Rendering a 2D dataset

Abstract

In some examples, a two-dimensional (2D) dataset, such as an image, is converted into a three-dimensional (3D) dataset (e.g., a volume). The values ​​and material properties of the voxels of the 3D dataset are based at least in part on the values ​​of the pixels in the 2D dataset. The 3D scene in the 3D dataset is rendered from a plane parallel to the plane of the 2D dataset to produce a "top-down" rendering that looks like a 2D image. In some examples, additional coloring is added to the rendered 2D image based on intensity or other properties of the 2D dataset.

Description

[Technical Field]

[0001] This application relates to generating two-dimensional images, and more particularly to applying three-dimensional rendering techniques to two-dimensional data sets. [Background technology]

[0002] It is often difficult for medical image evaluators to perceive depth within medical images. This can be due to the lack of familiar reference points for determining relative distances and / or the lack of reflections and shadows when the images are not acquired using a light source (e.g., x-ray, ultrasound). Some types of medical imaging may use photorealistic rendering, which applies physically based ray tracing algorithms to generate a two-dimensional (2D) projection of a three-dimensional (3D) (e.g., volumetric) dataset. Photorealistic rendering applies one or more virtual light sources to the 3D dataset to simulate shadows and reflections. These virtual lighting effects improve depth perception. However, photorealistic rendering only works with volumetric datasets or surface meshes obtained from 3D acquisitions. This does not benefit traditional 2D imaging. Despite advances in 3D imaging, many evaluators (e.g., radiologists, surgeons) still prefer to view 2D images rather than 3D renderings. Therefore, it is desirable to provide an enhanced depth perception to 2D images. Summary of the Invention

[0003] Disclosed herein are examples of post-processing techniques for simulating 3D photorealistic rendering without the need for a 3D volumetric image dataset. Depth perception is added to the 2D image, and structure is enhanced by lighting effects. As disclosed herein, a conversion from the 2D image to a 3D volumetric representation of density voxels is performed. Material properties such as absorption and scattering are assigned to each voxel. The 3D scene is rendered with at least one light source overlooking the image from a viewer position. Optionally, color mapping of the voxels may be performed to add coloring according to depth and / or intensity.

[0004] According to at least one example disclosed herein, an apparatus includes a processor configured to convert a two-dimensional (2D) dataset including a plurality of pixels into a three-dimensional dataset including a plurality of voxels, wherein a plane of the 2D dataset includes some voxels of the plurality of voxels corresponding to the plurality of pixels, the plurality of voxels including additional voxels located off-plane of the 2D dataset to define a height dimension of the 3D dataset, the processor configured to assign material properties to the plurality of voxels, and render a 3D scene of the 3D dataset from a viewing plane parallel to the plane of the 2D dataset, the rendering simulating at least one virtual light source propagated through the 3D dataset, the propagation of the virtual light source through the 3D dataset based at least in part on the material properties assigned to the plurality of voxels.

[0005] In some examples, the apparatus further comprises an ultrasound probe configured to acquire ultrasound signals from the subject, the 2D data set being generated from the ultrasound signals.

[0006] In some examples, the location of the simulated light source is based at least in part on the location of the ultrasound probe relative to the location where the ultrasound signal was acquired.

[0007] In some examples, the processor is further configured to assign color values ​​to the plurality of voxels based at least in part on a distance from the voxel to the viewing surface.

[0008] In some examples, the apparatus further comprises a user interface configured to receive user input that determines a location of the virtual light source relative to the 3D dataset.

[0009] According to at least one example disclosed herein, a method includes converting a two-dimensional (2D) set including a plurality of pixels into a three-dimensional (3D) dataset including a plurality of voxels, wherein a plane of the 2D dataset includes some voxels of the plurality of voxels corresponding to the plurality of pixels, the plurality of voxels including additional voxels located out of the plane of the 2D dataset and defining a height dimension of the 3D dataset; assigning material properties to the plurality of voxels; and rendering a 3D scene of the 3D dataset from a viewing plane parallel to the plane of the 2D dataset, wherein the rendering includes simulating at least one virtual light source propagated through the 3D dataset, the propagation of the virtual light source through the 3D dataset being based at least in part on the material properties assigned to the plurality of voxels.

[0010] In some examples, the height dimension is based at least in part on intensity values ​​of a plurality of pixels.

[0011] In some examples, assigning the material properties includes defining at least one transfer function for the at least one material property.

[0012] In some examples, the transfer function includes a threshold, and voxels of the plurality of voxels having a value below the threshold are rendered as transparent for at least one material property.

[0013] In some examples, the material properties include at least one of density, absorption, reflectance, or scattering.

[0014] In some examples, at least one of the material properties is wavelength dependent.

[0015] In some examples, the 2D dataset is a multi-channel dataset, and the different material properties of the multiple voxels are each based on values ​​from a different channel of the multi-channel dataset.

[0016] In some examples, the method further comprises assigning color values ​​to the plurality of voxels based at least in part on the distance from the voxels to the viewing surface.

[0017] In some examples, converting the 2D dataset to a 3D dataset comprises assigning binary values ​​to a plurality of voxels.

[0018] In some examples, converting the 2D dataset to a 3D dataset includes assigning values ​​to a plurality of voxels based at least in part on intensity values ​​of a plurality of pixels.

[0019] In some instances, the values ​​assigned to the voxels decrease with height.

[0020] In some examples, the plurality of pixels of the 2D dataset includes a first plurality of pixels and a second plurality of pixels, and the plurality of voxels of the 3D dataset includes a first plurality of voxels corresponding to the first plurality of pixels and a second plurality of voxels corresponding to the second plurality of pixels, the first plurality of pixels and the second plurality of pixels corresponding to the first and second 2D images, respectively.

[0021] In some examples, material properties of the first plurality of voxels are assigned independently from material properties of the second plurality of voxels.

[0022] In some examples, the first plurality of voxels are rendered separately from the second plurality of voxels to generate first and second 3D scenes, respectively, and the first and second 3D scenes are combined to provide the 3D scene.

