VISUALIZATION OF MEDICAL IMAGE DATA

Description

[0001] The invention relates to a method for visualizing medical image data as volumetric data. In this method, medical image data is acquired. A three-dimensional mask is then generated. To generate the three-dimensional mask, the image data is segmented, and the segmented areas are divided into predetermined classes. The invention further relates to a visualization device. The visualization device comprises an acquisition unit for acquiring medical image data. The visualization device also includes a mask generation unit for generating a 3D mask by segmenting the image data and dividing the segmented areas into predetermined classes. Finally, the invention relates to a medical imaging system. The medical imaging system comprises an image acquisition unit for generating image data of an examination area of ​​an object under investigation.

[0002] Modern imaging techniques often generate two- or three-dimensional image data that can be used to visualize a subject under investigation and for other applications. The resulting image data, such as that produced by computed tomography or magnetic resonance imaging, is usually provided as tomographic images for review by medical professionals.

[0003] However, three-dimensional images, also known as volume graphics, are necessary for better illustration. The volumetric data required for such representations are visualized three-dimensionally using a process called volume rendering to illustrate case-specific anatomy. In this process, a captured volume area initially forms a single unit and therefore cannot be easily broken down into individual anatomical structures for closer examination, for example.

[0004] Volumetric image representation is based on a technique called rendering. Initially, image data exists as slice data, with each voxel, at least in the case of computed tomography, assigned a gray value corresponding to the density measured at that location. However, this data cannot be directly converted into image data for three-dimensional perspective visualization, as such a visualization requires consideration of the observational perspective and the position of each voxel relative to the observer. Furthermore, individual voxels initially only possess a single value, which, for example, provides information about the X-ray density in CT imaging or the proton or hydrogen nuclei content in MRI, but it contains no information about the appearance of the material within a voxel, such as its color or reflectivity.Therefore, a texture is assigned to each voxel within a given volume. This process is also known as classification. A further step deals with shading, i.e., how much light is reflected from a voxel towards the viewer and what color it is. Next, a sampling step is performed to scan the volume. In this step, lines of sight are projected into the volume to be imaged. After the interaction of the light with the volume is calculated, the contributions along the lines of sight are summed to create a pixel in the image. This approach is implemented, for example, using raycasting. However, it is not conventionally possible to display only a portion of the generated volume data.

[0005] However, such detailed visualization of specific areas within the entire image field is often desirable. Traditionally, so-called polygonal surface models are used to represent anatomical details. With these models, an artificially created model serves as the data basis for the visualization. This allows each structure to be modeled individually and moved independently within the image. However, surface representations do not allow for the examination of the interior of anatomical structures. For example, it is not possible to virtually dissect an organ and examine it for pathologies that may not be visible externally. Furthermore, surface models require intermediate representations, which demand a significant amount of data and can contribute to inaccurate visualizations.

[0006] In Stefan Bruckner et al., "Exploded Views for Volume Data," IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, IEEE SERVICE CENTER, LOS ALAMITOS, CA, US, Vol. 12, No. 5, September 1, 2006 (2006-09-01), pages 1077-1084, XP011150904, ISSN: 1077-2626, DOI: 10.1109 / TVCG.2006, the rendering of exploded views of volume data is described. Raycasting techniques are also used to create these exploded views.

[0007] In Kang DS et al: "An interactive exploded view generation using block-based re-rendering", MEDICAL IMAGING 2011: VISUALIZATION, IMAGE-GUIDED PROCEDURES, AND MODELING; SPIE, 1000 20TH ST. BELLINGHAM WA 98225-6705 USA, BD. 7964, No. 1, March 3, 2011 (2011-03-03), pages 1-7, XP060008164, DOI: 10.1117 / 12.877895, the generation of exploded views using a block-based re-rendering method is described.

[0008] In Subramanian N et al: "Volume rendering segmented data using 3D textures: a practical approach for intra-operative visualization", PROCEEDINGS OF THE SPIE - THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING USA, Vol. 6141, 2006, pages 614120-1, XP002793564, ISSN: 0277-786X, a volume rendering method based on 3D textures is described.

