Computer-implemented method and system for navigation and display of 3D image data

Abstract

A computer-implemented method and system for navigating and displaying 3D image data is described. In the method, a 3D image dataset to be displayed is obtained, and a highlight location is identified within the 3D image dataset. A scalar opacity map is calculated for the 3D image dataset, the opacity map having a value for each of a plurality of locations within the 3D image dataset, each value being dependent on the respective location relative to the highlight location and the value of the 3D image at the respective location relative to the value of the 3D image at the highlight location. The opacity is applied to the 3D image dataset to generate a modified 3D image view.

Description

[Technical Field]

[0001] The present invention relates to a system and computer-implemented method for navigation and display of three-dimensional images, and is particularly applicable to three-dimensional imaging of the human body for purposes of medical diagnosis and treatment planning. [Background technology]

[0002] Conventional imaging scanners are used for a variety of purposes, including imaging the human and animal body for diagnosis and guidance during medical interventions such as surgery. Other uses of imaging scanners include structural analysis of buildings, pipes, etc.

[0003] Traditional medical ultrasound scanners create two-dimensional B-mode images of tissue, where pixel brightness is based on the intensity of the echo return. Other types of imaging scanners can capture blood flow, tissue motion over time, blood location, the presence of specific molecules, tissue stiffness, or the anatomy of a three-dimensional (3D) area.

[0004] Traditionally, imaging scanners generate 2D images. 2D images, such as 2D ultrasound images, capture only a single 2D slice of a cross section and therefore cannot represent the three-dimensional structures typical of human or animal organs. However, mechanically or electronically sweeping a probe, such as an ultrasound probe, over an area of ​​interest generates a three-dimensional image volume. Alternatively, some ultrasound probes, such as "matrix" probes, contain multiple piezoelectric crystals and can build a "real-time" 3D ultrasound image. This can then be displayed, for example, through 3D holographic technology, making the anatomical structures much easier to visualize for both trained and untrained observers, as they better represent the true underlying structure / anatomy. Other technologies are also capable of acquiring or building 3D images.

[0005] 3D imaging (in the form of ultrasound, CT, and MR) has become available to clinicians in recent years and has proven extremely valuable due to its ability to convey imaging information in an intuitive format. For example, in the field of cardiology, such data is used to plan and guide surgical and catheter interventions.

[0006] A current limitation of 3D imaging is that while the data is inherently 3D, traditional 2D displays can only render a flat representation (projected, sliced, cast, etc.) of the image on the screen.

[0007] As mentioned above, 3D imaging devices are available. However, most computing systems (including imaging systems) have two-dimensional displays and user interface designs for two-dimensional navigation. Only recently has technology become available to display 3D images using computed reality technologies, such as holograms, virtual reality, mixed reality, or augmented reality. However, such technologies have not primarily been developed for the specific requirements of clinical settings. 3D systems tend to be expensive, and their interfaces are foreign to users accustomed to working in 2D.

[0008] A further problem with 3D rendering of data is that the amount of information depicted to the user increases significantly, which can be considered practical, but also makes navigation and changing views in 3D space, as well as assimilating information about features of interest, more difficult.

[0009] In many cases, these problems mean that users revert to working with two-dimensional slices of 3D image data using 2D displays and user interfaces. While this may be preferred by users, it misses information from three-dimensional views (e.g., from different directions) that may be relevant or interesting to the user. As a result, many of the benefits of 3D systems are lost, and 3D systems ultimately become expensive 2D systems. Summary of the Invention [Means for solving the problem]

[0010] According to one aspect of the present invention, there is provided a method and apparatus for navigation and display of 3D image data, the method comprising: obtaining a 3D image dataset to display; receiving an identification of a highlight location within the 3D image dataset; calculating a scalar opacity map of the 3D image dataset, the opacity map having respective values ​​for a plurality of locations in the 3D image dataset, each value depending on the respective location relative to the highlight location and the value of the 3D image at the respective location relative to the value of the 3D image at the highlight location; applying the opacity to the 3D image dataset to generate a modified 3D image view.

[0011] In embodiments of the present invention, a 3D image dataset refers to a 3D array of scalar or vector values, and possibly associated range, orientation, and resolution, that enable establishing correspondence between a 3D image and a real-world or imaginary object. The methods and apparatus described herein, and the outlined claims, apply to this definition of 3D image data, but also to other definitions, including, but not limited to, non-Cartesian spatial sampling of 3D scalar or vector fields, such as 3D spatial sampling (e.g., used in some 3D ultrasound systems), 3D unstructured datasets (e.g., results from computational fluid dynamics simulations), and point clouds (e.g., results from particle image velocimetry). In all cases, the values ​​of points in the 3D image may be color values ​​or other scalar or vector image-related values. The values ​​may or may not have been acquired by an imaging sensor. They may be ultrasound, MRI, Doppler, or other data, but may also represent detected blood velocity or other modalities or measurable / calculatable values ​​that are mapped to the 3D image.

