阅读说明：本技术 具有改进的动态范围控件的超声成像系统 (Ultrasound imaging system with improved dynamic range control ) 是由 F·G·G·M·维尼翁 D·W·克拉克 D·P·亚当斯 D·普拉特 A·佩里亚帕特纳纳根德拉 于 2019-07-19 设计创作，主要内容包括：一种超声成像系统具有改进的动态范围控件,该动态范围控件使得用户能够减少图像雾,而对图像中的明亮组织几乎没有影响,并且也不会增大散斑变化。动态范围处理器处理输入图像以产生近似图像,该近似图像是输入图像的空间低通滤波版本。对近似图像的动态范围进行压缩,并且重新添加不受压缩影响的图像细节以产生经动态压缩的图像以供显示。(An ultrasound imaging system has an improved dynamic range control that enables a user to reduce image fog with little effect on bright tissue in the image and without increasing speckle variation. The dynamic range processor processes the input image to generate an approximation image that is a spatially low pass filtered version of the input image. The dynamic range of the approximate image is compressed, and image details unaffected by the compression are re-added to produce a dynamically compressed image for display.)

1. An ultrasound imaging system with image dynamic range control, comprising:

a transducer array probe;

a beamformer coupled to the probe;

an image processor coupled to the beamformer and configured to produce an ultrasound image;

a dynamic range processor coupled to the image processor and adapted to generate an approximation image in response to an input image, to apply dynamic range compression to the approximation image and to add image details to the dynamic range compressed approximation image; and

a display coupled to the dynamic range processor and adapted to display a dynamic range compressed approximation image with added image detail.

2. The ultrasound imaging system of claim 1, wherein the approximation image further comprises a spatial low-pass version of the input image.

3. The ultrasound imaging system of claim 1, wherein the dynamic range processor is further adapted to one or more of: converting the approximate low image value range to a black value or converting the approximate high image value range to a white value.

4. The ultrasound imaging system of claim 1, wherein the image details further comprise a difference between the input image and the approximate image.

5. The ultrasound imaging system of claim 3, further comprising a user control coupled to the dynamic range processor.

6. The ultrasound imaging system of claim 5, wherein the user control is further adapted to define a low range of input values to be converted to black values.

7. The ultrasound imaging system of claim 6, wherein the user control is further adapted to simultaneously define a high range of input values to be converted to white values.

8. The ultrasound imaging system of claim 5, wherein the user control is further adapted to define a high range of input values to be converted to white values.

9. The ultrasound imaging system of claim 2, wherein the dynamic range processor is further adapted to generate an approximate image value from a plurality of input image values.

10. The ultrasound imaging system of claim 9, wherein the dynamic range processor is further adapted to spatially low pass filter the input image values with spatially adjacent image input values.

11. The ultrasound imaging system of claim 1, wherein the image details added to the dynamic range compressed approximation image further include speckle dynamic range information.

12. The ultrasound imaging system of claim 11, wherein the image details added to the dynamic range compressed approximation image further comprise image details weighted by a speckle dynamic range term.

13. The ultrasound imaging system of claim 12, further comprising: a user interface control coupled to the dynamic range processor and adapted to enable a user to change the speckle dynamic range term.

14. The ultrasound imaging system of claim 1, further comprising: a user control adapted to weight the image detail to be added to the dynamic range compressed approximation.

15. The ultrasound imaging system of claim 14, wherein the user control is further adapted to smooth the image produced by the display.

Technical Field

The present invention relates to ultrasound imaging systems, and in particular to ultrasound imaging systems having dynamic range controls for adjusting fog removal and tissue filling without affecting the dynamic range of image speckle.

Background

Ultrasound imaging systems typically have two types of user controls for adjusting the appearance of an image: an image gain control and an image dynamic range control. These controls are typically adjusted to approve the image appearance when the user is viewing the ultrasound image of the region of interest. For example, a user may be viewing an image of the heart of a heart chamber containing a blood pool. If the gain is set too high, the blood pool is often obscured by the mist on the heart chamber. The user can reduce or eliminate the fog by adjusting the gain down so that the resulting heart chamber has the desired solid black shading. While gain reduction does not generally affect image contrast, adjusting the gain downward will darken the white shade of tissue, which can reduce the clarity and resolution of the tissue in the image.

The dynamic range control can also be adjusted to reduce unwanted fog. By adjusting down or reducing the dynamic range of the displayed image, low levels of haze can be attenuated, and as the image contrast increases, the bright white level of the tissue is substantially unaffected. The contrast to noise ratio does not change because the change in speckle (the range of different shades of speckle in the image) increases with increasing contrast.

Disclosure of Invention

Aspects of the present invention provide a user control that reduces unwanted image fog while not affecting the shade of bright white tissue, and both without detrimentally increasing speckle variation.

