on a user's electronic device or remotely from an electronic device. The electronic device may be a mobile electronic device.

[0210] The computer system 2501 includes a central processing unit (CPU, also referred to herein as “processor” and “computer processor”) 2505, which may be a single-core or multi-core processor, or multiple processors for parallel processing. The computer system 2501 also includes memory or memory locations 2510 (e.g., random-access memory, read-only memory, flash memory), electronic storage devices 2515 (e.g., hard disks), a communication interface 2520 for communicating with one or more other systems (e.g., a network adapter), and peripheral devices 2525 such as caches, other memory, data storage and / or electronic display adapters. The memory 2510, storage devices 2515, interface 2520, and peripheral devices 2525 communicate with the CPU 2505 via a communication bus (solid line), such as a motherboard. The storage device 2515 may be a data storage device (or data repository) for storing data. The computer system 2501 may be operably coupled to a computer network ("network") 2530 with the assistance of the communication interface 2520. Network 2530 may be the Internet, the Internet and / or an extranet, or an intranet and / or extranet communicating with the Internet. In some cases, network 2530 may be a telecommunications and / or data network. Network 2530 may include one or more computer servers, thereby enabling distributed computing such as cloud computing. Network 2530 may, in some cases, implement a peer-to-peer network with the assistance of computer system 2501, thereby enabling devices coupled to computer system 2501 to act as clients or servers.

[0211] The CPU 2505 can execute a sequence of machine-readable instructions that can be embodied in a program or software. These instructions may be stored in a storage location, such as memory 2510. The instructions can be directed to the CPU 2505, which can then be programmed or otherwise configured to perform the methods of this disclosure. Examples of operations performed by the CPU 2505 may include fetching, decoding, executing, and writing back.

[0212] CPU2505 may be part of a circuit, such as an integrated circuit. One or more other components of system 2501 may be included in the circuit. In some cases, the circuit is an application-specific integrated circuit (ASIC).

[0213] The storage device 2515 can store files such as drivers, libraries, and saved programs. The storage device 2515 can also store user data, such as user preferences and user programs. The computer system 2501 may, in some cases, include one or more additional data storage devices that are external to the computer system 2501, such as those located on a remote server communicating with the computer system 2501 via an intranet or the internet.

[0214] Computer system 2501 can communicate with one or more remote computer systems through network 2530. For example, computer system 2501 can communicate with a user's remote computer system (e.g., to provide processed data, provide updates, provide usage instructions, solve problems, etc.). Examples of remote computer systems include personal computers (e.g., portable PCs), slate or tablet PCs (e.g., Apple® iPad®, Samsung® Galaxy Tab), telephones, smartphones (e.g., Apple® iPhone®, Android® compatible devices, BlackBerry®), or personal digital assistants. Users can access computer system 2501 via network 2530.

[0215] The methods described herein can be implemented, for example, by machine (e.g., computer processor) executable code stored in an electronic storage location of a computer system 2501, such as memory 2510 or electronic storage device 2515. The machine executable code or machine-readable code can be provided in software form. In use, the code can be executed by the processor 2505. In some cases, the code may be retrieved from storage device 2515 and stored in memory 2510 for immediate access by the processor 2505. In some situations, electronic storage device 2515 may be omitted, and the machine executable instructions are stored in memory 2510.

[0216] The code can be pre-compiled and configured for use on a machine with a processor adapted to run the code, or it can be compiled at runtime. The code may be provided in a programming language that can be chosen to run the code either pre-compiled or in a pre-compiled manner.

[0217] Embodiments of the systems and methods provided herein, for example, computer system 2501, can be embodied in programming. Various embodiments of the technology can typically be considered “products” or “manufactured articles” in the form of machine (or processor) executable code and / or associated data carried on or embodied therein in some kind of machine-readable medium. Machine-executable code can be stored in electronic storage devices such as memory (e.g., read-only memory, random-access memory, flash memory) or hard disks. “Storage” type media include any or all of tangible memory such as computers and processors, or associated modules such as various semiconductor memories, tape drives, disk drives, etc., which can provide non-temporary storage at any time for software programming. All or part of the software can be communicated from time to time over the Internet or various other telecommunication networks. Such communication may enable, for example, loading software from one computer or processor to another, for example, from a management server or host computer to an application server computer platform. Therefore, other types of media capable of carrying software elements include optical waves, radio waves, and electromagnetic waves, such as those used across physical interfaces between local devices via wired and optical terrestrial communication networks and various air links. Physical elements that carry such waves, such as wired or wireless links and optical links, can also be considered media capable of carrying software. As used herein, unless limited to non-temporary and tangible “storage” media, terms such as computer or machine “readable media” refer to any medium involved in giving instructions to a processor for execution.

