Systems and methods for visualizing and quantitative imaging - Patents.com

Abstract

Disclosed herein are systems and methods for providing data for visualization and data for quantification. The data for visualization is used to generate an image for presentation to a user on a display. The data for quantification is used to calculate various physiologically relevant parameters, such as a Hepatorenal Index (HRI) value. In some examples, the quantification data may not be used to generate an image. In some examples, a user selects regions of interest (ROIs) in an image generated from the visualization data, and corresponding quantification data of the ROIs is used to calculate one or more parameters. The visualization data and quantification data, in some examples, are generated from different imaging modes or the same imaging mode with different data processing.

Description

Technical Field

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[0003]

[0001] This application relates to providing imaging data for visualization purposes and imaging data for quantification purposes. More specifically, this application relates to providing ultrasonic data for generating images for display and providing ultrasonic data for calculating parameters such as physiologically relevant indicators.

Background Art

[0002] Non-alcoholic fatty liver disease (NAFLD) has become one of the main causes of liver diseases due to the high morbidity of obesity and diabetes. Its incidence has been steadily increasing, affecting approximately 25% - 30% of the population in Western countries and developing countries. The clinical term for fatty liver is hepatic steatosis, which is defined as the excessive accumulation of fat (from more than 5% to up to 10% by weight) in hepatocytes as neutral fat. The initial stage of hepatic steatosis is reversible by simple lifestyle changes, such as regular exercise and a healthy diet. Hepatic steatosis may turn into more advanced liver diseases such as non-alcoholic steatohepatitis (NASH) and liver fibrosis. If left untreated at these stages, fatty liver will progress to end-stage diseases including cirrhosis and primary hepatocellular carcinoma.

[0003] In current clinical practice, the gold standard for fatty liver diagnosis is liver biopsy, which is an invasive procedure subject to sampling error and variability in interpretation. Magnetic resonance proton density fat fraction (MR-PDFF) is considered a new reference standard for NAFLD diagnosis because it can provide a quantitative biomarker for liver fat content. However, MR-PDFF is an expensive diagnostic tool and is not always available in all hospitals. Compared with MR, ultrasound is a widely available and cost-effective imaging modality and is more suitable for examinations of the general population and low-risk groups.

Summary of the Invention

Problems to be Solved by the Invention

[0004] The hepatorenal index (HRI), an ultrasound-based method, has been clinically used to detect fatty liver. Excessive fatty infiltration in the liver increases the acoustic backscatter coefficient, resulting in higher grayscale values ​​in ultrasound B-mode imaging. Under normal conditions, the liver parenchyma and renal cortex have similar echogenicity. With significant fatty deposition, the liver appears more echogenic (i.e., brighter) than the renal cortex. HRI is often calculated as the ratio of the liver's echo intensity to that of the kidney. Based on B-mode data, the echo intensities from the liver and kidney are estimated by selecting regions of interest (ROIs) within the liver parenchyma and renal cortex at similar depths and then averaging the grayscale echo intensity values ​​in the ROIs. However, HRI has reliability issues that limit its applicability, mainly due to the nonlinear relationship between the displayed intensity (logarithmically scaled or grayscale) and the true in-situ echo intensity. Furthermore, the less-than-ideal selection of ROIs can also affect the reliability and reproducibility of HRI. Therefore, improved techniques for obtaining HRI are desired. [Means for solving the problem]

[0005] Systems and methods for providing medical imaging data for visualization and quantification are disclosed. In some examples, medical imaging data for visualization is acquired and / or processed to provide the user with images having high image quality (e.g., low signal-to-noise ratio, high contrast, artifact reduction or none). The user selects regions of interest (ROIs) from the images via a user interface. Medical imaging data for quantification is acquired and / or processed to provide data that provides more accurate, reliable, and / or quantifiable results for one or more calculations. In some examples, medical imaging data is acquired and / or processed in a manner that does not introduce unknown relationships (e.g., nonlinearity) between signal intensity and the resulting medical imaging data. In some examples, medical imaging data for visualization is registered to medical imaging data for quantification. Thus, when the user selects an ROI in an image generated from medical imaging data for visualization, the medical imaging data for quantification corresponding to the ROI is used to calculate parameters such as physiological indices, e.g., hepatorenal indices.

[0006] According to at least one example disclosed herein, an ultrasonic imaging system includes: an ultrasonic probe configured to transmit ultrasonic signals, receive echoes in response to ultrasonic signals, and provide radio frequency (RF) data corresponding to the echoes; a display configured to display an image; a user interface configured to receive indications of a first region of interest (ROI) in the image; and a processor configured to receive RF data and further configured to generate visualization data from at least a first portion of the RF data, the image, at least partially based on the visualization data, and quantification data from at least a second portion of the RF data, the ROI indication, and to calculate physiological parameters at least partially based on a portion of the quantification data associated with the ROI.

[0007] According to at least one example disclosed herein, the method includes receiving radio frequency (RF) data corresponding to echoes generated in response to ultrasonic signals transmitted by an ultrasonic transducer array; generating visualization data from a first portion of the RF data acquired by a first imaging mode; generating quantification data from a second portion of the RF data acquired by a second imaging mode; generating an image based at least partially on the visualization data; receiving indication of a region of interest (ROI) in the image from a user interface; and calculating physiological parameters based at least partially on a portion of the quantification data associated with the ROI.