In accordance with the principles of the present invention, a user gain control is provided that reduces image fog with little effect on the appearance of bright tissue in the image and improves contrast without increasing speckle variation. In a preferred embodiment, this is done by a dynamic range processor that processes the input image values to produce an approximation image of the low spatial frequency of the tissue structure in the image. The difference between the input image and the approximation image contains image details, which preferably contain the speckle characteristics of the image. Dynamic range compression is then applied to the approximation image and not to the image details, which are then recombined with the dynamic range adjusted approximation image to produce an image to be displayed. While dynamic range compression can be accomplished with values preselected or optimized by the imaging system, it is preferable to provide user controls that enable a user to adjust the dynamic range processing to produce an image having a user-preferred appearance.

It should be understood that one or more processors (whether the same processor or different processors) may be used to run processing such as a beamformer, dynamic range processor, image processor.

Drawings

In the drawings:

figure 1 illustrates, in block diagram form, an ultrasound system configured in accordance with the principles of the present invention.

FIG. 2 illustrates a nine element kernel that may be used to generate an approximation image in accordance with the present invention.

Fig. 3 illustrates a dynamic range curve for an imaging system constructed in accordance with the present invention.

Fig. 4 illustrates a second dynamic range curve for an imaging system constructed in accordance with the present invention that stabilizes mid-range gray values during dynamic range adjustment.

Fig. 5 is a cardiac ultrasound image with unprocessed image speckle and no dynamic range adjustment.

Fig. 6 is the same cardiac ultrasound image as fig. 5 after dynamic range adjustment by the ultrasound imaging system of the present invention.

Detailed Description

Referring initially to FIG. 1, an ultrasound imaging system constructed in accordance with the principles of the present invention is shown in block diagram form. A transducer array 12 is provided in the ultrasound probe 10 for transmitting ultrasound waves and receiving echo information. The transducer array 12 may be a one-dimensional array or a two-dimensional array of transducer elements capable of scanning in two or three dimensions, such as in both elevation (in 3D) and azimuth. The transducer array 12 is coupled to an optional microbeamformer 14 in the probe, which microbeamformer 14 controls the transmission and reception of signals by the array elements. The microbeamformer is capable of at least partially beamforming signals received by groups or "tiles" of transducer elements, as described in U.S. patents US 5997479(Savord et al), US 6013032(Savord) and US 6623432(Powers et al). The microbeamformer is coupled by the probe cable to a transmit/receive (T/R) switch 16, which T/R switch 16 switches between transmit and receive and protects the main beamformer 20 from high energy transmit signals. The transmission of ultrasound beams from the transducer array 12 under the control of the microbeamformer 14 is directed by a beamformer controller 18 coupled to the T/R switches and the main beamformer 20, the beamformer controller 18 receiving input from the user's operation of a user interface or control panel 38. The transmission characteristics controlled by the transmission controller include the number, spacing, amplitude, phase, frequency, polarity, and diversity of the transmission waveforms. The beam formed in the pulse transmit direction may be steered straight forward from the transducer array or steered at different angles on either side of the non-steered beam to obtain a wider sector field of view. For some applications, unfocused plane waves may be used for transmission. Most one-dimensional array probes having a relatively short array length (e.g., 128 element array) do not use a microbeamformer, but rather are driven from and respond directly to the main beamformer.

Echoes received by a set of adjacent transducer elements are beamformed by being appropriately delayed and then combined. The partially beamformed signals produced from each tile by the microbeamformer 14 are coupled to a main beamformer 20 where the partially beamformed signals from the individual tiles of transducer elements are combined into fully beamformed coherent echo signals or the echo signals from the elements of the one-dimensional array are delayed and combined without the microbeamformer. For example, the main beamformer 20 may have 128 channels, each of which receives partially beamformed signals from a tile of 12 transducer elements or partially beamformed signals from individual elements. In this way, signals received by over 1500 transducer elements of a two-dimensional array transducer can effectively contribute to a single beamformed signal, and the signals received from the image plane are combined.

The signal processor 26 performs signal processing on the coherent echo signals, including filtering by digital filters and optionally noise reduction by spatial or frequency compounding. The filtered echo signals are coupled to a quadrature bandpass filter (QBP) 28. The QBP performs three functions: limiting the frequency band of the RF echo signal data, producing in-phase and quadrature pairs (I and Q) of echo signal data, and decimating the digital sample rate. The QBP comprises two separate filters, one producing in-phase samples and the other producing quadrature samples, wherein each filter is formed by a plurality of multiply-accumulators (MACs) implementing FIR filters. The signal processor can also shift the frequency band to a lower or baseband frequency range, as the QBP can do.