[0023] In some examples, the first 2D image is acquired from a first imaging mode and the second 2D image is acquired from a second imaging mode that is different from the first imaging mode. [Brief explanation of the drawings]

[0024] [Figure 1A] 1 is a diagram of existing two-dimensional (2D) shading. [Figure 1B] FIG. 1 is a diagram of volumetric rendering of a 2D image according to an example of the present disclosure. [Figure 2] FIG. 1 is a block diagram of an ultrasound imaging system arranged in accordance with an example of the present disclosure. [Figure 3] FIG. 2 is a block diagram illustrating an exemplary processor, according to an example of the present disclosure. [Figure 4] FIG. 10 illustrates an example of converting a 2D image into a three-dimensional (3D) volume, according to an example of the present disclosure. [Figure 5] FIG. 10 illustrates a step of assigning material properties to voxels in a volume, according to an example of the present disclosure. [Figure 6] 10 is a graph illustrating an example of two-stage rendering, according to an example of the present disclosure. [Figure 7] 1A-1C illustrate an example 2D image and a corresponding example of a rendered 2D image according to an example of the present disclosure. [Figure 8] 10 is a graph illustrating an example of depth-based shading, according to an example of the present disclosure. [Figure 9] 1A-1C illustrate an example 2D image and a corresponding example of a rendered 2D image according to an example of the present disclosure. [Figure 10] FIG. 1 is a graphical illustration of a method according to an example of the present disclosure. [Figure 11]1A-1C illustrate examples of a 2D Doppler image overlaid on a 2D B-mode image, and a rendered 2D image incorporating the Doppler and B-mode image, according to examples of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0025] The following description of specific illustrative examples is merely exemplary in nature and is not intended to limit the present disclosure, its application, or uses. In the following detailed description of exemplary devices, systems, and methods of the present invention, reference is made to the accompanying drawings that form a part hereof, and in which are shown, by way of illustration, specific examples in which the described devices, systems, and methods may be practiced. These examples are described in sufficient detail to enable those skilled in the art to practice the presently disclosed devices, systems, and methods, and it should be understood that other examples may be utilized and structural and logical changes may be made without departing from the spirit and scope of the present disclosure. Moreover, for purposes of clarity, detailed descriptions of specific features will not be discussed unless they would be apparent to those skilled in the art, in order to avoid obscuring the description of the present disclosure. Therefore, the following detailed description is not to be taken in a limiting sense, and the scope of the present systems and methods is defined only by the appended claims.

[0026] Users of medical imaging systems, such as radiologists, surgeons, and sonographers, often feel more comfortable making decisions based on two-dimensional (2D) images compared to three-dimensional (3D) renderings. However, 2D medical images typically provide little or no visual cues to assist the viewer with depth perception. Some post-processing image enhancement methods use local illumination shading to artificially add depth perception to 2D images. A well-known example is the emboss filter.

[0027] Applying local illumination shading to 2D images involves interpreting image brightness values ​​as height measurements for 3D surfaces. Adding ambient, diffuse, and specular effects to surfaces results in better perception of shape. By depicting local image features, virtual lighting can create sharper contours and better separation of objects. Existing 2D shading uses local gradient-based surface illumination. In other words, the rate of change of pixel intensity values ​​in one or more directions within a 2D image is used to determine shading. However, existing 2D shading suffers from several limitations. For example, shading requires the input surface to be smoothed, and therefore shading cannot be directly applied to high-frequency texture components, such as coarse-grained ultrasound images. Furthermore, global effects such as shadows or volumetric scattering are not considered, thereby limiting the added perceived contrast. Therefore, improving depth perception in 2D images is desirable.

[0028] According to examples of the present disclosure, a 2D dataset is converted into a 3D dataset. The 3D dataset is a volumetric representation of density voxels. Material properties (absorption, scattering) are assigned to each voxel in the 3D dataset. A 3D scene is rendered from the 3D dataset using at least one light source. The rendering is from a viewer's position looking down on the 3D dataset. Rendering from this perspective provides the viewer with the appearance of a 2D image (rather than the appearance of a volume as in traditional renderings of 3D datasets). Therefore, the 3D scene is also referred to as a rendered 2D image or a "top-down rendering." Optionally, in some examples, voxels in the 3D dataset may be assigned a color based on the voxel's depth and / or intensity value.

[0029] In some applications, material property assignment and volumetric rendering improves depth perception and contrast within the rendered 2D image compared to 2D images augmented by traditional local surface illumination techniques.

[0030] 1A and 1B illustrate the difference between existing 2D shading and volumetric rendering of a 2D image according to an example of the present disclosure. In FIG. 1A, plot 100 shows an exemplary cross-section of a solid surface 104, plotted as height (z) versus distance (x) illuminated by a light source 102. Existing methods simulate the height of the solid surface 104 using the gradient (e.g., rate of change) of pixel intensity values ​​of the 2D image or the pixel intensity values ​​alone. The light source 102 is then projected onto the solid surface 104 as light rays 106, and the light rays 106 reflected from the solid surface 104 to a viewing surface 108 are used to generate a shaded 2D image.

[0031] In FIG. 1B, plot 110 shows a cross-section of volume 114, illuminated by light source 102, plotted as height (z) versus distance (x), according to an example of the present disclosure. Unlike the solid surface 104 of FIG. 1A, volume 114 is made up of a voxel grid that can be filled with values ​​that are non-binary. These non-binary values ​​define the material properties of the voxels. The material properties determine how light interacts with volume 114 and / or determine the appearance of volume 114. As shown in plot 110, some light rays, such as ray 112 projected from light source 102, pass (at least partially) through a portion of volume 114 before reflecting from volume 114, rather than just reflecting from the surface, such as ray 106. This results in more realistic shading, which improves contrast by darkening areas surrounding high-intensity regions in the 2D image. In some examples, some light rays, such as ray 112, are partially absorbed by volume 114. When such light transmission, absorption, and / or scattering is not equal across the spectrum (eg, different absorption coefficients for red, green, and blue), a gray value scalar 2D image is converted to a color image by rendering.

[0032] In some applications, volume 114 is less dependent on local gradients compared to 2D surface shading techniques, which allows the technique to be used with non-smooth texture images. An example of a volume rendered from a non-smooth 2D image is shown in plot 120 of FIG. 1B.