[0008] According to at least one example disclosed herein, the method includes receiving radio frequency (RF) data corresponding to echoes generated in response to ultrasonic signals transmitted by an ultrasonic transducer array; generating visualization data from the RF data by processing the RF data by at least one nonlinear processing method; generating quantification data from the RF data by processing the RF data by at least one linear processing method; generating an image based at least partially on the visualization data; receiving indication of a region of interest (ROI) in the image from a user interface; and calculating physiological parameters based at least partially on a portion of the quantification data associated with the ROI. [Brief explanation of the drawing]

[0009] [Figure 1] This is a block diagram of an ultrasonic imaging system arranged in accordance with the principles of this disclosure. [Figure 2] A list of exemplary processors according to the principles of this disclosure. [Figure 3] This is a graph illustrating a data processing path according to one embodiment of the present disclosure. [Figure 4] This is a graph illustrating a data processing path according to one embodiment of the present disclosure. [Figure 5] The following are example images generated from visualization and quantification data based on the principles of this disclosure. [Figure 6] This disclosure shows example images generated from visualization data and quantification data on a display device based on the principles of this disclosure. [Figure 7] This is a flowchart of the method based on the principles of this disclosure. [Modes for carrying out the invention]

[0010] The following description of some exemplary embodiments is purely illustrative and is not intended to limit the disclosure, application, or use. The following detailed description of embodiments of the System and Methods refers to the accompanying drawings, which form part of this Specification and illustrate specific examples of the System and Methods described herein. These embodiments are described in sufficient detail to enable those skilled in the art to implement the System and Methods of this Disclosure, and it should be understood that other embodiments may be utilized, and that structural and logical modifications may be made without departing from the spirit and scope of this Disclosure. Furthermore, for clarity, detailed descriptions of some features are omitted if they are 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 constrained, and the scope of the System and Methods is defined solely by the accompanying claims.

[0011] The hepatic-renal index (HRI) is typically acquired based on the pixel intensity of B-mode images displayed on an ultrasound imaging system. HRI can be a powerful diagnostic indicator of fatty liver disease when measured using a clearly defined grayscale imaging mode with optimal instrument settings such as gain, time-gain control (TGC), dynamic range, and display map (ideally, a linear transformation from a decibel scale of echo intensity to a grayscale). The reliability of HRI can be affected by the highly nonlinear relationship between a logarithmically scaled (or often more complex) and further threshold-limited grayscale display (0-255) and the true in-situ echo intensity of B-mode image data. Therefore, a specially designed imaging mode and / or processing method is desirable for calculating HRI. For example, HRI can be calculated using basic imaging, analysis of radio frequency (RF) data, and / or appropriately linearized imaging data.

[0012] Another issue affecting the reliability of HRI is the inappropriate selection of regions of interest (ROIs) in the liver and / or kidneys. ROIs are preferably located in the liver and kidneys at positions with high homogeneity (e.g., few or no brighter or darker pixels) and an unobstructed acoustic path (e.g., no beam distortion or shadows). Therefore, imaging modes and / or processing methods that provide high-quality images (e.g., low artifacts, high signal-to-noise ratio) are desirable to provide users with enhanced visualization and facilitate the appropriate selection of ROIs. For example, harmonic imaging and / or nonlinear processing may be performed to provide high-quality images.

[0013] However, imaging modes and / or processing that provide improved visualization for users to select ROIs may introduce nonlinearity or unknown relationships between ultrasound signals, making HRI calculation impossible or unreliable. On the other hand, imaging modes and / or processing that provide improved quantification of HRI may produce images that are difficult for users to interpret, leading to less-than-ideal selection of ROIs.

[0014] This disclosure relates to a system and method for generating separate sets of medical imaging data, one set for visualization and one set for quantification. Medical imaging data for visualization is generated by an imaging mode and / or processing for producing high-quality images that allow the user to easily interpret the images. Medical imaging data for quantification is generated by an imaging mode and / or processing for generating medical imaging data that has a known relationship (e.g., linear) with respect to signals received from a subject (e.g., echoes responding to ultrasound signals). In some examples, the same imaging mode is employed to generate one set of medical imaging data for visualization and another set of medical imaging data for quantification, but the medical imaging data undergo different processing steps. In some examples, different imaging modes are utilized by alternately superimposing sequences for generating, for example, two different sets of medical imaging data for visualization and quantification, using the same or different processing steps. Medical imaging data for visualization is used to provide images to the user, for example, to select ROIs, and medical imaging data for quantification corresponding to the locations indicated by the ROIs is analyzed to determine parameters such as HRI. Thus, in some applications, the selection of ROIs is improved while the reliability of HRI is maintained or improved.

[0015] Figure 1 shows a block diagram of an ultrasound imaging system 100 constructed according to the principles of the present disclosure. The ultrasound imaging system 100 according to the present disclosure includes a transducer array 114, which is contained within an ultrasound probe 112. In other examples, the transducer array 114 is in the form of a flexible array configured to be applied isometrically to the surface of the subject being imaged (e.g., a patient). The transducer array 114 is configured to transmit an ultrasound signal (e.g., a waveform) and to receive echoes (e.g., reflected and / or backscattered ultrasound signals) in response to ultrasound signals (e.g., beams) transmitted in various directions. Various transducer arrays, such as linear arrays, curved arrays, or phase arrays, are used. The transducer array 114 may include, for example, a two-dimensional array of transducer elements that can be scanned in both elevation and azimuth dimensions for 2D and / or 3D imaging (as shown). As is generally known, the axial direction is perpendicular to the array's plane (in the case of a curved array, the axial direction spreads out in a sector shape), the azimuth direction is generally defined by the longitudinal dimension of the array, and the elevation direction is transverse to the azimuth direction.

[0016] In some examples, the transducer array 114 is coupled to a microbeam former 116, which is located on an ultrasonic probe 112 and controls the transmission and reception of signals by the transducer elements in the array 114. In some examples, the microbeam former 116 controls the transmission and reception of signals by active elements in the array 114 (e.g., an active subset of elements of the array that define an active aperture at any given time).

[0017] In some examples, the microbeamformer 116 is coupled, for example, by a probe cable or wirelessly, to a transmit / receive (T / R) switch 118, which switches between transmit and receive, protecting the main beamformer 122 from high-energy transmit signals. In some examples, for example, in a portable ultrasound system, the T / R switch 118 and other elements in the system may be included in the ultrasound probe 112 rather than in an ultrasound system base housing image processing electronics. The ultrasound system base typically includes software and hardware components, including circuits for signal processing and image data generation, as well as executable instructions to provide a user interface.

