Patent Description:
Ultrasound Doppler imaging is typically used in a qualitative manner to determine the presence or absence of flow. A more quantitative measure such as blood velocities can be estimated from Doppler shifts occurring in a sample volume after correction for the Doppler angle in modes such as spectral Doppler. While velocities are an important measure, total flow is a measure that may better represent the well-being of an organ or body. Current methods for estimation of blood volume flow through a blood vessel implemented on several clinical ultrasound systems are based on one dimensional (1D) spectral Doppler. Estimation of blood volume flow entails steps that are operator dependent such as determination of the diameter of the vessel based on cursors placed by the operator on the B-Mode image and a sample volume from which the velocity is estimated is also selected by the operator. Additionally, the angle of the probe relative to the volume is based on how the operator holds the probe. Typically, spectral Doppler requires angle correction to obtain accurate velocities, however the angle correction vector may be subjective, thus also prone to operator errors. Thus, this method has high variability, limited accuracy, makes geometric assumptions, and is not operator friendly. Furthermore, total flow estimated using this method is not a true three dimensional (3D) measurement.

To overcome the above limitations, a 3D ultrasound method for measuring volume blood flow has been developed as described in <CIT>, <CIT>, and <NPL>. The method performs a surface integration of velocity vectors from the Doppler data based on Gauss' theorem. It may be implemented by defining a surface called a Gaussian surface that is locally perpendicular to the ultrasound beam (also referred to as a C-plane or Z-surface). In each of the Gaussian surfaces (Z-surfaces or C-planes) an integral of the product of velocity and surface area provide a total flow through the Gaussian surface. The calculation of this method must be limited to a blood vessel of interest. In addition, limited resolution of the ultrasound voxels produces partial volume effects at the boundary of the blood vessel. These are corrected for by using a Doppler power based weighting. While Doppler data is available from all spatial locations in the 3D volume, potentially only a single cross-section is enough to estimate the total volumetric flow. Multiple Z-surfaces may be used to improve robustness. The measurements at multiple Z-surfaces may be used to obtain a mean estimate of the volume flow. However, the data from these Z-surfaces could be of variable quality. The variability affects the repeatability of the measurements and usability of the measurements for diagnostic purposes.

Publication "<NPL>, investigates the relationship between 3D Color flow power and fractionally perfused voxels.

Apparatuses, systems, and methods for providing user feedback on acquisition of ultrasound data are disclosed herein. The ultrasound data may include Doppler data used for calculating volume flow, such as blood flow through one or more blood vessels. The feedback may indicate a quality of the acquisition and/or the reliability of the measurements calculated from the ultrasound data, for example, the volume flow measurements calculated from Doppler data. In some examples, a signal-to-noise ratio (SNR) may be calculated to provide an indication of quality of the acquisition. In some examples, motion of the ultrasound probe and/or subject may be detected to provide an indication of quality. In some examples, Doppler angle, vessel size, and/or vessel depth may be determined to provide an indication of quality. In some examples, variance in velocities in the blood vessels may be determined to provide an indication of quality. In some examples, the SNR and/or other quality factors may be provided to the user. In some examples, one or more of the quality factors may be combined into a quality indicator (e.g., index). In some examples, a qualitative indication of the quality of the acquisition may be provided to the user.

In accordance with at least one example disclosed herein, an ultrasound imaging system may be configured to provide feedback on a quality of volume flow measurements, and the system may include a user interface, a non-transitory computer readable medium encoded with instructions and configured to store power Doppler data for a plurality of Z-surfaces of a volume including a region of interest (ROI), and at least one processor in communication with the non-transitory computer readable medium configured to execute the instructions, wherein when executed, the instructions cause at least one processor to generate a histogram for individual ones of the plurality of Z-surfaces based, at least in part, on the power Doppler data, wherein the histogram has a first curve based on the power Doppler data from within the ROI and a second curve based on the power Doppler data from outside the ROI, calculate a signal-to-noise ratio (SNR) in the logarithmic domain by subtracting a peak of the second curve from a peak of the first curve for at least one of the plurality of Z-surfaces, and generate a quality factor based, at least in part, on the SNR, generate display data for a quality indicator based, at least in part, on the quality factor, wherein the quality indicator is indicative of the quality of the volume flow measurements, wherein the user interface is configured to display the quality indicator to a user based on the display data.

In accordance with at least one example disclosed herein, a method for providing feedback on a quality of volume flow measurements may include generating a histogram from power Doppler data for individual ones of a plurality of Z-surfaces in a volume of a subject, wherein the histogram has a first curve based on the power Doppler data from within a region of interest (ROI) and a second curve based on the power Doppler data from outside the ROI, calculating a signal-to-noise ratio (SNR) by subtracting a peak the second curve from a peak of the first curve for at least one of the plurality of Z-surfaces, and generating a quality factor based, at least in part, on the SNR, generating display data for a quality indicator based, at least in part, on the quality factor, wherein the quality indicator is indicative of the quality of the volume flow measurements, and displaying the quality indicator to a user based on the display data.

The following description of certain exemplary examples is merely exemplary in nature and is in no way intended to limit the invention or its applications or uses. In the following detailed description of examples of the present apparatuses, systems and methods, reference is made to the accompanying drawings which form a part hereof, and in which are shown by way of illustration specific examples in which the described apparatuses, systems and methods may be practiced. These examples are described in sufficient detail to enable those skilled in the art to practice the presently disclosed apparatuses, systems and methods, and it is to be understood that other examples may be utilized and that structural and logical changes may be made without departing from the scope of the present disclosure Moreover, for the purpose of clarity, detailed descriptions of certain features will not be discussed when they would be apparent to those with skill in the art so as not to obscure the description of the present system. The following detailed description is therefore not to be taken in a limiting sense, and the scope of the present system is defined only by the appended claims.