[0218] Therefore, machine-readable media such as computer executable code can take many forms, including but not limited to tangible storage media, carrier media, or physical transmission media. Non-volatile storage media include optical or magnetic disks, such as any storage device in any computer(s), which may be used to implement a database as shown in the drawings. Volatile storage media include dynamic memory, such as the main memory of such a computer platform. Tangible transmission media include copper wires and optical fibers, including coaxial cables and wires that constitute buses within computer systems. Carrier media can take the form of electrical or electromagnetic signals, or acoustic or optical waves, such as those generated during radio frequency (RF) and infrared (IR) data communications. Therefore, common forms of computer-readable media include, for example, floppy disks, flexible disks, hard disks, magnetic tapes, or any other magnetic media; CD-ROMs, DVDs or DVD-ROMs, or any other optical media; punched card paper tapes, any other physical storage media having a pattern of holes; RAM, ROMs, PROMs and EPROMs, FLASH-EPROMs, any other memory chips or cartridges; carriers that transport data or instructions; cables or links that transport such carriers; or any other media from which a computer can read programming code and / or data. Many of these forms of computer-readable media may be involved in transporting one or more sequences of one or more instructions to a processor for execution.

[0219] The computer system 2501 includes or can communicate with an electronic display 2535, which includes a user interface (UI) 2540 for providing a display of one or more traces relating to, for example, the characterization of the state or condition of the eardrum, the location or movement of tissue, etc. Examples of UIs include, but are not limited to, graphical user interfaces (GUIs) and web-based user interfaces.

[0220] The methods and systems of this disclosure can be implemented by one or more algorithms. The algorithms can be implemented by software when executed by the central processing unit 2505. The algorithms can, for example, calculate a covariance matrix from ultrasound data from a depth group of ultrasound data and over multiple pulse periods, calculate one or more eigenvalues ​​of the covariance matrix, analyze the frequency components of the ultrasound data over multiple pulse periods, use the frequency components to calculate a second trace of tissue location, determine one or more regions in which the first trace contains non-physical tissue location or motion, and replace the first trace with at least a portion of the second trace in one or more regions.

[0221] While preferred embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided merely as examples. The present invention is not intended to be limited by any specific examples provided herein. While the present invention has been described with reference to the preceding specification, the descriptions and illustrations of embodiments herein are not intended to be constrained. Numerous variations, changes, and substitutions will be conceivable hereby to those skilled in the art without departing from the present invention. Furthermore, it should be understood that all aspects of the present invention are not limited to any specific descriptions, configurations, or relative proportions described herein, depending on various conditions and variables. It should be understood that in practice the present invention, various substitutes may be used for the embodiments of the present invention described herein. Accordingly, the present invention is intended to encompass any such substitutes, modifiers, variations, or equivalents. The following claims define the scope of the present invention, and the methods and structures contained within these claims, as well as their equivalents, are intended to be encompassed thereto.

[0222] Methods and System Applications The methods and systems disclosed herein can be used to characterize numerous biological tissues and provide a variety of diagnostic information. Biological tissues may include the organs of a patient. Microscopes may be placed in cavities of the body to characterize patient tissues. Patient organs or cavities of the body may include, to name a few, muscles, tendons, ligaments, mouth, tongue, pharynx, esophagus, stomach, intestines, anus, liver, gallbladder, pancreas, nose, larynx, trachea, lungs, kidneys, bladder, urethra, uterus, vagina, ovaries, testes, prostate, heart, arteries, veins, spleen, glands, brain, spinal cord, nerves, and so on.

[0223] Methods and systems disclosed herein may be used to classify tympanic membranes. For example, the membrane may be classified to determine ear conditions such as acute otitis media (AOM), chronic otitis media, serous otitis media, and / or chronic suppurative otitis media. Classification of ear showing AOM may include detecting the presence of exudate and characterizing the type of exudate as serous, mucous, suppurative, or a combination thereof. In AOM, middle ear exudate (MEE) may be induced by infectious agents and may be thin or serous in viral infections and thicker and suppurative in bacterial infections. Thus, information that can be used to characterize the membrane may be provided by determining various properties of the fluid adjacent to the tympanic membrane. For example, an ultrasonic transducer may be provided in a microscope and placed in the external auditory canal. An excitation generator may apply impact pressure to the tympanic membrane, and the transducer may direct ultrasound to the tympanic membrane, and the reflected ultrasonic energy may be measured from the surface of the tympanic membrane. The phase change of reflected ultrasound during and / or after the application of non-contact excitation may exhibit elasticity that can correlate with the type of fluid behind the eardrum; for example, air indicates a healthy ear, a clear fluid indicates a viral infection, or an opaque fluid indicates a bacterial infection.

[0224] Figure 29A shows a side section view of the spectrum of an otoscope 650 positioned in an ear 651 according to several embodiments. Figure 29B shows a front section view of an otoscope 650 according to several embodiments. Region 150 (shown in enlarged figure 29A) shows a section view of the middle ear and tympanic membrane 130 of the object to be examined. The tympanic membrane 130 may be examined by an ultrasonic beam 128 from an ultrasonic transducer. The transducer may be mounted on the inner surface of the otoscope tip 124. The otoscope tip may be detachable from the otoscope 650 via an otoscope mounting adapter 126. The otoscope tip may be operably coupled to or include an excitation generator. In some cases, the excitation generator may produce pressure excitation. In some cases, the excitation generator may produce pressure excitation which is sonic excitation, quasi-sonic excitation, or supersonic excitation. The pressure excitation generated by the excitation generator may be a shock step or delta (impulse) generation, sinusoidal pressure excitation, square wave excitation, or any combination thereof, and the excitation may be a gated burst or continuous. The pressure excitation may be provided with or without a static positive or negative pressure bias.

