Apparatus, system, and method for providing acquisition feedback

Abstract

User feedback regarding ultrasound data acquisition can be provided to a user. The feedback can indicate acquisition quality and / or reliability of measurements computed from the ultrasound data, e.g., volumetric flow measurements computed from Doppler data. Various quality factors such as signal-to-noise ratio (SNR), motion, Doppler angle, vessel size, vessel depth, and / or variance of velocity values can be determined to provide an indication of acquisition quality. The quality factors can be provided individually or in combination. In some examples, one or more quantitative values of the quality factors can be provided. In some examples, one or more qualitative indications of acquisition quality can be provided.

Description

Technical Field

[0001] This application relates to providing acquisition feedback. More specifically, this application relates to providing feedback on acquired ultrasound data. Background Technology

[0002] Ultrasound Doppler imaging is typically used qualitatively to determine the presence or absence of blood flow. After correcting the Doppler angle in modes such as spectral Doppler, more quantitative measurements, such as blood flow velocity, can be estimated based on the Doppler frequency shift occurring in the sample volume. While velocity is an important measure, total flow is a better indicator of organ or body health. Current methods implemented on many clinical ultrasound systems for estimating blood flow through vessels are based on one-dimensional (1D) spectral Doppler. Estimation of blood volumetric flow depends on operator actions, such as determining the vessel diameter based on the cursor placed by the operator on the B-mode image, and the sample volume from which the velocity is estimated is also chosen by the operator. Furthermore, the angle of the probe relative to the volume depends on how the operator holds the probe. Typically, spectral Doppler requires angle correction to obtain accurate velocity, but the angle correction vector can be subjective and therefore susceptible to operator error. Thus, the method suffers from high variability, limited accuracy, makes geometric assumptions, and is operator-unfriendly. Moreover, the total flow estimated using this method is not a true three-dimensional (3D) measurement.

[0003] To overcome the aforementioned limitations, a 3D ultrasound method for measuring volumetric blood flow has been developed, as described in U.S. Patent US6535835, U.S. Patent US6780155, and ODKripfgans, JMRubin, ALHall, MBGordon, and JBFowlkes, “Measurement of Volumetric Flow,” J Ultrasound Med 2006; 25:1305-1311, which are incorporated herein by reference for any purpose. This method performs a surface integral over the velocity vector from Doppler data according to Gauss's law. This can be achieved by defining a surface (also known as the C-plane or Z-plane) locally perpendicular to the ultrasound beam, called a Gaussian surface. In each Gaussian surface (Z-plane or C-plane), the integral of the product of velocity and surface area provides the total flow through the Gaussian surface. The calculations of this method must be limited to the vessel of interest. Furthermore, the limited resolution of the ultrasound voxels introduces partial volumetric effects at the vessel boundaries. These can be corrected using a weighting method based on Doppler power. While Doppler data can be obtained from all spatial locations within a 3D volume, a single cross-section may be sufficient to estimate the total volumetric flow rate. Multiple Z-surfaces can be used to improve robustness. Measurements at multiple Z-surfaces can be used to obtain an average estimate of the volumetric flow rate. However, the quality of data from these Z-surfaces may be variable. This variability affects the repeatability of the measurement and its availability for diagnostic purposes. Summary of the Invention

[0004] This document discloses apparatus, systems, and methods for providing user feedback on the acquisition of ultrasound data. The ultrasound data may include Doppler data for calculating volumetric flow rate, such as blood flow through one or more vessels. The feedback may indicate the acquisition quality and / or the reliability of measurements calculated from the ultrasound data, such as volumetric flow rate measurements calculated from the Doppler data. In some examples, a signal-to-noise ratio (SNR) may be calculated to provide an indication of acquisition quality. In some examples, motion of the ultrasound probe and / or object may be detected to provide an indication of quality. In some examples, Doppler angles, vessel size, and / or vessel depth may be determined to provide an indication of quality. In some examples, changes in velocity within the vessel may be determined to provide an indication of quality. In some examples, SNR and / or other quality factors may be provided to the user. In some examples, one or more quality factors may be combined into a quality index (e.g., an index). In some examples, a qualitative indication of acquisition quality may be provided to the user.

[0005] According to at least one example disclosed herein, an ultrasound imaging system can be configured to provide feedback on the quality of a volumetric flow rate measurement, and the system may include: a user interface; a non-transient computer-readable medium encoded with instructions and configured to store power Doppler data comprising a plurality of Z-surfaces for a volume of a region of interest (ROI); and at least one processor communicating with the non-transient computer-readable medium, the processor being configured to execute instructions, wherein, when executed, the at least one processor: generates a histogram for an individual Z-surface among the plurality of Z-surfaces, at least in part based on the power Doppler data, wherein the histogram... The figure has a first curve based on power Doppler data from within the ROI and a second curve based on power Doppler data from outside the ROI. The signal-to-noise ratio (SNR) is calculated in the logarithmic domain by subtracting the peak value of the second curve from the peak value of the first curve for at least one of the plurality of Z-surfaces. A quality factor is generated at least in part based on the SNR, and display data for a quality indicator is generated at least in part based on the quality factor, wherein the quality indicator indicates the quality of the volumetric flow rate measurement result, and the user interface is configured to display the quality indicator to the user based on the display data.

[0006] According to at least one example disclosed herein, a method for providing feedback on the quality of volumetric flow rate measurement results may include: generating a histogram based on power Doppler data of individual Z-surfaces among a plurality of Z-surfaces in the volume of an object, wherein the histogram has a first curve based on power Doppler data from within a region of interest (ROI) and a second curve based on power Doppler data from outside the ROI; calculating a signal-to-noise ratio (SNR) by subtracting the peak value of the second curve from the peak value of the first curve for at least one of the plurality of Z-surfaces; generating a quality factor at least in part based on the SNR; generating display data for a quality index at least in part based on the quality factor, wherein the quality index indicates the quality of the volumetric flow rate measurement results; and displaying the quality index to the user based on the display data. Attached Figure Description

[0007] Figure 1 This is a block diagram of an ultrasound imaging system arranged according to an example of this disclosure.

[0008] Figure 2 This is a block diagram illustrating a method for calculating the signal-to-noise ratio from Doppler data acquired for three-dimensional volumetric flow quantization, based on the principles of this disclosure.

[0009] Figures 3A-3BExample images of color and power Doppler data acquisitions for calculating the signal-to-noise ratio, and corresponding power histograms, are shown in accordance with the principles of this disclosure.

[0010] Figure 4 This is a block diagram illustrating an overview of providing an indication of acquisition quality according to the principles of this disclosure.

[0011] Figure 5 This is a flowchart of a method for providing feedback on the quality of volumetric flow rate measurement results, based on the principles of this disclosure.