Blood volume flow quantification features currently on commercial ultrasound imaging systems are based on two dimensional (2D) pulsed wave Doppler (e.g., spectral Doppler) measurements. As described in the Background, techniques for 3D volume flow measurements have been developed and will likely be implemented on commercial ultrasound imaging systems in the future. The techniques for 3D volume flow measurements have the potential for quick, accurate, and reliable quantification of volume flow. However, the volume flow quantification results are dependent on the quality of the acquisition of the ultrasound data (e.g., Doppler data). There are multiple factors that may affect the quality of the acquisitions such as motion, attenuation, Doppler angle, vessel size, vessel depth, and/or improper imaging settings (e.g., focus, gain, pulse repetition frequency).

Because 3D volume flow is a new measurement not yet widely available, users do not have experience acquiring data for generating volume flow measurements nor an understanding of how to achieve quality (e.g., accurate/reliable, repeatable) results. While users may be familiar with assessing the quality of the spectral trace in 2D Doppler, there is no such equivalence for 3D volume flow. Currently, there is a lack of a feedback mechanism that users can understand that provides information regarding the quality of the acquisition (e.g., the Doppler data to generate the volume flow measurements). This lack of feedback may lead to poor reliability and/or repeatability of volume flow measurements (e.g., poor quality of the volume flow measurements). Such poor measurements may lead to failed exams. Therefore, a meaningful and easy-to-understand feedback mechanism for data acquisition may be desirable.

The present disclosure is directed to apparatuses, systems, and methods for providing feedback to a user including an indication of a quality of an acquisition of data. For example, the quality of Doppler data acquired by the user with an ultrasound probe. The indication of quality (e.g., quality indicator) may be based on one or more quality factors. Quality factors may include, but are not limited to, SNR, size of blood vessel, location (e.g., depth) of blood vessel, motion of the probe and/or subject, variance in velocity values, and/or Doppler angle. Examples for calculation of these quality factors and generating one or more quality indicators are described in more detail herein. In some examples, feedback may be provided to the user to provide suggestions for improving the quality of the acquisition. For example, if motion is detected, the feedback may alert the user to hold the ultrasound probe steady or advise the subject to remain still. In another example, if a sub-optimal Doppler angle is determined, the feedback may advise the user to adjust an angle of the ultrasound probe.

In some applications, the quality of the acquisition may be an indication of the accuracy, reliability, and/or repeatability of measurements generated from the acquired data. For example, measurements may include volume flow measurements generated from the acquired Doppler data. The feedback may encourage users to reacquire data during an exam if the original data acquisition was poor, which may lead to more accurate and/or repeatable measurements. In some applications, the feedback may help users improve their data acquisition techniques, which may reduce the need to reacquire data.

<FIG> shows a block diagram of an ultrasound imaging system <NUM> constructed in accordance with the principles of the present disclosure. An ultrasound imaging system <NUM> according to the present disclosure may include a transducer array <NUM>, which may be included in an ultrasound probe <NUM>, for example an external probe or an internal probe. The transducer array <NUM> is configured to transmit ultrasound signals (e.g., beams, waves) and receive echoes (e.g., received ultrasound signals) responsive to the transmitted ultrasound signals. A variety of transducer arrays may be used, e.g., linear arrays, curved arrays, or phased arrays. The transducer array <NUM>, for example, can include a two dimensional array (as shown) of transducer elements capable of scanning in both elevation and azimuth dimensions for 2D and/or 3D imaging. As is generally known, the axial direction is the direction normal to the face of the array (in the case of a curved array the axial directions fan out), the azimuthal direction is defined generally by the longitudinal dimension of the array, and the elevation direction is transverse to the azimuthal direction.

In some examples, the transducer array <NUM> may be coupled to a microbeamformer <NUM>, which may be located in the ultrasound probe <NUM>, and which may control the transmission and reception of signals by the transducer elements in the array <NUM>. In some examples, the microbeamformer <NUM> may control the transmission and reception of signals by active elements in the array <NUM> (e.g., an active subset of elements of the array that define the active aperture at any given time).

In some examples, the microbeamformer <NUM> may be coupled, e.g., by a probe cable or wirelessly, to a transmit/receive (T/R) switch <NUM>, which switches between transmission and reception and protects the main beamformer <NUM> from high energy transmit signals. In some examples, for example in portable ultrasound systems, the T/R switch <NUM> and other elements in the system can be included in the ultrasound probe <NUM> rather than in the ultrasound system base, which may house the image processing electronics. An ultrasound system base typically includes software and hardware components including circuitry for signal processing and image data generation as well as executable instructions for providing a user interface.

The transmission of ultrasonic signals from the transducer array <NUM> under control of the microbeamformer <NUM> is directed by the transmit controller <NUM>, which may be coupled to the T/R switch <NUM> and a main beamformer <NUM>. The transmit controller <NUM> may control the direction in which beams are steered. Beams may be steered straight ahead from (orthogonal to) the transducer array <NUM>, or at different angles for a wider field of view. The transmit controller <NUM> may also be coupled to a user interface <NUM> and receive input from the user's operation of a user control. The user interface <NUM> may include one or more input devices such as a control panel <NUM>, which may include one or more mechanical controls (e.g., buttons, encoders, etc.), touch sensitive controls (e.g., a trackpad, a touchscreen, or the like), and/or other known input devices.

In some examples, the partially beamformed signals produced by the microbeamformer <NUM> may be coupled to a main beamformer <NUM> where partially beamformed signals from individual patches of transducer elements may be combined into a fully beamformed signal. In some examples, microbeamformer <NUM> is omitted, and the transducer array <NUM> is under the control of the beamformer <NUM> and beamformer <NUM> performs all beamforming of signals. In examples with and without the microbeamformer <NUM>, the beamformed signals of beamformer <NUM> are coupled to processing circuitry <NUM>, which may include one or more processors (e.g., a signal processor <NUM>, a B-mode processor <NUM>, a Doppler processor <NUM>, and one or more image generation and processing components <NUM>) configured to produce an ultrasound image from the beamformed signals (i.e., beamformed RF data).