[0225] In some examples, the excitation generator produces pressure excitation, such as an air jet. For example, the otoscope mounting adapter 126 and the otoscope tip 124 may have a common internal volume. The common internal volume can provide coupling of dynamic pressure from the excitation generator to the external auditory canal via the coupling 122, causing displacement of the tympanic membrane 130 by air pressure. The excitation generator can generate a pressure change that is coupled to the external auditory canal through the otoscope tip 126.

[0226] In some examples, the excitation generator may be an air displacement generator that produces an air bag, alternating pressure, stepped pressure, or air jet, which is operated by an operator to apply force to a membrane or surface. The output of the excitation generator may be sealed to the surrounding area of ​​the surface, or it may not be sealed and may use a gas jet, such as air or another suitable gas.

[0227] In some examples, excitation generators can produce sonic excitations, subsonic excitations, or supersonic excitations. For example, an excitation generator can produce sub-audio frequencies below 20 Hz, audio frequencies between 20 Hz and 20 kHz, or super-audio frequencies above 20 kHz. In one example, sonic excitations, subsonic excitations, or supersonic excitations can be produced by a piezoelectric transducer. A piezoelectric transducer can convert an electrical signal into a physical displacement, which can then induce a pressure wave. In one example, sonic excitations, subsonic excitations, or supersonic excitations may be produced by a cMUT transducer. In one example, an audio speaker with a voice coil actuator can be used to generate excitations.

[0228] An ultrasonic transducer may be provided in the otoscope in addition to, and separately from, the excitation generator. The ultrasonic transducer may include any transducer element or a variation, example, or embodiment of the ultrasonic transducer disclosed herein. In some cases, the ultrasonic transducer and the excitation generator may be the same element.

[0229] As shown in Figure 29B, the otoscope may include a handle 601 for positioning the microscope 602. The otoscope may include a video display 603. The display may show the user an optical image of the membrane being characterized. The display 603 may show an ultrasound image. The display 603 may provide a user interface for controlling various aspects of the otoscope 650 and / or the analysis of ultrasound data. The otoscope may include an onboard digital processing device, for example, located in the device's handle 601. The otoscope can connect to remote devices such as a server, remote memory, or remote processing device. The analysis of ultrasound data can be performed onboard or remotely.

[0230] Figure 30A shows a side section view of the microscope 701 according to several embodiments. Figure 30B shows a front section view of the microscope 702 according to several embodiments. In some examples, an ultrasonic transducer 703 is located inside the microscope 701. The microscope may be disposable. The microscope may comprise an ultrasonic transducer 703 located near the tip region 702 of the microscope 701. The microscope may comprise a lens assembly 704 that can help provide the user with an optical image to guide the positioning of the ultrasonic transducer. In some examples, the ultrasonic transducer 703 may be at the center of the microscope, and as a result, photodetection is achieved around the ultrasonic transducer 704. The ultrasonic transducer may be supported by a mesh 705. The mesh 705 may allow the transmission of electrical signals to a digital processing device.

[0231] The ultrasonic transducer described herein may comprise a base 706. The base may be mounted inside the otoscope via a plate 704. The plate 704 may allow the transducer to be positioned in the center or near the center of the opening of the otoscope. The plate 704 may be optically transparent. In one example, the plate 704 is glass. The plate 704 may include one or more openings that may allow pressure excitation to be transmitted from the inside of the tip of the otoscope to the outside of the tip of the otoscope. The plate 704 may include one or more conductive portions that may allow a drive voltage and / or current to be supplied to the transducer. The plate 704 may include one or more insulating layers. The plate 704 itself may include conductive or insulating portions. The plate 704 may be insulated from conductive portions mounted on it.

[0232] In another example, the methods and systems disclosed herein can be used to characterize animal or human organs, such as the eye. For example, an excitation generator may apply impact pressure to the eye, a transducer may direct ultrasound towards the eye, and the reflected ultrasound energy may be measured from the surface of the eye. The phase change of the reflected ultrasound during and / or after non-contact excitation is removed may exhibit elasticity that can correlate with intraocular pressure for the measurement or diagnosis of glaucoma.

[0233] In another example, the methods and systems disclosed herein can be used to characterize the lungs of animals or humans. For example, audible sounds from the chest (e.g., frequencies between 3 and 20 Hz) can be demodulated from a transducer. The transducer can be integrated into a device such as a stethoscope, and the transducer can be moved over the chest during a "knock test" (auscultation) to identify changes in reflected ultrasound that may indicate fluids (e.g., mucus or water) within the lungs. In some cases, multiple arrays or arrays of transducers are provided and can be placed or attached over the chest. Phase changes in reflected ultrasound during application may indicate changes in fluid viscosity, which may correlate with lung diseases such as pneumonia, lung cancer, chronic obstructive pulmonary disease (COPD), and idiopathic pulmonary fibrosis (IPF).

