Determining a color mapping function

Abstract

Thus, the proposed concept aims at providing schemes, solutions, concepts, designs, methods and systems related to dynamically determining a color mapping function for Doppler data of a regurgitant flow in a heart valve. In particular, embodiments aim at providing a method for dynamically determining a color mapping function for Doppler data of a regurgitant flow in a heart valve by obtaining and processing Doppler data and a blood flow model of the regurgitant flow, the Doppler data comprising at least two Doppler data frames representing the regurgitant flow associated with an orifice in the heart valve. In other words, it is proposed that for each Doppler data frame of the Doppler data, by determining a region of interest in the blood flow model based on the blood flow model and a color map, a target iso velocity surface of the model can be determined, and a color mapping function for the Doppler data frame can be determined based on the target iso velocity surface.

Description

Technical Field

[0001] This invention relates to the field of color mapping functions, and more particularly to the field of color mapping functions for Doppler data. Background Technology

[0002] In mitral regurgitation (MR), the mitral valve does not close properly, and therefore blood is allowed to flow back from the left ventricle to the left atrium when the left ventricle contracts (systole). Based on Doppler velocity, the algorithm can create a hydrodynamic model. Based on this model, the flow for each frame and the total regurgitation volume can be calculated.

[0003] Quantifying the orifice area and flow volume of mitral regurgitation is clinically important, but manual ultrasound techniques are difficult and often inaccurate. Several algorithms have been developed to automatically estimate the orifice area and flow volume. These algorithms iteratively adjust the orifice geometry and the proximal flow convergence model using the proximal isokinetic surface area (PISA) method to match 3D color flow data.

[0004] However, results from common methods for analyzing mitral regurgitation flow (or any other heart valve regurgitation) often involve coarse simplifications, leading to inaccurate and / or unreliable results. Furthermore, users often find it difficult or impossible to view and evaluate the correctness of the algorithm's results for each frame, particularly due to the inadequacy of the color mapping function used to visualize Doppler data. Therefore, it is currently impossible for users to evaluate the algorithm's output to determine if it has correctly processed the data. Moreover, it is even more difficult for users to view volumetric data because internal values ​​(or colors in the case of a 3D color map) may be obscured by external values.

[0005] US 2014 / 052001 A1 discloses the use of both B-mode data representing tissue and flow data representing the backflow jet to automatically detect the mitral valve using a machine learning classifier.

[0006] Anayiotos AS et al. "Morphological evaluation of a regurgitant orifice by 3-D echocardiography: applications in the quantification of valvular regurgitation A method for calculating the proximal flow field region using three-dimensional echocardiography combined with Doppler velocimetry is disclosed. Summary of the Invention

[0007] This invention is defined by the claims.

[0008] According to an example of one aspect of the invention, a method is provided for dynamically determining a color mapping function for Doppler data of regurgitant flow in a heart valve.

[0009] The method includes: obtaining Doppler data, wherein the Doppler data includes at least two Doppler data frames from a time series and represents regurgitant flow associated with an orifice in a heart valve, and wherein each Doppler data frame includes velocity values; obtaining a blood flow model of the regurgitant flow, wherein the blood flow model includes the orifice and a model of blood flow velocity values ​​through and around the orifice; and for each Doppler data frame of the Doppler data: determining a region of interest in the blood flow model based on the Doppler data frame and the velocity values ​​of the blood flow model; determining a target isokinetic surface of the blood flow model based on data from the blood flow model within the region of interest; and determining a color mapping function for the Doppler data frame based on the target isokinetic surface, wherein the color mapping function is used to map the velocity values ​​of the Doppler data frame to colors.

[0010] Therefore, the proposed concepts aim to provide schemes, solutions, ideas, designs, methods, and systems related to dynamically determining a color mapping function for Doppler data used for regurgitant flow in heart valves. Specifically, embodiments aim to provide a method for dynamically determining a color mapping function for Doppler data used for regurgitant flow in heart valves by acquiring and processing Doppler data and a blood flow model of the regurgitant flow, said Doppler data including at least two Doppler data frames representing regurgitant flow associated with orifices in the heart valve.

[0011] In other words, a method is proposed that for each Doppler data frame, by determining the region of interest in the blood flow model based on the blood flow model and the Doppler data frame, the target isokinetic surface of the model can be determined. Then, a color mapping function for the Doppler data frame can be determined based on this target isokinetic surface.

[0012] By following this process for each Doppler data frame, the color mapping function for the Doppler data can be determined substantially dynamically and automatically. This allows for the generation of color renderings for each Doppler data frame, each utilizing a potentially different color mapping function, without any user intervention, compared to existing methods. This facilitates the optimization of determining the color mapping function for each Doppler data frame in the time series of Doppler data, rather than attempting to optimize a single color mapping function for all Doppler data frames or requiring user input to manually adjust the color mapping function for each frame. Therefore, information contained within the Doppler data and blood flow model can be automatically provided to the user, information that would otherwise be inaccessible or uninterpretable.

[0013] For example, a color mapping function can be determined for each Doppler data frame such that backflow in each subsequently generated color render (using its corresponding determined color mapping function for each Doppler data frame) can be present / more clearly defined. In other words, determining the color mapping function for each Doppler data frame can contribute to a dynamic aliasing baseline for subsequent color renders / graphs across each Doppler data frame, making backflow visible (or more visible) to the user across the entire time series of the Doppler data frames. In contrast, current methods typically analyze the time series of Doppler data holistically and produce a single color mapping function, which is then used to generate color renders for each frame. However, this results in a suboptimal provision of information in each color render, where important information may be lost in some color renders because, for example, the aliasing baseline and / or color range of the color renders are not suitable for the Doppler data frame.