The signal processor <NUM> may be configured to process the received beamformed RF data in various ways, such as bandpass filtering, decimation, I and Q component separation, and harmonic signal separation. The signal processor <NUM> may also perform additional signal enhancement such as speckle reduction, signal compounding, and electronic noise elimination. The processed signals (also referred to as I and Q components or IQ signals) may be coupled to additional downstream signal processing circuits for image generation. The IQ signals may be coupled to a plurality of signal paths within the system, each of which may be associated with a specific arrangement of signal processing components suitable for generating different types of image data (e.g., B-mode image data, contrast image data, Doppler image data). For example, the system <NUM> may include a B-mode signal path <NUM> which couples the signals from the signal processor <NUM> to a B-mode processor <NUM> for producing B-mode image data. The B-mode processor <NUM> can employ amplitude detection for the imaging of organ structures the body.

In some examples, the system may include a Doppler signal path <NUM> which couples the output from the signal processor <NUM> to a Doppler processor <NUM>. The Doppler processor <NUM> may be configured to estimate the Doppler shift and generate Doppler image data. The Doppler image data may include color data which is then overlaid with B-mode (e.g., grayscale) image data for display. The Doppler processor <NUM> may be configured to filter out unwanted signals (e.g., noise or clutter associated with non-moving tissue), for example using a wall filter. The Doppler processor <NUM> may be further configured to estimate velocity and power in accordance with known techniques. For example, the Doppler processor may include a Doppler estimator such as an auto-correlator, in which velocity (Doppler frequency, spectral Doppler, color Doppler) estimation is based on the argument of the lag-one (R1) autocorrelation function and Doppler power estimation is based on the magnitude of the lag-zero (R0) autocorrelation function. Motion can also be estimated by known phase-domain (for example, parametric frequency estimators such as MUSIC, ESPRIT, etc.) or time-domain (for example, cross-correlation) signal processing techniques. Other estimators related to the temporal or spatial distributions of velocity such as estimators of acceleration or temporal and/or spatial velocity derivatives can be used instead of or in addition to velocity estimators. In some examples, the velocity and power estimates may undergo further threshold detection to further reduce noise, as well as segmentation and post-processing such as filling and smoothing. The velocity and power estimates may then be mapped to a desired range of display colors in accordance with a color map. The color data, also referred to as Doppler image data, may then be coupled to the scan converter <NUM>, where the Doppler image data may be converted to the desired image format and overlaid on the B-mode image of the tissue structure to form a color Doppler or a power Doppler image. For example, Doppler image data may be overlaid on a B-mode image of the tissue structure.

The signals produced by the B-mode processor <NUM> and/or Doppler processor <NUM> may be coupled to a scan converter <NUM> and/or a multiplanar reformatter <NUM>. The scan converter <NUM> may be configured to arrange the echo signals from the spatial relationship in which they were received to a desired image format. For instance, the scan converter <NUM> may arrange the echo signal into a two dimensional (2D) sector-shaped format, or a pyramidal or otherwise shaped three dimensional (3D) format. The multiplanar reformatter <NUM> can convert echoes which are received from points in a common plane in a volumetric region of the body into an ultrasonic image (e.g., a B-mode image) of that plane, for example as described in <CIT>). The scan converter <NUM> and multiplanar reformatter <NUM> may be implemented as one or more processors in some examples.

A volume renderer <NUM> may generate an image (also referred to as a projection, render, or rendering) of the 3D dataset as viewed from a given reference point, e.g., as described in <CIT>). The volume renderer <NUM> may be implemented as one or more processors in some examples. The volume renderer <NUM> may generate a render, such as a positive render or a negative render, by any known or future known technique such as surface rendering and maximum intensity rendering. Although shown in <FIG> as receiving data from the multiplanar reformatter <NUM>, in some examples, the volume renderer <NUM> may receive data from the scan converter <NUM>.

Output (e.g., B-mode images, Doppler images) from the scan converter <NUM>, the multiplanar reformatter <NUM>, and/or the volume renderer <NUM> may be coupled to an image processor <NUM> for further enhancement, buffering and temporary storage before being displayed on an image display <NUM>. A graphics processor <NUM> may generate graphic overlays for display with images, such as the accumulation image generated by the image processor <NUM>. These graphic overlays can contain, e.g., standard identifying information such as patient name, date and time of the image, imaging parameters, and the like. For these purposes the graphics processor may be configured to receive input from the user interface <NUM>, such as a typed patient name or other annotations. The user interface <NUM> can also be coupled to the multiplanar reformatter <NUM> for selection and control of a display of multiple multiplanar reformatted (MPR) images.

The system <NUM> may include local memory <NUM>. Local memory <NUM> may be implemented as any suitable non-transitory computer readable medium or media (e.g., flash drive, disk drive, dynamic random access memory). Local memory <NUM> may store data generated by the system <NUM> including B-mode images, Doppler images, instructions capable of being executed by one or more of the processors included in the system <NUM> (e.g., Doppler processor <NUM>, image processor <NUM>), inputs provided by a user via the user interface <NUM>, or any other information necessary for the operation of the system <NUM>.

As mentioned previously, system <NUM> includes user interface <NUM>. User interface <NUM> may include display <NUM> and control panel <NUM>. The display <NUM> may include a display device implemented using a variety of known display technologies, such as LCD, LED, OLED, or plasma display technology. In some examples, display <NUM> may comprise multiple displays. The control panel <NUM> may be configured to receive user inputs (e.g., exam type, selection of ROI in image). The control panel <NUM> may include one or more hard controls (e.g., buttons, knobs, dials, encoders, mouse, trackball or others). In some examples, the control panel <NUM> may additionally or alternatively include soft controls (e.g., GUI control elements or simply, GUI controls) provided on a touch sensitive display. In some examples, display <NUM> may be a touch sensitive display that includes one or more soft controls of the control panel <NUM>.

In some examples, various components shown in <FIG> may be combined. For instance, image processor <NUM> and graphics processor <NUM> may be implemented as a single processor. In another example, the scan converter <NUM> and multiplanar reformatter <NUM> may be implemented as a single processor. In some examples, various components shown in <FIG> may be implemented as separate components. For example, image processor <NUM> may be implemented as multiple processors. In some examples, the multiple image processors may perform different tasks (e.g., image segmentation, SNR calculation, motion detection, etc.). In another example, local memory <NUM> may include multiple memories which may be the same or different memory types (e.g., flash, DRAM).