[0033] FIG. 2 shows a block diagram of an ultrasound imaging system 200 constructed in accordance with the principles of the present disclosure. The ultrasound imaging system 200 according to the present disclosure includes a transducer array 214. The transducer array is contained within an ultrasound probe 212, such as an external probe or an internal probe such as an intravascular ultrasound (IVUS) catheter probe. In another example, the transducer array 214 is in the form of a flexible array configured to be conformally applied to the surface of a subject (e.g., a patient) being imaged. The transducer array 214 is configured to transmit ultrasound signals (e.g., beams, waves) and receive echoes (e.g., received ultrasound signals) in response to the transmitted ultrasound signals. Various transducer arrays may be used, such as linear arrays, curved arrays, or phased arrays. For example, the transducer array 214 may include a two-dimensional array (as shown) of transducer elements that can be scanned in both elevation and azimuth dimensions for 2D and / or 3D imaging. As is commonly known, the axial direction is the direction perpendicular to the plane of the array (or, in the case of a curved array, the axial direction fans out), the azimuthal direction is generally defined by the longitudinal dimension of the array, and the elevation direction is transverse to the azimuthal direction.

[0034] In some examples, the transducer array 214 is coupled to a microbeamformer 216. The microbeamformer is located within the ultrasound probe 212 and controls the transmission and reception of signals by the transducer elements in the array 214. In some examples, the microbeamformer 216 controls the transmission and reception of signals by the active elements in the array 214 (e.g., an active subset of the elements of the array that define an active aperture at any given time).

[0035] In some examples, the microbeamformer 216 is coupled to a transmit / receive (T / R) switch 218, for example, by a probe cable or wirelessly. The transmit / receive (T / R) switch switches between transmit and receive and protects the main beamformer 222 from high-energy transmit signals. In some examples, for example, in portable ultrasound systems, the T / R switch 218 and other elements in the system may be contained within the ultrasound probe 212 rather than in an ultrasound system base that houses the image processing electronics. The ultrasound system base typically includes circuitry for signal processing and image data generation, as well as software and hardware components containing executable instructions for providing a user interface.

[0036] Transmission of ultrasound signals from the transducer array 214 under control of the microbeamformer 216 is directed by a transmit controller 220, which may be coupled to a T / R switch 218 and a main beamformer 222. The transmit controller 220 controls the direction in which the beam is steered. The beam may be steered straight ahead (or orthogonal) from the transducer array 214 or at different angles for a wider field of view. The transmit controller 220 is also coupled to a user interface 224 to receive input from user manipulation (e.g., user control) of a user input device. The user interface 224 includes one or more input devices, such as a control panel 252. The input devices may include one or more mechanical controls (e.g., buttons, sliders, etc.), touch-sensitive controls (e.g., trackpads, touchscreens, etc.), and / or other known input devices.

[0037] In some examples, the partially beamformed signals generated by the microbeamformer 216 are coupled to a main beamformer 222, where the partially beamformed signals from individual patches of transducer elements are combined into a complete beamformed signal. In some examples, the microbeamformer 216 is omitted. In these examples, the transducer array 214 is under the control of the main beamformer 222, which performs all beamforming of the signals. In examples with and without the microbeamformer 216, the beamformed signals of the main beamformer 222 are coupled to processing circuitry 250. The processing circuitry includes one or more processors (e.g., signal processor 226, B-mode processor 228, Doppler processor 260, and one or more image generation and processing computers 268) configured to generate ultrasound images from the beamformed signals (i.e., beamformed RF data).

[0038] The signal processor 226 is configured to process the received, beamformed RF data in various ways, such as bandpass filtering, decimation, I and Q component separation, and harmonic signal separation. The signal processor 226 also performs additional signal enhancements, such as speckle reduction, signal combining, and electronic noise removal. The processed signals (also referred to as I and Q components or IQ signals) are coupled to additional downstream signal processing circuitry for image generation. The I and Q signals are coupled to multiple signal paths within the system, each associated with a unique arrangement of signal processing components suitable for generating a different type of image data (e.g., B-mode image data, Doppler image data). For example, the system includes a B-mode signal path 258, which couples a signal from the signal processor 226 to a B-mode processor 228 to generate B-mode image data.

[0039] The B-mode processor 228 can use amplitude detection for imaging structures within the body. The B-mode processor 228 generates signals for tissue and / or contrast images. The signals produced by the B-mode processor 228 are coupled to a scan converter 230 and / or a multiplanar reformatter 232. The scan converter 230 is configured to arrange the echo signals into a desired image format based on the spatial relationship in which the echo signals were received. For example, the scan converter 230 arranges the echo signals into a two-dimensional (2D) fan-shaped format or a pyramidal or otherwise shaped three-dimensional (3D) format.

[0040] In some examples, the system includes a Doppler signal path 262 that couples the output from the signal processor 226 to a Doppler processor 260. The Doppler processor 260 is configured to estimate the Doppler shift and generate Doppler image data. The Doppler image data includes color data. The color data is then overlaid on B-mode (i.e., grayscale) image data for display. The Doppler processor 260 is configured to filter out undesired signals (e.g., noise or clutter associated with stationary tissue), for example, using a wall filter. The Doppler processor 260 is further configured to estimate velocity and power by known techniques. For example, the Doppler processor includes a Doppler estimator, such as an autocorrelator, in which the velocity (Doppler frequency) estimate is based on the argument of a lag-one autocorrelation function (e.g., R1) and the Doppler power estimate is based on the magnitude of a lag-zero autocorrelation function (e.g., R0). The velocity estimate is referred to as color Doppler data, and the power estimate is referred to as power Doppler data. Motion can also be estimated by known phase-domain (e.g., parametric frequency estimators such as MUSIC and ESPRIT) or time-domain (e.g., cross-correlation) signal processing techniques. Other estimators related to the temporal or spatial distribution of velocity, such as estimators of acceleration or temporal and / or spatial velocity derivatives, can be used instead of or in addition to the velocity estimator. In some examples, the velocity and power estimates (e.g., color and power Doppler data) undergo post-processing such as segmentation and filling and smoothing, along with additional thresholding to further reduce noise. The velocity and / or power estimates are then mapped to desired ranges of color and / or intensity for the display according to one or more color and / or intensity maps. The map data, also referred to as Doppler image data, is then coupled to the scan converter 230. In the scan converter, the Doppler image data is converted to the desired image format to form a color Doppler or power Doppler image.