[0014] The inventors have recognized that by using both Doppler data and a blood flow model, an isokinetic surface of the blood flow model can be found that corresponds to the Doppler velocity values ​​of each Doppler data frame, allowing for the beneficial determination of a color mapping function for each Doppler data frame. In this way, beneficial information that users would not otherwise have access to can be provided; for example, the color mapping function can be used to reveal information, for instance, in subsequent color maps, which can then help users compare and / or match the Doppler data with the blood flow model.

[0015] Finally, an improved method for dynamically adjusting Doppler data frames for regurgitation flow in heart valves is provided.

[0016] In some embodiments, at least two Doppler data frames from a time series can be temporally adjacent. This allows two adjacent frames from the Doppler data to each have their own defined color mapping function, such that color rendering (e.g., color maps) subsequently generated for the Doppler data can be dynamically adjusted on a frame-by-frame basis. This substantially contributes to better color rendering for each frame of, for example, the Doppler data / video.

[0017] In some embodiments, the method may further include generating a color rendering for the Doppler data frame based on the velocity value of the Doppler data frame and a color mapping function determined for the Doppler data frame. In this way, a color rendering generated using a more beneficial / optimal color mapping function can be provided to the user.

[0018] In some embodiments, the method may further include generating a display control signal that describes color rendering for a Doppler data frame. In this way, the generated color rendering can be presented to the user.

[0019] In some embodiments, the display control signal may also describe the color mapping function of the Doppler data frame. In this way, a user can analyze color rendering in the context of the color mapping function (which may be, for example, a lookup table for a color map) to see, for example, what speed the aliasing baseline represents.

[0020] In some embodiments, the color mapping function may include a lookup table. This is an efficient and / or effective form of the color mapping function.

[0021] In some embodiments, the color mapping function can be configured to map at least one velocity value to a transparent color. This can help generate color renderings that highlight, for example, a subset or even a single velocity value by making other velocity values ​​appear transparent (i.e., without color) in the color rendering.

[0022] In some embodiments, determining the region of interest (ROI) in the blood flow model may include: comparing velocity values ​​of the blood flow model with velocity values ​​of a Doppler data frame; and determining the ROI in the blood flow model based on the comparison. In this way, for example, the region where different velocities best match, i.e., the region where the voxels of the blood flow model are most reliable, can be used to determine the target isokinetic surface.

[0023] In some embodiments, determining the color mapping function of the Doppler data frame may include determining the color mapping function based on the velocity of the target isokinetic surface. For example, the color mapping function may be determined such that the aliasing baseline of the color rendering of the Doppler data frame generated, for example, using the color mapping function, is at the velocity of the target isokinetic surface.

[0024] In some embodiments, the velocity of the target isodynamic surface can describe the average velocity of the region of interest. This is a useful parameter for the isodynamic surface to be used.

[0025] In some embodiments, the region of interest can also be based on the requirement that the region of interest is upstream of the heart valve. This helps to determine the color mapping function based on the regurgitant flow upstream of the heart valve rather than the flow downstream of the heart valve.

[0026] In some embodiments, the region of interest can also be based on a requirement that the region of interest be within a predetermined distance of the heart valve. This ensures that the region of interest is close to the heart valve, thereby providing more useful data for adjusting the color map.

[0027] In some embodiments, the method may further include adjusting the blood flow model based on Doppler data before determining the region of interest. This allows the blood flow model to be adjusted to, for example, better correspond to the actual Doppler data.

[0028] In some embodiments, determining a target isokinetic surface may include: determining the target isokinetic surface of the blood flow model based on velocity values ​​from the Doppler data within the region of interest. This facilitates the use of useful information to determine the target isokinetic surface.

[0029] In some embodiments, the display control signals can also describe Doppler data. In this way, the actual Doppler data and the generated color rendering can be presented to the user for a better context and understanding of both.

[0030] In some embodiments, the method may further include generating a grid representation of the proximal flow convergence of the blood flow model; and wherein a display control signal may further describe the grid representation. In this way, a further useful representation of the data can be presented to the user for better context and understanding.

[0031] According to another aspect of the invention, a computer program including code units is provided for implementing any of the methods disclosed herein when the program is run on a processing system.

[0032] According to another aspect of the invention, a system is provided for dynamically determining a color mapping function for Doppler data of regurgitant flow in a heart valve. The system includes an input interface configured to: acquire Doppler data, wherein the Doppler data includes at least two Doppler data frames from a time series and represents regurgitant flow associated with an orifice in a heart valve, and wherein the Doppler data frames include velocity values; acquire a blood flow model of the regurgitant flow, wherein the blood flow model includes the orifice and a model of blood flow velocity values ​​through and around the orifice; and a processing unit configured to: for each Doppler data frame of the Doppler data: determine a region of interest in the blood flow model based on the Doppler data frame and the velocity values ​​of the blood flow model; determine a target isokinetic surface of the blood flow model based on data from the blood flow model within the region of interest; and determine a color mapping function for the Doppler data frame based on the target isokinetic surface, wherein the color mapping function maps the velocity values ​​of the Doppler data frame to colors.

[0033] Therefore, a concept can be proposed for dynamically determining the color mapping function for Doppler data of regurgitation flow in heart valves, and this can be done based on the obtained Doppler data and a blood flow model of the regurgitation flow, which includes at least two Doppler data frames representing the regurgitation flow associated with the orifice in the heart valve.