[0012] Figure 6 This is a block diagram illustrating an example processor according to an example of this disclosure. Detailed Implementation

[0013] The following description of specific exemplary embodiments is merely illustrative in nature and is in no way intended to limit the invention or its application or use. In the following detailed description of embodiments of the apparatuses, systems, and methods of the invention, reference is made to the accompanying drawings, which form part of the description, and in which specific embodiments of the described systems and methods are illustrated by way of illustration. These embodiments are described in sufficient detail to enable those skilled in the art to practice the currently disclosed apparatuses, systems, and methods, and it should be understood that other embodiments may be utilized and structural and logical changes may be made without departing from the spirit and scope of this disclosure. Furthermore, for clarity, detailed descriptions of certain features will not be discussed where obvious to those skilled in the art, so as not to obscure the description of the system. Therefore, the following detailed description should not be regarded in a limiting sense, and the scope of the system is defined only by the claims.

[0014] Current blood volume flow quantification features in commercial ultrasound imaging systems are based on two-dimensional (2D) pulse-wave Doppler (e.g., spectral Doppler) measurements. As described in the background section, 3D volume flow measurement techniques have been developed and are likely to be implemented in commercial ultrasound imaging systems in the future. Techniques for 3D volume flow measurement have the potential to quantify volume flow rapidly, accurately, and reliably. However, the volume flow quantification results depend on the acquisition quality of the ultrasound data (e.g., Doppler data). Several factors can affect acquisition quality, such as motion, attenuation, Doppler angle, vessel size, vessel depth, and / or incorrect imaging settings (e.g., focus, gain, pulse repetition frequency).

[0015] Because 3D volumetric flow rate is a relatively new measurement, users lack experience in generating data for volumetric flow rate measurements and are unfamiliar with how to achieve high-quality (e.g., accurate / reliable, repeatable) results. While users may be familiar with evaluating the quality of 2D Doppler spectral traces, there is no equivalent for 3D volumetric flow rate. Currently, there is a lack of user-understandable feedback mechanisms to provide information about acquisition quality (e.g., Doppler data used to generate volumetric flow rate measurement results). This lack of feedback can lead to poor reliability and / or repeatability of volumetric flow rate measurements (e.g., poor quality of volumetric flow rate measurements). Such poor measurement results can result in failed inspections. Therefore, a meaningful and easily understood data acquisition feedback mechanism is likely needed.

[0016] This disclosure relates to apparatus, systems, and methods for providing feedback to a user, including indications of data acquisition quality. For example, the quality of Doppler data acquired by a user using an ultrasound probe. Indications of quality (e.g., quality metrics) may be based on one or more quality factors. Quality factors may include, but are not limited to, SNR, vessel size, vessel location (e.g., depth), probe and / or object motion, changes in velocity values, and / or Doppler angles. Examples of calculating these quality factors and generating one or more quality metrics are described in more detail herein. In some examples, feedback may be provided to the user to offer suggestions for improving acquisition quality. For example, if motion is detected, the feedback may remind the user to stabilize the ultrasound probe or suggest that the object remain stationary. In another example, if a suboptimal Doppler angle is determined, the feedback may suggest that the user adjust the angle of the ultrasound probe.

[0017] In some applications, acquisition quality can be an indication of the accuracy, reliability, and / or repeatability of the measurements generated from the acquired data. For example, measurement results may include volumetric flow rate measurements generated from acquired Doppler data. If the raw data acquisition is poor, feedback can encourage the user to reacquire data during inspection, which can lead to more accurate and / or repeatable measurements. In some applications, feedback can help users improve their data acquisition techniques, which can reduce the need for reacquiring data.

[0018] Figure 1A block diagram of an ultrasound imaging system 100 constructed according to the principles of this disclosure is shown. The ultrasound imaging system 100 according to this disclosure may include a transducer array 114, which may be included in an ultrasound probe 112, such as an external or internal probe. The transducer array 114 is configured to transmit ultrasound signals (e.g., beams, waves) and receive echoes (e.g., received ultrasound signals) in response to the transmitted ultrasound signals. Various transducer arrays can be used, such as linear arrays, curved arrays, or phased arrays. The transducer array 114 may, for example, include a two-dimensional array (shown in the figure) of transducer elements capable of scanning in the height and azimuth dimensions for 2D and / or 3D imaging. It is well known that the axis is the direction perpendicular to the array plane (in the case of a curved array, the axis fans out), the azimuth direction is generally defined by the longitudinal dimension of the array, and the elevation direction is transverse to the azimuth direction.

[0019] In some examples, transducer array 114 may be coupled to microwave beamformer 116, which may be located within ultrasonic probe 112, and which may control the transmission and reception of signals by the transducer elements in array 114. In some examples, microwave beamformer 116 may control the transmission and reception of signals by active elements in array 114 (e.g., an active subset of array elements that defines the active aperture at any given time).

[0020] In some examples, the microwave beamformer 116 may be coupled, for example via a probe cable or wirelessly, to a transmit / receive (T / R) switch 118, which switches between transmitting and receiving and protects the main beamformer 122 from high-energy transmitted signals. In some examples, such as in a portable ultrasound system, the T / R switch 118 and other components of the system may be included in the ultrasound probe 112 rather than in the ultrasound system base, which may house image processing electronics. The ultrasound system base typically includes software and hardware components, including circuitry for signal processing and image data generation, and executable instructions for providing a user interface.

[0021] Under the control of microwave beamformer 116, the transmission of ultrasonic signals from transducer array 114 is guided by transmit controller 120, which can be coupled to T / R switch 118 and main beamformer 122. Transmit controller 120 can control the direction in which the beam is steered. The beam can be steered vertically forward from transducer array 114 (perpendicular to transducer array 26), or at different angles for a wider field of view. Transmit controller 120 can also be coupled to user interface 124 and receive input based on user operations on user controls. User interface 124 may include one or more input devices, such as control panel 152, which may include one or more mechanical controls (e.g., buttons, encoders, etc.), touch-sensitive controls (e.g., touchpad, touchscreen, or similar), and / or other known input devices.

[0022] In some examples, the partially beamformed signal generated by microwave beamformer 116 can be coupled to beamformer 122, wherein the partially beamformed signals from individual patches of transducer elements can be combined into a fully beamformed signal. In some examples, microwave beamformer 116 is omitted, and transducer array 114 is under the control of beamformer 122, and beamformer 122 performs all beamforming of the signal. In examples with and without microwave beamformer 116, the beamformed signal from beamformer 122 is coupled to processing circuitry 150, which may include one or more processors (e.g., signal processor 126, mode-B processor 128, Doppler processor 160, and one or more image generation and processing units 168) configured to generate ultrasound images based on the beamformed signal (i.e., beamformed RF data).