[0041] The multiplanar reformatter 232 can convert echoes received from points within a common plane (e.g., slice) within a volumetric region of the body into an ultrasound image (e.g., a B-mode image) of that plane, as described, for example, in U.S. Pat. No. 6,443,896 (Detmer). In some examples, the user interface 224 is coupled to the multiplanar reformatter 232 for selection and control of the display of multiple multiplanar reformat (MPR) images. In other words, the user selects a desired plane within the volume for which a 2D image is to be generated. In some examples, in addition to selecting the location and / or orientation of the plane within the volume, the user also selects the thickness of the plane. In some examples, the planar data of the multiplanar reformatter 232 is provided to a volume renderer 234. The volume renderer 234 generates an image (also referred to as a projection, 3D scene, or rendering) of the 3D dataset as viewed from a given reference point, as described, for example, in U.S. Pat. No. 6,530,885 (Entrekin et al.). In some examples, the image generated by volume renderer 234 is based on a volume containing voxels (e.g., a 3D data set), but the final image rendered by volume renderer 234 is a 2D data set containing pixels (e.g., a 2D image), which is then displayed on a conventional display (e.g., a liquid crystal display).

[0042] According to examples of the present disclosure, the volume renderer 234 receives 2D images from the scan converter 230 and / or the multiplanar reformatter 232. The 2D images include 2D datasets, each including pixels with intensity and / or color values. The volume renderer 234 converts the 2D dataset into a 3D dataset. In some examples, the 3D dataset includes voxels that define a volume. Material properties (e.g., density, absorption, scattering) are assigned to each voxel in the 3D dataset. The volume renderer 234 generates, from the 3D dataset, a rendered 2D image from the viewing plane of a virtual viewer that observes the 3D dataset from an angle orthogonal to the imaging plane of the 2D image. The volume renderer 234 simulates at least one light source when generating the rendered 2D image. In some examples, the light source is co-located with the virtual viewer. In other examples, the light source is at a different location. Optionally, in some examples, the volume renderer 234 may assign colors to voxels in the 3D dataset based on the depth and / or intensity value of the voxels.

[0043] Output from the scan converter 230 (e.g., B-mode image, Doppler image), multiplanar reformatter 232, and / or volume renderer 234 (e.g., volume, rendered 2D image) is coupled to an image processor 236 for further enhancement, buffering, and temporary storage before being displayed on an image display 238. In some examples, the Doppler image is overlaid by the scan converter 230 and / or image processor 236 onto a B-mode image of the tissue structure for display.

[0044] Graphics processor 240 generates graphic overlays for display with the images. These graphic overlays include standard identifying information, such as the patient name, date and time of the image, imaging parameters, etc. For these purposes, graphics processor 240 is configured to receive input from user interface 224, such as a typed patient name or other annotations.

[0045] System 200 includes local memory 242. Local memory 242 may be implemented as any suitable non-transitory computer-readable medium (e.g., flash drive, disk drive). Local memory 242 stores data generated by system 200, including images, 3D models, executable instructions, input provided by a user via user interface 224, or any other information necessary for the operation of system 200.

[0046] The system 200 described above includes a user interface 224. The user interface 224 includes a display 238 and a control panel 252. The display 238 includes a display device implemented using various known display technologies, such as LCD, LED, OLED, or plasma display technology. In some examples, the display 238 includes multiple displays. The control panel 252 is configured to receive user input (e.g., desired image plane, desired light source, etc.). The control panel 252 includes one or more hard controls (e.g., buttons, knobs, dials, encoders, mouse, trackball, etc.). In some examples, the control panel 252 additionally or alternatively includes soft controls (e.g., GUI control elements, or simply GUI controls) provided on a touch-sensitive display. In some examples, the display 238 is a touch-sensitive display that includes one or more soft controls of the control panel 252.

[0047] In some examples, the various components shown in FIG. 2 are combined. For example, image processor 236 and graphics processor 240 are implemented as a single processor. In another example, Doppler processor 260 and B-mode processor 228 are implemented as a single processor. In some examples, the various components shown in FIG. 2 are implemented as separate components. For example, signal processor 226 is implemented as a separate signal processor for each imaging mode (e.g., B-mode, Doppler). In some examples, one or more of the various processors shown in FIG. 2 are implemented by a general-purpose processor and / or microprocessor configured to perform designated tasks. In some examples, one or more of the various processors are implemented as application-specific circuitry. In some examples, one or more of the various processors (e.g., image processor 236) are implemented with one or more graphical processing units (GPUs).

[0048] Figure 3 is a block diagram illustrating an exemplary processor 300 in accordance with the principles of the present disclosure. Processor 300 may be used to implement one or more of the processors described herein, such as image processor 236 shown in Figure 2. Processor 300 may be any suitable processor type, including, but not limited to, a microprocessor, a microcontroller, a digital signal processor (DSP), a field programmable gate array (FPGA) where the FPGA is programmed to form a processor, a graphics processing unit (GPU), an application specific circuit (ASIC) where the ASIC is designed to form a processor, or a combination thereof.

[0049] Processor 300 includes one or more cores 302. Core 302 includes one or more arithmetic logic units (ALUs) 304. In some examples, core 302 includes a floating-point logic unit (FPLU) 306 and / or a digital signal processing unit (DSPU) 308 in addition to or instead of ALU 304.

[0050] The processor 300 includes one or more registers 312 communicatively coupled to the core 302. The registers 312 may be implemented using dedicated logic gate circuits (e.g., flip-flops) and / or any memory technology. In some examples, the registers 312 may be implemented using static memory. The registers provide data, instructions, and addresses to the core 302.