[0034] These and other aspects of the invention will become apparent from and be set forth with reference to the embodiments described below. Attached Figure Description

[0035] To better understand the invention and to more clearly illustrate how it can be implemented, reference will now be made to the accompanying drawings by way of example only, wherein:

[0036] Figure 1 It is a method according to the proposed embodiment for dynamically determining a color mapping function for Doppler data used for regurgitation flow in a heart valve;

[0037] Figure 2 It is a diagram showing the regurgitation flow in the heart valves;

[0038] Figure 3 This is a flowchart of a method for dynamically determining a color mapping function for Doppler data used for regurgitation flow in a heart valve, according to the proposed embodiment.

[0039] Figure 4 This is a flowchart of a method for dynamically determining a color mapping function for Doppler data used for regurgitation flow in a heart valve, according to the proposed embodiment.

[0040] Figure 5 This is a simplified block diagram of a system for dynamically determining a color mapping function for Doppler data used for regurgitant flow in a heart valve, according to the proposed embodiment; and

[0041] Figure 6 An example of a computer in which one or more parts of an embodiment may be employed is illustrated. Detailed Implementation

[0042] The invention will be described with reference to the accompanying drawings.

[0043] It should be understood that the detailed descriptions and specific examples, when indicating exemplary embodiments of the apparatuses, systems, and methods, are intended for illustrative purposes only and are not intended to limit the scope of the invention. These and other features, aspects, and advantages of the apparatuses, systems, and methods of the present invention will become better understood from the following description, claims, and drawings. It should be understood that the drawings are merely schematic and not to scale. It should also be understood that the same reference numerals are used throughout the drawings to indicate the same or similar parts.

[0044] By studying the accompanying drawings, the disclosure, and the appended claims, those skilled in the art can understand and implement variations of the disclosed embodiments in practicing the claimed invention. In the claims, the word "comprising" does not exclude other elements or steps, and the words "a" or "an" do not exclude a plurality.

[0045] Embodiments of the present invention relate to various techniques, methods, schemes, and / or solutions related to dynamically determining a color mapping function for Doppler data used for regurgitant flow in heart valves. Based on the proposed concepts, multiple possible solutions can be implemented individually or in combination. That is, although these possible solutions may be described individually below, two or more of these possible solutions may be implemented in one combination or another.

[0046] Embodiments of the present invention aim to provide a method for dynamically determining a color mapping function for Doppler data used for regurgitant flow in a heart valve. This can be achieved by acquiring and processing Doppler data and a blood flow model of the regurgitant flow, the Doppler data including at least two Doppler data frames representing regurgitant flow associated with orifices in the heart valve.

[0047] In other words, a method is proposed that for each Doppler data frame, by determining the region of interest in the blood flow model based on the blood flow model and the Doppler data frame, the target isokinetic surface of the model can be determined. Then, a color mapping function for the Doppler data frame can be determined based on this target isokinetic surface.

[0048] Now for reference Figure 1 The flowchart depicts a method 100 for dynamically determining a color mapping function for Doppler data of regurgitation flow in a heart valve, according to the proposed embodiment.

[0049] Method 100 begins with step 110 of obtaining Doppler data, wherein the Doppler data includes at least two Doppler data frames from a time series and represents regurgitant flow associated with orifices in a heart valve. Each Doppler data frame includes velocity values. For example, the Doppler data may include three-dimensional (3D) color Doppler volume data of at least a portion of a heart valve throughout the cardiac cycle. A Doppler data frame can be understood as a single frame of Doppler data, i.e., describing a single moment including velocity values.

[0050] In this embodiment, at least two Doppler data frames from the time series are temporally adjacent. This allows two adjacent frames from the Doppler data to each have their own defined color mapping function, such that color rendering (e.g., color maps) subsequently generated for the Doppler data can be dynamically adjusted substantially on a frame-by-frame basis. This substantially contributes to optimized color rendering for each frame of, for example, the Doppler data / video. However, this is not essential for the invention, and in other embodiments, the Doppler data frames do not need to be temporally adjacent. For example, the method may include determining a color mapping function for every other Doppler data frame in the time series. In embodiments where the Doppler data comprises multiple Doppler frames, each Doppler frame may be temporally adjacent.

[0051] Of course, as those skilled in the art of Doppler ultrasound know, the velocity measured using the Doppler effect is merely a labeled projection along the direction of the ultrasonic beam.

[0052] Step 120 includes obtaining a blood flow model of the backflow, wherein the blood flow model includes the orifice and a model of blood flow velocity values ​​through and around the orifice. For example, the blood flow model may include a 3D vector model of the orifice and the blood flow through and around the orifice.

[0053] Then, steps 130, 140, and 150 are performed (independently) on each Doppler data frame.

[0054] Step 130 includes determining the region of interest (ROI) in the blood flow model based on the velocity values ​​of the Doppler data frame and the blood flow model. In this embodiment, determining the ROI is also based on the requirement that the ROI is upstream of the heart valve. This facilitates adjusting the Doppler data frame based on regurgitant flow upstream of the heart valve rather than flow downstream of the heart valve. However, this requirement is not essential for the operation of this invention.

[0055] Furthermore, in this embodiment, the determination of the region of interest is also based on the requirement that the region of interest be within a predetermined distance of the heart valve. For example, the predetermined distance may be based on constraints imposed by a blood flow model. This ensures that the region of interest is close to the heart valve, thereby providing more useful data for adjusting the Doppler data frames. Again, however, this requirement is not essential for the operation of this invention.