[0234] The methods and systems disclosed herein can be used to characterize, for example, food items. For example, an excitation generator may apply shock pressure to the surface of a food item such as a vegetable or fruit, and ultrasonic energy may be applied to the food item to measure the time-dependent surface response of the fruit or vegetable, determining elasticity or other physical properties that may correlate with the ripeness of the fruit or vegetable. For example, the food item may be placed in a holder, the surface may be excited by a gas jet such as air, and the deflection response of the surface may estimate the ripeness or other properties. For example, the excitation may be a gas that can be delivered to the surface of the food item at supersonic speed and / or scanning angle, or one or more food items may be placed in a chamber with variable pressure for measuring low-frequency surface responses to pressure, such as deflection to pressure. For example, the excitation may be applied to one surface, and the response may be measured on different surfaces of the same item, such as measuring propagating surface waves or shear waves propagating through the item being characterized.

[0235] The methods and systems disclosed herein can be used to characterize industrial processes. For example, the small-sized transducers disclosed herein can be applied to any industrial process where larger transducers, ultrasound, or other modalities such as LiDAR are prohibited due to their size. The high resolution achieved by the transducers of this disclosure with movement of 10 to 20 micrometers over short distances, e.g., in the range of less than 25 to 35 millimeters (e.g., by Doppler integration), makes the present invention applicable to a variety of industrial processes where it is necessary to analyze without physically touching the analyte. For example, an excitation generator can apply shock pressure to the surface of a manufactured part to determine the uniformity of a viscous fluid, such as a lubricant, and ultrasonic energy can be applied to a part to measure the time-dependent surface response of the viscous fluid, determining elasticity or other physical properties that may correlate with the quality of the lubricant. The thickness of paint can be measured by comparing a painted portion of an object with an unpainted portion using a transducer. A transducer can be used to measure whether a painted object is dry by comparing a painted object with a similar object recently painted with the same paint. Transducers may be used as part of a manufacturing process to identify objects as part of counting objects in production. Transducers may be used to measure changes in the density or composition of an object by comparing an object that has undergone a process (e.g., cooked food items, curing processes) with an object that has undergone a process. Other industrial examples may include distance measuring applications, ultrasonic transit time gas flow meters for measuring dynamic gas flow, wind speed measuring applications, and various other ultrasonic detection applications.

[0236] Methods and systems disclosed herein may include using an ultrasonic transducer, directing the tip of a microscope within a lumen adjacent to the membrane, directing perturbations to the surface of the membrane, measuring ultrasonic signals reflected from the surface of the membrane, and characterizing the viscosity or elasticity of the membrane in response to the perturbations and reflected ultrasonic waves. [Examples]

[0237] Example 1: Analysis of covariance matrices across phase tracks In some non-restrictive cases, a covariance matrix was used to identify the time-dependent location of tissue 3100 in response to a stimulus (Figure 26). In some non-restrictive cases, a covariance matrix was used to determine the time-dependent location of tissue 3100 in response to pneumatic excitation generated by an otoscope 3104 (Figure 31).

[0238] Ultrasound data, derived from ultrasound waveforms reflected from tissue, was received for analysis (2601). A covariance matrix was calculated from the ultrasound data for depth groups and for numerous pulse periods of the ultrasound data, and one or more eigenvectors were calculated from the covariance matrix (2602). The covariance matrix was constructed by taking Hilbert-transformed RF ultrasound samples within the clear echo region of the ultrasound data. The covariance matrix was analyzed from the Hilbert transform. For example, the analyzed pulse echo signal obtained from the Hilbert transform of a single pulse period of the kth pulse period, denoted as "k", is expressed as follows:

number

number

number

number

[0239] The completed covariance matrix is ​​the sum of adjacent depth groups m={m0,m1,m2,...,mR}.

number

number

number

[0240] The covariance matrix resulting from a series of contributions across consecutive depths spanning a specific tympanic membrane echo is expressed as follows:

number

number

number

number

number

number

[0241] The covariance matrix was calculated based on the mathematical description above. The resulting matrix has eigenvectors and eigenvalues ​​expressed as follows:

number

number

number

[0242] The overall signal energy was clustered at a very small number of eigenvectors.

number

number

[0243] M was evaluated at various depths along the ultrasonic beam over the number of pulse periods included in the covariance calculation. In the absence of a target, M can be a value between 0 and 1. In some cases, M may have a higher variance than when a target is present.