[0023] Signal processor 126 can be configured to process received beamformed RF data in various ways, such as bandpass filtering, decimation, I and Q component separation, and harmonic signal separation. Processor 126 can also perform signal enhancement, such as ripple reduction, signal recombination, and electronic noise cancellation. The processed signal (also referred to as the I and Q components or IQ signal) can be coupled to additional downstream signal processing circuitry to generate an image. The IQ signal can be coupled to multiple signal paths within the system, each signal path potentially 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, system 100 may include a B-mode signal path 158 that couples the signal from signal processor 126 to B-mode processor 128 to generate B-mode image data. B-mode processor 128 can employ amplitude detection to image tissue structures in the body.

[0024] In some examples, the system may include a Doppler signal path 162 that couples the output from signal processor 126 to Doppler processor 160. Doppler processor 160 may be configured to estimate the Doppler frequency 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. Doppler processor 160 may be configured to, for example, use a wall filter to filter out unwanted signals (e.g., noise or clutter associated with non-moving tissue). Doppler processor 160 may also be configured to estimate velocity and power according to known techniques. For example, the Doppler processor may include a Doppler estimator such as an autocorrelation function, where the velocity (Doppler frequency, spectral Doppler, color Doppler) estimation is based on the parameters of a hysteresis-1 (R1) autocorrelation function and the Doppler power estimation is based on the amplitude of a hysteresis-zero (R0) autocorrelation function. Motion can also be estimated using known phase-domain (e.g., parametric frequency estimators, such as MUSIC, ESPRIT, etc.) or time-domain (e.g., cross-correlation) signal processing techniques. Other estimators related to the temporal or spatial distribution of velocity, such as estimators of acceleration or temporal / spatial velocity derivatives, can be used instead of the velocity estimator or as an adjunct to it. In some examples, velocity and power estimates may undergo further thresholding to further reduce noise, as well as segmentation and post-processing, such as padding and smoothing. The velocity and power estimates can then be mapped to a desired range of display colors based on the color map. Color data, also known as Doppler image data, can then be coupled to a scan converter 130, whereby the Doppler image data can be converted to the desired image format and overlaid on a B-mode image of the tissue structure to form a color Doppler or power Doppler image. For example, Doppler image data can be overlaid on a B-mode image of the tissue structure.

[0025] Signals generated by the B-mode processor 128 and / or the Doppler processor 160 can be coupled to the scan converter 130 and / or the multiplane reformer 132. The scan converter 130 can be configured to arrange the echo signals in a desired image format according to the spatial relationships in which the echo signals are received. For example, the scan converter 130 can arrange the echo signals in a two-dimensional sector format, or a three-dimensional (3D) format of a cone or other shape. The multiplane reformer 132 is capable of converting echoes received from points in a common plane within a volumetric region of the body into an ultrasound image (e.g., a B-mode image) of that plane, for example, as described in U.S. Patent US 6,443,896 (Detmer). In some examples, the scan converter 130 and the multiplane reformer 132 can be implemented as one or more processors.

[0026] Volume renderer 134 can generate an image (also known as a projection, drawing, or plotted image) of a 3D dataset viewed from a given reference point, for example, as described in U.S. Patent US6530885 (Entrekin et al.). In some examples, volume renderer 134 can be implemented as one or more processors. Volume renderer 134 can generate the drawing using any known or future known techniques such as surface drawing and maximum intensity drawing, such as positive or negative drawing. Although in Figure 1 The diagram shows data being received from a multiplane redistributor 132, but in some examples, the volume plotter 134 may receive data from a scan converter 130.

[0027] Outputs from the scan converter 130, the multiplane reformer 132, and / or the volume plotter 134 (e.g., B-mode images, Doppler images) can be coupled to the image processor 136 for further enhancement, caching, and temporary storage before being displayed on the image display 138. The graphics processor 140 can generate graphic overlays for display alongside the images, such as cumulative images generated by the image processor 136. These graphic overlays may include standard identification information, such as the patient's name, date and time, imaging parameters, etc., of the images. For these purposes, the graphics processor can be configured to receive input from the user interface 124, such as a typed patient's name or other annotations. The user interface 124 can also be coupled to the multiplane reformer 132 for selecting and controlling the display of multiple multiplane reformulated (MPR) images.

[0028] System 100 may include local memory 142. Local memory 142 may be implemented as any suitable non-transient computer-readable medium or medium (e.g., flash drive, disk drive, dynamic random access memory). Local memory 142 may store data generated by system 100, including B-mode images, Doppler images, instructions executable by one or more processors included in system 100 (e.g., Doppler processor 160, image processor 136), input provided by the user via user interface 124, or any other information required for the operation of system 100.

[0029] As previously described, system 100 includes a user interface 124. User interface 124 may include a display 138 and a control panel 152. Display 138 may include a display device implemented using various known display technologies such as LCD, LED, OLED, or plasma display technologies. In some examples, display 138 may include multiple displays. Control panel 152 may be configured to receive user input (e.g., selection of inspection type, ROI in an image). Control panel 152 may include one or more hardware controls (e.g., buttons, knobs, dials, encoders, mice, trackballs, or others). In some examples, control panel 152 may additionally or alternatively include software controls (e.g., GUI control elements, or simply GUI controls) provided on a touch-sensitive display. In some examples, display 138 may be a touch-sensitive display that includes one or more software controls of control panel 152.

[0030] In some examples, Figure 1 The various components shown can be combined. For example, image processor 136 and graphics processor 140 can be implemented as a single processor. In another example, scan converter 130 and multi-plane reformer 132 can be implemented as a single processor. In some examples, Figure 1 The various components shown can be implemented as individual components. For example, image processor 136 can be implemented as multiple processors. In some examples, multiple image processors can perform different tasks (e.g., image segmentation, SNR calculation, motion detection, etc.). In another example, local memory 142 can include multiple memories that can be the same or different memory types (e.g., flash memory, DRAM).

[0031] In some examples, Figure 1 One or more of the various processors shown are implemented by a general-purpose processor and / or microprocessor configured to perform a specified task. For example, a processor may be configured by instructions stored in non-transient computer-readable memory (e.g., local memory 142), which are executed by the processor to perform the specified task. In some examples, one or more of the various processors may be implemented as an application-specific circuit (ASIC). In some examples, one or more of the various processors (e.g., image processor 136) may be implemented using one or more graphics processing units (GPUs).