[0056] In some embodiments, color noise and potentially aliased values ​​in the subsequently generated color rendering / mapping (using a determined color mapping function) can be removed from the region of interest.

[0057] Step 140 includes determining a target isokinetic surface for the blood flow model based on data from the blood flow model within the region of interest. The target isokinetic surface can also be understood as the optimal isokinetic surface, where the optimal isokinetic surface describes the proximal flow convergence zone of the backflow. In other words, the target isokinetic surface can be understood as the surface that provides the best consistency between the velocity values ​​of the Doppler data frame and the velocity values ​​of the blood flow model; that is, the surface guided by the best fit between the Doppler data frame and the blood flow model.

[0058] For example, a blood flow model covers a volume of interest (VOI). The target isokinetic surface (or shell) is only a potential subset of the VOI. Therefore, there can be multiple such isokinetic surfaces / shells within the same VOI (e.g., each isokinetic surface / shell has a different velocity). For example, a color map can then be designed in a way that can represent multiple velocities simultaneously (e.g., stacking several red-to-blue scales).

[0059] In this embodiment, determining the target isokinetic surface includes: determining the target isokinetic surface of the blood flow model based on the velocity values ​​of the Doppler data within the region of interest. This helps in using useful information to determine the target isokinetic surface, but it is not essential for the work of this invention.

[0060] Step 150 describes determining a color mapping function for the Doppler data frame based on the target isovelocity surface, where the color mapping function maps velocity values ​​of the Doppler data frame to colors. For example, the color mapping function may include a set of colors, where each color is mapped to a velocity value from the set of velocity values. For example, the color mapping function could indicate that a velocity of 5 cm / s should be mapped to blue, and a velocity of -5 cm / s should be mapped to red, for example, with a color gradient between these two values. For example, the velocity color mapping function could be designed to smoothly transition from cool hues (such as blue or green) for low velocities, gradually shifting to warmer hues (such as red or orange) as velocity increases, thereby creating an intuitive visual representation where color is correlated with the magnitude of velocity. This approach allows users to quickly interpret velocity changes through a perceptually meaningful color spectrum.

[0061] In this embodiment, the color mapping function includes a lookup table. This is an efficient and / or effective form of the color mapping function. For example, the lookup table can be determined such that the aliasing baseline of the color render / map generated using the lookup table can be located substantially in a favorable position in the color render / map. For example, this can help make changes in flow velocity / direction visible (or more visible) on the color map / render. In some embodiments, the lookup table can be determined such that the color render / map generated using the lookup table can include at least two aliasing baselines—this can provide a more volumetric impression of velocity values. Of course, in other embodiments, the color mapping function can take any suitable form.

[0062] Furthermore, in this embodiment, the color mapping function is configured to map at least one velocity value to a transparent color, i.e., transparent. This facilitates the generation of color rendering that can highlight, for example, a subset or even a single velocity value by making other velocity values ​​appear transparent (i.e., without color) in the color rendering. However, this is not essential for the present invention, and in other embodiments, the color mapping function cannot map any velocity value to transparent, i.e., without color.

[0063] Furthermore, in this embodiment, determining the color mapping function includes determining the color mapping function based on the velocity of the target isodynamic surface. For example, a color mapping function can be determined such that the aliasing baseline of subsequent color rendering of the Doppler data frame generated using, for example, the color mapping function is at the velocity of the target isodynamic surface. However, this is not necessary for the invention to function, and in other embodiments, determining the color mapping function can be based on other parameters of the target isodynamic surface.

[0064] In this embodiment, the velocity of the target isostatic surface describes the average velocity of the region of interest. This is a useful parameter for the region of interest to be used. However, in other embodiments, the velocity of the target isostatic surface may include the average velocity of the region of interest, the pattern velocity of the region of interest, the highest velocity of the region of interest, and / or the lowest velocity of the region of interest, etc.

[0065] The present invention can then substantially facilitate a method for automatically adjusting, on a frame-by-frame basis, the aliasing baseline (via a determined color mapping function) of a series of generated color maps / renders for color flow data including regurgitant flow from orifices on heart valves, based on orifice geometry and a PISA-like (proximal isokinetic surface area) proximal flow pooling model. In some embodiments, the PISA-like proximal flow pooling can be adapted to better match the color flow data prior to performing the above-described method.

[0066] Therefore, according to the present invention, the method may include: receiving color flow data including regurgitant flow from an orifice on a heart valve; receiving a model (adapted to the color flow data) of the orifice geometry and a PISA-like proximal flow convergence; and for each Doppler data frame of the color flow data: determining a region of interest near the valve orifice based on a comparison between the values ​​of the color flow data and corresponding estimates obtained from the (adapted) model; determining a target isokinetic surface of the (adapted) model based on the voxels of the color flow data in the region of interest; and determining a color mapping function for the Doppler data frame based on the velocity of the target isokinetic surface.

[0067] By automatically selecting a target (e.g., optimal) isokinetic value and determining the color mapping function for each frame, the present invention approximates the information that would be presented if an expert user manually performed PISA techniques. In a preferred embodiment, the method may further include displaying the color stream data, wherein the color rendering / map (generated using the determined color mapping function) is placed side-by-side with a grid representation of the proximal flow convergence of the (adapted) model. In this way, the present invention can provide the user with more context and information about automated measurements.