[0244] The behavior of the noise process covariance (no target present) yielded many eigenvectors of covariance necessary to describe the various motions observed across the sample volume, in contrast to the behavior of the target covariance (target present), which yielded one or very few eigenvectors necessary to describe the narrow range of motions observed across the sample volume. The principal eigenvector (largest λ) contained a description of the target motion, with contributions from all a / d samples within the target echo. Target motion information was embedded in the covariance principal eigenvector. Eigenvector analysis involved repeating the analysis in 10 ms steps within an adjustable 20 millisecond (ms) analysis window. Eigenvector analysis also involved analyzing depth range values ​​and creating a set of principal eigenvectors every 10 millisecond (ms) steps.

[0245] The set of principal eigenvectors, one for each window position, given in 10ms increments over a 1-second time range of 100 windows, can be expressed as follows:

number

[0246] The membrane motion information contained in the eigenvectors was obtained by the following equation.

number

number

number

number

[0247] Similar to the set of eigenvectors described above, there was a set of displacement vectors that could be expressed as follows.

number

[0248] A set of vectors D containing the positional changes between each covariance calculation time interval shows the relative positional changes made by the tympanic membrane, which are "stitched" together with adjacent ones to show tympanic membrane motion. Next, a first trace of the tissue position associated with a component or one or more eigenvalues ​​was determined.2603

[0249] Example 2: Signal decomposition using autoregression In some non-restrictive cases, an autoregressive (AR) model was used to estimate the frequency spectrum of the signal trace.2701 The autoregression was applied to the eigenvectors of the analysis of covariance to estimate the frequency spectrum of the signal trace (Figure 27). The autoregression was, for example, cubic autoregression, or, in some non-restrictive cases, higher than cubic autoregression. The application of one or more autoregressive models generated one or more poles, and one or more poles were excluded or selected using one or more features of one or more poles, e.g., the calculated frequency characteristics.2702 The pulse repetition frequency was, for example, 5 kHz. The shift frequency of the ultrasonic data was, for example, + / -2.5 kHz. The angle of the complex unit circle representing the ultrasonic shift frequency was, for example, + / -π. These angles also represented the velocity of sound in air, for example, + / -227.9 mm / s. Velocity estimates were calculated by multiplying the radian angle of each pole on the complex unit circle by 72.54 (mm / s) / radian. The variance of the estimated frequency spectrum was determined using the radius of each pole.

[0250] The variance of the estimated frequency spectrum is calculated using the following formula:

number

[0251] A cubic autoregressive model was applied to the first eigenvector of the covariance in Example 1. It was determined that pole 1 primarily estimates the low-frequency velocity of the ultrasonic data, while poles 2 and 3 were found to primarily estimate the velocity from the high-frequency ultrasonic data.

[0252] The cubic autoregressive pole was found by solving the square root of the denominator of the cubic estimate of the power spectral density using the following power spectral density equation.

number

[0253] Using the poles of the cubic equation, the zeros of the cubic polynomial in the denominator of the power spectral density equation are as follows:

number

[0254] The poles of the cubic equation were used to determine the frequency estimates of the ultrasound data and the poles of the cubic autoregression.

[0255] Three poles were obtained by applying the poles of the cubic equation of the autoregressive model to the first eigenvector of the covariance result. Pole 1 is, for example, a frequency estimator with low-pass filtering characteristics. Poles 2 and 3 are, for example, frequency estimators with high-pass filtering characteristics.

[0256] The velocity over time was determined by plotting the angles of a given pole for all pulse periods. The pole angles were calculated by finding the inverse tangent obtained by dividing the real component of a given pole by the imaginary component of the given pole. The angles of a given pole were converted to velocity by applying the poles to a cubic equation and the power spectral density equation. For example, the displacement was obtained by integrating the velocity estimated by pole 1 over time. The magnitude of the displacement was found by comparing the position trace of pole 1 with the covariance position trace. Using the selection or exclusion of one or more poles, an optimized signal trace of the tissue position was generated from the remaining or selected poles.2703

[0257] Example 3: Selection of the AR pole In a non-restrictive example, a pole, e.g., pole 1, was found to be discontinuous, and a different pole was selected to improve the trace (Figure 28). Numerous autoregressive algorithms were applied to select the pole (Figure 28). Ultrasonic measurements were, for example, 5000 pulse periods. The pulse repetition frequency was, for example, 5 kHz. The total measurement time was 1 second. A 12-point autoregressive analysis was applied over the first 4800 pulse periods, excluding data from the last 200 pulse periods. The first pulse period, e.g., pole 1, was automatically selected. The remaining pulse periods had, for example, pole 1, pole 2, and pole 3 as options for the selected pole. One or more discriminators listed in Tables 2 and 3 were applied to each pole in each of the 4799 pulse periods after the first pulse period. A predictive anti-aliasing model was applied to select regions of the trace that did not follow the desired phase track (Figure 28).

[0258] Example 4: Anti-aliasing method acting on covariance and eigenvector motion detection In some non-restrictive cases, autoregression was applied to the region of the trace where the covariance missed the pressure transition to create an anti-aliasing fix-up trace (Figure 28). The difference in absolute error between the covariance velocity trace and the autoregressive trace was calculated. If the difference in absolute error was greater than π / 3 and the velocities had opposite signs, the covariance displacement trace was replaced with the autoregressive trace (Figure 28). An optimized signal trace of the tissue position from the poles of the anti-aliasing model was output (Figure 28).