[0018] The transmission of ultrasonic signals from the transducer array 114, under the control of the microbeamformer 116, is directed by the transmit controller 120, which is coupled to the T / R switch 118 and the main beamformer 122. The transmit controller 120 controls the characteristics of the ultrasonic signal waveform transmitted by the transducer array 114, such as amplitude, phase, and / or polarity. The transmit controller 120 controls the direction in which the beam is directed. The beam is directed straight forward (orthogonally) from the transducer array 114, or at different angles for a wider field of view. The transmit controller 120 is also coupled to the user interface 124 and receives input from user-controlled user operations. For example, the user selects whether the transmit controller 120 will cause the transducer array 114 to operate in harmonic imaging mode, basic imaging mode, Doppler imaging mode, or a combination of imaging modes (e.g., alternating different imaging modes). The user interface 124 includes one or more input devices, such as a control panel 152, the control panel including one or more mechanical controls (e.g., buttons, encoders, etc.), touch-sensitive controls (e.g., trackpads, touchscreens, etc.), and / or other known input devices.

[0019] In some examples, the partially beamformed signal created by the microbeamformer 116 is coupled to a main beamformer 122 where the partially beamformed signals from the individual patches of transducer elements are combined into a fully beamformed signal. In some examples, the microbeamformer 116 is omitted and the transducer array 114 is under the control of the beamformer 122, and the beamformer 122 performs all beamforming of the signals. With or without the microbeamformer 116, in an example, the beamformed signal of the beamformer 122 is coupled to a processing circuit 150, which is configured with one or more processors (e.g., signal processor 126, B-mode processor 128, RF image processor 144, parameter calculator 146, and one or more image generation and processing components 168) to create an ultrasonic image (e.g., beamformed RF data) from the beamformed signal.

[0020] The signal processor 126 receives the beamformed RF data and processes the received beamformed RF data in various ways such as band-pass filtering, decimation, and separation of I and Q components. The signal processor 126 also performs additional signal enhancement such as speckle reduction, signal combining, and electronic noise removal. The processing of the beamformed RF data performed by the signal processor 126 varies at least in part based on whether visualization data (e.g., medical imaging data for visualization) and / or quantification data (e.g., medical imaging data for quantification) is desired. As shown in FIG. 1, the signal processor 126 includes a visualization data processor 170 that generates visualization data and a quantification data processor 172 that generates quantification data by processing the beamformed RF data.

[0021] The visualization data processor 170 processes the beamformed RF data to generate visualization data that is used to generate an image with enhanced quality (e.g., improved signal-to-noise ratio, reduced artifacts, improved contrast) compared to the raw image data. In some embodiments, the visualization data processor 170 may, in some cases, perform processing operations (e.g., non-linear operations) that cause the linear and / or known relationship with the echoes directly received by the transducer array 114 to be lost in the visualization data. Examples of processing operations that change the relationship with the echoes include, but are not limited to, adjustment of the dynamic range of pixel values, smoothing algorithms, and / or reverberation removal algorithms. In some examples, imaging processing techniques such as speckle smoothing / reduction and / or edge enhancement are used so that homogeneous portions are clearly visualized and segmented from defects (e.g., blood vessels and small cavities) in the liver parenchyma and renal cortex.

[0022] [[ID={4}]]The quantification data processor 172 processes the beamformed RF data to generate quantification data for calculating parameters including physiologically relevant parameters such as HRI. The quantification data processor 172 processes the beamformed RF data such that the relationship between the echo signal received by the transducer array 114 and the quantification data is linearly proportional and / or known. In some embodiments, the relationship between the echo signal and the quantification data is linear. Examples of processing operations that maintain the relationship include, but are not limited to, fixed gain and fixed time gain compensation.

[0023] In some embodiments, the beamformed RF data provided to the visualization data processor 170 is the same as the beamformed RF data provided to the quantification data processor 172. In these embodiments, there are two data paths for processing the beamformed RF data, and the processing of the beamformed RF data by the visualization data processor 170 is different from the processing of the beamformed RF data by the quantification data processor 172.

[0024] In some embodiments, beamforming RF data provided to the visualization data processor 170 differs from beamforming RF data provided to the quantification data processor 172. For example, beamforming RF data acquired by one imaging mode is provided to the visualization data processor 170, and data acquired by another imaging mode is provided to the quantification data processor 172. Different imaging modes cause the transducer array 114 to transmit an ultrasonic signal and receive an echo using different settings (e.g., bandwidth, center frequency, power, number of transmit pulses, number and position of transmit and receive transducer elements). In some embodiments, the imaging system 100 switches between imaging modes in various patterns. For example, frames (e.g., image slices) for two different imaging modes are alternated during acquisition (e.g., interleaved). Interleaving imaging modes helps ensure that frames acquired in different imaging modes spatially correspond to each other; that is, probe and / or subject movement is minimized between frames of different imaging modes. In some embodiments, the imaging mode and the pattern in which the imaging mode is applied by the transducer array 114 are based on control signals provided by the transmit controller 120.

[0025] In some embodiments, the imaging mode used to provide beamforming RF data to the visualization data processor 170 is an imaging mode that provides visualization data that can be used to generate high-quality images, such as harmonic imaging. In some embodiments, the imaging mode used to provide beamforming RF data to the quantification data processor 172 is an imaging mode that preserves the relationship between echo signals and quantification data, such as basic imaging. Basic imaging and harmonic imaging modes are provided as examples, but the principles of this disclosure are not limited to these imaging modes. When different beamforming RF data is provided to the visualization processor 170 and the quantification processor 172, the processing performed on the beamforming RF data by the visualization processor 170 and the quantification processor 172 may be the same or different.

