Systems and methods for vessel mapping

Abstract

In some examples, color Doppler data can be separated into luminance data and chrominance data. The luminance data can be modified without modifying the chrominance data. In some examples, the luminance data can be adjusted based at least in part on power Doppler data. The adjusted luminance data can be recombined with the chrominance data to provide enhanced color Doppler data. In some examples, the power Doppler data can be enhanced by filtering, e.g., by applying a Frangi vessel filter, prior to adjusting the luminance data of the color Doppler data.

Description

Technical Field

[0001] This application relates to the mapping of vascular systems in ultrasound imaging. More specifically, this application relates to mapping vascular systems based on Doppler data. Background Technology

[0002] Doppler ultrasound is an ultrasound technique that detects blood flow by repeatedly sampling a region of interest at a high pulse repetition frequency (PRF). Example PRFs include >200 Hz for power Doppler and kHz ranges for color Doppler. Motion is detected as the phase shift of each PRF in the in-phase / quadrature echo signal (IQ), proportional to the axial velocity. The moving blood echo can be separated from the stationary tissue background by applying a high-pass filter (e.g., a wall filter) along a slow-time direction. The autocorrelation (R) of the flow IQ signal is then typically calculated, and power Doppler is extracted based on the zeroth hysteresis (R0), while color Doppler is estimated based on the phase of the first hysteresis (~arg[R1]).

[0003] Color Doppler provides quantitative information about blood flow, including velocity and direction, but offers limited vascular mapping capabilities. Power Doppler is generally more sensitive than color Doppler and provides better vascular mapping, but it offers less quantitative information. Therefore, an improved technique for mapping blood vessels based on color Doppler data is desired. Summary of the Invention

[0004] Systems and methods for mapping vascular systems based on Doppler data are disclosed. In some examples, the luminance portion of a velocity color map from color Doppler data can be enhanced. Color Doppler data (e.g., a red-green-blue (RGB) velocity color map) can be decomposed into its luminance and chromaticity (e.g., chromaticity) channels. The luminance can be adjusted at least partially based on power Doppler data, while the chromaticity remains unchanged. The adjusted luminance can be recombinated with the chromaticity to generate enhanced color Doppler data. In some applications, adjusting the luminance of color Doppler based on power Doppler can improve the mapping of blood vessels.

[0005] According to at least one example disclosed herein, an ultrasound imaging system may include a processor configured to analyze ultrasound signals to generate power Doppler data and color Doppler data, separate the color Doppler data into luminance data and chrominance data, adjust the luminance data at least in part based on the power Doppler data to generate adjusted luminance data, and combine the adjusted luminance data and the chrominance data to generate enhanced color Doppler data.

[0006] According to at least one example disclosed herein, a method may include: decomposing color Doppler data into luminance data and chrominance data, adjusting the luminance data at least in part based on power Doppler data to generate adjusted luminance data, and combining the adjusted luminance data and chrominance data to generate enhanced color Doppler data.

[0007] According to at least one example disclosed herein, a non-transient computer-readable medium comprising instructions that, when executed, enable an ultrasound imaging system to: analyze ultrasound signals to generate power Doppler data and color Doppler data, separate the color Doppler data into luminance data and chrominance data, adjust the luminance data at least in part based on the power Doppler data to generate adjusted luminance data, and combine the adjusted luminance data and chrominance data to generate enhanced color Doppler data. Attached Figure Description

[0008] Figure 1 This is a block diagram of an ultrasound imaging system arranged according to an example of this disclosure.

[0009] Figure 2 This is a block diagram illustrating an example processor according to an example of this disclosure.

[0010] Figure 3 This is a functional block diagram of a portion of a Doppler processor according to an example of this disclosure.

[0011] Figure 4 Example enhanced maps, color Doppler velocity maps, and enhanced color Doppler velocity maps are shown according to examples of this disclosure.

[0012] Figure 5 It is a flowchart of a method based on the principles 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 this disclosure or its application or use. In the following detailed description of embodiments of this system and method, reference is made to the accompanying drawings, which form part of the description, and in which specific embodiments of the described system and method are illustrated by way of illustration. These embodiments are described in sufficient detail to enable those skilled in the art to practice the systems and methods currently disclosed, 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 this disclosure. Therefore, the following detailed description should not be regarded in a limiting sense, and the scope of this system and method is defined only by the appended claims.