[0012] For multi-channel 3D image data, different approaches can be taken (sometimes selectable in the user interface, sometimes pre-selected depending on the expected data), for example: The system may apply the same opacity mask to multiple (but not necessarily all) of the channels. Opacity masks may be calculated for one channel, calculated for multiple channels, merged, or calculated for flattened versions of the channels. The system can selectively (by the system or the user) calculate and apply an opacity mask to only one channel. For example, in the case of color flow Doppler, the anatomical channel (B-mode) can be faded out, but the blood flow (CFD channel) does not. The system may apply opacity masks to multiple channels in a weighted manner (determined by the system or specified by the user via a user interface). For example, in the case of color flow Doppler, the anatomical channel (B-mode) can be faded out more compared to the blood flow fade out (CFD channel), which fades out less compared to the same distance from the highlight point. Or the fade-out distance may be different for each channel, for example, anatomical structures may fade out very close to the highlight point, while color flow may fade away from the highlight point.

[0013] The step of calculating may include using a masking kernel, instead of which a predefined shape or another 3D image can be used as the masking function.

[0014] The modified 3D image view may be rendered as a 2D or 3D image (or other rendering) for display to the user.

[0015] Preferably, the scalar opacity map is calculated for a region of the 3D image dataset, the region comprising a portion of the 3D image dataset between the highlight location in the field of view and an edge of the 3D image dataset.

[0016] One example of such a 3D image is a 3D ultrasound image of the heart. These images contain many structures surrounding the heart that are opaque to ultrasound. As a result, these structures obscure the view of the internal cardiac structures. An example of this is shown in FIG. 2a. Embodiments of the present invention include methods and systems that allow users to peel back obscuring structures and focus on structures of interest. A preferred embodiment has an intuitive user interface that uses a center of view from a specified highlight location within the 3D imaged volume to define the structure of interest. In this way, the user simply identifies the location and direction they want to highlight (similar to shining a torch in an unlit scene), and the system can focus on the structure in that view. Preferably, a masking kernel is used (or the user can be given the ability to select from one of multiple masking kernels), and the user interface includes a user interface feature that allows the user to adjust the kernel's parameters. The parameters are preferably adjustable during use, allowing the user to change the degree to which the surrounding structures can be seen.

[0017] One type of kernel that can be used is a Gaussian kernel, described below, although other kernels are understood, such as kernels based on uniform / rectangular distributions, radial basis functions, spherical step functions, or exponential distributions (in the case of exponential distributions, the user selects points / regions to obscure rather than points / regions to highlight).

[0018] A preferred embodiment applies a position-dependent opacity kernel so that the opacity of image features in a rendered 2D view (or 3D view) of a 3D image dataset varies depending on the location of the highlight point. Preferably, the user interface allows the user to move the highlight point and optionally other parameters used to control opacity, as described in more detail below. Advantageously, the user is provided with an intuitive user interface for navigating the 3D image using a 2D display. Preferably, the user interface receives input from a keyboard and / or mouse and / or other controller that interacts with the user interface via the 2D display. In this way, the user can change the perspective / view around the 3D image and display highlighted structures / regions in various perspectives. As a 3D image defined by voxels or the like, the volume can be navigated and displayed using existing 3D rendering systems (or 2D slices or other renderings of the 3D image).

[0019] While the focus of the following description is on 3D images, it will be understood that embodiments of the present invention are applicable to higher dimensional datasets, such as, for example, 4D (3D image + time). In such cases, the user interface may include functionality for the user to set the time point (or range) to be displayed, or it may automatically loop through recorded images for viewing.

[0020] Similarly, the dimensions need not correspond (or correspond exactly) to data from the visible spectrum, but may include representations of ultrasound, MRI (magnetic resonance imaging), or other data used to form the multispectral or hyperspectral image to be displayed.

[0021] for example: 3D color Doppler data This modality consists of three channels of 3D imaging data over time. Each time frame is a volume of data, and each voxel of imaging data has two values ​​(channels): a background value corresponding to the anatomical image in B-mode (brightness), which is usually visualized in grayscale; and a Doppler velocity value, which measures the blood flow velocity along a specific direction, usually in cm / s, and is usually visualized on a red-to-blue color scale.