[0051] In some examples, processor 300 includes one or more levels of cache memory 310 communicatively coupled to cores 302. Cache memory 310 provides computer-readable instructions to cores 302 for execution. Cache memory 310 provides data for processing by cores 302. In some examples, computer-readable instructions are provided to cache memory 310 by local memory, for example, local memory attached to external bus 416. Cache memory 310 may be implemented using any suitable cache memory type, for example, static random access memory (SRAM), metal-oxide-semiconductor (MOS) memory such as dynamic random access memory (DRAM), and / or any other suitable memory technology.

[0052] Processor 300 includes a controller 314. The controller controls input to processor 300 from other processors and / or components included in the system (e.g., control panel 252 and scan converter 230 shown in FIG. 2 ) and / or output from processor 300 to other processors and / or components included in the system (e.g., display 238 and volume renderer 234 shown in FIG. 2 ). Controller 314 controls data paths within ALU 304, FPLU 306, and / or DSPU 308. Controller 314 is implemented as one or more state machines, data paths, and / or dedicated control logic. The gates of controller 314 are implemented as standalone gates, FPGAs, ASICs, or any other suitable technology.

[0053] Registers 312 and cache memory 310 communicate with controller 314 and core 302 via internal connections 320A, 320B, 320C, and 320D. The internal connections may be implemented as buses, multiplexers, crossbar switches, and / or any other suitable connection technology.

[0054] Inputs and outputs of processor 300 are provided via bus 316, which may include one or more conductive lines. Bus 316 is communicatively coupled to one or more components of processor 300, such as controller 314, cache memory 310, and / or registers 312. Bus 316 is coupled to one or more components of the system, such as display 238 and control panel 252, as previously described.

[0055] The bus 316 is coupled to one or more external memories. The external memories include read-only memory 332. The ROM 332 may be masked ROM, electrically programmable read-only memory (EPROM), or any other suitable technology. The external memories include random access memory (RAM) 333. The RAM 333 may be static RAM, battery-backed static RAM, dynamic RAM (DRAM), or any other suitable technology. The external memories include electrically erasable programmable read-only memory (EEPROM) 335. The external memories include flash memory 334. The external memories include magnetic storage devices such as disks 336. In some examples, the external memories are included within a system such as the ultrasound imaging system 200 shown in FIG. 2, for example, local memory 242.

[0056] The functions performed by a volume renderer, such as volume renderer 234, to generate rendered 2D images according to examples of this disclosure will now be described in more detail with reference to the following figures.

[0057] 4 shows an example of converting a 2D image (e.g., a 2D dataset) into a 3D volume (e.g., a 3D dataset) according to an example of the present disclosure. Values ​​on a discrete pixel grid (x, y) of the 2D image are used to generate a 3D volume V(x, y, z) defined on a 3D voxel grid, where x, y are the planar dimensions (e.g., length and width) and z is the height of the volume. While the examples provided herein use scalar-valued images, this technique can also be used with multi-channel images by creating one volume per channel.

[0058] In a first exemplary technique 401, a volume 400 can be filled with binary values ​​402, where the intensity of a pixel in a 2D image 404 is interpreted as the height of the solid object surface at each point corresponding to the pixel. This technique produces a binary height volume 400 and is described by the following equation: V(x,y,z)=I(x,y)≧z Equation 1

[0059] The representation produced by Equation 1 is similar in principle to the surface shading method described with reference to FIG. 1, but this first exemplary technique 401 allows for at least some volumetric effects during the generation of the rendered 2D image.

[0060] In a second exemplary technique 403, the volume 406 is filled with image-dependent values. For example, the volume 406 is constructed as a stack of identically repeated 2D images 404, or as an arbitrary function of image value and height. This is described by the following equation: V(x,y,z)=I(x,y)×I(x,y)≧z Equation 2

[0061] The second technique 403 provides greater flexibility for assigning material properties and generating rendered 2D images in some applications. Optionally, Equation 2 may be modified so that values ​​assigned to voxels based on the intensity of pixels in the 2D image 404 decrease with height. For example, if density is assigned to voxels, the density decreases as z increases. In some examples, whether the first technique 401 or technique 403 is used is based at least in part on user input provided via a user interface (e.g., user interface 224).

[0062] 5 illustrates steps for assigning material properties to voxels in a volume, according to an example of the present disclosure. Voxel values ​​V(x,y,z), defined during generation of a 3D dataset as described with reference to FIG. 4, are mapped to optical material properties. These physically based properties define how lighting calculations affect any given voxel in subsequent rendering steps. The material properties selected depend on the choice of a specific volume renderer in some applications. The material properties assigned to each voxel are represented by one or more transfer functions.

[0063] For photorealistic materials, the properties assigned to a voxel include the material's density (e.g., opacity), absorption, scattering, and / or reflection coefficients. Optionally, these coefficients can be specified in red-green-blue (RGB) space to model the dependence of the physics of light-matter interaction on light frequency. In other words, the coefficients vary based on the color (e.g., wavelength) of the light. For example, wavelength-dependent absorption effects are selected to add color to light propagation during rendering.

[0064] Plot 500 shows an example of a scalar transfer function. The scalar transfer coefficient controls material density by mapping the intensity of voxels V(x,y,z) of volume 502 through a transfer function F(V), such that density is equal to F(V). Note that because the values ​​of V(x,y,z) were based on I(x,y) as discussed with reference to FIG. 4, the densities assigned to voxels are based at least in part on the intensities in the original 2D image 501. In some examples, the transfer function includes a threshold T. Voxels with values ​​below the threshold T are rendered as transparent. Including the threshold reduces noise in some examples.

[0065] RGB absorption coefficients A[R,G,B] are defined. If a wavelength-dependent effect is desired, the R, G, and B factors are different values ​​(e.g., unequal). For example, to mimic a tissue-like appearance where red light is absorbed less than other frequencies, the R value of the absorption coefficient is smaller than the G and B coefficients. The total absorption is obtained by multiplying the scalar density F(V) by the coefficient value for each color in the absorption coefficients A[R,G,B]. Thus, both a density and an absorption coefficient are assigned to each voxel in the volume 502.

[0066] While assigning density and absorption coefficients is described herein, these are provided merely as examples, and additional material properties may be assigned to voxels of a 3D dataset (e.g., a volume). For example, to obtain lighting effects such as specular highlights or soft shadows, similar mappings from voxel values ​​to scattering and reflectance properties are defined. In another example, if a segmentation map of an anatomical structure is available for a 2D image, different material properties can be assigned to different regions of the volume in addition to the intensity mapping.