[0068] For example, it should be clear that different velocities do not necessarily need to be mapped to different colors via a color mapping function, nor is any monotonicity required. A color mapping function can alternatively be understood as a color mapping relation. For instance, if multiple target velocities and associated isokinetic surfaces are determined for the same volume of interest, it might be desirable to intentionally map all these different velocities to the same color. A color mapping function / relationship can then facilitate this.

[0069] Now for reference Figure 2 Figure 200 depicts regurgitation flow in a heart valve 230. For example, an orifice 220 is provided in the heart valve 230 (such as the mitral valve). Thus, fluid such as blood or any other liquid can be transferred from one side of the heart valve 230 to the other through the orifice 220. Isokinetic curves (i.e., potential target isokinetic surfaces) 240 and 245 are positioned around the orifice 220. For example, a first isokinetic curve 240 and a second isokinetic curve 245 are positioned around the orifice 220. Thus, isokinetic curves 240 and 245 can represent the velocity of the fluid approaching the orifice 220 before passing through it, thereby creating a fluid jet 210.

[0070] For example, as the fluid approaches orifice 220, it can accelerate, reaching its maximum velocity as it passes through orifice 220. As a result, the fluid located at the first isovelocity curve 240 can maintain a constant velocity at all points along curve 240. Furthermore, although curves 240 and 245 are shown as two-dimensional, the isovelocity curve can be represented as a three-dimensional hemispherical shell, representing all points with the same velocity within the fluid surrounding orifice 220.

[0071] Isokinetic curves 240 and 245 are observed upstream of orifice 220. Therefore, in this particular example, fluid can flow from below the heart valve 230 to above the heart valve 230.

[0072] For example, in current clinical practice, the quantification of reflux flow using ultrasound typically relies on the 2D proximal isokinetic surface area (PISA) principle. The 2D PISA method uses the proximal flow convergence zone to measure the volume of reflux.

[0073] The underlying principle of this method is simple and based on basic fluid dynamics. The flow convergence zone corresponds to countercurrent flow. The blood flow velocity increases as it approaches the countercurrent orifice 220. In the proximal flow convergence zone, blood flows over continuous isokinetic surfaces 245 and 240, where isokinetic means that the velocity on each individual isokinetic surface is equal.

[0074] The inventors have recognized that visual elements of the PISA method can be used to provide information to users that would otherwise be unavailable to them. The PISA method uses a proximal flow confluence to measure the volume of reflux. It is assumed that the flow confluence corresponds to reflux flow. Blood flow velocity increases as it approaches the reflux orifice 220. As described above, the proximal flow confluence can therefore be described as hemispherical shells 240 and 245, wherein the velocity is equal on the surface of each shell.

[0075] In the PISA method, which typically operates within a single frame, aliasing effects in color Doppler (e.g., a Doppler color map) are utilized. Because pulse wave Doppler is cyclic, there are abrupt transitions from yellow to blue, which can easily create speckles in the frame (color map). In the PISA method, this artifact (aliasing baseline) is used as a feature. By allowing the user to shift the baseline so that the aliased area is in a certain position, the user can determine the velocity at that position.

[0076] Therefore, the inventors proposed significantly modifying the PISA method in several ways. First, instead of treating the idea of ​​a hemispherical isokinetic shell as merely a concept, the algorithm returns an object representing the convergence region of the algorithm's (blood flow) model. For example, it could be a triangular mesh resembling a hemispherical sphere (the optimal isokinetic shell). When this mesh intersects the view plane, it provides the user with an overview of the convergence region upon which the algorithm's (blood flow model) results are based. This is performed frame-by-frame.

[0077] Secondly, in the PISA method, the user shifts the aliasing baseline. Here, conversely, for each frame viewed by the user, a color mapping function can be automatically determined, thereby essentially facilitating a shift of the baseline to a velocity corresponding to the optimal isovelocity shell for each frame by placing aliasing artifacts there. This makes it possible to provide the user with regions in the color Doppler data having that velocity (which may / will be different frame by frame). Essentially, the present invention provides a dynamic (frame-by-frame) automatic adaptation of velocity transitions in Doppler data frames.

[0078] Then, a pooling region can be presented to the user side-by-side with a PISA-like aliasing transformation at the same rate, as seen by the algorithm (blood flow model). The user is then able to compare the two and is thus provided with more context and information for evaluating both results.

[0079] Therefore, while the algorithm used (the blood flow model) does not need to share the many limitations of the PISA method and its often coarse simplifications, the idea is to leverage clinicians' familiarity with the PISA method. The output of the model-based flow detection is presented in such a way that it can be expressed in an optical language similar to PISA, but with significant modifications that prevent it from being simplified to that. Using the model-based representation, optimal isokinetic values ​​can be extracted from the model to construct a display of the backflow.

[0080] For example, model-based algorithms can track and quantify cardiac regurgitation flow while establishing the regurgitation flow shape upstream of the valve. This relies on assuming orifice shape and distribution, as well as the corresponding upstream flow distribution. By comparing 3D color Doppler voxels with the assumed flow model, the orifice extension can be iteratively updated, and the resulting updated flow can be measured. The flow model can then be superimposed in 2D or 3D using a mesh defined by a specific isokinetic threshold.

[0081] In this invention, a blood flow model can be formed by adding contributions from multiple orifice elements. This model can be a 3D vector model. Each element's contribution can be a vector field oriented towards that element, with an amplitude determined by (1 / R). 2Modulation. The accumulated vector field can then be projected following the orientation of the ultrasound beam to simulate the color Doppler effect. The velocity amplitude of this "virtual Doppler" can then be adjusted to better correspond to the actual Doppler data.