[0032] To acquire volumetric flow rate measurements, probe 112 can acquire volumetric (e.g., 3D) ultrasound data (e.g., B-mode data, Doppler data) of one or more blood vessels within the field of view. Volumetric ultrasound data can be acquired continuously, ensuring sufficient spatial and temporal coverage of the region of interest (ROI) including one or more blood vessels. In some applications, the ROI can be sampled at a sufficient temporal rate to cover the cardiac cycle (e.g., heartbeat). In some examples, the ROI can be selected by the user via user interface 124. In some examples, the user can select the ROI before acquiring volumetric Doppler data. For example, 2D or 3D ultrasound images can be acquired, and the user can select the ROI from the 2D or 3D ultrasound images. In other examples, the entire field of view can be sampled sufficiently in both time and space, and the user can select the ROI after volumetric ultrasound data has been acquired.

[0033] In some examples, the field of view and / or region of interest (ROI) can be divided into sub-volumes. In some applications, acquiring volumetric ultrasound data from sub-volumes may require less time than acquiring the complete volume. In some examples, volumetric ultrasound data from sub-volumes can be acquired sequentially over multiple cardiac cycles. The volumetric ultrasound data acquired from sub-volumes can be retrospectively stitched together to generate the complete volume through sufficient time sampling. In some examples, acquisition can capture both constant and pulsating flow profiles. In some examples, segmentation and stitching can be performed by signal processor 126, Doppler processor 160, B-mode processor 128, scan converter 130, and / or image processor 136.

[0034] At least some of the ultrasound beams emitted by probe 112 can intersect with blood vessels in the ROI, allowing the definition of a Z-surface (e.g., a Gaussian plane) that includes the entire cross-section of the blood vessel with points equidistant from the surface of probe 112. The Z-surface can have a specific depth. In volumetric acquisition, multiple such Z-surfaces may exist, each encompassing the complete cross-section of the blood vessel. Some or all of the Z-surfaces can be used for volumetric flow rate measurement calculations.

[0035] Within each Z-surface, vessels of interest can be segmented manually or automatically. Manual segmentation may involve a user placing the ROI around a vessel boundary via user interface 124, the boundary surrounding the vessel as seen by the user in Doppler velocity (e.g., spectrum) and / or Doppler power images provided on display 138. Automatic segmentation may involve a combination of thresholding and other morphological image processing operations on individual power, velocity, or B-mode images, or combinations thereof, to set the ROI as the vessel boundary in the volumetric ultrasound data. Segmentation may also, or alternatively, involve artificial intelligence algorithms that identify vessels across multiple Z-surfaces in the volumetric ultrasound data. In some examples, segmentation may be performed by image processor 136.

[0036] In some examples, once the vessel boundary is segmented, a histogram can be calculated using the power Doppler values ​​in pixels inside and outside the vessel. For example, the histogram may include one curve (e.g., a profile) of power Doppler values ​​located within a region of interest (ROI) on the Z-surface (e.g., inside the vessel) and another curve (e.g., a profile) of power Doppler values ​​located outside the ROI on the Z-surface. The power Doppler histogram method can be used to derive fractional weights that determine the fractional weights of voxel regions inside and outside the vessel, as well as partial volumetric voxels on the boundary. That is, some voxels on the vessel boundary may include data from tissues inside and outside the vessel. Once the partial volumetric weights are determined based on the power Doppler histogram, the velocity from the spectral Doppler data and the surface area of ​​the voxels can be multiplied to obtain the volumetric flow rate in each voxel. The integral of the flow rate values ​​in the voxels inside the vessel gives the volumetric flow rate in the Z-surface. These volumetric flow rate values ​​may be stored, for example, in local memory 142, and / or made available to the user on display 138.

[0037] According to the principles of this disclosure, in addition to providing volumetric flow rate measurement, system 100 can also provide a user with an indication of the quality of the volumetric flow rate measurement results using a quality index 170, such as text or graphics on display 138. The quality index 170 can indicate the level of reliability / accuracy and / or repeatability of the volumetric flow rate measurement results. In some examples, the quality index 170 can provide a qualitative indication of the acquisition quality. For example, the quality index 170 can provide different colors, shapes, or descriptors / adjectives (e.g., good, average, poor) to indicate the acquisition quality. In some examples, the quality index 170 can provide a quantitative value indicating the acquisition quality. The indication of quality can be based at least in part on one or more quality factors. Quality factors can include, but are not limited to, SNR, the presence of motion (e.g., motion detection), Doppler angle, vessel size, vessel depth, and / or changes in intravascular flow velocity.

[0038] In some examples, multiple quality factors can be combined to generate a quality indication provided by quality index 170. In some examples, only one quality factor can be used to generate a quality indication. In some examples, one or more quality factors can be used to generate a quality indication, while the same or different quality factors can be used to provide guidance to the user for improving acquisition. For example, SNR can be used to determine the quality indication provided by quality index 170, while Doppler angle can be used to guide the user in adjusting the orientation of probe 112. In some examples, quality index 170 can also provide guidance.

[0039] In some examples, SNR can be the primary quality factor used to determine the quality of acquisition. Figure 2This is a block diagram illustrating a method for calculating the signal-to-noise ratio based on Doppler data acquired for three-dimensional volumetric flow quantization, according to the principles of this disclosure. An ultrasound imaging system 200 can acquire image data 202 from the volume within an object. In some examples, the ultrasound imaging system 200 may be included in or used to implement system 100. The image data 202 may include power Doppler data 204, which in some examples may be color power Doppler data, such as… Figure 2 The example shown. Image data 202 may include color spectral Doppler data 206 (e.g., hue), echo data 208 (e.g., B-mode data), and / or volume geometry 210. In some examples, image data 202 may have already been acquired by a transducer array of a probe (e.g., transducer array 114 of probe 112). In some examples, power Doppler data 204 and / or spectral Doppler data 206 may be extracted from image data 202 by a Doppler processor (such as Doppler processor 160). In some examples, echo data 208 may be extracted from image data (e.g., acquisition data) 202 by a B-mode processor such as B-mode processor 128. In some examples, volume geometry 210 may be provided by a scan converter, a multiplane redistributor, and / or a volume plotter (e.g., scan converter 130, multiplane redistributor 132, and / or volume plotter 134). In some examples, the computation of image data 202 may be performed by one or more processors, such as image processor 136, Doppler processor 160, B-mode processor 128, scan converter 130, multiplane reformer 132 and / or volume plotter 134.

[0040] like Figure 2 As shown, in some examples, power Doppler data 204 can be used to provide power Doppler data in the individual Z-surface 212 within a volume and / or in a Region of Interest (ROI) within the volume. In some examples, power Doppler data 204, color Doppler data 206, and / or echo data 208 can be used to segment and / or identify one or more blood vessels within a volume and / or ROI (e.g., vessel segmentation 214). In some examples, segmentation can be performed by an image processor (e.g., image processor 136). In some examples, segmentation of blood vessels and volume can be used to define an ROI within a volume. In other examples, a user can define an ROI (e.g., via a user interface, such as user interface 124), and one or more blood vessels can be segmented from the ROI.