[0009] In HENRY SONNET ET AL: "Integrating expanding annotations with a 3D explosion probe", AVI'04 INTERNATIONAL CONFERENCE ON ADVANCED VISUAL INTERFACES; GALLIPOLI, ITALY; MAY 25-28, 2004, ASSOCIATION FOR COMPUTING MACHINERY, NEW YORK, NY, USA, May 25, 2004 (2004-05-25), pages 63-70, XP058274606, DOI:10.1145 / 989863.989871 ISBN: 978-1-58113-867-2, an exploded view enriched with annotations is described.

[0010] US 5,825,365 describes a method for three-dimensional dynamic representation.

[0011] In WILMOT Li et al: "Automated generation of interactive 3D exploded view diagrams", ACM TRANSACTIONS ON GRAPHCS, ACM, 2 PENN PLAZA, SUITE 701 NEW YORKNY 10121-0701 USA, Vol. 27, No. 3, August 1, 2008 (2008-08-01), pages 1-7, XP058355394, ISSN:0730-0301, DOI:10.1145 / 1360612.1360700, an automated generation of interactive exploded views is described.

[0012] The object of the present invention is therefore to enable a more accurate and simplified representation of even individual anatomical structures based on three-dimensional image data of an area of ​​investigation of an object under investigation.

[0013] This task is solved by a method for visualizing medical image data as volume data, a visualization device, a medical imaging system, a computer program product and a computer-readable data carrier according to the independent claims.

[0014] In the inventive method for visualizing medical image data as volumetric data, medical image data is first acquired from an object to be examined. The image data can, for example, originate from a medical imaging device such as a CT or MRI system. The image data is then preferably segmented automatically, and the segmented areas are divided into predetermined classes that characterize different organs or structure types. In this way, a mask is generated that assigns markers or labels to each segmented volume element for characterization purposes. Using these markers, a voxel can be uniquely assigned to a specific organ or tissue type. The image data and the mask data are stored in two separate 3D texture files.

[0015] One of these texture files contains information about the appearance of the individual voxels, while the other file contains the information, derived from segmentation or masking, about which structure or organ each voxel corresponds to. The "image data" file includes not only the density or concentration information generated by the medical imaging equipment, but also volume texture data, which, as briefly explained earlier, defines the appearance of a voxel more precisely. Furthermore, a translation vector is calculated, which describes the displacement of a segmented volume element between an origin and a target position. The origin and target positions can be specified by a user, for example, using an input device.Furthermore, a visual representation of the image data is generated by applying a beam scanning method to the stored image data. Additionally, a segmented volume element is shifted in the visual representation by the translation vector. That is, the segmented volume element is shifted in the visual representation by the translation vector. This shift can be triggered, for example, by user-controlled movement of the input medium. The shift is performed in such a way that the pixels in the target area are generated by a scan shifted by the translation vector, so that the pixels in the target area inherit the texture data of the original area. The shifting of volume data enables improved visualization of sub-areas of the object under investigation.

[0016] In contrast to the use of polygonal surface models, the visualization of volumetric image data according to the invention also enables the illustration of the internal structure of anatomical structures. For example, an organ can be virtually dissected and examined for pathological phenomena that are not visible externally. Furthermore, no intermediate representations are required when visualizing volumetric data, which reduces data requirements and avoids artifacts during visualization.

[0017] Furthermore, the data overhead is reduced because, despite segment translation, only one and the same dataset needs to be stored. Instead of modifying textures within the dataset and thus requiring additional storage space, the image data is manipulated only during the scanning process for visualization. The rendering process can therefore always run on the same data and the same routine, regardless of whether and what kind of shift is performed. Moreover, only three different inputs are required for the shift: the image data with a volume texture, a mask with labels for segmentation, and a translation vector for shifting a segment.

[0018] The visualization device according to the invention comprises an acquisition unit for acquiring the medical image data. The visualization device according to the invention also includes a mask generation unit for generating a 3D mask by segmenting the image data and dividing the segmented areas into predetermined classes. The visualization device according to the invention also includes a storage unit for saving the image data and the mask data in two separate 3D texture files.