[0014] As previously mentioned, in Doppler ultrasound, the autocorrelation (R) of the in-phase / quadrature echo (IQ) signal is calculated, and the power Doppler is extracted based on the zeroth lag (R0), while the color Doppler is estimated based on the phase of the first lag (~arg[R1]).

[0015] Color Doppler provides quantitative information about blood flow, including velocity and direction. arg[R1] is not scaled proportionally to blood volume, which may limit vascular morphology rendering based on color Doppler data. Power Doppler's R0 is scaled proportionally to blood volume, which is beneficial for morphological rendering. However, power Doppler provides less quantitative information than color Doppler because it is independent of angle, velocity, and direction.

[0016] Currently, to overcome the limitations of color Doppler, when mixing color Doppler images with anatomical B-mode ultrasound images, blood flow / tissue mixing can be arbitrated using the R0 of the power Doppler data. A fixed threshold is typically selected from the R0 receiver operating characteristic (ROC) curve to hide color pixels that may belong to noise. This process may improve vascular morphology mapping but loses fine details of the vascular tree. Furthermore, overshooting the ROC threshold reduces sensitivity, while a threshold that is too low can cause the color Doppler signal to overflow the vessel wall in B-mode images. Therefore, in some applications, it may be beneficial to improve vascular mapping while preserving the quantitative information provided by color Doppler.

[0017] According to examples of this disclosure, color Doppler data can be decomposed into luminance channels and chroma (also known as chroma) channels. The luminance channel data (e.g., luminance data) can be adjusted (e.g., modified) based at least in part on the power Doppler data. The adjusted luminance channel data can be recombinated with the unchanged chroma channel data to provide enhanced color Doppler data. In some examples, the enhanced color Doppler data can provide improved vascular mapping. In some examples, quantitative data on blood flow from the color Doppler data can be preserved within the enhanced color Doppler 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, for example, an intravascular ultrasound (IVUS) catheter probe. In other examples, the transducer array 114 may be in the form of a flexible array configured to conformally apply to the surface of the object to be imaged (e.g., a patient). The transducer array 114 is configured to emit ultrasound signals (e.g., beams, waves) and receive echoes (e.g., received ultrasound signals) in response to the emitted ultrasound signals. Various transducer arrays may be used, such as linear arrays, curved arrays, or phased arrays. The transducer array 114 may, for example, include a two-dimensional array (as shown) of transducer elements capable of scanning in the height and azimuth dimensions for 2D and / or 3D imaging. As is well known, 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 usually 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 from the user for operation of user input devices (e.g., user control). 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. In these examples, transducer array 114 is under the control of master beamformer 122, and master beamformer 122 performs all beamforming of the signal. In examples with and without microwave beamformer 116, the beamformed signal from master 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 an ultrasound image 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 signals (also referred to as I and Q components or IQ signals) can be coupled to additional downstream signal processing circuitry to generate an image. IQ signals 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, Doppler image data). For example, the system may include a B-mode signal path 158 that couples signals from signal processor 126 to B-mode processor 128 to generate B-mode image data.

[0024] B-mode processor 128 can employ amplitude detection to image structures within the body. B-mode processor 128 can generate signals for tissue images and / or contrast images. The signals generated by B-mode processor 128 can be coupled to scan converter 130 and / or multi-plane reformer 132. Scan converter 130 can be configured to arrange echo signals in a desired image format according to the spatial relationships in which the echo signals are received. For example, 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.

[0025] Multiplane reformer 132 is capable of converting echoes received from points in a common plane of a volumetric region of the body into an ultrasound image (e.g., a B-mode image) of that plane, as described, for example, in U.S. Patent US 6,443,896 (Detmer). The planar data from multiplane reformer 132 can be provided to volume plotter 134. Volume plotter 134 can generate an image (also referred to as a projection, plot, or drawing) of a 3D dataset viewed from a given reference point, as described, for example, in U.S. Patent US 6,530,885 (Entrekin et al.). Volume plotter 134 can generate the drawing using any known or future known techniques, such as surface plotting and maximum intensity plotting, such as positive or negative plotting.

[0026] 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 (i.e., grayscale) image data for display. Doppler processor 160 may be configured, for example, to use a wall filter to filter out unwanted signals (i.e., 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) estimate is based on the parameters of a hysteresis-autocorrelation function (e.g., ~arg[R1]) and the Doppler power estimate is based on the magnitude of a hysteresis-zero autocorrelation function (e.g., R0). The velocity estimate may be referred to as color Doppler data, and the power estimate may be referred to as power Doppler data.