[0041] Vessel segmentation can be used to acquire color velocities within one or more vessels. Volume geometry 210 can be used to calculate the surface area 224 of each Z-surface within the volume and / or ROI.

[0042] The vessel segmentation 214 can also be used to access power Doppler data within the vessel ROI 220. The Doppler image 218 is an image of the Z-surface having an ROI 238 including the vessel 240. Power Doppler data throughout the Z-surface 212 and within the ROI 220 can be used to generate a power Doppler histogram 228. Histogram peaks 230 of the power Doppler data outside the ROI 238 represent noise, while histogram peaks 232 of the power Doppler data within the ROI 220 represent the signal of interest.

[0043] Data from histogram 228 can be used to generate a partial volumetric weight mask 234. The partial volumetric weight mask 234 can be combined with velocity 222 and surface area 224 data to provide a volumetric flow rate measurement 226. In some examples, data from the power Doppler histogram 228 can be used to calculate the SNR 236. As noted, the Doppler histogram 228 can have curves based on power Doppler data (signal) from within the ROI and curves based on power Doppler data (noise) from outside the ROI. In some examples, in a logarithmic scale (e.g., dB), the difference between the signal peak and the noise peak represents the SNR of the Z-surface (e.g., SNR = signal / noise). 峰值 -noise 峰值 Therefore, the SNR for each Z-surface can be calculated and stored. For example, the data can be stored in local memory, such as local memory 142.

[0044] Due to inhomogeneities in ultrasound beam intensity, structures in the field of view that cause reflections and reverberation, attenuation, and other factors affecting Z-surface quality, the SNR values ​​across multiple Z-surfaces along the depth can vary. For example, reflections may exist at specific depths, resulting in higher noise in the region outside the vessel. Similarly, beam angle and / or attenuation can lead to reduced signal values ​​within the vessel. To determine acquisition quality based on these multiple and variable SNR values, one or more techniques can be used to identify a set of Z-surfaces whose SNR values ​​are used to determine the acquisition quality factor. In some examples, Z-surfaces can be sorted based on SNR values. In some examples, the average and / or median SNR of all Z-surfaces of the vessel within the ROI can be provided as a quality factor (e.g., dB).

[0045] In some examples, the interquartile range median (IQR / median) can be used to determine which range of data on a Z-surface is used in combination with the mean or median to determine the SNR. The interquartile range (IQR) is a measure of variability based on dividing the dataset into four parts (e.g., for the SNR of a Z-surface). The top and bottom quarters are removed from the dataset, leaving a “middle fifty” around the median of the dataset. Dividing the interquartile range median by the median yields the quality factor. For example, in Z-surfaces ranked by SNR, using Z-surface groups greater than 5, such as 1-5, 1-6, 1-7, etc., the IQR / Med for the SNR is calculated: IQR / Med. 1-5 IQR 1-6 IQR 1-7 And so on. Select the mean or median SNR of the group with the lowest IQR / Med as the SNR-based quality factor.

[0046] In some examples, the coefficient of variability (COV) or relative standard deviation (e.g., standard deviation divided by the mean) can be used to determine the range of data to be used in combination with the mean or median (e.g., which SNR values ​​of a Z-surface). For example, in Z-surfaces ranked by SNR, using more than three Z-surface groups, such as 1-3, 1-4, 1-5, etc., the COV of the SNR values ​​is calculated: COV 1-3 COV 1-4 COV 1-5 The mean or median of the Z surface group representing the minimum COV can be selected as the mean or median for generating the quality factor based on SNR.

[0047] In another example, the average SNR of several Z surfaces (e.g., 3, 5, 10) that are closest to and / or close to the focal point of the ultrasonic beam provided by the ultrasonic probe 112 can be used.

[0048] In some examples, multiple techniques used to determine the SNR and / or which SNR values ​​to use can be combined to generate a quality factor. For example, the average SNR calculated using various techniques can be used as a quality factor. In some examples, different weights can be applied to different SNR values ​​determined by different techniques when calculating the average. In some examples, SNR values ​​calculated by different techniques can be provided as separate quality factors to determine an indication of quality provided by a quality indicator such as quality index 170. In some examples, the SNR may be the sole quality factor used to generate the quality indication.

[0049] Figures 3A-3BThe illustration shows example images of spectral and power Doppler data acquisitions used to calculate the signal-to-noise ratio, along with corresponding power histograms, in accordance with the principles of this disclosure. The images and histograms can be generated by an ultrasound imaging system (e.g., ultrasound imaging system 100 and / or ultrasound imaging system 200).

[0050] exist Figure 3A In the image, a velocity map generated from color Doppler velocity data of the Z-surface is shown in image 302, and a power Doppler map for the same acquired Z-surface is shown in image 304. Blood vessels are shown within ROI 302 in image 304. Histogram 300 is a graph of power Doppler data, showing curves of noise (e.g., power Doppler data outside ROI 306) and signal (e.g., power Doppler data inside ROI 306). Below the graph, the power (dB) difference between the signal peak and the noise peak, i.e., the SNR, is shown, which is 18 dB.

[0051] exist Figure 3B In the image, a velocity map generated from color Doppler velocity data of the Z-surface is shown in image 310, and a power Doppler map of the same Z-surface acquired in another image is shown in image 312. Vessels are shown within ROI 314 in image 312. Histogram 308 is a graph of the power Doppler data, showing curves of noise (e.g., power Doppler data outside ROI 314) and signal (e.g., power Doppler data inside ROI 314). Velocity map 310 shows less distinct vessels compared to velocity map 302. Furthermore, power Doppler map 312 has lower contrast than power Doppler map 304. Below the image is the power (dB) difference between the signal peak and noise peak, with an SNR of 2dB, significantly lower than... Figure 3A SNR.

[0052] Figure 3A and 3B The data shown is from umbilical cord images. In this application, good acquisition quality is associated with an SNR of 12 dB or higher. However, different ranges of SNR can be used to categorize acquisition quality (e.g., 0–5 dB poor, 6–14 dB fair, 15–20 dB good, 20–24 dB very good, 25+ dB excellent). SNR can be used to generate quantitative quality metrics. For example, numerical values ​​(e.g., in dB) can be provided on quality metrics (e.g., quality metric 170). In some examples, SNR can be used to generate qualitative quality metrics; for example, different colors and / or other descriptors can be associated with different ranges of SNR values.

[0053] In other examples, other quality factors besides SNR can be identified to calculate quality indicators and / or provide suggestions to users to improve acquisition quality.