In some examples, one or more of the various processors shown in <FIG> may be implemented by general purpose processors and/or microprocessors configured to perform the specified tasks. For example, the processors may be configured by instructions stored in a non-transitory computer readable memory (e.g., local memory <NUM>) which are executed by the processors to perform the specified tasks. In some examples, one or more of the various processors may be implemented as application specific circuits (ASICs). In some examples, one or more of the various processors (e.g., image processor <NUM>) may be implemented with one or more graphical processing units (GPU).

For acquiring volume flow measurements, the probe <NUM> may acquire volume (e.g., 3D) ultrasound data (e.g., B-mode data, Doppler data) with one or more blood vessels in a field of view. The volume ultrasound data may be acquired continuously such that there is sufficient spatial and temporal coverage of a region of interest (ROI) including one or more vessels. In some applications, the ROI may be sampled at a sufficient temporal rate to cover a cardiac cycle (e.g., heart beat). In some examples, the ROI may be selected by a user via the user interface <NUM>. In some examples, the user may select the ROI prior to collection of volume Doppler data. For example, a 2D or 3D ultrasound image may be acquired, and the user may select an ROI from the 2D or 3D ultrasound image. In other examples, the entire field of view may be sampled sufficiently temporally and spatially, and a user may select an ROI after the volume ultrasound data has been acquired.

In some examples, the field of view and/or ROI may be divided into sub-volumes. In some applications, acquiring volume ultrasound data from the sub-volumes may require less time than a complete volume. The volume ultrasound data from the sub-volumes may be acquired continuously over multiple cardiac cycles in some examples. The acquired volume ultrasound data from the sub-volumes may be retrospectively stitched together in order to generate a full volume with adequate temporal sampling. The acquisition may capture both constant and pulsatile flow profiles in some examples. The dividing and stitching may be performed by the signal processor <NUM>, Doppler processor <NUM>, B-mode processor <NUM>, scan converter <NUM>, and/or image processor <NUM> in some examples.

At least some of the ultrasound beams transmitted by the probe <NUM> may intersect a blood vessel in the ROI such that a Z-surface (e.g., Gaussian plane) can be defined including the entire cross-section of the vessel that has points equidistant to the probe <NUM> surface. The Z-surface may have a certain depth. In a volumetric acquisition, there may be multiple such Z-surfaces that include complete cross-sections of the blood vessel. Some or all of the Z-surfaces may be utilized for the volume flow measurement computations.

In each of those Z-surfaces, the blood vessel of interest may be either manually or automatically segmented. Manual segmentation may involve a user, via the user interface <NUM>, placing an ROI around the vessel boundary that encompasses the blood vessel as seen by the user in Doppler velocity (e.g., spectral) and/or Doppler power images provided on display <NUM>. Automatic segmentation may involve a combination of threshold and other morphological image processing operations on the individual power, velocity or B-Mode images or a combination of the above to set the ROI to the vessel boundary in the volume ultrasound data. Segmentation could also or alternatively involve artificial intelligence algorithms that identifies the vessel across multiple Z-surfaces in the volume ultrasound data. The segmenting may be performed by the image processor <NUM> in some examples.

Once the vessel boundary is segmented, power Doppler values in the pixels inside and outside the vessel may be utilized to compute a histogram in some examples. For example, the histogram may include one curve (e.g., profile) of power Doppler values located within the ROI (e.g., within the blood vessel) in a Z-surface and another curve of power Doppler values located outside the ROI in the Z-surface. The power Doppler histogram method may be utilized to derive fractional weights that determines regions of voxels inside and outside the vessel as well as partial volume voxels on the boundary with fractional weights. That is, some voxels on the border of the vessel may include data from both inside the vessel and tissue outside the vessel. Once the partial volume weights are determined from the power Doppler histogram, the velocity from the spectral Doppler data and surface area of the voxels may be multiplied to obtain volumetric flow in the individual voxels. An integral of the flow values in the voxels inside the vessel gives the volumetric flow in that Z-surface. These volume flow values may be stored, for example, in local memory <NUM>, and/or provided to the user on display <NUM>.

According to principles of the present disclosure, in addition to providing the volume flow measurements, the system <NUM> may provide an indication of quality of the volume flow measurements with a quality indicator <NUM> to the user, for example, as text or a graphic on display <NUM>. The quality indicator <NUM> may indicate a level of reliability/accuracy and/or repeatability of the volume flow measurements. In some examples, the quality indicator <NUM> may provide a qualitative indication of quality of the acquisition. For example, the quality indicator <NUM> may provide different colors, shapes, or descriptors/adjectives (e.g., good, fair, bad) to indicate the quality of the acquisition. In some examples, the quality indicator <NUM> may provide a quantitative value indicating the quality of the acquisition. The indication of quality may be based, at least in part, on one or more quality factors. Quality factors may include, but are not limited to, SNR, presence of motion (e.g., detection of motion), Doppler angle, vessel size, vessel depth, and/or variance of flow velocity inside the vessel.

In some examples, multiple quality factors may be combined to generate the indication of quality provided by the quality indicator <NUM>. In some examples, only one of the quality factors may be used to generate the indication of quality. In some examples, one or more quality factors may be used to generate the indication of quality while the same or different quality factors may be used to provide guidance to a user for improving the acquisition. For example, the SNR may be used to determine the indication of quality provided by the quality indicator <NUM> while Doppler angle may be used to provide guidance to the user to adjust the orientation of the probe <NUM>. In some examples, the quality indicator <NUM> may also provide the guidance.