[0026] Although the visualization processor 170 and the quantification processor 172 are shown as separate components, in some embodiments, the visualization processor 170 and the quantification processor 172 are implemented by a single processor, particularly when beamforming RF data is provided sequentially from interleaved imaging modes. However, in some embodiments, a single processor capable of parallel processing is used when the same beamforming RF data is provided to the visualization processor 170 and the quantification processor 172. Alternatively, each of the visualization processor 170 and the quantification processor 172 may be implemented by multiple processors (e.g., multiple graphics processing units) in some embodiments.

[0027] A portion of the processed signal output from the signal processor 126 (e.g., the I and Q components of the visualization data or the IQ signal generated by the visualization data processor 170) is coupled to additional downstream signal processing components (e.g., hardware and / or software) for image generation. The IQ signal is coupled to one or more signal paths within the system, each of which relates to a component designating a signal processing component suitable for generating different types of image data (e.g., amplitude-based B-mode image data, phase-based Doppler image data). For example, the system includes a B-mode signal path 158 that couples the signal from the signal processor 126 to a B-mode processor 128 to produce B-mode image data. The B-mode processor 128 may employ amplitude detection for imaging structures in the body.

[0028] A portion of the processed signal output from the signal processor 126 (e.g., quantified data generated by the quantification data processor 172) is coupled to additional downstream data processing circuits for analysis. For example, the system includes a parameter calculation path 160 that couples the signal from the signal processor 126 to a parameter calculator 146 for calculating one or more parameters such as HRI.

[0029] Optionally, quantified data may be provided to the B-mode processor 128 and / or the scan transducer 130. As described herein, in some embodiments, images based on quantified data are provided to the user in addition to images based on visualization data.

[0030] The signal generated by the B-mode processor 128 is coupled to the scan transducer 130 and / or the multi-section reconstructor 132. The scan transducer 130 is configured to arrange the echo signal from the spatial relationship in which the echo signal was received into a desired image format. For example, the scan transducer 130 arranges the echo signal into a two-dimensional (2D) sector-shaped format, or a three-dimensional (3D) format of a pyramidal or other shape.

[0031] The multi-plane reconstructor 132 can convert echoes received from points in a common plane within a volume region of the body into ultrasound images of that plane (e.g., B-mode images, harmonic images, basic images, mixed images), as described, for example, in U.S. Patent No. 6,443,896 (Detmer). The scanning transducer 130 and the multi-plane reconstructor 132 are implemented as one or more processors in some examples.

[0032] The volume renderer 134 generates an image of a 3D dataset viewed from a given reference point (also referred to as projection, rendering, or rendering), as described, for example, in U.S. Patent No. 6,530,885 (Entrekin et al.). The volume renderer 134 is implemented as one or more processors in some examples. The volume renderer 134 generates renders, such as positive renders or negative renders, by any known or future known techniques, such as surface rendering and maximum intensity rendering.

[0033] The outputs from the scanning converter 130, the multi-section reconstructor 132, and / or the volume renderer 134 (e.g., B-mode images) are coupled to the image processor 136 for further enhancement, buffering, and temporary storage before being displayed on the image display 138. For example, images generated from visualization data produced by the visualization processor 170 are provided to the user on the display.

[0034] The graphics processor 140 generates graphic overlays for display alongside the images. These graphic overlays may contain standard identifying information, such as patient name, date and time of the image, and imaging parameters. For these purposes, the graphics processor is configured to receive input, such as typed patient name or other annotations, from the user interface 124. The user interface 124 can also be coupled to the multiplanar reconstructor 132 for the selection and control of the display of multiple multiplanar reconstruction (MPR) images.

[0035] System 100 includes local memory 142. Local memory 142 is implemented as any suitable non-temporary computer-readable medium (e.g., a flash drive, a disk drive). Local memory 142 stores data generated by system 100 and including images, calculated parameters, executable instructions, inputs provided by the user via the user interface 124, or any other information necessary for the operation of system 100.

[0036] As previously mentioned, system 100 includes a user interface 124. The user interface 124 includes a display 138 and a control panel 152. The display 138 includes a display device implemented using various known display technologies such as LCD, LED, OLED, or plasma display technology. In some examples, the display 138 includes multiple displays. The control panel 152 is configured to receive user input (e.g., selection of imaging mode, selection of region of interest). The control panel 152 includes one or more hardware controls (e.g., buttons, knobs, dials, encoders, mice, trackballs, etc.). In some examples, the control panel 152 additionally or alternatively includes software controls (e.g., GUI control elements or simply GUI controls) provided on a touch-sensitive display. In some examples, the display 138 is a touch-sensitive display including one or more software controls of the control panel 152.

[0037] According to the principles of this disclosure, system 100 includes a parameter calculator 146. The parameter calculator 146 is implemented in software, hardware, or a combination thereof. For example, the parameter calculator 146 is implemented by a processor that executes instructions for calculating one or more parameters, such as HRI. The parameter calculator 146 receives quantification data from a quantification data processor 172. The parameter calculator 146 receives instructions for selecting ROIs from a user interface 124 based on an image generated from visualization data. In embodiments where the parameter to be calculated is HRI, one of the ROIs is from an area of ​​the image corresponding to the liver parenchyma, and the other ROI is from an area of ​​the image corresponding to the renal cortex. The parameter calculator 146 calculates one or more HRIs using the quantification data obtained from the areas corresponding to the ROIs. The parameters calculated by the parameter calculator 146 are provided to local memory 142 for storage and / or to image processor 136 for display to the user on display 138.