[0259] The absolute value of the error between the autoregressive rate and the covariance rate was measured and logged in an anti-aliasing fix-up chart (Figure 21).

Claims

1. A method for determining the time-dependent location of tissue in response to a stimulus, wherein the method is: (a) Receiving ultrasonic data derived from ultrasonic waveforms reflected from the tissue, (b) Calculate a covariance matrix from the ultrasound data from the depth group and for multiple pulse periods of the ultrasound data. (c) Calculate one or more eigenvectors of the covariance matrix and their associated eigenvalues, and (d) The method comprising determining a first trace of the location of the tissue relating to the principal eigenvector components of one or more eigenvectors.

2. (i) Analyzing the frequency components of the ultrasonic data over the multiple pulse periods, and (ii) The method according to claim 1, further comprising calculating a second trace of the location of the tissue based at least partially on the frequency components.

3. The method according to claim 1, further comprising outputting an indication of disease state, health state, or undetermined state of the tissue in response to the second trace of the location of the tissue.

4. The method according to claim 1, wherein the plurality of pulse periods are continuous.

5. The method according to claim 1, wherein the tissue is the eardrum.

6. The method according to claim 1, further comprising outputting an indication of disease state, health state, or undetermined state of the tissue in response to the first trace of the location of the tissue.

7. The method according to claim 1, wherein each pulse period of the plurality of pulse periods is associated with one associated covariance matrix from a plurality of associated covariance matrices, and the method further comprises calculating a set of displacement vectors of the plurality of associated covariance matrices.

8. The method according to claim 7, wherein the plurality of related covariance matrices relate to pulse durations over consecutive and adjacent depths of the ultrasonic data.

9. The method according to claim 7, wherein the set of displacement vectors of the plurality of related covariance matrices is calculated based on the target quality signal ratio.

10. The method according to claim 9, wherein the target quality signal ratio is a value between 0.00 and 1.

00.

11. The method according to claim 9, further comprising stitching together segments of the set of displacement vectors to generate a time trace of the location of the tissue, wherein the time trace of the location of the tissue is the first trace.

12. The method according to claim 1, further comprising analyzing one or more eigenvectors of the covariance matrix, each of which includes an orbital rotation related to the phase of the ultrasonic data.

13. Applying a bias to each of the one or more eigenvectors such that the eigenvectors orbit the origin in phase space, and The method of claim 12, further comprising calculating a second trace of the location of the tissue using the resulting biased eigenvectors.

14. The method according to claim 12 or 13, wherein analyzing the one or more eigenvectors further comprises analyzing membrane motion information of the one or more eigenvectors.

15. The method according to claim 14, further comprising analyzing one or more eigenvectors in 10 ms steps within an adjustable 20 millisecond (ms) analysis window.

16. The method according to claim 15, wherein analyzing one or more eigenvectors further comprises analyzing depth range values.

17. The method according to claim 15, wherein the analysis of one or more eigenvectors further comprises creating a set of principal eigenvectors at 10 ms steps.

18. The method according to claim 15, further comprising analyzing one or more eigenvectors to create a set of principal eigenvectors within each 20 ms analysis window.

19. The method according to claim 1, wherein the stimulus is pneumatic excitation.

20. The method according to claim 1, wherein the air pressure excitation is an air injection.

21. The first trace determines one or more regions including non-physical tissue location or movement, and The method according to claim 2, further comprising replacing the first trace with at least a portion of the second trace in one or more of the aforementioned regions.

22. The method according to claim 2, wherein (i) utilizes autoregression.

23. The method according to claim 22, wherein the autoregression is of order three or higher.

24. A method for improving the accuracy of the time-dependent position of tissue in response to a stimulus, wherein the method is: Applying autoregression to a signal trace to generate multiple poles, Using the characteristics of the aforementioned multiple poles, excluding or selecting one of the aforementioned multiple poles, and The method comprising generating an improved signal trace of the tissue location from the remaining or selected poles of the plurality of poles.

25. The method according to claim 24, wherein the autoregression is applied to one or more points in the signal trace.

26. The method according to claim 24, wherein the autoregression is of order three or higher.

27. The method according to claim 26, wherein the dimension of the autoregression corresponds to the number of frequency peaks of the separated signal traces.

28. The method according to claim 24, wherein the signal trace includes a covariance motion detection trace.

29. The method according to claim 28, wherein the autoregression is applied to one or more eigenvectors of the covariance motion detection trace.

30. The method according to claim 24 or claim 28, further comprising estimating the average frequency of the signal trace using the angular value between each of the plurality of poles and the center of the complex unit circle in the complex plane.

31. The method according to claim 30, wherein the angle value is calculated by taking the inverse tangent obtained by dividing the real component of the pole by the imaginary component of the pole.