In some examples, SNR may be the main quality factor used to determine the quality of the acquisition. <FIG> is a block diagram illustrating a method to calculate the signal-to-noise ratio from Doppler data acquired for three dimensional volume flow quantification according to principles of the present disclosure. An ultrasound imaging system <NUM>, may acquire image data <NUM> from a volume within a subject. The ultrasound imaging system <NUM> may be included in or used to implement system <NUM> in some examples. The image data <NUM> may include power Doppler data <NUM>, which may be color power Doppler data in some examples, such as the one shown in <FIG>. The image data <NUM> may include color spectral Doppler data <NUM> (e.g., color phase), echo data <NUM> (e.g., B-mode data), and/or volume geometry <NUM>. In some examples, the image data <NUM> may have been acquired by a transducer array of a probe, such as transducer array <NUM> of probe <NUM>. In some examples, the power Doppler data <NUM> and/or spectral Doppler data <NUM> may be extracted from the image data <NUM> by a Doppler processor, such as Doppler processor <NUM>. In some examples, the echo data <NUM> may be extracted from the image data (e.g., acquisition data) <NUM> by a B-mode processor such as B-mode processor <NUM>. In some examples, the volume geometry <NUM> may be provided by a scan converter, multiplanar reformatter, and/or volume renderer, such as scan converter <NUM>, multiplanar reformatter <NUM>, and/or volume renderer <NUM>. In some examples, the computations on the image data <NUM> may be performed by one or more processors, for example, image processor <NUM>, Doppler processor <NUM>, B-mode processor <NUM>, scan converter <NUM>, multiplanar reformatter <NUM>, and/or volume renderer <NUM>.

As shown in <FIG>, in some examples, the power Doppler data <NUM> may be used to provide the power Doppler data in individual Z-surfaces <NUM> in the volume and/or in a ROI within the volume. In some examples, the power Doppler data <NUM>, color Doppler data <NUM>, and/or echo data <NUM> may be used to segment and/or identify one or more blood vessels (e.g., vessel segmentation <NUM>) within the volume and/or ROI. In some examples, the segmentation may be performed by an image processor, such as image processor <NUM>. In some examples, the segmentation of a vessel from the volume may be used to set the ROI within the volume. In other examples, a user may define the ROI (e.g., via a user interface, such as user interface <NUM>), and the one or more blood vessels may be segmented from the ROI.

The vessel segmentation may be used to access the color velocities within the one or more vessels. The volume geometry <NUM> may be used to calculate the surface area <NUM> of every Z-surface within the volume and/or ROI.

The vessel segmentation <NUM> may also be used to access the power Doppler data within the vessel ROI <NUM>. Doppler image <NUM> is an image of a Z-surface with an ROI <NUM> including a blood vessel <NUM>. The power Doppler data in the entire Z-surface <NUM> and the power Doppler within the ROI <NUM> may be used to generate a power Doppler histogram <NUM>. The histogram peak <NUM> of power Doppler data outside the ROI <NUM> represents the noise and the histogram peak <NUM> of power Doppler data within the ROI <NUM> represents the signal of interest.

The data from the histogram <NUM> may be used to generate a partial volume weight mask <NUM>. The partial volume weight mask <NUM> may be combined with the velocity <NUM> and surface area <NUM> data to provide volume flow measurements <NUM>. The data from the power Doppler histogram <NUM> may be used to calculate the SNR <NUM> in some examples. As noted, the Doppler histogram <NUM> may have a curve based on the power Doppler data from within the ROI (signal) and a curve based on the power Doppler data from outside the ROI (noise). In some examples, in a logarithmic scale (e.g., decibels dB), the difference between the signal and the noise peaks represent the SNR of the Z-surface (e.g., SNR=Signalpeak-Noisepeak). Thus, an SNR for each Z-surface may be computed and stored. For example, data may be stored in a local memory, such as local memory <NUM>.

The SNR values in multiple Z-surfaces along the depth may present some variation on account of non-uniformity in the ultrasound beam intensities, structures in the field of view causing reflections and reverberations, attenuation, and other factors that affect the quality of the Z-surfaces. For example, at certain depths there may be reflections leading to higher noise in the regions outside the vessel. Similarly, the beam angle and/or the attenuation may cause a diminished value of the signal inside the vessel. To determine the quality of the acquisition based on these multiple and variable SNR values, one or techniques may be used to identify a set of Z-surfaces whose SNR values are used to determine the quality factor for the acquisition. In some examples, the Z-surfaces may be sorted based on the SNR values. In some examples, the mean and/or median SNR for all the Z-surfaces of the vessel within the ROI may be provided as the quality factor (e.g., dB).

In some examples, interquartile range median (IQR/Median) may be used to determine what range of data Z-surfaces are used in combination with the mean or median to determine the SNR. The interquartile range (IQR) is a measure of variability, based on dividing a data set into quartiles (e.g., the SNRs for the Z-surfaces). The top and bottom quartiles are removed from the data set, thus leaving the "middle fifty" around the median of the data set. The interquartile range divided by the median value provides a quality factor. For example, among the SNR sorted Z-surfaces, using Z-surface groups of greater than five such as <NUM>-<NUM>, <NUM>-<NUM>, <NUM>-<NUM>, etc., IQR/Med of the SNR are computed: IQR<NUM>-<NUM>, IQR<NUM>-<NUM>, IQR<NUM>-<NUM>, and so on. The mean or median SNR of the group that has the minimum IQR/Med is selected as the one to provide as the quality factor based on SNR.

In some examples, coefficient of variation (COV) or relative standard deviation (e.g., the standard deviation divided by the mean) may be used to determine the range of data (e.g., which SNR values of the Z-surfaces) to be used in combination with the mean or median. For example, among the SNR sorted Z-surfaces, using Z-surface groups of greater than three such as <NUM>-<NUM>, <NUM>-<NUM>, <NUM>-<NUM>, etc., the COV of the SNR values are computed: COV<NUM>-<NUM>, COV<NUM>-<NUM>, COV<NUM>-<NUM>, etc. The mean or median of the group of Z-surfaces that represents the minimum COV may be selected as the one used to generate the quality factor based on SNR.

In another example, the mean SNR of a few Z-surfaces (e.g., <NUM>, <NUM>, <NUM>) closest to and/or proximate the focus of the ultrasound beam provided by the ultrasound probe <NUM> may be used.