[0019] The visualization device according to the invention also includes a vector generation unit for calculating a translation vector, which describes the displacement of a segmented volume element between an origin position and a target position. The displacement or translation vector can be determined, for example, by a user-controlled movement of an input medium or by an origin and target position of an input medium controlled by the user.

[0020] Furthermore, the visualization device according to the invention also includes a translation unit for moving the segmented volume element around the translation vector. Part of the visualization device according to the invention is also a visualization unit for generating a pictorial representation of the image data by applying a beam scanning method to the stored image data. The visualization device according to the invention shares the advantages of the inventive method for visualizing medical image data as volume data.

[0021] The medical imaging system according to the invention comprises the visualization device according to the invention and an image acquisition unit for generating image data from an examination area of ​​an object to be examined.

[0022] The image acquisition unit can, for example, include a CT scan unit, an MR scan unit, or a tomosynthesis unit.

[0023] The medical imaging system according to the invention shares the advantages of the visualization device according to the invention.

[0024] Parts of the visualization device according to the invention can be predominantly implemented as software components. This applies in particular to parts of the acquisition unit, the mask generation unit, the vector generation unit, the translation unit, and the visualization unit. However, these components can also be partially implemented as software-supported hardware, such as FPGAs or the like, especially when particularly fast calculations are required. Likewise, the necessary interfaces, for example, when it is only a matter of transferring data from other software components, can be implemented as software interfaces. Alternatively, they can be implemented as hardware interfaces controlled by suitable software.

[0025] A partial software-based implementation has the advantage that computer systems already used in imaging systems can be easily retrofitted with hardware units for acquiring patient image data via a software update to operate according to the invention. In this respect, the problem is also solved by a corresponding computer program product with a computer program that can be directly loaded into a storage device of such a computer system, containing program sections to execute all steps of the method for visualizing medical image data as volume data when the computer program is executed in the computer system.

[0026] Such a computer program product may, in addition to the computer program itself, include additional components such as documentation and / or additional components, including hardware components such as hardware keys (dongles, etc.) for using the software.

[0027] For transport to the computer system's storage device and / or for storage on the computer system, a computer-readable medium, such as a memory stick, a hard drive, or other portable or permanently installed data carrier, can be used, on which the program sections of the computer program that can be read and executed by a computer unit are stored. The computer unit can, for example, include one or more cooperating microprocessors or similar components.

[0028] The dependent claims and the subsequent description each contain particularly advantageous embodiments and further developments of the invention. In particular, the claims of one claim category may also be further developed analogously to the dependent claims of another claim category and their descriptive parts. Furthermore, within the scope of the invention, the various features of different embodiments and claims may be combined to form new embodiments.

[0029] In a particularly preferred embodiment of the inventive method for visualizing medical image data as volume data, the segmented volume element is made transparent at its original position. This transparency can be achieved by setting the opacity value of the segmented volume element at its original position to zero. Advantageously, the volume element to be moved does not appear twice in the image. This prevents it from being obscured by other structures at its original position in the image.

[0030] In one embodiment of the inventive method for visualizing medical image data as volume data, a collider object is created for each segmented volume element, the shape of which is adapted to the dimensions of the segmented volume element. Such a collider object is a structure that can be attached to a three-dimensional object. The collider object itself takes on the shape of the three-dimensional object and is responsible for registering a collision or contact event of the three-dimensional object with other collider objects and forwarding it to other interfaces. Furthermore, the translation vector is determined from the difference between the current position of an input medium and the position at which the input medium first contacts the collider object and at the same time the user indicates that they want to move the segmented volume element.This variant allows for a shift effect to be achieved for individual structures to be highlighted within an area under investigation.

[0031] In a specific embodiment of the inventive method for visualizing medical image data as volume data, a distance between the segmented volume element to be moved and the surrounding volume is predetermined. The translation vector is then calculated as the product of the difference between a distance factor and the value 1, and the difference between the position of the center of the segmented volume element to be moved at its initial position and the current position of an input medium. The length of the translation path depends on both a predetermined distance factor and the distance between the input medium and the segment to be moved. Individual anatomical structures can thus be highlighted and made more visible.Advantageously, a kind of exploded view of individual or several different anatomical structures can be obtained, and in this view the anatomical structures can then be examined in more detail.