[0027] 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 in place of the velocity estimator or as an addition to it.

[0028] In some examples, velocity and power estimates (e.g., color and power Doppler data) may undergo further thresholding to further reduce noise, as well as segmentation and post-processing such as filling and smoothing. The velocity and / or power estimates can then be mapped to a desired range of display colors and / or intensities based on one or more color and / or intensity maps. The map 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 to form a color Doppler or power Doppler image.

[0029] In some examples, color Doppler data may include a red-green-blue (RGB) color Doppler image (e.g., a velocity map). An RGB color Doppler image may include a luminance channel and a chrominance channel. The luminance channel may include data (e.g., luminance data) corresponding to the intensity (e.g., brightness) of pixels or voxels in the RGB color Doppler image. The chrominance channel may include data (e.g., hue data) corresponding to the tint (e.g., color) of pixels or voxels in the RGB color Doppler image.

[0030] According to examples of this disclosure, Doppler processor 160 can decompose (e.g., separate) color Doppler data, such as an RGB color Doppler image, into its luminance and chrominance channels. Doppler processor 160 can adjust (e.g., modify) the luminance data based at least in part on power Doppler data. In some examples, the power Doppler data can be used to generate an enhancement map, and in some examples, the enhancement map can include luminance (e.g., intensity) data. The enhancement map can be mixed with the luminance data of the color Doppler data to generate adjusted luminance data. Doppler processor 160 can recombine the chrominance and luminance channels (which now include the adjusted luminance data) to generate enhanced color Doppler data, which can be used to generate enhanced color Doppler images (e.g., RGB color Doppler images, such as velocity maps). In some examples, the enhanced color Doppler image may have improved vascular representation compared to the original color Doppler image. In some examples, because only the luminance data is changed and not the chrominance data, the quantitative information provided by the color Doppler data can be preserved.

[0031] The output from the scan converter 130, the multi-plane 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, buffering, and temporary storage before being displayed on the image display 138. In some examples, the Doppler image can be superimposed on a B-mode image of a tissue structure by the scan converter 130 and / or the image processor 136 for display.

[0032] Graphics processor 140 can generate graphic overlays for display alongside images. These overlays may include, for example, standard identification information such as patient name, date and time of image, imaging parameters, etc. For these purposes, graphics processor 140 can be configured to receive input from user interface 124, such as typed patient name or other annotations. User interface 124 may also be coupled to multiplane reformer 132 for selecting and controlling the display of multiple multiplane reformulated (MPR) images.

[0033] System 100 may include local memory 142. Local memory 142 may be implemented as any suitable non-transitory computer-readable medium (e.g., flash drive, disk drive). Local memory 142 may store data generated by system 100, including images, graphs, executable instructions, input provided by the user through user interface 124, or any other information required for the operation of system 100.

[0034] 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., blend ratio, saturation, gain level). 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.

[0035] 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, Doppler processor 160 and mode-of-effect processor 128 can be implemented as a single processor. In some examples, Figure 1 The various components shown can be implemented as individual components. For example, signal processor 126 can be implemented as a separate signal processor for each imaging mode (e.g., B-mode, Doppler). In some examples, Figure 1One or more of the various processors shown are implemented by general-purpose processors and / or microprocessors configured to perform specified tasks. In some examples, one or more of the various processors may be implemented as dedicated circuitry. 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).

[0036] Figure 2 This is a block diagram illustrating an example processor 200 according to the principles of this disclosure. Processor 200 can be used to implement one or more processors described herein, for example... Figure 2 The image processor 136 is shown. Processor 200 can be any suitable processor type, including but not limited to microprocessors, microcontrollers, digital signal processors (DSPs), field-programmable arrays (FPGAs) where the FPGA has been programmed to form a processor, graphics processing units (GPUs), application-specific integrated circuits (ASICs) where the ASIC is designed to form a processor, or combinations thereof.

[0037] Processor 200 may include one or more cores 202. Core 202 may include one or more arithmetic logic units (ALUs) 804. In some examples, in addition to or instead of ALU 204, core 202 may include a floating-point logic unit (FPLU) 206 and / or a digital signal processing unit (DPU) 208.

[0038] Processor 200 may include one or more registers 212 communicatively coupled to core 202. Registers 212 may be implemented using dedicated logic gates (e.g., flip-flops) and / or any memory technology. In some examples, registers 212 may be implemented using static memory. Registers may provide data, instructions, and addresses to core 202.