[0054] In some examples, vessel size and / or depth can be determined from segmented data. Smaller diameter vessels imaged using a wide Doppler ultrasound beam may be resolution-limited. That is, there may not be enough ultrasound beam inside the vessel, which could limit the effectiveness of partial volume correction algorithms. Similarly, deeper vessels may be affected by increased intravascular attenuation and / or reduced power. Furthermore, even deeper vessels interacting with diverging beams may again be resolution-limited. The effects of size and depth on partial volume weight can limit accuracy and may increase the variability of volumetric flow rate measurements. Therefore, small and / or deep vessels may provide lower quality factor values ​​for determining quality indications. In some examples, a weighting table can be calculated based on the ultrasound beam profile, vessel depth, and size, and used for the quality factor.

[0055] In addition to being used for quality indication or alternatively, the determination can be used to provide the user with suggestions for improving acquisition. For example, if the vessel diameter and / or beam density within the vessel is below a threshold, an ultrasound system such as System 100 can prompt the user to select different vessel and / or imaging settings (e.g., increase beam density) to acquire flow measurements. The prompts can be made via text, graphics, audio signals, and / or tactile feedback (e.g., probe vibration). In another example, if the vessel depth and / or power level within the vessel is below a threshold, the system can prompt the user to select different vessel and / or imaging settings (e.g., increase power).

[0056] In some examples, the effect of motion on Doppler data can be considered as a quality factor for indicative of quality and / or user recommendations. In the iSTIC acquisition framework, each sub-volume (e.g., the elevation plane) can be sampled continuously at high temporal resolution throughout the cardiac cycle. This process can then be repeated for the remaining sub-volumes. Motion-affected data may reduce the robustness of the segmentation boundaries of vessels visualized on the Z-plane throughout the cardiac cycle. Furthermore, flash artifacts on the velocity map may increase and / or transient reflections may affect the power map. Any method known now or in the future for motion detection can be used. Greater motion may result in a reduced quality factor. In some examples, if the motion exceeds a threshold (e.g., the velocity and / or amplitude of displacement), the ultrasound system may prompt the user to keep the probe stationary and / or instruct the subject to remain still.

[0057] In some applications, the Doppler angle can also be an important parameter that potentially influences partial volume correction at the vessel boundary, and therefore may be an important quality factor. As the angle between the ultrasound beam and the flow axis increases, the number of beams entirely within the vessel decreases. This increases the partial volume beam interacting with the vessel boundary. Therefore, similar to the effects in small vessels, resolution limitations can adversely affect the accuracy of volumetric flow rate measurements. In some examples, the Doppler angle can be provided as a quality factor. Alternatively, if the Doppler angle exceeds a threshold (e.g., 60 degrees, 65 degrees), the ultrasound system can prompt the user to adjust the probe angle to reduce the Doppler angle.

[0058] In some applications, an increase in the variance of segmented intravascular velocity values ​​may indicate poor acquisition quality. In some examples, the variance of velocity values ​​can be calculated for each Z-surface. In some examples, the mean and / or median variance of the Z-surface can be provided as a quality factor. In some examples, one or more of the techniques described with reference to SNR can be used to perform the selection of which Z-surfaces to use to provide the velocity variance factor.

[0059] Figure 4 This is a block diagram illustrating an overview of providing an indication of acquisition quality according to the principles of this disclosure. Overview 400 can be implemented on an ultrasound imaging system such as ultrasound imaging system 100 and / or 200.

[0060] Image data 402 may have been acquired by a transducer array such as transducer array 114. In some examples, image data 402 may include image data 202. Image data 402 can be used to generate various quality factors 430, such as SNR 404, motion detection 406, Doppler angle 408, vessel size and / or depth 410, and / or velocity variance within vessel 412. Quality factors 430 can be used to generate quality indicators 414 and / or user suggestions 420, which can be provided to the user via a user interface such as user interface 124.

[0061] In some examples, quality metric 414 may include text, graphics, sound, animation, and / or light (e.g., light-emitting diodes). In some examples, quality metric 414 may be provided on a display such as display 138. In some examples, quality metric 414 may be used to implement quality metric 170.

[0062] In some examples, quality indicator 414 may include qualitative and / or semi-qualitative indications of the acquisition quality of image data 402. For example, qualitative descriptors (e.g., poor, average, good), colors (e.g., red, yellow, green), and / or emojis associated with different quality levels may be used to indicate acquisition quality. Example graph 416 illustrates text, shading, dials, and emojis. However, graph 416 is presented as an example only, and qualitative indicators according to this disclosure are not limited to the examples shown. Qualitative indicators may be based on one or more of quality factors 430.

[0063] In some examples, in addition to or instead of a qualitative indicator, quality indicator 414 may provide a quantitative value 418 of the acquisition quality. In the example shown, the quantitative value 418 is an SNR 404 value in decibels. However, in other examples, the quantitative value 418 may be generated by one or more quality factors 430, which may or may not include SNR 404.

[0064] In some examples, user suggestions 420 may include text, graphics, sound, animation, and / or light (e.g., light-emitting diodes). In some examples, user suggestions 420 may be provided on a display such as display 138. For example, text 422 may provide suggestions to the user to improve data acquisition. Figure 4 In the example shown, text 422 suggests adjusting the angle of the ultrasound probe to improve the Doppler angle (e.g., if the Doppler angle 408 is found to be above a threshold). As another example, graph 424 indicates how the user should move and / or position the ultrasound probe to improve the Doppler angle. Text 422 and graph 424 are provided only as examples, and user suggestions 420 are not limited to the examples shown.

[0065] Figure 5 This is a flowchart of a method for providing feedback on the quality of volumetric flow rate measurement results, based on the principles of this disclosure. In some examples, method 500 may be performed wholly or partially by an ultrasound imaging system such as imaging system 100 and / or imaging system 200. In some examples, method 500 may be performed by one or more processors that execute computer-readable instructions, such as image processor 136, Doppler processor 160, B-mode processor 128, and / or... Figure 1 Other processors are shown. In some examples, computer-readable instructions may be stored in at least one processor-accessible non-transient computer-readable medium, such as local memory 142.

[0066] As indicated in box 502, at least one processor (e.g., image processor 136) can generate a histogram based on power Doppler data for individual Z-surfaces among a plurality of Z-surfaces in an object volume. The power Doppler data may have been acquired by an ultrasound imaging system, for example, by a probe of the ultrasound imaging system. The histogram may have a first curve based on power Doppler data from within the ROI and a second curve based on power Doppler data from outside the ROI. The ROI may be based on automatic segmentation of image data from the object volume or on user input (e.g., via a user interface, such as user interface 124).