In some examples, multiple ones of the techniques for determining the SNR and/or which SNR values to use may be combined to generate the quality factor. For example, an average SNR calculated from the various techniques may be used for the quality factor. In some examples, the different SNR values determined by the different techniques may be weighted differently when computing the average. In some examples, SNR values calculated by different techniques may be provided as separate quality factors used to determine an indication of quality provided by a quality indicator, such as quality indicator <NUM>. In some examples, SNR may be the only quality factor used to generate the indication of quality.

<FIG> illustrates examples images of spectral and power Doppler data acquisitions and corresponding power histograms for calculating the signal-to-noise ratio according to principles of the present disclosure. The images and histograms may have been generated by an ultrasound imaging system, such as ultrasound imaging system <NUM> and/or ultrasound imaging system <NUM>.

In <FIG>, a velocity map generated from color Doppler velocity data for a Z-surface is shown in image <NUM>, and a power Doppler map for the same Z-surface is shown in image <NUM> for a same acquisition. A vessel is shown within a ROI <NUM> in image <NUM>. Histogram <NUM> is a plot of power Doppler data showing curves for the noise (e.g., power Doppler data outside the ROI <NUM>) and the signal (e.g., power Doppler data inside the ROI <NUM>). Below the images the difference in power (dB) between the signal and noise peaks, the SNR, which is <NUM> dB.

In <FIG>, a velocity map generated from color Doppler velocity data for a Z-surface is shown in image <NUM>, and a power Doppler map for the same Z-surface is shown in image <NUM> for another acquisition. A vessel is shown within a ROI <NUM> in image <NUM>. Histogram <NUM> is a plot of power Doppler data showing curves for the noise (e.g., power Doppler data outside the ROI <NUM>) and the signal (e.g., power Doppler data inside the ROI <NUM>). The velocity map <NUM> has less well-defined vessels compared to the velocity map <NUM>. Furthermore, the power Doppler map <NUM> has less contrast than the power Doppler map <NUM>. Below the images the difference in power (dB) between the signal and noise peaks, the SNR, which is <NUM> dB, much lower than the SNR for <FIG>.

The data shown in <FIG> were from umbilical cord images. In this application, good quality acquisitions were associated with SNR of <NUM> dB and higher. However, different ranges of SNR may be used to classify the quality of the acquisition (e.g., <NUM>-5dB bad, <NUM>-<NUM> dB fair, <NUM>-<NUM> dB good, <NUM>-<NUM> dB better, <NUM>+ dB great). The SNR may be used to generate a quantitative quality indicator. For example, the numerical value (e.g., in dB) may be provided on a quality indicator (e.g., quality indicator <NUM>). In some examples, the SNR may be used to generate a qualitative quality indicator, for example, different colors and/or other descriptors may be associated with different ranges of SNR values.

Other quality factors in addition to the SNR may also be determined in other examples to compute the indication of quality and/or provide advice to a user to improve quality of the acquisition.

In some examples, the size and/or depth of the blood vessel may be determined from the segmented data. Vessels with smaller diameters imaged with a broad Doppler ultrasound beam may be resolution limited. That is, there may not be enough ultrasound beams inside the blood vessel, which may limit the effectiveness of the partial volume correction algorithms. Similarly, a deeper vessel may be affected by an increased attenuation and/or diminished power inside the vessel. In addition, a deeper vessel interacting with a diverging beam may again be resolution limited. The effect on the partial volumes weights caused by both the size and depth of the vessel may limit the accuracy and may increase the variability in the volume flow measurements. Thus, small and/or deep vessels may provide lower valued quality factors for determining the indication of quality. In some examples, based on the ultrasound beam profile, depth and size of the vessel, a table of weights can be computed and used in the quality factor.

Additionally or alternatively to being used for the indication of quality, the determination may be used to provide suggestions to the user for improving the acquisition. For example, if a vessel diameter and/or beam density within the vessel is below a threshold value, the ultrasound system, such as system <NUM>, may prompt the user to select a different vessel and/or imaging settings (e.g., increase beam density) to acquire flow measurements. Prompting the user may be through text, graphics, audio signals, and/or haptic feedback (e.g., probe vibrations). In another example, if a vessel depth and/or power level within the vessel is below a threshold value, the system may prompt the user to select a different vessel and/or imaging setting (e.g., increase power).

In some examples, the effect of motion on the Doppler data may be taken into account as a quality factor for the indication of quality and/or user suggestions. In the iSTIC acquisition framework, each sub-volume (e.g., elevation planes(s)) may be continuously sampled with high temporal resolution through the cardiac cycle. This may then be repeated for the rest of the sub-volumes. Data affected by motion may have decreased robustness of the segmented boundary of the vessel as visualized on the Z-surfaces through the cardiac cycle. Furthermore, there could be increased flash artifacts on the velocity map and/or an increase in transient reflections affecting the power map. Any method for detecting motion known now or in the future may be used. Greater motion may lead to lower valued quality factors. In some examples, if motion is above a threshold (e.g., velocity and/or magnitude of displacement), the ultrasound system may prompt the user to hold the probe still and/or ask the subject to remain still.

In some applications, the Doppler angle may also be an important parameter that has potential implications on the partial volume correction at the boundaries of the vessel, and thus may be an important quality factor. As the angle between the ultrasound beam and the flow axis increases, the number of beams completely inside the vessel decreases. This increases the partial volume beams interacting with the boundary of the vessel. Consequently, similar to effects in small vessels, a resolution limited condition could adversely affect the volume flow measurement accuracy. In some examples, the Doppler angle may be provided as a quality factor. Additionally or alternatively, if the Doppler angle is over a threshold value (e.g., <NUM> degrees, <NUM> degrees), the ultrasound system may prompt the user to adjust an angle of the probe to reduce the Doppler angle.

In some applications, an increase in the variance of the velocity values inside the segmented vessel may indicate a poor quality acquisition. The variance of the velocity values may be calculated for each Z-surface in some examples. In some examples, the mean and/or median variance of the Z-surfaces may be provided as the quality factor. In some examples, selection of which Z-surfaces to use to provide the velocity variance factor may be performed using one or more of the techniques described with reference to the SNR.