[0039] In some examples, processor 200 may include one or more levels of cache memory 210 communicatively coupled to core 202. Cache memory 210 may provide computer-readable instructions to core 202 for execution. Cache memory 210 may provide data for core 202 to process. In some examples, computer-readable instructions may already be provided to cache memory 210 by local memory (e.g., local memory attached to external bus 3216). Cache memory 210 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.

[0040] Processor 200 may include controller 214, 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 200 and / or inputs from the processor 200 to other processors and / or components included in the system (e.g., Figure 1 The outputs of the display 138 and volume plotter 134 shown are illustrated. The controller 214 can control the data paths in the ALU 204, FPLU 206, and / or DSPU 208. The controller 214 can be implemented as one or more state machines, data paths, and / or dedicated control logic. The gates of the controller 214 can be implemented as stand-alone gates, FPGAs, ASICs, or any other suitable technology.

[0041] Register 212 and cache 210 can communicate with controller 214 and core 202 via internal connections 220A, 220B, 220C and 220D. These internal connections can be implemented as buses, multiplexers, cross switches and / or any other suitable connection technology.

[0042] The processor 200's inputs and outputs can be provided via bus 216, which may include one or more wires. Bus 216 may be communicatively coupled to one or more components of the processor 200, such as controller 214, cache 210, and / or register 212. Bus 216 may also be coupled to one or more components of the system, such as the previously mentioned display 138 and control panel 152.

[0043] Bus 216 may be coupled to one or more external memories. The external memory may include read-only memory (ROM) 232. ROM 232 may be a mask ROM, electrically programmable read-only memory (EPROM), or any other suitable technology. The external memory may include random access memory (RAM) 233. RAM 233 may be static RAM, battery-backed static RAM, dynamic RAM (DRAM), or any other suitable technology. The external memory may include electrically erasable programmable read-only memory (EEPROM) 235. The external memory may include flash memory 234. The external memory may include a magnetic storage device, such as a disk 236. In some examples, the external memory may be included in the system, for example... Figure 1 The ultrasound imaging system 100 shown includes, for example, local memory 142.

[0044] Figure 3This is a functional block diagram of a portion of a Doppler processor 300 according to an example of this disclosure. In some examples, the Doppler processor 300 may be included within a Doppler processor 160. In some examples, color Doppler data 302 may be provided (e.g., from reference). Figure 1 The velocity estimator in another part of the described Doppler processor 300 generates the data. Color Doppler data 302 may include an RGB image (e.g., an RGB color Doppler image). In some examples, the RGB image may include a velocity map. In some examples, power Doppler data 304 may be provided (e.g., from a reference image). Figure 1 The power estimator is generated in another part of the described Doppler processor 300.

[0045] The Doppler processor 300 can decompose the color Doppler data 302 into its luminance channel (luminance data 306) and chrominance channel (chrominance data 308) components. In other words, the color Doppler data 302 is re-represented in a color space that separates image luminance from chrominance (e.g., hue). Various techniques for decomposing the Doppler data 302 can be used to obtain the luminance data 306 and chrominance data 308, such as YCbCr, HSV, or CIEL*A*B. For illustrative purposes, the YCbCr technique will be provided as an example. However, this disclosure is not limited to this technique.

[0046] YCbCr technology includes approximations of color processing and perceived uniformity, which can be estimated through the following linear transformation:

[0047]

[0048] Where Y is luminance (e.g., luminance information), Cb is blue chromaticity, Cr is red chromaticity, and R, G, and B are inputs to the original color Doppler data 302 (e.g., an RGB image). Y obtained from Equation 1 is luminance data 306, while CbCr obtained from Equation 1 is chromaticity data 308. Decomposing the color Doppler data 302 into luminance data 306 and chromaticity data 308 allows modification of luminance data 306 and / or chromaticity data 308 without affecting the other. That is, the data for the luminance and chromaticity channels can be adjusted independently.