[0082] In this invention, the last described step can be used to define a target isovelocity value, which can then be applied to determine a color mapping function, for example, a function showing the aliasing rate applied to the subsequently generated color map, and it can also be used to define the flow model shell. A region of interest can be defined in the flow model to compare a “virtual Doppler” with actual Doppler data. This can be performed when adjusting the flow model to better match the Doppler data. The region of interest can be located upstream of the valve and kept close to the valve. In this region, color noise and potentially obfuscated values ​​can be removed.

[0083] The target isovelocity (and therefore the surface) can then be defined using the region of interest (which includes only reliable voxels). The optimal value for this velocity shell can be the average velocity from the region of interest or a combination of that value and the orifice velocity value. Most importantly, this setup is done frame-by-frame (per-Doppler data frame), allowing for continuous and full visualization of the reverse flow structure.

[0084] Now for reference Figure 3 A flowchart depicts a method 300 for dynamically determining a color mapping function for Doppler data used for regurgitant flow in a heart valve, according to a proposed embodiment. Steps 110, 120, 140, and 150 are consistent with previously discussed... Figure 1 The method described in 100 is essentially the same.

[0085] Steps 330, 335, 140, and 150 are performed (independently) for each Doppler data frame. Step 330 includes comparing the velocity values ​​of the blood flow model with the velocity values ​​of the Doppler data frame, and step 335 includes determining the region of interest in the blood flow model based on this comparison. In this way, for example, the region where different velocities best match, i.e., the region where the voxels of the blood flow model are most reliable, can be used to determine the target isokinetic surface.

[0086] Now for reference Figure 4 A flowchart depicts a method 300 for dynamically determining a color mapping function for Doppler data used for regurgitant flow in a heart valve, according to a proposed embodiment. Steps 110, 120, 130, 140, and 150 are consistent with previously stated... Figure 1 The method described in 100 is essentially the same.

[0087] Steps 425, 130, 140, 150, 460, and 470 are performed (independently) for each Doppler data frame. Step 425 includes adjusting the blood flow model based on the Doppler data. This allows the blood flow model to be adjusted to, for example, better correspond to the actual Doppler data. In other words, the blood flow model can be compared to the actual Doppler data (frames) and then adjusted so that the blood flow model better matches the actual flow seen in the Doppler data to ensure accuracy. For example, the blood flow model can be adjusted for each Doppler data frame to better match each Doppler data frame.

[0088] Step 460 includes generating a color rendering for the Doppler data frame based on the velocity value of the Doppler data frame and a color mapping function determined for the Doppler data frame. In this way, a color rendering generated using a more beneficial / optimal color mapping function can be provided to the user.

[0089] Step 470 includes generating a display control signal that describes the color rendering for the Doppler data frame. In this way, the generated color rendering, and optionally, the color mapping function of the Doppler data frame, can be presented to the user. In this way, the user can analyze the color rendering in the context of the color mapping function (which may be, for example, a lookup table for a color map) to see, for example, what speed the aliasing baseline represents. Furthermore, in this embodiment, the display control signal also describes the Doppler data. In this way, the actual Doppler data and the generated color rendering / mapping can be presented to the user for a better context and understanding of both.

[0090] In some embodiments, method 400 may further include generating a grid representation of the proximal flow convergence of the blood flow model. Therefore, the display control signal in step 470 may also describe the grid representation. In this way, a further useful representation of the data can be presented to the user for better context and understanding.

[0091] Now for reference Figure 5 The present invention describes a system 500 for dynamically determining a color mapping function for Doppler data used for regurgitation flow in a heart valve, according to a proposed embodiment. System 500 includes an input interface 510 and a processing unit 520.

[0092] System 500 is configured to regulate Doppler data frames of regurgitant flow in a heart valve by processing input 515. Input 515 includes: Doppler data, wherein the Doppler data comprises at least two Doppler data frames from a time series and represents regurgitant flow associated with an orifice in the heart valve, and wherein each Doppler data frame includes a velocity value; and a blood flow model of the regurgitant flow, wherein the blood flow model includes the orifice and a model of blood flow velocity values ​​through and around the orifice. Input interface 510 receives input 515, and then processing unit 520 is configured to process input 515 to generate output 530. Processing unit 520 is configured to: for each Doppler data frame, determine a region of interest (ROI) in the blood flow model based on the Doppler data frame and the velocity values ​​of the blood flow model; determine a target isovelocity surface of the blood flow model based on the data of the blood flow model within the ROI; determine the target isovelocity surface of the blood flow model based on the data of the blood flow model within the ROI; and determine a color mapping function for the Doppler data frame based on the target isovelocity surface, wherein the color mapping function is used to map the velocity values ​​of the Doppler data frame to colors. Therefore, output 530 includes the color mapping function for each Doppler data frame.

[0093] Figure 6 An example of a computer 600 in which one or more parts of an embodiment may be employed is illustrated. The various operations discussed above can utilize the capabilities of computer 600. In this regard, it should be understood that system functional blocks may run on a single computer or may be distributed across several computers and locations (e.g., via an Internet connection).

[0094] Computer 600 includes, but is not limited to, PCs, workstations, laptops, PDAs, handheld devices, servers, storage devices, etc. Typically, in terms of hardware architecture, computer 600 may include one or more processors 610, memory 620, and one or more I / O devices 630 communicatively coupled via a local interface (not shown). As is known in the art, the local interface may be, for example, but not limited to, one or more buses or other wired or wireless connections. The local interface may have additional elements such as controllers, buffers (caches), drivers, repeaters, and receivers to enable communication. Furthermore, the local interface may include address, control, and / or data connections to enable appropriate communication between the aforementioned components.