<FIG> is a block diagram illustrating an overview of providing an indication of quality of an acquisition according to principles of the present disclosure. The overview <NUM> may be implemented on an ultrasound imaging system, such as ultrasound imaging system <NUM> and/or <NUM>.

Image data <NUM> may have been acquired by a transducer array, such as transducer array <NUM>. In some examples image data <NUM> may include image data <NUM>. Image data <NUM> may be used to generate a variety of quality factors <NUM> such as SNR <NUM>, motion detection <NUM>, Doppler angle <NUM>, vessel size and/or depth <NUM>, and/or velocity variance inside the vessel <NUM>. The quality factors <NUM> may be used to generate a quality indicator <NUM> and/or user suggestions <NUM>, which may be provided to a user via a user interface, such as user interface <NUM>.

In some examples, the quality indicator <NUM> may include text, graphics, sounds, animations, and/or lights (e.g., light emitting diodes). In some examples, the quality indicator <NUM> may be provided on a display, such as display <NUM>. In some examples, quality indicator <NUM> may be used to implement quality indicator <NUM>.

In some examples, the quality indicator <NUM> may include a qualitative and/or semi-qualitative indication of quality of the acquisition of the image data <NUM>. For example, qualitative descriptors (e.g., bad, fair, good), colors (e.g., red, yellow, green), and/or emojis associated with different levels of quality may be used to indicate the quality of the acquisition. An example graphic <NUM> shows text, shades, a dial, and emojis. However, the graphic <NUM> is presented merely as an example, and qualitative indicators according to the present disclosure is not limited to the example shown. The qualitative indication may be based on one or more of the quality factors <NUM>.

In some examples, in addition to or instead of a qualitative indication, the quality indicator <NUM> may provide a quantitative value <NUM> of quality of the acquisition. In the example shown, the quantitative value <NUM> is a value of SNR <NUM> with units in decibels. However, in other examples, the quantitative value <NUM> may be generated by one or more quality factors <NUM>, which may or may not include the SNR <NUM>.

In some examples, user suggestions <NUM> may include text, graphics, sounds, animations, and/or lights (e.g., light emitting diodes). In some examples, the user suggestions <NUM> may be provided on a display, such as display <NUM>. For example, text <NUM> may provide a suggestion to the user to improve data acquisition. In the example shown in <FIG>, the text <NUM> suggests adjusting an angle of the ultrasound probe to improve the Doppler angle (e.g., if the Doppler angle <NUM> is found to be above a threshold value). As another example, a graphic <NUM> indicates how the user should move and/or position the ultrasound probe to improve the Doppler angle. Text <NUM> and graphic <NUM> are provided merely as examples, and the user suggestions <NUM> are not limited to the examples shown.

<FIG> is a flow chart of a method for providing feedback on a quality of volume flow measurements according to principles of the present disclosure. In some examples, the method <NUM> may be performed, all or in part, by an ultrasound imaging system, such as imaging system <NUM> and/or imaging system <NUM>. In some examples, the method <NUM> may be performed by one or more processors executing computer-readable instructions, for example, image processor <NUM>, Doppler processor <NUM>, B-mode processor <NUM>, and/or other processors shown in <FIG>. In some examples, the computer-readable instructions may be stored in a non-transitory computer-readable medium accessible to the at least one processor, such as local memory <NUM>.

As indicated at block <NUM>, at least one processor, such as image processor <NUM> may generate a histogram from power Doppler data for individual ones of a plurality of Z-surfaces in a volume of a subject. The power Doppler data may have been acquired by the ultrasound imaging system, for example, by a probe of the ultrasound imaging system. The histogram may have a first curve based on the power Doppler data from within a ROI and a second curve based on the power Doppler data from outside the ROI. The ROI may be based on auto-segmentation of image data from the volume of the subject or based on a user input (e.g., via a user interface, such as user interface <NUM>).

At block <NUM>, the at least one processor may calculate a SNR by subtracting a peak the second curve from a peak of the first curve for at least one of the plurality of Z-surfaces. The at least one processor may then generate a quality factor based, at least in part, on the SNR as indicated by block <NUM>. In some examples, the SNR may be calculated for all Z-surfaces, and an average and/or median SNR may be used to generate the quality factor. In some examples, a subset of the SNR values from the Z-surfaces may be used to generate the quality factor as described with reference to <FIG> and <FIG>.

At block <NUM>, the at least one processor may generate display data for a quality indicator based, at least in part, on the quality factor. The quality indicator may be indicative of the quality of the volume flow measurements. As indicated at block <NUM>, the quality indicator may be displayed to a user based on the display data. For example, the quality indicator may be displayed on display <NUM>. Of course, in other examples, the quality indicator may be an auditory signal provided on a speaker, one or more lights on a control panel of the ultrasound imaging system, and/or haptic feedback provided on the control panel and/or ultrasound probe.

In some examples, the at least one processor may calculate at least one more quality factor, wherein the quality indicator is further based on the at least one more quality factor as shown by block <NUM>. In some examples, as indicated by block <NUM>, the at least one processor may generate a suggestion for improving the quality of the volume flow measurements, based at least in part, on the quality factor and the at least one more quality factor.

<FIG> is a block diagram illustrating an example processor <NUM> according to principles of the present disclosure. Processor <NUM> may be used to implement one or more processors described herein, for example, image processor <NUM> shown in <FIG>. Processor <NUM> may be capable of executing computer-readable instructions stored on a non-transitory computer-readable medium in communication with the processor <NUM>, for example, local memory <NUM> shown in <FIG>. Processor <NUM> may be any suitable processor type including, but not limited to, a microprocessor, a microcontroller, a digital signal processor (DSP), a field programmable array (FPGA) where the FPGA has been programmed to form a processor, a graphical processing unit (GPU), an application specific circuit (ASIC) where the ASIC has been designed to form a processor, or a combination thereof.

The processor <NUM> may include one or more cores <NUM>. The core <NUM> may include one or more arithmetic logic units (ALU) <NUM>. In some examples, the core <NUM> may include a floating point logic unit (FPLU) <NUM> and/or a digital signal processing unit (DSPU) <NUM> in addition to or instead of the ALU <NUM>.