[0067] For multi-channel input images, each channel can be used to separately control different parameters, e.g., channel #1 controls height z, #2 controls density, #3 controls scatter, etc. Examples of multi-channel inputs include: i) different complex bandpass (QBP) filter outputs, (ii) intensity and coherence images, iii) power, variance, and mean angle of a color sequence, iv) a combination of intensity with one or more tissue properties such as stiffness, attenuation, and / or speed of sound.

[0068] 6 graphically illustrates an example of two-stage rendering according to an example of the present disclosure. In some examples, a 3D scene is rendered from voxel volumes generated as described with reference to FIGS. 4 and 5 using a two-stage process: a lighting pass and a compositing pass.

[0069] In illumination path 601, a virtual light source 600 is simulated by casting light rays 602 into volume 604. In some examples, such as the one shown in FIG. 6, light source 600 is a point light source that emits light rays 602 in all directions. However, in other examples, light source 600 is a directional light source (e.g., a spotlight, a beam). In some examples, light source 600 is centered on volume 604 (e.g., at the center point of the x- and y-coordinates of volume 604), while in other examples, light source 600 is off-center or offset from volume 604 (e.g., light source 600 emits light rays 602 onto volume 604 from a position outside the x- and y-coordinates of the volume). In some examples, the distance of light source 600 from volume 604 (or the distance from voxels 608 of volume 604) is preset or based on one or more algorithms (e.g., based on desired lighting conditions, location of anatomical structures to be detected). In other examples, the distance is selected by user input via a user interface, such as user interface 224. In some examples, two or more light sources 600 are used.

[0070] In examples where the 2D image used to generate volume 604 is an ultrasound image, light source 600 is positioned such that the location of light source 600 is where the transducer array that acquired the 2D image was located relative to the 2D image. In some applications, this allows for the creation of virtual optical shadows that are in the same position as acoustic shadows corresponding to actual ultrasound attenuation.

[0071] A ray of light 602 propagates through a volume 604 that includes voxels 608. How the ray of light 602 interacts with the voxels 608 is based at least in part on the material properties assigned to the voxels 608. In some examples, the light for each voxel 608 is calculated based on an exponential decay model. In some examples, the decay model allows different colors of light (e.g., RGB) to be treated differently by having different absorption coefficients for R, G, and B. For example, the light for each voxel is provided as follows:

number

[0072] In the composite path 603, parallel rays 612 are cast from the viewing plane of the virtual viewer 610. To generate a 3D scene that resembles the original 2D image (rather than a perspective view of the volume), the viewing plane 614 is parallel to the xy plane of the volume 604, and the rays 612 are cast in a direction orthogonal to the xy plane of the volume 604 (e.g., parallel to the z-axis of the volume). That is, the virtual viewer 610 is looking straight down into the volume 604. Hence, the rendering is referred to as a top-down rendering. In some examples, the virtual viewer 610 is centered with respect to the volume 604, similar to the virtual light source 600. In some examples, the virtual viewer 610 is co-located with the virtual light source 600. In some examples, the distance from the volume 604 to the viewing plane 614 is preset or selected by the user via a user interface.

[0073] During compositing pass 603, the final values ​​of the pixels of the rendered 2D image are calculated using a front-to-back RGB compositing scheme calculated by ray marching and trilinear volume sampling, based at least in part on the material properties of the voxels 608 and / or the light at each voxel 608 calculated during lighting pass 601.

[0074] FIG. 7 illustrates an exemplary 2D image and a corresponding example of a rendered 2D image according to an example of the present disclosure. 2D image 700 is a four-chamber view of the heart acquired by an ultrasound system. To produce 2D image 702, a volumetric data set was generated based on image 700 and the rendered 3D scene as described with reference to FIGS. 4-6. The top-down rendered 2D image 702 provides shadowing in region 704, including, for example, a portion of the wall between the right and left ventricles. The shadowing and increased contrast provide an improved depth perception to some viewers. Optionally, in some examples, depth-based shading may be performed during the compositing pass, which in some applications further increases the contrast in the rendered 2D image.

[0075] FIG. 8 graphically illustrates an example of depth-based shading, according to an example of the present disclosure. As discussed with reference to FIG. 6, during the compositing pass of the two-stage rendering process, parallel light rays 804 propagate from a viewing plane 802 of a virtual viewer 800 through a volume 806. In addition to the material properties and light calculations used during compositing, the hue (e.g., color) of a given voxel 808 is modified during ray marching based on the distance from that voxel 808 to the viewing plane 802 along the ray 804. For example, voxels 808 closer to the viewing plane 802 are modified to have a more reddish color, while voxels 808 farther from the viewing plane 802 have a more bluish color. This is illustrated in FIG. 8 as different shading of voxels 808 in the volume 806. Red and blue are provided by way of example only; other colors may be used. Furthermore, in some examples, more than two colors are used for the depth-dependent modulation of the color of voxels 808. Note that the color of voxels 808 is height (e.g., z-axis) dependent in some examples, such as those described with reference to FIG. 4, but the height is based on the intensity of the pixel in the 2D image. Thus, the “depth” or “distance”-based shading of voxels 808 indicates signal intensity within the 2D image. Signal intensity may not necessarily correspond to the actual physical distance of a point within the 2D image from the viewer. For example, one tissue type may provide a stronger signal than another tissue type despite having the same physical distance from the viewer (e.g., the same physical distance from the transducer array). Nevertheless, providing different color tones for different intensities further enhances the viewer's ability to interpret the rendered 2D image compared to the original 2D image.

[0076] 9 shows an exemplary 2D image and a corresponding example of a rendered 2D image according to the present disclosure. 2D image 900 is a view of a blood vessel 904 and surrounding tissue acquired by an ultrasound imaging system. To produce rendered 2D image 902, a volumetric data set was generated based on image 900 and the rendered 3D scene as described with reference to FIGS. 4-6 and 8. In addition to shadowing and contrast (see, e.g., region 906), rendered 2D image 902 provides depth-dependent coloring, for example, in regions 908 and 910. The depth-dependent coloring further enhances the contrast between low-signal and high-signal regions to suit some viewers.