[0095] Processor 610 is a hardware device for executing software that can be stored in memory 620. Processor 610 can actually be any custom or commercially available processor, central processing unit (CPU), digital signal processor (DSP), or auxiliary processor among several processors associated with computer 600, and processor 610 can be a semiconductor-based microprocessor (in the form of a microchip) or microprocessor.

[0096] Memory 620 may include any one or a combination of the following: volatile memory elements (e.g., random access memory (RAM), such as dynamic random access memory (DRAM), static random access memory (SRAM), etc.) and non-volatile memory elements (e.g., ROM, erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic tape, optical disc read-only memory (CD-ROM), magnetic disk, floppy disk, cassette tape, magnetic tape cartridge, etc.). Furthermore, memory 620 may contain electronic, magnetic, optical, and / or other types of storage media. Note that memory 620 may have a distributed architecture, in which various components are geographically separated but accessible by processor 610.

[0097] The software in memory 620 may include one or more individual programs, each including an ordered list of executable instructions for implementing logical functions. According to an exemplary embodiment, the software in memory 620 includes a suitable operating system (O / S) 650, a compiler 660, source code 670, and one or more applications 680. As shown, application 680 includes numerous functional components for implementing features and operations of the exemplary embodiment. Application 680 of computer 600 may represent various applications, computing units, logic, functional units, processes, operations, virtual entities, and / or modules according to the exemplary embodiment, but application 680 is not intended to be limiting.

[0098] Operating system 650 controls the execution of other computer programs and provides scheduling, input-output control, file and data management, memory management, communication control, and related services. The inventors anticipate that application 680 for implementing exemplary embodiments can be applied to all commercially available operating systems.

[0099] Application 680 can be a source program, an executable program (object code), a script, or any other entity including a set of instructions to be executed. When it is a source program, the program is typically translated by a compiler (such as compiler 660), assembler, interpreter, etc., which may or may not be included in memory 620 to operate appropriately in conjunction with O / S 650. Furthermore, application 680 can be written in an object-oriented programming language with data and method classes, or a procedural programming language with routines, subroutines, and / or functions, such as, but not limited to, C, C++, C#, Pascal, Python, BASIC, API calls, HTML, XHTML, XML, ASP scripts, JavaScript, FORTRAN, COBOL, Perl, Java, ADA, .NET, etc.

[0100] I / O device 630 may include input devices, such as, but not limited to, a mouse, keyboard, scanner, microphone, camera, etc. Furthermore, I / O device 630 may also include output devices, such as, but not limited to, a printer, monitor, etc. Finally, I / O device 630 may also include devices that transmit both input and output, such as, but not limited to, a NIC or modulator / demodulator (for accessing remote devices, other files, devices, systems, or networks), radio frequency (RF) or other transceivers, telephone interfaces, bridges, routers, etc. I / O device 630 also includes components for communication over various networks, such as the Internet or intranets.

[0101] If the computer 600 is a PC, workstation, intelligent device, etc., the software in the memory 620 may also include a Basic Input / Output System (BIOS) (omitted for simplicity). The BIOS is a collection of basic software routines that initializes and tests the hardware at startup, boots the O / S 650, and supports data transfer between hardware devices. The BIOS is stored in some type of read-only memory (such as ROM, PROM, EPROM, EEPROM, etc.) so that it can be executed when the computer 800 is activated.

[0102] When the computer 600 is in operation, the processor 610 is configured to execute software stored in the memory 620 to transfer data to and from the memory 620, and typically controls the operation of the computer 600 according to the software. Applications 680 and O / S 650 are read, in whole or in part, by the processor 610, possibly buffered within the processor 610, and then executed.

[0103] When application 680 is implemented as software, it should be noted that application 680 can be stored on virtually any computer-readable medium for use by or in connection with any computer-related system or method. In the context of this document, a computer-readable medium can be an electronic, magnetic, optical, or other physical device or module that can contain or store computer programs for use by or in connection with a computer-related system or method.

[0104] Application 680 can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device (such as a computer-based system, a processor-containing system, or other system that can fetch and execute instructions from and to an instruction execution system, apparatus, or device). In the context of this document, "computer-readable medium" can be any module that can store, deliver, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. Computer-readable media can be, for example, but not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, devices, or propagation media.

[0105] Figure 1 and Figure 3-4 Methods and Figure 5 The system can be implemented in hardware, software, or a combination of both (e.g., as firmware running on a hardware device). Where embodiments are implemented partially or entirely in software, the functional steps shown in the process flowchart can be executed by appropriately programmed physical computing devices, such as one or more central processing units (CPUs) or graphics processing units (GPUs). Each process and its individual component steps, as shown in the flowchart, can be executed by the same or different computing devices. According to embodiments, a computer-readable storage medium stores a computer program including computer program code configured to cause one or more physical computing devices to perform the encoding or decoding methods described above when the program is run on one or more physical computing devices.

[0106] Storage media can include volatile and non-volatile computer memories, such as RAM, PROM, EPROM and EEPROM, optical discs (such as CD, DVD, BD), and magnetic storage media (such as hard disks and magnetic tapes). Various storage media can be fixed within a computing device or can be transportable, allowing one or more programs stored thereon to be loaded into a processor.