[0022] Diffusion MRI data This modality consists of N channels of 3D imaging data (N>0). Each voxel of the imaging data contains N+1 values. The first value is called the B0 signal, and all subsequent values ​​correspond to the diffusion-weighted signal at the voxel location. N is typically 6, but can be up to several hundred channels. This type of modality is often used to investigate unique tissue orientations within organs.

[0023] PET-MRI data This modality is generated by a dedicated MRI scanner equipped with a PET imaging device. It consists of two channels of 3D imaging data. Each voxel of the imaging data contains two values: the first value corresponds to the MR-weighted signal (which can be T1, T2-weighted, or other MR modalities), and the second value corresponds to the PET signal. This type of imaging modality is often used to highlight the concentrated presence of radioactive tracers attached to tumor tissue and superimposed on the structural MRI signal.

[0024] MR (or CT)-ultrasound fusion While not a modality itself, (usually live) 3D or 2D ultrasound data can be fused with MR or CT data. This can provide structural / functional views or show features that may not be as clear in one modality as in the other. This can be used for guidance. The two sets of data can be kept in separate coordinate systems or fused into a single volume, with one modality registered to the other and then resampled.

[0025] It will be appreciated that the calculation of masking kernels, opacity channels, and 2D or 3D rendered images can be performed on the fly or cached / recorded. In particular, for looped (in-time) display, it may be desirable to generate rendered images in the first loop and cache them until the position or kernel parameters are moved. Furthermore, it will be appreciated that embodiments of the present invention are applicable for use in live image acquisition situations. The user interface can be used in place of a view used by a technician to guide the probe as it scans a patient, or as an alternative view for the clinician that can be controlled independently of the probe's operation.

[0026] Embodiments of the present invention can operate substantially in real time, allowing a user to navigate the imaged volume and change what is and is not displayed simply by moving the highlight position and kernel parameters.

[0027] It will be appreciated that in contrast to existing systems that involve slicing a plane through a volume and then manually cropping the image, embodiments of the present invention provide great power and flexibility while reducing the expertise and skill required to operate an imaging system.

[0028] The preferred embodiment utilizes full 3D interaction, allowing the user to select a position in 3D (e.g., by hand tracking or using an interaction tool) and for the structure to fade out as it moves away from this point.

[0029] It will be appreciated that the user's interactions can be recorded for later playback (and since the recording allows the view itself to be recalculated at display time, it is only necessary to record the viewpoint and parameters for playback; this approach is particularly advantageous, especially if different display devices are used to render the 3D image dataset, as different clinicians or specialists may have different display technologies available). [Brief explanation of the drawings]

[0030] Embodiments of the present invention will now be described, by way of example only, with reference to the accompanying description, which includes the following.

[0031] [Figure 1] 1 is a schematic diagram of an imaging system according to an embodiment of the present invention.

[0032] [Figure 2a-2b] 2A and 2B are exemplary line drawings and corresponding images showing a 3D rendered image without processing (FIG. 2a) and with processing (FIG. 2b) according to an embodiment of the present invention.

[0033] [Figure 3a-3b] 3A and 3B are images of ultrasound scans showing a conventional image (FIG. 3a) and the change in the image after applying an embodiment of the present invention (FIG. 3b).

[0034] [Figure 4] 1 shows an image where the method of the present invention has been applied and the trade-off between the color distance λ parameter and the Euclidean distance parameter is illustrated by the steepness of the Gaussian kernel through θ. DETAILED DESCRIPTION OF THE INVENTION

[0035] SUMMARY OF THE INVENTION Embodiments of the present invention relate to methods and systems for displaying and applying user input to manipulate 3D images.

[0036] There are many sources of 3D image data, including 3D imaging scanners, and embodiments may receive data directly from the 3D image data source, or may receive data that has previously been acquired and stored in a data repository or the like.

[0037] 3D image data is typically encoded in the form of a 3D array of voxels. In 3D imaging, the term "voxel" is used to refer to a scalar or vector value on a regular grid in three-dimensional space. Similar to pixels in a bitmap, voxels themselves typically do not have a location (spatial coordinates) explicitly encoded with their value. Instead, rendering systems infer a voxel's location based on its position relative to other voxels (i.e., its position within the data structures that make up a single volumetric image).