The beamformed and processed coherent echo signals are coupled to an image processor (B mode processor 30 which produces B mode images of in vivo structures such as tissue). B mode processor by computation (I)²+Q²)^1/2To perform amplitude (envelope) detection of quadrature demodulated I and Q signal components. The quadrature echo signal components are also coupled to a doppler processor 34. The Doppler processor 34 stores the data from the image fieldAn aggregate set of echo signals at discrete points, which is then used to estimate the doppler shift at a point in the image using a Fast Fourier Transform (FFT) processor. The rate at which the aggregate is acquired determines the range of speeds at which the system can accurately measure and delineate motion in the image. The doppler shift is proportional to the motion (e.g., blood flow and tissue motion) at a point in the image field. For color doppler images, the estimated doppler flow values at each point in the blood vessel will be wall filtered and converted to color values using a look-up table. The wall filter has an adjustable cutoff frequency above or below which motion (e.g., low frequency motion of the vessel wall) will be rejected when imaging flowing blood. The B-mode image or the doppler image can be displayed separately or together in an anatomical registration in which a color doppler overlay shows the flow of blood in tissue and blood vessels in the tissue structure of the B-mode image.

The image data produced by the B-mode processor 30 and the doppler processor 34 are coupled to an image data memory 33, in which image data memory 33 the image data is stored in memory locations addressable according to the spatial location at which the image values were acquired. In accordance with the principles of the present invention, the sequential B-mode images are coupled to a dynamic range processor 40, which dynamic range processor 40 processes each image for dynamic range control as described below. The dynamic range adjusted image is coupled back to the image memory from where it is coupled to the display processor 36 for further enhancement, buffering, and temporary storage for display on the image display 22.

In the embodiment shown in fig. 1, the dynamic range processor 40 is an image processor that provides two functions: it computes an approximation image, which is preferably a spatially low-pass filtered or speckle-reduced version of the input image received from the image memory; and it performs dynamic range compression on the approximation image, after which image details not present in the approximation image are recombined into the approximation image. A simple space suitable for use with embodiments of the present invention is conceptually illustrated in FIG. 2A low pass filter. The diagram shows a nine-element kernel for spatially processing nine contiguous image elements of the input image I. By weighting and summing the eight surrounding image values and the central image value of the input image I, at the central position I of the kernel_AThe image values for the approximation image a are calculated. Mathematically, this process can be expressed as:

that is, byEach of the nine image elements of the kernel is weighted and then the weighted values of the kernel are summed to calculate an approximate image value. Therefore, the approximation image will contain mainly low spatial frequency structure information of the image. Although this simple example operates on only a small partial image area of nine image elements, the preferred kernel size will typically be significantly larger, preferably large enough to encompass input image elements that encompass a wide (and preferably complete) range of speckle values. Thus, the speckle range can be included in an image "detail" value D, which can be calculated as the difference between the input image I and the approximate image A at each image element position, or

D＝I-A [2]

In conventional dynamic range compression, image values below a low luminance level M are mapped to full black, and image values above a high luminance level M are mapped to full white, or

(I-m)/(M-m) [3]

In an embodiment of the invention, dynamic range compression is applied to the approximation image a, but not to the image detail D. The algorithm to perform dynamic range compression in this way is:

where SDR is the speckle dynamic range. The algorithm takes the form of a mapping expression that maps an input image of a value of arbitrary bit length to an 8-bit range of 0 to 255 bits of standard display word length. The operation of the algorithm is to map all low level values of the approximate image a below level M to black and all high level values of the approximate image above level M to white. The speckle detail D of the original image, determined as shown in expression [2] above, is added back to the dynamic range adjusted approximate image weighted by the speckle dynamic range SDR, thus preserving the original speckle variation. In a constructed implementation, the SDR will typically be determined by an optimization process that first sets the SDR to the dynamic range of the input image. With the increase of SDR, speckle variation is reduced to realize image smoothing processing. However, such image smoothing will typically come at the expense of reducing image resolution, since detail D, while ideally containing only speckle, will actually also include some structural image detail.

The imaging system can use pre-selected values for the variables to automatically perform the above dynamic range processing, preferably by optimization when processing a greater number of images. Preferably, however, the dynamic range processor 40 is controlled by user input from the user interface 38, either in combination with or as an alternative to conventional gain and dynamic range controls. For many users, the ideal set of controls would be those that darken the chamber (reduce fog) and fill the tissue (lighten the tissue structure). The dynamic range processor described above can achieve this by providing the user with two controls: one control adjusts the M value for the dynamic range processor, while the other adjusts the M value. As the user adjusts the "m" control to increase the value of m for the processor, low levels of fog in the image will become increasingly darker. Unlike conventional gain adjustments, as the user adjusts the "M" control to reduce the value of M, the high echo generating tissue will become increasingly white and have little effect on the fog level. Alternatively, both variables can be changed by a single control that increases M while decreasing M; the haze is reduced while the tissue is brightened. The effect of these individual or combination controls is illustrated graphically in fig. 3. Without dynamic range compression, the compression curves 50-52-54 would be a continuous line from zero at the lower left corner to 255 at the upper right corner (all white in the 8-bit nomenclature). When the user control increases m (as shown by curve segment 50), all image values from zero to m will be set to zero (all black), thereby reducing the appearance of low level fog in the image. When the user control decreases by M (as shown by curve segment 54), all image values from M to 255 will be set to full white (255). As shown by curve segment 52, dynamic range compression is achieved.