The processor <NUM> may include one or more registers <NUM> communicatively coupled to the core <NUM>. The registers <NUM> may be implemented using dedicated logic gate circuits (e.g., flip-flops) and/or any memory technology. In some examples the registers <NUM> may be implemented using static memory. The register may provide data, instructions and addresses to the core <NUM>.

In some examples, processor <NUM> may include one or more levels of cache memory <NUM> communicatively coupled to the core <NUM>. The cache memory <NUM> may provide computer-readable instructions to the core <NUM> for execution. The cache memory <NUM> may provide data for processing by the core <NUM>. In some examples, the computer-readable instructions may have been provided to the cache memory <NUM> by a local memory, for example, local memory attached to the external bus <NUM>. The cache memory <NUM> may be implemented with any suitable cache memory type, for example, metal-oxide semiconductor (MOS) memory such as static random access memory (SRAM), dynamic random access memory (DRAM), and/or any other suitable memory technology.

The registers <NUM> and the cache memory <NUM> may communicate with controller <NUM> and core <NUM> via internal connections 620A, 620B, 620C and 620D. Internal connections may implemented as a bus, multiplexor, crossbar switch, and/or any other suitable connection technology.

Inputs and outputs for the processor <NUM> may be provided via a bus <NUM>, which may include one or more conductive lines. The bus <NUM> may be communicatively coupled to one or more components of processor <NUM>, for example the controller <NUM>, cache memory <NUM>, and/or register <NUM>. The bus <NUM> may be coupled to one or more components of the system, such as display <NUM> and control panel <NUM> mentioned previously.

The bus <NUM> may be coupled to one or more external memories. The external memories may include Read Only Memory (ROM) <NUM>. ROM <NUM> may be a masked ROM, Electronically Programmable Read Only Memory (EPROM) or any other suitable technology. The external memory may include Random Access Memory (RAM) <NUM>. RAM <NUM> may be a static RAM, battery backed up static RAM, Dynamic RAM (DRAM) or any other suitable technology. The external memory may include Electrically Erasable Programmable Read Only Memory (EEPROM) <NUM>. The external memory may include Flash memory <NUM>. The external memory may include a magnetic storage device such as disc <NUM>. In some examples, the external memories may be included in a system, such as ultrasound imaging system <NUM> shown in <FIG>, for example local memory <NUM>.

The apparatuses, systems, and methods disclosed herein may allow for providing feedback to a user including an indication of a quality of an acquisition of data based on one or more quality factors. In some examples, the apparatuses, systems, and methods disclosed herein may allow for providing suggestions for improving the quality of the acquisition to the user. The feedback may encourage users to reacquire data during an exam if the original data acquisition was poor, which may lead to more accurate and/or repeatable measurements. In some applications, the feedback may help users improve their data acquisition techniques, which may reduce the need to reacquire data.

In various examples where components, systems and/or methods are implemented using a programmable device, such as a computer-based system or programmable logic, it should be appreciated that the above-described systems and methods can be implemented using any of various known or later developed programming languages, such as "C", "C++", "FORTRAN", "Pascal", "VHDL" and the like. Accordingly, various storage media, such as magnetic computer disks, optical disks, electronic memories and the like, can be prepared that can contain information that can direct a device, such as a computer, to implement the above-described systems and/or methods. Once an appropriate device has access to the information and programs contained on the storage media, the storage media can provide the information and programs to the device, thus enabling the device to perform functions of the systems and/or methods described herein. For example, if a computer disk containing appropriate materials, such as a source file, an object file, an executable file or the like, were provided to a computer, the computer could receive the information, appropriately configure itself and perform the functions of the various systems and methods outlined in the diagrams and flowcharts above to implement the various functions. That is, the computer could receive various portions of information from the disk relating to different elements of the above-described systems and/or methods, implement the individual systems and/or methods and coordinate the functions of the individual systems and/or methods described above.

In view of this disclosure it is noted that the various methods and devices described herein can be implemented in hardware, software, and/or firmware. Further, the various methods and parameters are included by way of example only and not in any limiting sense. In view of this disclosure, those of ordinary skill in the art can implement the present teachings in determining their own techniques and needed equipment to affect these techniques, while remaining within the scope of the invention. The functionality of one or more of the processors described herein may be incorporated into a fewer number or a single processing unit (e.g., a CPU) and may be implemented using application specific integrated circuits (ASICs) or general purpose processing circuits which are programmed responsive to executable instructions to perform the functions described herein.

Of course, it is to be appreciated that any one of the examples, examples or processes described herein may be combined with one or more other examples, examples and/or processes or be separated and/or performed amongst separate devices or device portions in accordance with the present systems, devices and methods.

Claim 1:
An ultrasound imaging system (<NUM>, <NUM>) configured to provide feedback on a quality of volume flow measurements, the system comprising:
a non-transitory computer readable medium (<NUM>) encoded with instructions and configured to store power Doppler data (<NUM>) for a plurality of Z-surfaces of a volume including a region of interest, ROI, (<NUM>); and
at least one processor (<NUM>) in communication with the non-transitory computer readable medium configured to execute the instructions, wherein when executed, the instructions cause the at least one processor to:
generate a histogram (<NUM>) for individual ones of the plurality of Z-surfaces based, at least in part, on the power Doppler data, wherein the histogram has a first curve based on the power Doppler data from within the ROI and a second curve based on the power Doppler data from outside the ROI;
characterized in that the system further comprises a user interface (<NUM>); and the instructions further cause the at least one processor to:
calculate a signal-to-noise ratio, SNR, (<NUM>, <NUM>) by subtracting in the logarithmic scale a peak (<NUM>) of the second curve from a peak (<NUM>) of the first curve for at least one of the plurality of Z-surfaces; and
generate a quality factor (<NUM>) based, at least in part, on the SNR;
generate display data for a quality indicator (<NUM>, <NUM>) based, at least in part, on the quality factor, wherein the quality indicator is indicative of the quality of the volume flow measurements,
wherein the user interface is configured to display the quality indicator to a user based on the display data.