[0032] In one variant of the inventive method for visualizing medical image data as volume data, segmented volume elements located close to the input medium are shifted more than segmented volume elements located further away from the input medium.

[0033] The invention is explained in more detail below with reference to the accompanying figures and exemplary embodiments. The figures show: FIG 1 a flowchart illustrating a method for visualizing medical image data as volume data according to an embodiment of the invention, FIG 2 a schematic representation of a displacement of a segment of visualized volume data, FIG 3 a flowchart illustrating a method for visualizing medical image data as volume data according to an alternative embodiment of the invention, FIG 4 a schematic representation of a displacement of a segment of visualized volume data in the alternative embodiment, FIG 5 a schematic representation of a visualization device according to an embodiment of the invention, FIG 6 a schematic representation of a medical imaging system according to an embodiment of the invention.

[0034] In FIG 1 Figure 100 shows a flowchart illustrating a method for visualizing medical image data as volumetric data according to an embodiment of the invention. In step 1.I, image data BD of a patient's area to be examined is first received from a medical imaging device. This image data BD is in the form of grayscale values ​​that vary depending on location. In step 1.II, the image data BD is segmented. That is, individual organs or other body structures are identified and delineated from one another. This process can be fully automated, semi-automated, or performed by qualified personnel who draw boundary lines in the image data BD to divide the image data into segmented areas. During segmentation, a 3D mask MD is created, and specific classes or tissue types are assigned to areas within the image volume.This mask assigns so-called labels to individual voxels, uniquely identifying each voxel and assigning it to a segment or segment type. This results in two datasets: a volume dataset containing the grayscale values ​​of the image data (BD) and a mask dataset (MD) containing the mask data that assigns each voxel to a specific segment or body part. Furthermore, texture information is assigned to the image data (BD), derived from the knowledge of the optical characteristics associated with individual organs or tissue types, such as their color or specular reflections.

[0035] These two datasets, BD and MD, are saved as two separate textures, or in two separate texture files, in step 1.III. Subsequently, in step 1.IV, the image data is visualized as volume data (VD) using a raycasting technique. In this technique, virtual rays are placed within the image volume, and an image of the area under investigation is generated based on the scanned image data (BD). This image is then constructed from the corresponding visualized volume data (VD). Within this visualized image, a user can select and move individual segments.

[0036] In step 1.V, a user selects a segment to be moved using an input device, such as a hand or a controller. For example, the user points to a structure at position pos_HF on an image display and indicates the desired movement. Based on the known position of the structure's center and its dimensions within the image, a collider object is created, its shape adapted to the structure's dimensions. For this purpose, the selected segment's center and its dimensions (or boundaries) are determined. This process can be performed using the stored mask data MD, which assigns each pixel to a segment.Furthermore, by moving the input medium to the target position pos_HC, the user specifies to which position in the visualized image volume the structure should be moved.

[0037] In step 1.VI, a translation vector Vec_tr is determined based on a user's displacement movement. More precisely, the translation vector Vec_tr is calculated based on the initial position pos_HF and the target position pos_HC. Vec_tr = pos_HC − pos_HF calculated.

[0038] After the translation vector Vec_tr has been determined, the access to the texture coordinates is adjusted. Otherwise, the density value or texture of a sampled volume at the current sample position pos would be visualized by reading the image data BD and its associated texture at the current position pos. However, due to the translation around the translation vector Vec_tr, in step 1.VII a position shifted by the translation vector Vec_tr, pos_tr = pos - Vec_tr, is sampled.

[0039] If the mask data MD at this position has the label of the structure to be moved, which is checked in step 1.VIII, or in FIG 1 If the element is marked with "j", then in step 1.IX, the density value or texture data of the visualized volume data VD (pos_tr) is assigned to the sampling position pos, rather than the volume data VD (pos). This means that the current texture coordinate at the position pos in the visualized image is changed. Additionally, in step 1.X, the texture at the shifted position pos_tr is modified so that the structure being moved is transparent at its original position pos_tr. This prevents the structure being moved from being displayed twice.