[0107] With regard to the implementation of some or all of the embodiments in hardware, Figure 6The blocks shown in the block diagram can be individual physical components or logical subdivisions of a single physical component, or they can all be implemented in an integrated manner within a single physical component. The functionality of one block shown in the figures can be divided among multiple components in an implementation, or the functionality of multiple blocks shown in the figures can be combined in a single component in an implementation. Hardware components suitable for embodiments of the present invention include, but are not limited to, conventional microprocessors, application-specific integrated circuits (ASICs), and field-programmable gate arrays (FPGAs). One or more blocks can be implemented as a combination of dedicated hardware for performing some functions and one or more programmable microprocessors and associated circuitry for performing other functions.

[0108] A single processor or other unit can perform the functions of several items recited in the claims. Although specific measures are recited in dissimilar dependent claims, this does not imply that combinations of these measures cannot be advantageously used. If a computer program has been discussed above, it may be stored / distributed on suitable media, such as optical or solid-state media provided with or as part of other hardware, but it may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunications systems. Where the term “suitable” is used in the claims or description, it should be noted that the term “suitable” is intended to be equivalent to the term “configured as.” Any reference numerals in the claims should not be construed as limiting the scope.

[0109] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of instructions, including one or more executable instructions for implementing one or more specified logical functions. In some alternative implementations, the functions indicated in the blocks may occur in a different order than shown in the figures. For example, two blocks shown consecutively may actually be executed substantially simultaneously, or these blocks may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block illustrated in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented by a system based on dedicated hardware that performs the specified functions or actions or performs a combination of dedicated hardware and computer instructions, illustrating the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention.

Claims

1. A computer-implemented method (100) for dynamically determining a color mapping function for Doppler data of regurgitant flow in a heart valve, the method comprising: Obtain Doppler data (110), wherein the Doppler data includes at least two Doppler data frames from a time series and represents regurgitant flow associated with orifices in a heart valve, and wherein each Doppler data frame includes a velocity value; Obtain the blood flow model (120) of the backflow, wherein the blood flow model includes the orifice and a model of blood flow velocity values ​​through and around the orifice; and For each Doppler data frame of the Doppler data: The region of interest (130) in the blood flow model is determined based on the velocity values ​​of the blood flow model and the Doppler data frames; The target isokinetic surface (140) of the blood flow model is determined based on the data of the blood flow model within the region of interest; and A color mapping function (150) for the Doppler data frame is determined based on the target isotropic surface, wherein the color mapping function is used to map the velocity values ​​of the Doppler data frame to colors.

2. The computer-implemented method according to claim 1, wherein, The at least two Doppler data frames from the time series are temporally adjacent.

3. The computer-implemented method according to any one of claims 1 or 2, wherein, The method further includes generating a color rendering (460) for the Doppler data frame based on the velocity value of the Doppler data frame and a determined color mapping function for the Doppler data frame.

4. The computer-implemented method according to claim 3, wherein, The method further includes generating a display control signal (470) that describes the color rendering for the Doppler data frame and optionally describes the color mapping function of the Doppler data frame.

5. The computer-implemented method according to any of the preceding claims, wherein, The color mapping function includes a lookup table.

6. The computer-implemented method according to any of the preceding claims, wherein, The color mapping function is configured to map at least one velocity value to a transparent color.

7. The computer-implemented method according to any of the preceding claims, wherein, Determining the region of interest in the blood flow model includes: The velocity values ​​of the blood flow model are compared with the velocity values ​​of the Doppler data frames (330); and The region of interest (335) in the blood flow model is determined based on the comparison.

8. The computer-implemented method according to any of the preceding claims, wherein, Determining the color mapping function of the Doppler data frame includes determining the color mapping function based on the velocity of the target isokinetic surface.

9. The computer-implemented method according to claim 8, wherein, The velocity of the target isokinetic surface describes the average velocity of the region of interest.

10. The computer-implemented method according to any of the preceding claims, wherein, The region of interest is also based on the requirement that the region of interest is located upstream of the heart valve.

11. The computer-implemented method according to any of the preceding claims, wherein, The region of interest is also based on the requirement that the region of interest be within a predetermined distance of the heart valve.

12. The computer-implemented method according to any of the preceding claims, wherein, Before determining the region of interest, the method further includes adjusting the blood flow model (425) to more closely correspond to the Doppler data.

13. The computer-implemented method according to any of the preceding claims, wherein, Determining the target isokinetic surface involves determining the target isokinetic surface of the blood flow model based on the velocity values ​​of the Doppler data within the region of interest.

14. A computer program comprising code units configured to implement the method according to any of the preceding claims when the program is run on a processing system.

15. A system (500) for dynamically determining a color mapping function for Doppler data of regurgitant flow in a heart valve, the system comprising: Input interface (510), which is configured as follows: Obtain Doppler data, wherein the Doppler data includes at least two Doppler data frames from a time series and represents regurgitant flow associated with orifices in a heart valve, and wherein the Doppler data frames include velocity values; Obtain a blood flow model of the backflow, wherein the blood flow model includes the orifice and a model of blood flow velocity values ​​through and around the orifice; and Processing unit (520), which is configured as follows: For each Doppler data frame of the Doppler data: The region of interest in the blood flow model is determined based on the velocity values ​​of the blood flow model and the Doppler data frames. The target isovelocity surface of the blood flow model is determined based on the data of the blood flow model within the region of interest; and A color mapping function for the Doppler data frame is determined based on the target isodynamic surface, wherein the color mapping function is used to map the velocity values ​​of the Doppler data frame to colors.

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