[0038] In embodiments of the present invention, the 3D image data is preferably processed (preferably in real time or near real time) to suppress image features at the periphery of the field of view. Preferably, the system relies on a distance-dependent opacity map to determine how / if to depict image features in the rendered output. In this way, image features at the focal point (specified in the user interface) appear completely opaque, while peripheral image features become less visible as the opacity decreases, and distant image features are increasingly suppressed. In one embodiment, the further features are from the direct field of view, the more suppressed they are. It is important to note that the 3D image data is processed as an array of voxels (or other representation if voxels are not used). As such, the presence of structure is irrelevant to the system and no additional processing is required. Opacity varies based on distance from the focal point and also on color (or other scalar value if not color). Because blood vessels are likely to have similar colors, vascular voxels have similar opacity depending on their distance to the viewpoint.

[0039] FIG. 1 is a schematic diagram of an imaging system according to an embodiment of the present invention.

[0040] The imaging system includes an image data source 10, a processor 20, a display 30, and a user interface. The user interface in this embodiment includes position controls 40 and user input devices 45, although it will be understood that different representations and input devices can be used.

[0041] Processor 20 receives image data from image data source 10 and position data from position control 40. It generates an opacity channel from the position data and uses this to render the image data for display on display 30.

[0042] In the illustrated embodiment, the position control is separate from the display 30. In some embodiments, the position control 40 may be superimposed on the image displayed on the display 30. In other embodiments, it may be displayed separately.

[0043] A user (who may or may not be the operator of the imaging probe generating the imaging data provided by imaging data source 10) interacts with position control 40 to define the highlight position (base of arrow (A)) and direction (arrow direction), which in this embodiment is data provided to processor 20. Positioning can be achieved using, for example, X / Y / Z position using a mouse, tablet, keyboard, sliders, etc., and X / Y / Z highlight direction. In the illustrated example, the position cursor is represented by an arrow and moves from position A to position B.

[0044] Once the positioning and kernel parameters have been established and the opacity channel Vo has been calculated, the resulting two-channel image is output and visualized via a transfer function that maps intensity to color and the calculated opacity channel to opacity.

[0045] 2a and 2b are exemplary line drawings and corresponding images showing 3D rendered images without (FIG. 2a) and with (FIG. 2b) processing according to an embodiment of the present invention, and FIGS. 3a and 3b are images of an ultrasound scan showing the changes after applying an embodiment of the present invention (FIG. 3a is an illustration of the rendering without applying an embodiment of the present invention).

[0046] Given the intensity and opacity channels, application of the transfer function by processor 20 is straightforward. It will be appreciated that the output may be a 3D display device, the projection of a 3D image onto a 2D display, or the output may be communication or storage of the rendered data (or the underlying 3D image dataset and the opacity channel, or just the opacity channel).

[0047] It will be appreciated that both the target position and the kernel parameters (θ, λ) can be adjusted interactively. Preferably, the system includes a user interface that allows a user to move a cursor to select a target point and to select kernel parameters using sliders or other GUI elements.

[0048] The amount by which the surrounding area is obscured can be controlled by a kernel trade-off parameter (which, as mentioned above, is preferably provided to the user in the form of a GUI slider or the like). The trade-off in the above embodiment is between color distance λ and Euclidean distance, with the steepness of the opacity kernel through θ, as shown in Figure 4.

[0049] Figure 3a is an example of a conventional rendering of ultrasound image data used in medical imaging, for example, to assist clinicians in diagnostic and treatment decisions. Figure 3b is an image rendered using an embodiment of the present invention. In an embodiment of the present invention, the rendering is preferably modified depending on user input so that organs or other imaged structures at or near the highlight focus (crosshairs in Figure 4) are rendered, but when image features are encountered that are further away from the highlight focus, they are suppressed relative to the distance to the highlight focus.

[0050] Preferably, the system includes a user interface that allows the user to interact with the rendered 2D environment in 3D, allowing the user to select a position in 3D (e.g., using manual tracking or interaction tools) and fade out structures as they move away from this point (see Figures 2b and 3b).

[0051] In this preferred embodiment, 3D image data in the form of a scalar (single channel) or vector (multi-channel) image is received as input. The system calculates an opacity channel based on intensity and a kernel that operates on the relative position of voxels in the 3D image data to a user-defined position (typically the system has a default position that can be manipulated by the user via a user interface). It will be appreciated that image data in other formats can also be used as input.

[0052] The opacity channel is calculated relative to the focus of the highlight. The opacity channel is used to generate the rendered view in Figure 2b or 3b from the input image data. As can be seen in Figures 2b and 3b, the region of interest is opaque and can be seen through the semi-transparent structure.