[0049] Power Doppler data 304 can be used to generate an enhancement map 316. The enhancement map 316 may include luminance information (e.g., brightness), which in some examples may correspond to flow rate. That is, a brighter pixel may correspond to a larger volume of blood flowing at the location corresponding to that pixel than at the corresponding location to a darker pixel. In some examples, the power Doppler data 304 may be encoded into a dynamic range to generate the enhancement map 316. For example, the dynamic range may be [0,1], where the encoded luminance values ​​of the enhancement map 316 may have a minimum value of 0 and a maximum value of 1. In some examples, the noise floor may be set to 0, and max[R0] may be set to 1. In some examples, gamma compression may be applied. Prototype code for encoding and compressing the data is provided below:

[0050] normR0data=(R0-NoiseR0) / max(R0-NoiseR0);

[0051] normR0data(normR0data<0)=0; normR0data(normR0data>1)=1;

[0052] AugR0 = normR0data.^(gamma); % Gamma compression

[0053] Optionally, after encoding, the power Doppler data 304 (now AugR0) can be further processed to provide enhanced Doppler data 310, thereby generating an enhanced image 316. In some examples, advanced imaging filters (e.g., Xres, bilateral filters, etc.) can be applied to the power Doppler data 304 AugR0 to denoise speckle, improve edge sharpness, and / or enhance contrast.

[0054] In some examples, the principal curvature R0 of the power Doppler data 304 can be extracted from the power Doppler data 304 AugR0, which can improve the rendering of tubular structures such as blood vessels. In some examples, the Frangi vessel filter 312 can be applied to extract the principal curvature. In some examples, the principal curvature includes the second derivative calculated perpendicular to the vessel and is estimated based on the eigenvalues ​​of the Hessian matrix. Examples of such Frangi vessel filters can be found in the following articles: Frangi AF, Niessen WJ, Vincken KL, Viergever MA (1998) Multiscale vessel enhancement filtering; Wells WM; Colchester A., ​​Delp S. (eds) Medical Image Computing and Computer-Assisted Intervention—MICCAI'98. MICCAI 1998. Lecture Notes in Computer Science, vol. 1496. Springer, Berlin, Heidelberg. However, other tubular and / or morphological filters and / or techniques can be applied. For example, a filter designed to separate blood pool-like structures can be used instead of the Frangi blood vessel filter 312. In another example, a speckle despeculiarity filter, a bilateral filter, and / or a nonlocal mean filter can be used instead of the Frangi blood vessel filter.

[0055] In some examples, the output of the Frangi vascular filter 312 can be used to generate the enhanced image 316. In other examples, the output of the Frangi vascular filter 312 can be mixed by a combiner 314 with power Doppler data 304AugR0 (which can be the original encoded data or data processed by the additional filters described above). The mixing can be described by the following formula:

[0056] Aug = Aug R0 (1-α)+Aug Frangi Formula 2

[0057] Where Aug is the obtained enhancement map 316, AugR0 is the power Doppler data 304, Aug Frangi It is the output of the Frangi blood vessel filter 312, and α is the mixing factor.

[0058] The resulting enhancement map 316 can be combined with luminance data 306 by combiner 320 to generate adjusted luminance data 322. In some examples, combiner 320 can mix enhancement map 316 and luminance data 306 according to the following formula:

[0059] Y * =Y(1-β)+Aug(β+saturation) Formula 3

[0060] Where Y* is the adjusted luminance data 322, Y is the original luminance data 306, Aug is the enhancement map 316, and β is the blending factor. When β = 0, Equation 3 produces Y* from the original luminance data 306, and β = 1 allows the enhancement map 316 to have the greatest impact on the resulting adjusted luminance data 322. Optionally, a saturation bias, as indicated by "Saturation" in Equation 3, may be included. In some applications, a saturation bias can saturate the luminance to improve contrast. In some examples, the saturation bias may have a value from 0 to 1 (inclusive). As shown in Figure 3, in addition to or instead of the saturation bias in Equation 3, a saturation factor 318 (e.g., Cmap saturation) may be applied to the enhancement map 316 before being combined with the luminance data 306.

[0061] The adjusted luminance data 322 can be recombine with the original chrominance data 308 to generate enhanced color Doppler data 324. As previously described, the enhanced color Doppler data 324 may include a color (e.g., RGB) velocity map. The luminance and chrominance channels can be recombine by performing the inverse operation for channel decomposition. In the provided YCbCr example, the reciprocal of Equation 1 will be used to obtain the RGB data. In some examples, the enhanced color Doppler data 324 may be provided from the Doppler processor 300 to a scan converter (e.g., scan converter 130) and / or an image processor (e.g., image processor 136). The enhanced color Doppler data 324 can be used to generate a color Doppler image, such as a velocity color map, for display on a display (e.g., display 138). In some examples, the enhanced color Doppler image may be overlaid on a B-mode image.