[0040] If the moved position pos_tr is not assigned a label of the structure to be moved, which occurs in FIG 1 If the position is marked with "n", the density value of the original volume data VD (pos) is assigned to the sampling position pos in step 1.XI, and therefore the visualized image remains unchanged at this point. This means that in this case, the current sampling position pos is outside the area of ​​the structure to be moved.

[0041] In FIG 2 This is a schematic representation of the displacement of a segment SG from visualized volume data. The segment SG is to be shifted by the translation vector Vec_tr from position pos_HF to the current position pos_HC. A pixel at position pos is now assigned not the texture of position pos in the volume data, but rather the texture of position pos_Tr, which has been shifted by the translation vector Vec_tr. In this way, the structure SG to be shifted is moved from position pos_Tr to position pos.

[0042] In FIG 3 A flowchart is shown illustrating a method for visualizing medical image data as volume data according to an alternative embodiment of the invention. The diagram in FIG 3 The illustrated embodiment differs essentially from the one in FIG 1 The illustrated embodiment is achieved by defining the translation vector Vec_tr differently. In this way, by moving a selected structure, the distance between structures in the visualized image volume is increased within a defined area around the position of the input medium, similar to an exploded view. Such a representation allows for a more detailed examination of individual structures.

[0043] In step 3.I, the following steps are initially performed as in the one described above. FIG 1 The illustrated example shows image data (BD) of a patient's area to be examined being received from a medical imaging device. This image data (BD) is in the form of grayscale values ​​that vary depending on location. In step 3.II, the image data (BD) is segmented. During segmentation, a 3D mask (MD) is created, and segmented areas within the image volume are assigned to different classes or tissue types. The mask (MD) assigns so-called labels to individual voxels, uniquely assigning each voxel to a segment. This results in two datasets: an image dataset (BD) containing the grayscale values ​​of the image data and texture information, and a mask dataset (MD) containing the mask data that assigns each voxel to a specific segment or body part.

[0044] In step 3.III, the position pos_SC of the center of each segment or structure is determined. Such a center can, for example, be determined as the centroid of a segment.

[0045] The image data set BD and the mask data set MD are then saved as two separate textures in step 3.IV.

[0046] Subsequently, in step 3.V, volume data VD is visualized using a raycasting method, which has already been used in connection with FIG 1 This was briefly explained. In the visualized image, a user can now select and move individual segments.

[0047] In step 3.VI, the user selects a segment to be highlighted in the visualized representation using an input device. For example, the user approaches the segment to be highlighted from a specific direction with the input device until it reaches a current position pos_HC.

[0048] In step 3.VII, a translation vector Vec_tr is determined. In the FIG 3 In the illustrated example, the structure selected by the user is now shifted from its center pos_SC by a distance determined by a predetermined distance factor dist in the direction of the current position pos_HC of the input medium. The dimensionless distance factor dist can be chosen arbitrarily beforehand. For example, individual structures can all be shifted by the same factor, or structures located close to the input medium can be shifted by a greater amount than structures further away.

[0049] Based on the initial position or center position pos_SC, the current position of the input medium pos_HC and the distance factor dist, the translation vector Vec_tr is now calculated. Vec_tr = dist − 1 * pos_SC − pos_HC calculated.

[0050] After the translation vector Vec_tr has been determined, the access to the texture coordinates is adjusted. Otherwise, a texture of a sampled volume at a current sample position pos would be visualized by reading the image data BD at the current position pos. Due to the translation around the translation vector Vec_tr, in step 1.VIII a position pos_tr = pos - Vec_tr, shifted by the translation vector Vec_tr, is sampled.

[0051] If the mask data MD at this position displays the label of the structure to be moved, which is checked in step 1.IX, then in step 1.X the density value of the volume data VD(pos_tr) is assigned to the sample position pos, instead of the volume data VD(pos). This changes the current texture coordinate, or the texture at the current position pos in the visualized image. Additionally, in step 1.XI the texture at the moved position pos_tr is modified so that the structure to be moved is transparent at its original position. This prevents the structure to be moved from being displayed twice.