[0053] 3D images are visualized using this transfer function, preferably using volume rendering to generate 2D projections.

[0054] As understood, volume rendering refers to a set of techniques used to display a 2D projection of a 3D discretely sampled dataset, typically a 3D scalar field. To render a 2D projection of a 3D image dataset, one defines a camera in space relative to the volume, the opacity, and also the color of every voxel. This is typically defined using an RGBA (for red, green, blue, alpha) transfer function, which defines the RGBA values ​​of all possible voxel values.

[0055] For example, volumes can be displayed by extracting isosurfaces (surfaces of equal value) from the volume and rendering them as a polygon mesh, or by directly rendering the volume as a block of data. The marching cubes algorithm is a common technique for extracting isosurfaces from volume data. Ray-casting algorithms are common techniques for directly rendering volumes.

[0056] Preferably, the 3D image dataset is stored as a D-dimensional scalar map with samples on a uniform grid G. This can be performed as a transformation step at the time the 3D image dataset is received, or the dataset can be received and transformed / mapped as the first step before rendering is performed.

[0057] V(X):R D →R is a grid G ​​⊂ R D If we define V(G) as a D-dimensional scalar map containing the samples above, then V(G) is a D-dimensional scalar image. Similarly, V(X):R D →Define R as a vector-valued map, and V(G) is a vector-valued image of dimension D. In what follows, we denote all images V and assume that scalar images are vector images with d=1.

[0058] To calculate the opacity channel, the user preferably provides the following: 1) spatial location (preferably via a movable cursor) P∈R D and; 2) An M-dimensional parameter vector for the masking kernel θ (which can be provided, for example, via a slider or other control in the GUI).

[0059] In one embodiment, the masking kernel k maps a location X and an image V to a scalar opacity value and is of the form:

number

number

[0060] It will be appreciated from the above discussion that the kernel need not be Gaussian in form. Other examples include radial (spheroidal) step functions and inverse Gaussian kernels. i) Radial step function centered at P0:

number

number

[0061] Generalizing the above approach to any kernel, the preferred embodiment uses a kernel that combines intensity (relative to a reference intensity value) and position (Euclidean distance to the target of interest) to compute the opacity channel V o is defined as follows:

number

number

number

number

[0062] As above, VR is the reference image value (which can be the intensity at the target of interest or, in scalar ultrasound, is typically fixed at VR=255, i.e., the intensity of the bright white area).

[0063] It will be appreciated that parameters do not need to be provided by the user but can be system defaults. Additionally, kernel parameter positioning and masking can be provided via an external system that may record previous views of the dataset or have data from other sources (diagnostic, imaging, medical history, or other data) and is guided by that data to highlight features that may be of interest. The system may also include machine learning or other systems to provide assistance regarding the best selection of parameters for a particular feature in the focus of the field of view or highlight location (e.g., in the crosshairs, etc.).

[0064] Figure 4 shows an example image of the opacity channel obtained using an embodiment of the present invention. The image shows the opacity channel for increasing values ​​of λ (from left to right: 0.1, 0.2, 0.3, 0.4, and 0.5) and θ (from top to bottom: 0.05, 0.1, and 0.15) when selecting a point on the atrium (white crosses).

[0065] It will be appreciated that the above approaches can be implemented in software and / or hardware. A recently utilized technique for accelerating traditional volume rendering algorithms, such as ray casting, is the use of modern graphics cards. Starting with programmable pixel shaders, people recognized the power of parallel operations on multiple pixels and began to use graphics processing units (GPGPUs) and other high-performance hardware to perform general-purpose computing. Pixel shaders can randomly read and write from video memory and perform some basic mathematical and logical calculations. These single-instruction, multiple-data (SIMD) processors were used to perform common calculations such as polygon rendering and signal processing. In recent GPU generations, pixel shaders can now function as multiple-instruction, multiple-data (MIMD) processors (which can now branch independently) that utilize up to 1GB of texture memory in floating-point format. This capability allows virtually any algorithm with steps that can be executed in parallel, such as volume ray casting and tomographic reconstruction, to run with incredible acceleration. Programmable pixel shaders can be used to simulate changes in properties such as lighting, shadows, reflections, and emissive color. Such simulations can be created using high-level shading languages.