32. The method according to claim 24 or claim 28, further comprising estimating the frequency dispersion of the signal trace or the velocity represented by the signal trace using the radius value between each of the plurality of poles and the center of the complex unit circle in the complex plane.

33. The method according to claim 32, further comprising plotting a velocity trace over the time of the signal trace by plotting the angles between the plurality of poles and the center of the complex unit circle in the complex plane.

34. The method according to claim 33, further comprising calculating one or more displacement values ​​between the position of the velocity trace and the position of the signal trace.

35. The method according to claim 34, wherein the determination of the one or more displacement values ​​includes integrating the velocity of the velocity trace.

36. The method according to any one of claims 24 to 35, wherein the characteristics of the plurality of poles include the radius, the angle, the velocity, the complex plane distance between the plurality of poles, the change in velocity between the plurality of poles, the change in inertial velocity between one or more poles, or the momentum of the plurality of poles, or any combination thereof.

37. The method according to claim 24, wherein the plurality of poles are segmented into one or more frequency groups based on frequency.

38. The method according to claim 24, wherein generating the optimized signal trace further comprises replacing the plurality of poles of the signal trace with a plurality of poles of different frequency groups.

39. The method according to claim 24, wherein the plurality of poles of the signal trace are replaced if the absolute error of one or more of the features of the plurality of poles exceeds a threshold.

40. The method according to claim 39, wherein the threshold is π / 3 and the poles have opposite signs.

41. A system for determining the time-dependent location of tissue in response to a stimulus, The system includes a processor that stores executable instructions, and when an instruction is executed: (a) Receiving ultrasonic data derived from the ultrasonic waveform reflected from the tissue, (b) A covariance matrix is ​​calculated from the ultrasound data, from the depth group and for multiple pulse periods of the ultrasound data. (c) Calculate one or more eigenvectors of the covariance matrix and their associated eigenvalues, and (d) The system configured to determine a first trace of the location of the tissue relating to the principal eigenvector components of one or more eigenvectors.

42. The aforementioned processor is: (ii) Analyze the frequency components of the ultrasonic data over the plurality of pulse periods, and (ii) The system further configured to calculate a second trace of the location of the tissue based at least in part on the frequency components.

43. The system according to claim 41, wherein the processor is further configured to output an indication of disease state, health state, or undetermined state of the tissue in response to the second trace of the location of the tissue.

44. The system according to claim 41, further comprising an airtight otoscope.

45. The system according to claim 44, wherein the processor is operably connected to the airtight otoscope.

46. The system according to claim 41, further comprising a capacitive micromachine ultrasonic transducer.

47. The system according to claim 46, wherein the processor is operably connected to the capacitive micromachine ultrasonic transducer.

48. The system according to claim 46, wherein the capacitive micromachine ultrasonic transducer is located inside an otoscope.

49. The system according to claim 41, wherein the plurality of pulse periods are continuous.

50. The system according to claim 41, wherein the tissue is the eardrum.

51. The system according to claim 41, wherein the processor is further configured to output a display of disease state, health state, or undetermined state of the tissue in response to the first trace of the location of the tissue.

52. The system according to claim 41, wherein each pulse period of the plurality of pulse periods is associated with one associated covariance matrix from a plurality of associated covariance matrices, and the system further comprises calculating a set of displacement vectors of the plurality of associated covariance matrices.

53. The system according to claim 52, wherein the plurality of associated covariance matrices relate to pulse durations over consecutive and adjacent depths of the ultrasonic data.

54. The system according to claim 53, wherein the set of displacement vectors of the plurality of related covariance matrices is calculated based on the target quality signal ratio.

55. The system according to claim 54, wherein the target quality signal ratio is a value between 0.00 and 1.

00.

56. The system according to claim 54, wherein the processor is further configured to stitch together segments of the set of displacement vectors to generate a time trace of the location of the tissue, the time trace of the location of the tissue being the first trace.

57. The aforementioned processor is: (i) Analyze one or more eigenvectors of the covariance matrix, each of which includes an orbital rotation related to the phase of the ultrasonic data, (ii) Apply a bias to each of the one or more eigenvectors such that the eigenvectors orbit the origin in phase space, and (iii) The system according to claim 41, further configured to calculate a second trace of the location of the tissue using the resulting biased eigenvectors.

58. The system according to claim 57, wherein the processor is further configured to analyze the one or more eigenvectors by analyzing membrane motion information of the one or more eigenvectors.

59. The system according to claim 58, wherein the processor is further configured to repeat the analysis in 10 ms steps within an adjustable 20 ms analysis window.

60. The system according to claim 59, wherein the processor is further configured to analyze the one or more eigenvectors by analyzing the depth range values ​​of the one or more eigenvectors.

61. The system according to claim 59, wherein the processor is further configured to analyze one or more eigenvectors by generating a set of principal eigenvectors at 10ms steps.

62. The system according to claim 59, wherein the processor is further configured to analyze one or more eigenvectors by generating a set of principal eigenvectors within each 20 ms analysis window.