[0038] The user interface 124 is used to adjust various parameters of image acquisition, generation, and / or display. For example, the user can select the imaging mode, adjust the power, adjust the gain level and dynamic range, switch spatial synthesis on / off, and / or adjust the smoothing level. In some embodiments, user-adjustable settings affect the imaging mode and / or processing of visualization data (e.g., beamforming RF data processed by the visualization processor 170). In some embodiments, user-adjustable settings do not affect the imaging mode and / or processing of quantification data (e.g., beamforming RF data processed by the quantification processor 172). This allows the system 100 to provide a display image that is adjustable by the user without altering the quantification data. This helps ensure that the linear and / or known relationship between the quantification data and the echo signal is maintained regardless of user adjustments to the displayed image. In some applications, this prevents parameters calculated by the parameter calculator 146 from being compromised (e.g., becoming less accurate or unreliable).

[0039] In some examples, the various components shown in Figure 1 are combined. For example, the image processor 136 and the graphics processor 140 are implemented as a single processor. In another example, the parameter calculator 146 and the signal processor 126 are implemented as a single processor. In some examples, the various components shown in Figure 1 are implemented as separate components. For example, the signal processor 126 is implemented as a separate signal processor for each imaging mode (e.g., basic or harmonic grayscale mode). In some examples, one or more of the various processors shown in Figure 1 are implemented by a general-purpose processor and / or a microprocessor configured to perform a specified task (e.g., a grayscale mode designed for greater penetration or higher resolution). In some examples, one or more of the various processors are implemented as application-specific circuits (ASICS) or other components. In some examples, one or more of the various processors (e.g., the image processor 136) are implemented by one or more graphical processing units (GPUs).

[0040] Figure 2 is a block diagram illustrating an exemplary processor 200 according to the principles of this disclosure. Processor 200 is used to implement one or more processors described herein, for example, the image processor 136 shown in Figure 1. Processor 200 is any preferred type of processor, including, but not limited to, a microprocessor, a microcontroller, a digital signal processor (DSP), a field-programmable array (FPGA) programmed to form a processor, a graphical processing unit (GPU), an application-specific circuit (ASIC) designed to form a processor, or a combination thereof.

[0041] The processor 200 includes one or more cores 202. Each core 202 includes one or more arithmetic logic units (ALUs) 804. In some examples, the core 202 includes, in addition to or instead of, the ALUs 204, a floating-point logic unit (FPLU) 206 and / or a digital signal processing unit (DSPU) 208.

[0042] The processor 200 includes one or more registers 212 that are communicatively coupled to the core 202. The registers 212 are implemented using dedicated logic gate circuits (e.g., flip-flops) and / or any memory technology. In some examples, the registers 212 are implemented using static memory. The registers provide data, instructions, and addresses to the core 202.

[0043] In some examples, the processor 200 includes one or more levels of cache memory 210 that are communicatively coupled to the core 202. The cache memory 210 provides computer-readable instructions to the core 202 for execution. The cache memory 210 provides data for processing by the core 202. In some examples, computer-readable instructions are provided to the cache memory 210 by local memory, for example, local memory attached to an external bus 3216. The cache memory 210 is implemented in metal-oxide-semiconductor (MOS) memory, such as any preferred cache memory type, e.g., static random access memory (SRAM), dynamic random access memory (DRAM), and / or any other preferred memory technology.

[0044] The processor 200 includes a controller 314, which controls inputs to the processor 200 from other processors and / or components included in the system (e.g., the control panel 152 and the scan transducer 130 shown in Figure 1), and / or outputs from the processor 200 to other processors and / or components included in the system (e.g., the display unit 138 and the volume renderer 134 shown in Figure 1). The controller 214 controls the data paths in the ALU 204, FPLU 206, and / or DSPU 208. The controller 214 is implemented as one or more state machines, data paths, and / or dedicated control logic. The gates of the controller 214 are implemented as standalone gates, FPGAs, ASICs, or any other preferred technology.

[0045] The registers 212 and cache memory 210 communicate with the controller 214 and core 202 via internal connections 220A, 220B, 220C, and 220D. The internal connections are implemented as buses, multiplexers, crossbar switches, and / or any other suitable connection techniques.

[0046] Inputs and outputs for the processor 200 are provided via a bus 216, which includes one or more conductive wires. The bus 216 is communicatively coupled to one or more components of the processor 200, such as the controller 214, the cache 210, and / or the registers 212. The bus 216 is coupled to one or more components of the system, such as the indicator 138 and the control panel 152 mentioned earlier.

[0047] Bus 216 is coupled to one or more external memories. The external memory includes read-only memory (ROM) 232. ROM 232 is masked ROM, electronically programmable read-only memory (EPROM), or any other preferred technology. The external memory includes random access memory (RAM) 233. RAM 233 is static RAM, battery-backed static RAM, dynamic RAM (DRAM), or any other preferred technology. The external memory includes electrically erasable programmable read-only memory (EEPROM) 235. The external memory includes flash memory 234. The external memory includes magnetic storage devices such as disk 236. In some examples, the external memory is included in a system such as the ultrasound imaging system 100 shown in Figure 1, and is, for example, local memory 142.

[0048] Figure 3 is a graphical representation of the data processing path according to one embodiment of the present disclosure. Multiple ultrasonic transmission / reception events are performed, for example, by the transducer array 114 to acquire imaging data 300. The imaging data 300 is shown in Figure 3 as multiple image frames 302. However, this is for illustrative purposes only, and the imaging data 300 is RF data that has not yet been processed and / or placed into image frames at this point in the data path. In some examples, each of the image frames 302 may have been acquired by a separate transmission / reception event. The imaging data 300 is beamformed, for example, by the microbeamformer 116 and / or the main beamformer 122. After beamforming, the image data 300 is provided to the visualization data processor 170 and the quantification data processor 172. The visualization data processor 170 processes the imaging data 300 to generate visualization data used to generate an image 304. In some examples, the image 302 is provided on the display 138. The quantification data processor 170 processes the imaging data 300 to generate quantification data, which is provided to the parameter processor 146. The parameter processor 146 calculates one or more parameters based on the quantification data, for example, hepatic and renal indices. Optionally, in some examples, the quantification data is also used to generate an image 306, which is displayed on the display 138. Generating an image 306 from the quantification data involves, in some examples, further processing and / or arrangement of the quantification data by the B-mode processor 128 and / or the scan transducer 130.