[0077] 10 is a graphical illustration of a method according to an example of the present disclosure. In some examples, some or all of the method 1000 is performed by a volume renderer, such as the volume renderer 234 shown in FIG.

[0078] The method 1000 includes converting a two-dimensional (2D) dataset 1002 into a three-dimensional (3D) dataset 1006, as indicated by arrow 1004. In some examples, the 2D dataset 1002 includes pixels (medical images) and the 3D dataset 1006 includes voxels. In some examples, a height dimension (z) of the 3D dataset (e.g., the number of voxels extending from the x-y plane) is based at least in part on the intensity values ​​of the pixels. In some examples, binary values ​​are assigned to voxels or values ​​are assigned to voxels based at least in part on the intensity values ​​of the pixels assigned to the voxels, as discussed with reference to FIG. 4 .

[0079] The method 1000 includes assigning material properties to voxels of the volume 1006, as indicated by arrow 1008. As discussed with reference to FIG. 5, in some examples, the material properties are based at least in part on intensity values ​​of the pixels. In some examples, the material properties are defined based on one or more transfer functions. Different transfer functions define different material properties in some examples. In some examples, one or more of the transfer functions include a threshold. Voxels having values ​​below the threshold are rendered transparent (e.g., inactive) for that particular material property. In some examples, the material properties include density, absorption, reflectance, and / or scattering. In some examples, one or more of the material properties are wavelength dependent. In examples where the 2D dataset is a multi-channel dataset, different material properties of the voxels are each based on values ​​from a different channel of the multi-channel dataset.

[0080] The method 1000 includes rendering the 3D scene of the 3D dataset from a viewing plane parallel to the plane of the 2D dataset to generate a top-down rendering (e.g., a rendered 2D image) 1012, as indicated by arrow 1010. As discussed with reference to FIG. 6 , the rendering includes simulating at least one virtual light source propagating through the 3D dataset and marching parallel light rays through the 3D dataset. How the light propagates through the voxels is based at least in part on material properties assigned to the voxels. Optionally, in some examples, the method 1000 further includes assigning color values ​​to the voxels based at least in part on the distance from the voxels to the viewing plane, as indicated by arrow 1014. This, in some examples, generates a rendered 2D image with enhanced contrast between high-intensity signal regions and low-intensity signal regions.

[0081] Although the principles of the present disclosure have been described with reference to a single 2D image, examples of the present disclosure extend to the mixing and fusion of multiple 2D images, such as multiple B-mode images, harmonic and fundamental images, B-mode and Doppler images, etc.

[0082] FIG. 11 shows an example of a 2D Doppler image overlaid on a 2D B-mode image and a rendered 2D image incorporating Doppler and B-mode images, according to an example of the present disclosure. Image 1100 shows a 2D B-mode image 1102 of tissue with a 2D color Doppler image 1104 of a blood vessel overlaid on top of the B-mode image 1102. In some examples, separate volumes are generated for each 2D image, as described with reference to FIGS. 4-6 and 10 . In other words, a 3D dataset is generated for the B-mode image 1102, and a separate 3D dataset is generated for the Doppler image 1104. Material properties are, in some examples, assigned independently to the voxels of each 3D dataset. That is, the material properties of the voxels in the 3D dataset of the B-mode image 1102 do not affect the material properties assigned to the voxels in the 3D dataset of the Doppler image 1104. In some examples, the two 3D data sets are fused in 3D space by simultaneously rendering both 3D data sets in the same 3D scene under the same lighting conditions. In other examples, the two 3D data sets are mixed into a single volume before rendering. Image 1110 shows a rendered 2D image generated from a B-mode image 1102 and a Doppler image 1104. The B-mode portion 1106 of the rendered 2D image 1110 was rendered using optional depth-dependent shading as described with reference to FIG. 8. The Doppler portion 1108 of the rendered 2D image 1110 maintains the velocity color mapping data from the Doppler image 1104.

[0083] In some examples, different rendering and / or shading techniques are used for different images that are combined to generate a rendered 2D image. For example, a volume for one image is generated using a first technique shown in Figure 4, and another volume for a second image is generated using a second technique shown in Figure 4. In another example, a first image is rendered according to an example according to the present disclosure, and another image is overlaid on the rendered 2D image of the first image using local surface shading as described with reference to Figure 1.

[0084] The devices, systems, and methods disclosed herein enable the application of 3D rendering techniques, including photorealistic rendering techniques, to 2D images. These techniques provide the viewer with 2D images that have greater contrast and / or depth perception compared to traditional 2D images and / or 2D images enhanced with traditional surface enhancement techniques. While the examples disclosed herein utilize ultrasound images, the principles of this disclosure apply to other imaging modalities that generate 2D data sets (e.g., slices from x-ray or computed tomography).

[0085] In various examples in which the components, systems, and / or methods are implemented using computer-based systems or programmable devices such as programmable logic, it should be understood that the systems and methods described above can be implemented using any of a variety of programming languages, such as "C," "C++," "FORTRAN," "Pascal," "VHDL," etc., or various later-developed programming languages. Accordingly, various storage media, such as magnetic computer disks, optical disks, electronic memory, etc., capable of containing information capable of instructing a device such as a computer, can be provided to implement the systems and / or methods described above. When an appropriate device accesses the information and programs contained on the storage media, the storage media can provide the information and programs to the device, thereby enabling the device to perform the functions of the systems and / or methods described herein. For example, if a computer is provided with a computer disk containing appropriate material, such as source files, object files, executable files, etc., the computer can receive the information, configure itself appropriately, and execute the functions of the various systems and methods outlined in the above figures and flowcharts to perform various functions. That is, the computer can receive from the disk various portions of information relating to different elements of the systems and / or methods described above, implement the individual systems and / or methods, and coordinate the functionality of the individual systems and / or methods described above.