[0067] At box 504, at least one processor can calculate the SNR by subtracting the peak value of a second curve from the peak value of a first curve of at least one of the plurality of Z-surfaces. The at least one processor can then generate a quality factor at least partially based on the SNR, as shown in box 506. In some examples, the SNR can be calculated for all Z-surfaces, and the quality factor can be generated using the average and / or median SNR. In some examples, a subset of the SNR values ​​from the Z-surfaces can be used to generate the quality factor, as in the reference... Figure 2 As described in Figure 3.

[0068] At block 508, at least one processor can generate display data for a quality indicator based at least in part on the quality factor. The quality indicator can indicate the quality of a volumetric flow rate measurement. As shown in block 510, the quality indicator can be displayed to a user based on the display data. For example, the quality indicator can be displayed on display 138. Of course, in other examples, the quality indicator can be an audible signal provided on a speaker, one or more lights provided on the control panel of an ultrasound imaging system, and / or tactile feedback provided on the control panel and / or ultrasound probe.

[0069] In some examples, at least one processor can calculate at least one additional quality factor, wherein the quality metric is also based on the at least one additional quality factor, as shown in box 512. In some examples, as indicated in box 514, at least one processor can generate recommendations for improving the quality of volumetric flow rate measurements based at least in part on the quality factor and the at least one additional quality factor.

[0070] Figure 6 This is a block diagram illustrating an example processor 600 according to the principles of this disclosure. Processor 600 can be used to implement one or more processors described herein, for example... Figure 1 The image processor 136 shown is illustrated. Processor 600 is capable of executing data stored on a non-transient computer-readable medium (e.g., [missing information]) in communication with processor 600. Figure 1The computer-readable instructions are located on the local memory 142 shown in the diagram. The processor 600 can be any suitable processor type, including but not limited to a microprocessor, microcontroller, digital signal processor (DSP), field-programmable array (FPGA) where the FPGA has been programmed to form a processor, graphics processing unit (GPU), application-specific circuit (ASIC) where the ASIC is designed to form a processor, or a combination thereof.

[0071] Processor 600 may include one or more cores 602. Core 602 may include one or more arithmetic logic units (ALUs) 604. In some examples, in addition to or instead of ALU 604, core 602 may include a floating-point logic unit (FPLU) 606 and / or a digital signal processing unit (DPU) 608.

[0072] Processor 600 may include one or more registers 612 communicatively coupled to core 602. Registers 612 may be implemented using dedicated logic gates (e.g., flip-flops) and / or any memory technology. In some examples, registers 612 may be implemented using static memory. Registers may provide data, instructions, and addresses to core 602.

[0073] In some examples, processor 600 may include one or more levels of cache memory 610 communicatively coupled to core 602. Cache memory 610 may provide computer-readable instructions to core 602 for execution. Cache memory 610 may provide data for core 602 to process. In some examples, computer-readable instructions may already be provided to cache memory 610 by local memory (e.g., local memory attached to external bus 616). Cache memory 610 may be implemented using any suitable cache memory type, such as metal-oxide-semiconductor (MOS) memory, such as static random access memory (SRAM), dynamic random access memory (DRAM), and / or any other suitable memory technology.

[0074] Processor 600 may include controller 614, which can control components included in other processors and / or the system (e.g., Figure 1 The control panel 152 and scan converter 130 shown are inputs to the processor 600 and / or inputs from the processor 600 to other processors and / or components included in the system (e.g., Figure 1The outputs of the display 138 and volume plotter 134 shown are illustrated. The controller 614 can control the data paths in the ALU 604, FPLU 606, and / or DSPU 608. The controller 614 can be implemented as one or more state machines, data paths, and / or dedicated control logic. The gates of the controller 614 can be implemented as stand-alone gates, FPGAs, ASICs, or any other suitable technology.

[0075] Register 612 and cache 610 can communicate with controller 614 and core 602 via internal connections 620A, 620B, 620C and 620D. These internal connections can be implemented as buses, multiplexers, cross switches and / or any other suitable connection technology.

[0076] The processor 600's inputs and outputs can be provided via a bus 616, which may include one or more wires. The bus 616 may be communicatively coupled to one or more components of the processor 600, such as the controller 614, cache 610, and / or register 612. The bus 616 may also be coupled to one or more components of the system, such as the previously mentioned display 138 and control panel 152.

[0077] Bus 616 can be coupled to one or more external memories. The external memory may include read-only memory (ROM) 632. ROM 632 may be a mask ROM, electrically programmable read-only memory (EPROM), or any other suitable technology. External memory may include random access memory (RAM) 633. RAM 633 may be static RAM, battery-backed static RAM, dynamic RAM (DRAM), or any other suitable technology. External memory may include electrically erasable programmable read-only memory (EEPROM) 635. External memory may include flash memory 634. External memory may include magnetic storage devices, such as disk 636. In some examples, external memory may be included in the system, e.g. Figure 1 The ultrasound imaging system 100 shown includes, for example, local memory 142.

[0078] The apparatus, systems, and methods disclosed herein can allow for the provision of feedback to users, including indications of the quality of data acquisition based on one or more quality factors. In some examples, the apparatus, systems, and methods disclosed herein can allow for the provision of suggestions to users for improving acquisition quality. If the raw data acquisition is poor, feedback can encourage users to reacquire data during review, which can lead to more accurate and / or repeatable measurements. In some applications, feedback can help users improve their data acquisition techniques, which can reduce the need for reacquiring data.

[0079] In various examples of implementing components, systems, and / or methods using programmable devices such as computer-based systems or programmable logic, it should be understood that the aforementioned systems and methods can be implemented using various known or later-developed programming languages, such as "C", "C++", "FORTRAN", "Pascal", "VHDL", etc. Therefore, various storage media, such as magnetic computer disks, optical disks, electronic storage devices, etc., can be prepared, which can contain information that can instruct devices, such as computers, to implement the aforementioned systems and / or methods. Once a suitable device can access the information and programs contained on the storage medium, the storage medium can provide the information and programs to that device, thereby enabling that device to perform the functions of the systems and / or methods described herein. For example, if a computer disk containing appropriate materials (e.g., source files, object files, executable files, etc.) is provided to a computer, the computer can receive the information, appropriately configure itself, and perform the functions of the various systems and methods depicted in the above diagrams and flowcharts to achieve various functions. That is, a computer can receive various parts of information relating to different elements of the aforementioned systems and / or methods from the disk, implement individual systems and / or methods, and coordinate the functions of the individual systems and / or methods described above.

[0080] In view of this disclosure, it should be noted that the various methods and apparatuses described herein can be implemented in hardware, software, and firmware. Furthermore, the various methods and parameters are included by way of example only and are not intended to be limiting. In view of this disclosure, those skilled in the art can implement the teachings of this invention while still remaining within the scope of this disclosure, provided they determine their own techniques and the desired apparatus for influencing these techniques. The functionality of one or more processors described herein can be incorporated into a smaller number or a single processing unit (e.g., a CPU) and can be implemented using application-specific integrated circuits (ASICs) or general-purpose processing circuitry programmed to perform the functions described herein in response to operable instructions.