[0062] In some examples, at least some of the inputs to Equations 1-3 can be defined by user input received through a user interface (e.g., user interface 124). For example, the degree of mixing performed by combiner 314 and / or combiner 320 can be determined by user input (e.g., β and / or α). In another example, saturation bias can be determined by user input. In some examples, whether and / or how to enhance power Doppler data can be based at least in part on user input (e.g., whether to use the Frangi vascular filter 312).

[0063] Figure 4 Example enhanced image 400, color Doppler velocity map 405, and enhanced color Doppler velocity map 410 are shown according to examples of this disclosure. These images are generated from ultrasound data acquired from human thyroid imaging. Enhancement image 400 is calculated based on R0 power Doppler data, which is obtained from a reference... Figure 3 The Frangi vascular filter enhancement is described above. The color Doppler velocity map 405 is generated from the raw color Doppler data (e.g., ~arg[R1]). The color Doppler data for the color Doppler velocity map 405 uses the data as referenced. Figure 3 The described YCbCr technique is decomposed into its luminance and chrominance channels. The luminance data is then adjusted by blending in enhancement map 400 using Equation 3, where β = 0.7 and saturation = 0.15. The adjusted luminance data is recombinated with the chrominance data to generate enhanced color Doppler data. The enhanced color Doppler data is shown as enhanced color Doppler velocity map 410. It can be seen, for example, in the circular region 415, that enhanced color Doppler velocity map 410 improves the rendering of the vascular system compared to the original color Doppler velocity map 405.

[0064] Figure 5 This is a flowchart of method 500 based on the principles of this disclosure. In some examples, at least a portion of method 500 may be performed by ultrasound imaging system 100 and / or Doppler processor 300.

[0065] In box 502, the action "Decompose color Doppler data into luminance and chrominance data" can be performed. In some examples, the color Doppler data can be decomposed using the YCbCr technique. In other examples, other techniques (e.g., HSV) can be used. In some examples, the decomposition can be performed by a Doppler processor such as Doppler processor 160 and / or Doppler processor 300.

[0066] In box 504, it is possible to perform "adjusting the luminance data based at least in part on the power Doppler data to generate adjusted luminance data".

[0067] In block 506, the process of "combining adjusted luminance and chrominance data to generate enhanced color Doppler data" can be performed. In some examples, the adjusted luminance and chrominance data can be combined using the inverse of the technique used at block 502. For example, the inverse technique of YCbCr can be used.

[0068] In some examples, prior to box 504, at box 508, the process of "generating an enhancement map based at least in part on power Doppler data" can be performed. In these examples, adjusting the luminance data may include combining the enhancement map and the luminance data. In some examples, combining the enhancement map and the luminance data may include blending the enhancement map and the luminance data based at least in part on a blending factor. In some examples, the blending factor may be determined by user input. In some embodiments, the user input may be received from a user interface, such as user interface 124. In some examples, combining the enhancement map and the luminance data may include applying a saturation bias.

[0069] In some examples, generating the enhancement map may include encoding the power Doppler data, for example, encoding the power Doppler data into a dynamic range of [0,1]. In some examples, generating the enhancement map may include applying gamma compression to the power Doppler data.

[0070] In some examples, prior to box 508, the "Enhance Power Doppler Data" function can be performed at box 510. In some examples, the power Doppler data can be enhanced by applying a filter to generate filtered power Doppler data. In some examples, the filter can be a Frangi vascular filter (e.g., Frangi vascular filter 312). In some examples, after box 510 and before box 508, the "Combining Power Doppler Data and Filtered Power Doppler Data to Generate Enhanced Power Doppler Data" function can be performed at box 512. In these examples, the enhanced power Doppler data can be used to generate an enhancement map.

[0071] In some examples, a velocity color map can be generated based on enhanced color Doppler data after box 506. In some examples, the velocity color map can be overlaid on a B-mode image. In some examples, these actions can be performed by a scan converter (e.g., scan converter 130) and / or an image processor (e.g., image processor 136). The velocity color map and / or B-mode image can be provided on a display, such as display 138 in some examples.

[0072] The system and method described herein can decompose color Doppler data into luminance and chrominance channels. The luminance channel data can be adjusted, at least in part, based on power Doppler data, while the chrominance data remains unchanged. The adjusted luminance channel data can be recombine with the unchanged chrominance channel data to provide enhanced color Doppler data. In some applications, enhanced color Doppler data can provide improved vessel mapping, such as in color velocity maps. In some applications, quantitative data on blood flow based on color Doppler data can be preserved.