[0049] As discussed with reference to Figure 1, when the same imaging data 300 is provided to both the visualization data processor 170 and the quantification data processor 172, the imaging data 300 is processed differently by the two processors. For example, the visualization data processor 170 performs a nonlinear operation on the imaging data 300, while the quantification data processor 172 performs a linear operation.

[0050] Figure 4 is a graphical representation of the data processing path according to one embodiment of the present disclosure. Multiple ultrasonic transmission / reception events are performed, for example, by the transducer array 114 to acquire imaging data 400. The imaging data 400 is shown in Figure 4 as multiple image frames 402 and image frame 404. However, this is for illustrative purposes only, and the imaging data 400 is RF data that has not yet been processed and / or placed into image frames at this point in the data path. In some examples, each of image frames 402 and each of image frames 404 were acquired by separate transmission / reception events. In some examples, image frames 402 and image frame 404 were acquired by different imaging modes. For example, a harmonic-dominant imaging mode with sufficiently high resolution (e.g., high linear density and high frequency) is used and processed with a combination of various image enhancement techniques such as spot reduction and marginal / vascular enhancement, while a basic mode with moderate resolution (e.g., relatively low linear density and frequency) is used to generate image frame 404, although it has low beam distortion and reverberation at intermediate imaging depths. The imaging modes are shown interleaved so that the imaging mode changes in each consecutive image frame, but other acquisition patterns are used in other examples. The imaging data 400 is beamformed, for example, by a microbeamformer 116 and / or a main beamformer 122. The visualization processor 170 and the quantification processor 172 receive different portions of the imaging data 400. Before or after beamforming, image frame 402 is separated from image frame 404. Image frame 402 is provided to the visualization processor 170, and image frame 404 is provided to the quantification processor 172. Thus, the visualization processor 170 receives a portion of the imaging data 400 acquired by one imaging mode, while the quantification processor 172 receives a portion of the imaging data 400 acquired by the other imaging mode.

[0051] The visualization data processor 170 processes the imaging data 400 corresponding to image frame 402 to generate visualization data used to generate image 406. In some examples, image 406 is displayed on the display 138. The quantification data processor 170 processes the imaging data 400 corresponding to image frame 404 to generate quantification data provided to the parameter processor 146. The parameter processor 146 calculates one or more parameters based on the quantification data, e.g., hepatic and renal indices. Optionally, in some examples, the quantification data is used to generate image 408, which is displayed on the display 138. Generating image 408 from the quantification data includes, in some examples, further processing and / or arranging the quantification data by the B-mode processor 128 and / or the scan transducer 130.

[0052] As discussed with reference to Figure 1, in some examples, when different imaging data (e.g., images acquired by different imaging modes) are provided to the visualization data processor 170 and the quantification data processor 172, the imaging data 400 is processed similarly by both processors, and the characteristics of the visualization and quantification data are based on the imaging mode. However, in other examples, different imaging data are processed differently by the two processors. For example, the visualization data processor 170 performs a nonlinear operation on image frame 402 of the imaging data 400, while the quantification data processor 172 performs a linear operation on image frame 404 of the imaging data 400. In these examples, the characteristics of the visualization and quantification data are based on the imaging mode and processing technique.

[0053] The illustrated embodiment described with reference to Figure 3 provides a higher frame rate and / or is relatively easier to implement than the illustrated embodiment described with reference to Figure 4. However, the embodiment shown in Figure 4 provides better optimized imaging settings (e.g., frequency settings, linear density) for generating visualization data and quantification data, respectively.

[0054] Figure 5 shows exemplary images generated from visualization and quantification data according to the principles of this disclosure. Image 500 is an ultrasound image generated from beamforming RF data processed by a nonlinear processing method (e.g., dynamic range modification) and / or a nonlinear imaging mode (e.g., harmonic imaging). Image 502 is an ultrasound image generated from beamforming RF data processed by a linear processing method and / or a linear imaging mode (e.g., basic imaging). Both images 500 and 502 are generated from ultrasound data of image slices acquired from the same spatial locations in the liver 504 and kidney 506. In some examples, image 500 was generated from data provided by the visualization data processor 170, and image 502 was generated from data provided by the quantification data processor 172.

[0055] The contrast of image 500 is greater than that of image 502, and the mottled noise in image 500 is less than that in image 502. Therefore, the user can more easily distinguish which areas of the liver 504 and kidney 506 are preferably homogeneous for calculating physiological parameters such as HRI. The first region of interest (ROI) 508 on the liver 504 and the second ROI 510 on the kidney 506 are selected by the user, for example, by providing user input via the user interface 124. In some examples, ROIs 508, 510 are selected by the user by selecting a location on image 500 while viewing image 500 on a display such as a display unit 138. In some examples, image 502 may not be provided to the user on a display unit. In other examples, ROIs 508, 510 are selected by the imaging system and / or with at least partial assistance from the system.

[0056] In this example, ROIs 508 and 510 are selected based on image 500, but the HRI is calculated based on the corresponding data for ROIs 508 and 510 in image 502. In some applications, the HRI values ​​calculated from the quantified data in image 502 are more reliable than the HRI values ​​calculated from the visualized data in image 500.