[0081] Although this system may have been described with particular reference to ultrasound imaging systems, it is also contemplated that this system can be extended to other medical imaging systems that acquire one or more images in a systematic manner. Therefore, this system can be used to acquire and / or record image information relating to the kidneys, testes, breasts, ovaries, uterus, thyroid gland, liver, lungs, musculoskeletal system, spleen, heart, arteries, and vascular system, as well as other imaging applications related to ultrasound-guided interventions, but not limited thereto. Furthermore, this system may include one or more procedures that can be used with conventional imaging systems, enabling them to provide the features and advantages of this system. Certain other advantages and features of this disclosure may be apparent to those skilled in the art upon study of this disclosure, or may be experienced by those who employ the novel systems and methods of this disclosure. Another advantage of this system and method is the ease with which conventional medical imaging systems can be upgraded to incorporate the features and advantages of this system, device, and method.

[0082] Of course, it should be understood that any of the examples, paradigms or processes described herein may be combined with one or more other examples, paradigms and / or processes, or may be separate and / or executed in a discrete device or part of a device, depending on the system, device and method described herein.

[0083] Ultimately, the above discussion is intended merely to illustrate the system and method of the invention and should not be construed as limiting the appended claims to any particular paradigm or group of paradigms. Therefore, while the system has been described in detail with reference to exemplary examples, it should be appreciated that numerous variations and alternative examples can be devised by those skilled in the art without departing from the broader spirit and scope of the system and method as set forth in the claims. Consequently, the specification and drawings should be considered illustrative and not intended to limit the scope of the appended claims.

Claims

1. An ultrasonic imaging system (100) configured to provide feedback on the quality of a volumetric flow rate measurement, the system comprising: User interface (124); A non-transient computer-readable medium (142) is 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); as well as At least one processor (136) communicating with the non-transient computer-readable medium, the at least one processor being configured to execute the instructions, wherein the instructions, when executed, cause the at least one processor to: Histograms for individual Z-surfaces among the plurality of Z-surfaces are generated at least in part based on the power Doppler data, wherein the histograms have 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; The signal-to-noise ratio (SNR) is calculated by subtracting the peak value of the second curve from the peak value of the first curve on a logarithmic scale for at least one of a plurality of Z-surfaces; and The quality factor is generated at least in part based on the SNR. Display data for a quality index is generated, at least in part, based on the quality factor, wherein the quality index indicates the quality of the volumetric flow rate measurement result. The user interface is configured to display the quality indicators to the user based on the display data.

2. The ultrasound imaging system according to claim 1, wherein, The quality metric includes text indicating the value of the SNR.

3. The ultrasound imaging system according to claim 1, wherein, The quality metric includes a color-coded graph, wherein the color is based at least in part on the SNR value.

4. The ultrasound imaging system according to claim 1, wherein, The instruction also causes the at least one processor to: Calculate the SNR for each of the plurality of Z-surfaces; and Calculate at least one of the median SNR or the average SNR of the plurality of Z surfaces, wherein the quality factor is based on at least one of the median SNR or the average SNR.

5. The ultrasound imaging system according to claim 1, wherein, The instruction also causes the at least one processor to: Calculate the SNR for each of the plurality of Z-surfaces; Calculate the interquartile range median (IQR / M) for each of the multiple groups of Z-surfaces, wherein the individual groups in the multiple groups of Z-surfaces comprise subsets of the multiple Z-surfaces; Determine the group of Z surfaces with the minimum IQR / M; and Calculate at least one of the median SNR or the average SNR of one of the plurality of Z surfaces, wherein the quality factor is based on at least one of the median SNR or the average SNR.

6. The ultrasound imaging system according to claim 1, wherein, The instruction also causes the at least one processor to: Calculate the SNR for each of the plurality of Z-surfaces; Calculate the coefficient of variation (COV) for each group of Z-surfaces in a plurality of groups of Z-surfaces, wherein the individual groups in the plurality of Z-surfaces comprise subsets of the plurality of Z-surfaces; Determine the group with the minimum COV among the multiple groups of Z surfaces; and Calculate at least one of the median SNR or the average SNR of one of the plurality of Z surfaces, wherein the quality factor is based on at least one of the median SNR or the average SNR.

7. The ultrasound imaging system according to claim 1, wherein, The at least one Z-surface includes a plurality of Z-surfaces adjacent to the focal point of the ultrasonic beam used to collect the power Doppler data.

8. The ultrasound imaging system according to claim 1, wherein, The instruction also causes the at least one processor to: Generate at least one additional quality factor, wherein the quality index is also based on the at least one additional quality factor.

9. The ultrasound imaging system according to claim 8, wherein, The at least one additional quality factor includes at least one of the following: the presence of motion, Doppler angle, vessel size, vessel depth, or the variance of the volumetric flow rate measurement within the ROI.

10. The ultrasound imaging system according to claim 8, wherein, The instruction also causes the at least one processor to: Based at least in part on the quality factor and the at least one additional quality factor, second display data is generated to suggest improvements in the quality of the volumetric flow rate measurement. The user interface is also configured to display the suggestion based on the second display data.

11. A method (500) for providing feedback on the quality of a volumetric flow rate measurement, the method comprising: A (502) histogram is generated based on the power Doppler data of individual Z-surfaces among multiple Z-surfaces in the object volume, wherein the histogram has a first curve based on the power Doppler data from within the region of interest (ROI) and a second curve based on the power Doppler data from outside the ROI; (504) The signal-to-noise ratio (SNR) is calculated by subtracting the peak value of the second curve from the peak value of the first curve on a logarithmic scale for at least one of the plurality of Z-surfaces; and The (506) quality factor is generated at least in part based on the SNR; Display data for a quality index is generated (508) at least in part based on the quality factor, wherein the quality index indicates the quality of the volumetric flow rate measurement; and The quality indicator (510) is displayed to the user based on the displayed data.

12. The method of claim 11, further comprising calculating at least one additional quality factor, wherein, The quality indicator is also based on the at least one additional quality factor.

13. The method according to claim 12, wherein, The at least one additional quality factor includes at least one of the following: the presence of motion, Doppler angle, vessel size, vessel depth, or the variance of the volumetric flow rate measurement within the ROI.

14. The method according to claim 11, wherein, The quality indicator provides a qualitative indication of the quality of the volumetric flow rate measurement.

15. The method according to claim 11, wherein, The quality indicator provides a quantitative indication of the quality of the volumetric flow rate measurement.

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