[0073] 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.

[0074] 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 executable instructions.

[0075] 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.

[0076] 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.

[0077] 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 ultrasound imaging system, comprising: The processor is configured as follows: Analyze ultrasound signals to generate power Doppler data and color Doppler data; The enhancement map is generated at least in part based on the power Doppler data; The color Doppler data is separated into luminance data and chrominance data; The brightness data is adjusted at least in part based on the power Doppler data to generate adjusted brightness data, wherein adjusting the brightness data includes combining the enhancement map and the brightness data; and The adjusted luminance data and the chromaticity data are combined to generate enhanced color Doppler data.

2. The ultrasound imaging system according to claim 1, wherein, The processor is also configured to enhance the power Doppler data, at least in part, by applying a filter to the power Doppler data.

3. The ultrasound imaging system according to claim 2, wherein, The filter is a morphological filter.

4. The ultrasound imaging system of claim 1 further includes a user interface configured to receive user input, wherein, The user input at least partially defines how the brightness data is adjusted using the power Doppler data.

5. The ultrasound imaging system according to claim 4, wherein, The user input defines a mixing factor for mixing the luminance data and the power Doppler data to generate the adjusted luminance data.

6. An ultrasound imaging method, comprising: The color Doppler data is decomposed into luminance data and chrominance data; The enhancement map is generated at least in part based on power Doppler data; The brightness data is adjusted at least in part based on power Doppler data to generate adjusted brightness data, wherein adjusting the brightness data includes combining the enhancement map and the brightness data; and The adjusted luminance data and the chromaticity data are combined to generate enhanced color Doppler data.

7. The ultrasound imaging method according to claim 6, wherein, The color Doppler data is decomposed using a first technique, and the adjusted luminance data and chromaticity data are combined using the inverse of the first technique.

8. The ultrasound imaging method according to claim 7, wherein, Generating the enhancement map involves encoding the power Doppler data into a dynamic range.

9. The ultrasound imaging method according to claim 8, wherein, Generating the enhanced map involves applying gamma compression to the power Doppler data.

10. The ultrasound imaging method of claim 7 further includes enhancing the power Doppler data by applying a filter to generate filtered power Doppler data.

11. The ultrasound imaging method according to claim 10, wherein, The filter is a morphological filter.

12. The ultrasound imaging method of claim 10, further comprising combining the power Doppler data and the filtered power Doppler data to generate enhanced power Doppler data, wherein, The enhanced power Doppler data was used to generate the enhanced map.

13. The ultrasound imaging method according to claim 7, wherein, Combining the enhancement map and the brightness data includes at least partially based on a mixing factor to combine the enhancement map and the brightness data.

14. The ultrasound imaging method according to claim 13, wherein, The mixing factor is determined by user input received from the user interface.

15. The ultrasound imaging method according to claim 7, wherein, Combining the enhancement map and the brightness data also includes applying a saturation bias.

16. The ultrasound imaging method according to claim 6, further comprising: A velocity color map is generated based on the enhanced color Doppler data; and The velocity color map is overlaid on the B-mode image.

17. A non-transient computer-readable medium including instructions that, when executed, cause an ultrasound imaging system to perform the following operations: Analyze ultrasound signals to generate power Doppler data and color Doppler data; The enhancement map is generated at least in part based on the power Doppler data; The color Doppler data is separated into luminance data and chrominance data; The brightness data is adjusted at least in part based on the power Doppler data to generate adjusted brightness data, wherein, Adjusting the brightness data includes combining the enhancement map and the brightness data; and The adjusted luminance data and the chromaticity data are combined to generate enhanced color Doppler data.

18. The non-transient computer-readable medium of claim 17, further comprising instructions that, when executed, cause the ultrasound imaging system to perform the following operations: The power Doppler data is enhanced by applying at least one filter to the power Doppler data to generate enhanced power Doppler data; The enhancement map is generated at least in part based on the enhanced power Doppler data; and The enhancement map and the brightness data are combined to generate adjusted brightness data.

19. The non-transient computer-readable medium of claim 17, further comprising, when executed, instructions to cause the ultrasound imaging system to perform the following operation: generate and display a velocity color map based on the enhanced color Doppler data.

Images