[0066] The foregoing preferred embodiment has been disclosed for illustrative purposes. Variations and modifications of the basic concepts of the present invention will be readily apparent to those skilled in the art. For example, graphic symbols other than dots and crosshairs can be used to represent locations within a volume. Furthermore, the user interface is not limited to specific software elements. Various software GUI elements can be used, as well as hardware interface functions such as trackballs, rocker switches, rotary switches, and keys. Mice, joysticks, levers, sliders, or other input devices can also be used. Motion-based detectors, virtual controllers / environments, augmented reality, and the like can also be used. It will be appreciated that the generated rendered images can be used with many different display technologies, including 2D, 3D, virtual reality, augmented reality, holographic, and other display types. All such variations and modifications are intended to be encompassed by embodiments of the present invention.

[0067] It should be understood that certain embodiments of the present invention discussed below may be embodied as code (e.g., a software algorithm or program) residing on a computer-usable medium having firmware and / or control logic for enabling execution on a computer system having a computer processor. Such a computer system typically includes memory storage configured to provide output from the execution of code that configures the processor as it executes. The code may be arranged as firmware or software, and in an object-oriented programming environment, may be organized as a set of modules, such as individual code modules, function calls, procedure calls, objects, etc. When implemented using modules, the code may include a single module or multiple modules that operate in conjunction with each other.

[0068] Any embodiment of the present invention may be understood as including any or all combinations of two or more of the parts, elements and features referenced or shown herein, individually or collectively, and specific integers that have known equivalents in the art to which the present invention pertains are referred to herein, and such known equivalents are deemed to be incorporated herein as if individually set forth.

[0069] While illustrative embodiments of the present invention have been described, it should be understood that various changes, substitutions, and alterations could be made by those skilled in the art without departing from the invention as defined by the recitation in the claims and equivalents thereof.

[0070] This work is independent research funded by the National Institute for Health Research (Inventions for Innovation programme, 3DHeart project, II-LA-0716-20001). The views expressed are those of the authors and not necessarily those of the NHS, NIHR or Department of Health.

[0071] This application claims priority from GB Patent No. 1913832.0, the contents of which and the contents of the abstract accompanying this application are incorporated herein by reference.

Claims

1. 1. A computer-implemented method for navigation and display of 3D image data, comprising: obtaining a 3D image dataset to display; receiving an identification of a highlight location within the 3D image dataset; calculating a scalar opacity map of the 3D image dataset, the scalar opacity map having respective values ​​for a plurality of locations in the 3D image dataset, the respective values ​​depending on the respective distance from the highlight location and the value of the 3D image at the respective location relative to the value of the 3D image at the highlight location; applying the opacity to the 3D image dataset to generate a modified 3D image view; said calculating using a masking kernel; Computer-implemented methods.

2. The method of claim 1 , wherein the masking kernel is selected from a set including a Gaussian kernel, a uniform / rectangular distribution, a radial basis function, a spherical step function, or a kernel based on an exponential distribution.

3. receiving, via a user interface, the highlight location within the 3D image dataset; calculating the scalar opacity map dependent on the highlight location, the 3D image dataset, and the masking kernel; The method of claim 2 further comprising:

4. The method of claim 3 , further comprising receiving parameters for the masking kernel via the user interface.

5. The method of claim 3 or 4, wherein the user interface comprises a 2D representation of the 3D image dataset.

6. The method of claim 1 , wherein the calculating uses a predefined shape or a predefined 3D as a masking function.

7. The method of claim 1 , further comprising rendering the modified 3D image as a 2D or 3D image for display.

8. 8. The method of claim 1, further comprising calculating the scalar opacity map for a region of the 3D image dataset, the region comprising a portion of the 3D image dataset between the highlight location in a field of view and an edge of the 3D image dataset.

9. 1. A system for navigation and display of 3D image data, comprising: a data repository for storing the 3D image dataset to be displayed; a user interface configured to receive an identification of a highlight location within the 3D image dataset; a processor configured to calculate a scalar opacity map of the 3D image dataset and apply the opacity to the 3D image dataset to generate a modified 3D image view; the scalar opacity map having respective values ​​at a plurality of locations in the 3D image dataset, the respective values ​​depending on respective distances from the highlight location and the value of the 3D image at the respective locations relative to the value of the 3D image at the highlight location; said calculating using a masking kernel; system.

10. 10. The system of claim 9, wherein the system is configured to receive adjustable parameters for a masking kernel via the user interface, and the processor is configured to apply the received parameters when calculating the scalar opacity map.

11. The system of claim 10 , wherein the user interface includes a display showing a 2D representation of the 3D image dataset, and the system is configured to receive the specification of the highlight location via the 2D representation.

Images