[0086] In view of this disclosure, it should be noted that the various methods and devices described herein can be implemented in hardware, software, and / or firmware. Furthermore, the various methods and parameters are included by way of example only and not in a limiting sense. In view of this disclosure, one skilled in the art will be able to implement the teachings of the present invention in determining their own techniques and the apparatus necessary to effectuate these techniques while remaining within the scope of the present invention. One or more functions of the processors described herein may be incorporated into fewer or a single processing unit (e.g., a CPU), implemented using an application-specific integrated circuit (ASIC), or a general-purpose processing circuit programmed to perform the functions described herein in response to executable instructions.

[0087] While the devices, systems, and methods of the present invention have been described with particular reference to ultrasound imaging systems, it is contemplated that the present systems can be extended to other medical imaging systems in which one or more images are obtained in a systematic manner. Accordingly, the present systems may be used to obtain and / or record image information associated with, but not limited to, the kidneys, testes, breasts, ovaries, uterus, thyroid, liver, lungs, musculoskeletal, spleen, heart, arteries and vasculature, and other imaging applications related to ultrasound-guided interventions. Furthermore, the present systems may also include one or more programs used in conventional imaging systems, whereby these programs provide the features and advantages of the present systems. Certain additional advantages and features of the present disclosure will be apparent to those skilled in the art upon reviewing the present disclosure or will be experienced by adopters of the novel systems and methods of the present disclosure. Another advantage of the present systems and methods is that conventional medical imaging systems can be easily upgraded to incorporate the features and advantages of the present systems, devices, and methods.

[0088] Of course, it should be understood that any one, more, or processes of the examples described herein may be combined with one or more other examples, more, and / or processes, or separated among and / or performed as separate devices or device portions, by the apparatus, systems, devices, and methods of the present invention.

[0089] Finally, the foregoing discussion is intended to be merely illustrative of the inventive system and method and should not be construed as limiting the appended claims to any particular example or group of examples. Thus, while the present system has been described in particular detail with reference to illustrative examples, it should also be understood that numerous modifications and alternative examples will occur to those skilled in the art without departing from the broader intended spirit and scope of the inventive system and method as set forth in the appended claims. Accordingly, the specification and figures are to be considered in an illustrative manner and are not intended to limit the scope of the appended claims.

Claims

1. 1. An apparatus comprising a processor, the processor comprising: a transforming a two-dimensional (2D) image comprising a plurality of pixels into a three-dimensional (3D) volume comprising a plurality of voxels, the plurality of voxels including some voxels corresponding to the plurality of pixels in a plane of the 2D image, the plurality of voxels including some voxels located out of the plane of the 2D image defining a height dimension of the 3D volume, the height dimension being based at least in part on intensity values ​​of the plurality of pixels, the processor assigning properties to the plurality of voxels, the properties determining how light propagating through the 3D volume from at least one virtual light source interacts with the plurality of voxels and / or determining an appearance of the 3D volume, the processor generating a rendered 2D image by rendering the 3D volume from a viewing plane parallel to the plane of the 2D image, the rendering simulating the virtual light source, and propagation of light of the virtual light source through the 3D volume based at least in part on the properties assigned to the plurality of voxels.

2. The apparatus of claim 1 , further comprising an ultrasound probe that acquires ultrasound signals from the subject, the 2D image being generated from the ultrasound signals.

3. The apparatus of claim 2 , wherein the location of the simulated virtual light source relative to the 3D volume is based at least in part on the location of the ultrasound probe relative to where the ultrasound signals were acquired during use of the ultrasound probe.

4. 10. The apparatus of claim 1, wherein the processor is further configured to assign a color tone to the plurality of voxels, the color tone assigned to a voxel of the plurality of voxels being based at least in part on a distance from the voxel to the viewing surface.

5. The apparatus of claim 1 , further comprising a user interface for receiving user input that determines a location of the virtual light source relative to the 3D volume.

6. transforming a two-dimensional (2D) image comprising a plurality of pixels into a three-dimensional (3D) volume comprising a plurality of voxels, the plurality of voxels including some voxels corresponding to the plurality of pixels in a plane of the 2D image, the plurality of voxels including some voxels located out of the plane of the 2D image and defining a height dimension of the 3D volume, the height dimension being based at least in part on intensity values ​​of the plurality of pixels; assigning properties to the plurality of voxels, the properties determining how light propagating through the 3D volume from at least one virtual light source interacts with the plurality of voxels and / or determining the appearance of the 3D volume; generating a rendered 2D image by rendering the 3D volume from a viewing plane parallel to the plane of the 2D image, the rendering comprising simulating the virtual light source, wherein propagation of light of the virtual light source through the 3D volume is based at least in part on the properties assigned to the plurality of voxels.

7. The method of claim 6 , wherein assigning the properties to voxels of the plurality of voxels comprises defining at least one transfer function for at least one property of the voxels, or wherein the property is wavelength dependent.

8. The method of claim 7 , wherein the transfer function includes a threshold, and voxels of the plurality of voxels having a value below the threshold are rendered as transparent with respect to the at least one characteristic.

9. The method of claim 6 , wherein the 2D image is a multi-channel image, and wherein different properties of the plurality of voxels are each based on values ​​from a different channel of the multi-channel image.

10. The method of claim 6 , further comprising assigning a color tone to the plurality of voxels, the color tone assigned to a voxel of the plurality of voxels being based at least in part on a distance from the voxel to the viewing surface.

11. 7. The method of claim 6, wherein converting the 2D image to the 3D volume comprises assigning binary values ​​to the plurality of voxels, or wherein converting the 2D image to the 3D volume comprises assigning values ​​to the plurality of voxels based at least in part on intensity values ​​of the plurality of pixels.

12. The method of claim 11 , wherein the values ​​assigned to the voxels decrease with height.

13. 7. The method of claim 6, wherein the plurality of pixels of the 2D image comprises a first plurality of pixels and a second plurality of pixels, and the plurality of voxels of the 3D volume comprises a first plurality of voxels corresponding to the first plurality of pixels and a second plurality of voxels corresponding to the second plurality of pixels, the first plurality of pixels and the second plurality of pixels corresponding to first and second images, respectively.

14. 14. The method of claim 13, wherein the first plurality of voxels is rendered separately from the second plurality of voxels to generate first and second rendered 2D images, respectively, and the first and second rendered 2D images provide a combined 2D image.

Images