63. The system according to claim 41, wherein the stimulus is pneumatic excitation.

64. The system according to claim 41, wherein the air pressure excitation is an air injection.

65. The aforementioned processor is: The first trace determines one or more regions including non-physical tissue location or movement, and The system according to claim 41, further configured to replace the first trace with at least a portion of the second trace in one or more of the aforementioned regions.

66. The system according to claim 41, wherein the processor is further configured to analyze the frequency components of the complex demodulation of the ultrasonic data by applying autoregression.

67. The system according to claim 66, wherein the autoregression is of order three or higher.

68. A system for improving the accuracy of time-dependent localization of tissue in response to stimuli, the system comprising a processor storing executable instructions, the instructions, when executed: Applying autoregression to the signal trace generates multiple poles, Using the characteristics of the plurality of poles, one of the plurality of poles is excluded or selected, and The system is configured to generate an improved signal trace of the tissue location from the remaining or selected poles of the plurality of poles.

69. The system according to claim 68, wherein the processor is further configured to apply the autoregression to one or more points of the signal trace.

70. The system according to claim 69, wherein the autoregression is of order three or higher.

71. The system according to claim 70, wherein the dimension of the autoregression corresponds to the number of frequency peaks of the separated signal traces.

72. The system according to claim 68, wherein the signal trace includes a covariance motion detection trace.

73. The system according to claim 72, wherein the autoregression is applied to one or more eigenvectors of the covariance motion detection trace.

74. The system according to claim 68 or 72, wherein the processor is further configured to estimate the average frequency of the signal trace using the angular value between each of the plurality of poles and the center of the complex unit circle in the complex plane.

75. The system according to claim 74, wherein the processor is further configured to calculate the angle value by taking the inverse tangent obtained by dividing the real component of the pole by the imaginary component of the pole.

76. The system according to claim 68 or 72, wherein the processor is further configured to estimate the variance of the signal trace using the radius value between each of the plurality of poles and the center of the complex unit circle in the complex plane.

77. The system according to claim 68 or 72, wherein the processor is further configured to estimate the frequency dispersion of the signal trace or the velocity represented by the signal trace, using the radius value between each of the plurality of poles and the center of the complex unit circle in the complex plane.

78. The system according to claim 77, wherein the processor is further configured to plot a velocity trace over the time of the signal trace by plotting angles between the plurality of poles and the center of the complex unit circle in the complex plane.

79. The system according to claim 78, wherein the processor is further configured to calculate one or more displacement values ​​between the position of the velocity trace and the position of the signal trace.

80. The system according to claim 79, wherein the processor is further configured to calculate the one or more displacement values ​​by integrating the velocity of the velocity trace.

81. The system according to any one of claims 68 to 80, wherein the characteristics of the plurality of poles include the radius, the angle, the velocity, the complex plane distance between the plurality of poles, the change in velocity between the plurality of poles, the change in inertial velocity between one or more poles, or the momentum of the plurality of poles, or any combination thereof.

82. The system according to claim 68, wherein the processor is further configured to segment a plurality of poles into one or more frequency groups based on frequency.

83. The system according to claim 68, wherein the processor is further configured to generate the optimized signal trace by replacing the plurality of poles of the signal trace with a plurality of poles of different frequency groups.

84. The system according to claim 83, wherein the processor is further configured to replace the poles of the signal trace when one or more absolute errors between the features of the poles exceed a threshold.

85. The system according to claim 84, wherein the threshold is π / 3 and the poles have opposite signs.

86. A covariance analysis model that includes executable instructions, wherein the executable instructions, when executed by a processor: (a) Derive one or more covariance matrices from the ultrasound data, (b) Calculate one or more eigenvectors of the one or more covariance matrices, and (c) The covariance analysis model configured to generate traces from one or more eigenvectors, One or more autoregressives configured to generate multiple poles, An anti-aliasing model comprising executable instructions, wherein the executable instructions, when executed by a processor, are configured to select one or more regions of the trace that deviate from the phase track, A signal trace model including an executable instruction, wherein the executable instruction, when executed by a processor: (d) Eliminate one or more poles in the one or more regions selected by the anti-aliasing model, wherein the signal trace is configured to eliminate one or more poles based at least in part on the effect of one or more features of the one or more poles, and (e) Select one or more replacement poles of one or more alternative signal traces to replace the one or more poles excluded in (d), wherein the signal trace model is configured to select the one or more replacement poles at least partially based on the effect of one or more features of the one or more replacement poles, A system for improving time-dependent position determination, comprising: an output generator configured to produce an improved signal trace of the tissue position from one or more exchange electrodes output by the anti-aliasing model.

87. The system according to claim 86, wherein one or more of the features include the radius from one or more poles to the center of a complex unit circle, the angle between one or more poles and the center of a complex unit circle, the angular velocity between one or more poles, the distance in the complex plane between one or more poles, the change in velocity between one or more poles, the change in inertial velocity between one or more poles, or the momentum of one or more poles, or any combination thereof.

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