Fig. 5 and 6 show ultrasound images illustrating these combined effects. Figure 5 shows a standard cardiac ultrasound image without compensation for fog and brightness. Fig. 6 shows the same cardiac image with compensation for both. It can be seen that the haze in the heart chamber in figure 6 has been significantly reduced and does not substantially reduce the brightness of the myocardial tissue surrounding the heart chamber.

The combined effect of embodiments of the present invention is summarized in the following table in comparison with the effect of standard gain and dynamic range controls:

in many ultrasound examinations, mid-gray shades of B-mode images are clinically important. For example, myocardial tissue in cardiac images typically appears in medium gray shades. Therefore, it is often important to preserve the characteristics and appearance of medium gray tones in an image. Fig. 4 illustrates a compression curve that achieves this by anchoring a medium gray value near point G in the compression curve. As m increases to reduce low level haze, the mid-gray shade remains fixed near mid-gray level 127 of the 8-bit scale. Thus, as m increases to reduce heart chamber fog, the appearance of the myocardial tissue in the image will remain unchanged. This variation of the control, which links the control of G with M, has proven particularly effective. As M decreases to increase the brightness of the image, the proportion of G also decreases. This preserves the appearance of myocardial tissue relative to the appearance of the tissue generated by the strongest echoes in the image.

Other variations of the above-described dynamic range control of the present invention will be readily apparent to those skilled in the art. For example, a user interface control that controls M for brightness can also be used to control the value of the speckle dynamic range SDR for detail D. When the control is adjusted to decrease M and thus increase the overall tissue intensity in the image, SDR will increase simultaneously, which has the effect of reducing speckle variation and smoothing the image as the tissue intensity increases.

It should be noted that ultrasound systems (and in particular the component structure of the ultrasound system of figure 1) suitable for use with embodiments of the present invention may be implemented in hardware, software, or a combination thereof. The various embodiments and/or components of the ultrasound system, or components and controls therein, may also be implemented as part of one or more computers or microprocessors. The computer or processor may include, for example, a computing device for accessing the internet, an input device, a display unit, and an interface. The computer or processor may comprise a microprocessor. The microprocessor may be connected to a communication bus to, for example, access a PACS system or a data network to import training images. The computer or processor may also include memory. The memory devices (e.g., image memory 32) may include Random Access Memory (RAM) and Read Only Memory (ROM). The computer or processor may also include a storage device, which may be a hard disk drive or a removable storage drive (e.g., a floppy disk drive, an optical disk drive, a solid state thumb drive, etc.). The storage device may also be other similar means for loading computer programs or other instructions into the computer or processor.

The terms "computer" or "module" or "processor" or "workstation" as used herein may include any processor-based or microprocessor-based system, including systems using microcontrollers, Reduced Instruction Set Computers (RISC), ASICs, logic circuits, and any other circuit or processor capable of executing the functions described herein. The above examples are exemplary only, and thus are not intended to limit the definition and/or meaning of these terms in any way.

The computer or processor executes a set of instructions stored in one or more storage elements in order to process input data. The storage elements may also store data or other information as desired. The storage elements may be in the form of information sources or physical storage elements within the processing machine.

The set of instructions of the ultrasound system, including those instructions that control the acquisition, processing, and display of ultrasound images as described above, may include various commands that instruct the computer or processor as a processing machine to perform specific operations such as the methods and processes of the various embodiments of the present invention. The set of instructions may be in the form of a software program. The software may be in various forms (e.g., system software or application software) and may be embodied as tangible and non-transitory computer readable media. Additionally, the software may be in the form of a collection of separate programs or modules (e.g., a transmission control module), a program module or portion of a program module within a larger program. The software may also include modular programming in the form of object-oriented programming. The processing of input data by a processing machine may be in response to operator commands, or in response to results of previous processing, or in response to a request made by another processing machine.

Furthermore, the limitations of the claims are not written in the form of functional modules and are not intended to be interpreted based on the sixth paragraph of 35 u.s.c 112. Unless such claim limitations expressly use the phrase "a unit for … …" to recite a function without further structure.