[0057] Optionally, images generated from quantified data are also provided on the display. Figure 6 shows exemplary images generated from visualization and quantification data on the display according to the principles of this disclosure. Display 638 is used in some examples to implement display 138. Display 638 provides images 600 and 602. Image 600 is an ultrasonic image generated from beamforming RF data processed by a nonlinear processing method and / or a nonlinear imaging mode. Image 602 is an ultrasonic image generated from beamforming RF data processed by a linear processing method and / or a linear imaging mode. Image 600 is an image generated from visualization data generated using different signal processing on the acquired data used to generate image 602. Images 600 and 602 are shown or displayed to the user simultaneously, for example, as shown in Figure 6. In other examples, a toggle control is provided on the user interface (e.g., user interface 124) to allow the user to toggle between the two images by operating the toggle control on the user interface. Although shown at the same size in Figure 6, in other examples, one image, such as image 402, is smaller on the display than the other image (e.g., image 600). In some examples, only the image generated from the visualization data (e.g., image 600 or 500 in the example of Figure 5) is displayed to the user, allowing, for example, the user to select ROIs and / or visually confirm the location of the selected ROIs by the system and / or with the system's assistance.

[0058] In some examples, generating quantified data for a second image (e.g., image 602) involves additional processing, such as logarithmically compressed dynamic range processing. In other examples, “native” quantified data is used to generate image 602, as is the case with image 502. Native data means that the electrical signals generated by the transducer elements in response to the echo have been minimally processed; for example, the signals are beamformed and linear gain (e.g., signal amplification) is applied. In some examples, if the user adjusts the display and / or acquisition settings for image 600, the display and / or acquisition settings for image 602 are not adjusted (e.g., remain the same).

[0059] By providing both image 600 and image 602, the user can verify that images 600 and 602 are in the same spatial position when the images are generated from different data, such as different acquisitions (for example, when different imaging modes are interleaved). By providing both image 600 and image 602, the user can verify that no artifacts were introduced by nonlinear processing and / or, otherwise, the user can be confident that suitable ROIs were selected when images 600 and 602 are generated from different processing of the same data or from different acquisitions.

[0060] Figure 7 is a flowchart of the method according to the principles of this disclosure. In some examples, method 700 is performed by an ultrasonic imaging system, such as ultrasonic imaging system 100.

[0061] In block 702, the system (e.g., ultrasound imaging system 100) receives radio frequency (RF) data. The RF data corresponds to echoes generated in response to ultrasound signals transmitted by an ultrasound transducer array, such as ultrasound transducer array 114. The RF data is received by a signal processor, such as signal processor 126.

[0062] In block 704, visualization data is generated from at least a first portion of the RF data (for example, image frame 402 in the embodiment shown in Figure 4). In some examples, the visualization data is generated by a processor such as the visualization data processor 170. In block 706, quantification data is generated from at least a second portion of the RF data. In some examples, the quantification data is generated by a processor such as the quantification processor 172. As previously mentioned, in some examples, the signal processor 126 is a single processor that generates both the visualization data and the quantification data.

[0063] In some examples, generating visualization data involves processing RF data by at least one nonlinear processing method, and generating quantification data involves processing RF data by at least one linear processing method. In some examples, a first portion of the RF data is acquired by an ultrasonic transducer array using a first imaging mode, and a second portion of the RF data is acquired by an ultrasonic transducer array using a second imaging mode. In some examples, the first imaging mode is a harmonic imaging mode, and the second imaging mode is a fundamental imaging mode. In some examples, the first and second imaging modes are interleaved.

[0064] In block 708, an image is generated from visualization data. In some embodiments, the image is generated by one or more processors, such as a visualization data processor 170, a B-mode processor 128, a scanning transducer 130, a multi-section reconstructor 132, a volume renderer 134, and / or an image processor 136. In some examples, the image is provided on a display, such as a display 138. Optionally, in some examples, a second image is generated from quantification data. In some examples, one or more processors (e.g., the B-mode processor 128) further process and / or arrange the quantification data to generate the second image (e.g., logarithmically compressed dynamic range matching). In some examples, user input, including changes to the image, is received via a user interface, and one or more processors modify the generation of visualization data in response to the user input. However, in some examples, the quantification data remains unchanged.

[0065] In block 710, the user provides indication of a region of interest (ROI) in the image via a user interface. In some examples, the user interface includes user interface 124. In block 712, the processor calculates physiological parameters based at least partially on a portion of the quantification data associated with the ROI. In some examples, the physiological parameters are calculated by one or more processors, such as the quantification data processor 172 and / or the parameter calculator 146. The physiological parameters are provided on a display and / or stored in memory, such as local memory 142.

[0066] In some examples, the user provides a second instruction for a second ROI via a user interface, and the processor calculates physiological parameters based at least partially on the portion of quantified data associated with the ROI and the portion of quantified data associated with the second ROI. For example, if the ROI corresponds to a portion of the liver and the second ROI corresponds to a portion of the kidney, calculating the physiological parameters involves calculating the ratio of the echogenicity of the liver to the echogenicity of the kidney, i.e., the HRI.

[0067] The example method described with reference to Figure 7 illustrates different parts of the imaging data that are processed in different ways. However, in other examples, such as those described with reference to Figure 3, the same imaging data and / or the same parts of the imaging data are processed in different ways to generate visualization data and quantification data.

[0068] The systems and methods disclosed herein generate visualization data for generating an image to be displayed on a display and quantification data for calculating one or more parameters. The visualization data and quantification data are generated by different processing methods and / or imaging modes. In some applications, ROI selection is improved and / or more reliable parameter determination is made by providing data for generating one set of display images and another set of data for calculating parameters.

[0069] In various embodiments in which the components, systems, and / or methods are implemented using a computer-based system or a programmable device such as a programmable logic circuit, it should be understood that the above-described systems and methods can be implemented using any of the various known or later-developed programming languages, such as "C," "C++," "FORTRAN," "Pascal," and "VHDL." Accordingly, various storage media, such as magnetic computer disks, optical disks, and electronic memory, containing information that can be instructed to a device such as a computer, can be provided for implementing the above-described systems and / or methods. Once a suitable device accesses the information and programs contained in the storage media, the storage media can provide the information and programs to the device, thereby enabling the device to perform the functions of the systems and / or methods described herein. For example, if a computer disk containing suitable material such as source files, object files, and executable files is provided to a computer, the computer will receive the information, configure itself appropriately, and perform the functions of the various systems and methods outlined in the above-described drawings and flowcharts in order to perform various functions. In other words, the computer will receive various parts of information about different elements of the aforementioned systems and / or methods from the disk, implement the individual systems and / or methods, and coordinate the functions of the individual systems and / or methods described above.

[0070] In view of this disclosure, it should be noted that the various methods and devices described herein can be implemented in hardware, software, and / or firmware. Furthermore, the various methods and parameters are included merely as examples and are not included in any limiting sense. In view of this disclosure, those skilled in the art can use this teaching in determining their own techniques and the equipment required to influence those techniques, while remaining within the scope of the invention. One or more of the functions of the processors described herein may be incorporated into fewer numbers or a single processing unit (e.g., a CPU), and may be implemented using application-specific integrated circuits (ASICs) or general-purpose processing circuits programmed to perform instructions executable to perform the functions described herein.

[0071] Although this system has been described with particular reference to an ultrasound imaging system, it is also envisioned that the system can be extended to other medical imaging systems in which one or more images are systematically obtained for a combination of visualization and quantification. Therefore, the system can be used, but not limited to, to obtain and / or record imaging information relating to the kidneys, testes, breasts, ovaries, uterus, thyroid, liver, lungs, musculoskeletal system, spleen, heart, arteries, and vascular system, as well as for other imaging applications relating to ultrasound-guided interventions. Furthermore, the system also includes one or more programs to be used in conjunction with conventional imaging systems to provide the features and advantages of the system. Specific additional advantages and features of the disclosure will become apparent to those skilled in the art by examining the disclosure, or will be experienced by those adopting the novel systems and methods of the disclosure. Another advantage of the system and method is that conventional medical imaging systems can be easily upgraded to incorporate the features and advantages of the system, devices, and methods.

[0072] Naturally, it should be understood that any one of the examples, examples, or processes described herein may be combined with one or more other examples, examples, and / or processes, or may be separated and / or performed between separate devices or device parts by the System, Devices, and Methods.

[0073] Finally, the above discussion is intended to be merely illustrative of the System and Method and should not be construed as limiting the appended claims to any particular example or group of examples. Therefore, while the System has been described in particular detail with reference to exemplary embodiments, it should be recognized that many modifications and alternative embodiments can be devised by those skilled in the art without departing from the broader intended spirit and scope of the System and Method described in the following claims. Accordingly, this specification and the drawings should be considered illustrative and not intended to limit the scope of the appended claims.

Claims

1. an ultrasound probe that transmits ultrasound signals, receives echoes in response to the ultrasound signals, and provides radio frequency data corresponding to the echoes; a display for displaying an image; a user interface that receives an indication of a first region of interest in the image; a processor for receiving the radio frequency data, The processor further comprises: generating visualization data from a first portion of the radio frequency data; the image is based at least in part on the visualization data; The processor further comprises: generating quantified data from a second portion of the radio frequency data; receiving an indication of the region of interest; calculating a physiological parameter based at least in part on a portion of the quantified data associated with the region of interest; the first portion of the radio frequency data is acquired in a first imaging mode; the second portion of the radio frequency data is acquired by a second imaging mode different from the first imaging mode; the first imaging mode is interleaved with the second imaging mode; The processor: generating the visualization data by processing the first portion of the radio frequency data with at least one non-linear processing method; An ultrasound imaging system that generates the quantified data by processing the second portion of the radio frequency data by at least one linear processing method.

2. The user interface further receives input for adjusting the image; The ultrasound imaging system of claim 1 , wherein the processor is further configured to adjust the visualization data based on the input.

3. The ultrasound imaging system of claim 1 , wherein the physiological parameters include hepatorenal indices.

4. The ultrasound imaging system of claim 1 , wherein the display further displays a second image generated from the quantified data.

5. The ultrasound imaging system of claim 1 , wherein the first imaging mode is a harmonic imaging mode and the second imaging mode is a fundamental imaging mode.

6. receiving radio frequency data corresponding to echoes generated in response to ultrasound signals transmitted by the ultrasound transducer array; generating visualization data from a first portion of the radio frequency data; generating quantified data from a second portion of the radio frequency data; generating an image based at least in part on the visualization data; receiving an indication of a region of interest in the image from a user interface; calculating a physiological parameter based at least in part on a portion of the quantified data associated with the region of interest; the first portion of the radio frequency data is acquired in a first imaging mode; the second portion of the radio frequency data is acquired by a second imaging mode different from the first imaging mode; the first imaging mode is interleaved with the second imaging mode; 1. An ultrasound imaging method, wherein generating visualization data comprises processing the first portion of the radio frequency data with at least one nonlinear processing method, and generating quantification data comprises processing the second portion of the radio frequency data with at least one linear processing method.

7. receiving user input via the user interface, the user input comprising changes to the image; 7. The ultrasound imaging method of claim 6, further comprising the step of: adjusting the generating of visualization data in response to the user input.

8. The ultrasound imaging method of claim 6 , further comprising generating a second image based on the quantified data.

9. The ultrasonic imaging method according to claim 6 , wherein the first imaging mode is a harmonic imaging mode and the second imaging mode is a fundamental imaging mode.

10. receiving a second indication of a second region of interest from the user interface; and calculating the physiological parameter based at least in part on the portion of the quantified data associated with the region of interest and the portion of the quantified data associated with the second region of interest.

11. 11. The ultrasound imaging method of claim 10, wherein a first region of interest corresponds to a portion of a liver and the second region of interest corresponds to a portion of a kidney, and wherein calculating the physiological parameter comprises calculating a ratio of an echo intensity of the liver to an echo intensity of the kidney.

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