[0052] If the moved position pos_tr is not assigned a label of the structure to be moved, the density value of the original volume data VD(pos) is assigned to the sampling position pos in step 1.XII, and therefore the visualized image remains unchanged at this point. This means that in this case, the current sampling position pos is outside the area of ​​the structure to be moved.

[0053] In FIG 4 is a schematic representation of a shift of a segment of visualized volume data in the FIG 3 The illustrated alternative embodiment shows the following. The segment SG is to be shifted by the translation vector Vec_tr from the position pos_SC of the structure to the current position. A pixel at position pos is now not assigned the texture of position pos in the volume data, but rather the texture of the position pos_tr shifted by the translation vector Vec_tr. In this way, the structure SG to be shifted is moved from the original position pos_SC to the current position. The vector Vec_SH between the input medium and structure SG at the original position is given by Vec_SH = pos_SC − pos_HC

[0054] Another vector, the vector Vec_HTr between hand and shifted position of structure SG, results in Vec_HTr = dist * pos_SC − pos_HC

[0055] In FIG 5 Figure 50 shows a schematic representation of a visualization device according to an embodiment of the invention.

[0056] The visualization unit 50 has an input interface 51 as an acquisition unit. The input interface 51 receives image data BD of a patient's area to be examined from a medical imaging device (not shown). The image data BD is required as volume data VD for visualizing the area to be examined. Furthermore, the input interface 51 also receives data regarding the current position pos_HC of the input medium and the starting position pos_HF of the input medium. The image data BD is transmitted to a mask generation unit 52, which segments the image data BD with respect to individual organs or other body structures and generates a mask containing mask data MD for all segmented structures. The mask data MD assigns specific classes or tissue types to segmented areas within the image volume.Individual voxels are assigned so-called labels, which uniquely assign each voxel to a specific segment type. This results in two datasets: a volume dataset containing the grayscale values ​​of the image data (BD) along with corresponding texture data, and a mask dataset (MD) containing the mask data that assigns each voxel to a specific segment or body part.

[0057] The two data sets BD and MD are temporarily stored in a data storage DB, which is also part of the visualization device 50. The visualization device 50 also includes a vector generation unit 53, which calculates a translation vector Vec_Tr according to equation (1) or equation (2) based on the data received from the input interface 51 regarding the current position pos_HC and the output position pos_HF of the input medium. The data of the translation vector Vec_Tr are transmitted to a translation unit 54, which is also part of the visualization device 50. Based on the translation vector Vec_Tr, the translation unit 54 determines a translation position pos_tr and receives labels MD assigned to this translation position pos_tr from the data storage to determine whether a volume segment of the type of the volume segment to be moved SG is present at the translation position pos_tr.The type of volume segment to be moved is determined by the label at the initial position pos_HF. If this is the case, the translation position pos_tr is transmitted as a sampling position to a visualization unit 55, which is also part of the visualization device 50. Visualization unit 55 is also part of the visualization device 50. If no volume segment of the type SG to be moved is present at the translation position pos_tr, the original sampling position pos is forwarded from translation unit 54 to visualization unit 55, and raycasting takes place at the original sampling position pos. Visualization unit 55 also retrieves the image data BD and mask data MD stored in the data memory DB.Based on the received data BD, MD, and the scan position pos, pos_tr received by the translation unit, the visualization unit 55 uses a ray scanning method to generate a perspective image of the area under investigation. The information about the translation vector Vec_tr or the translation position pos_tr is used by the visualization unit 55 to access the image data BD or its associated textures within the area of ​​the volume segment SG to be moved, such that the desired displacement of the structure encompassed by the volume segment SG occurs. The visualized volume data VD is transmitted to an output interface 56, which forwards this data VD to an image display unit (not shown).

[0058] In FIG 6 Figure 6 illustrates a schematic representation of a medical imaging system 60 according to an embodiment of the invention. The medical imaging system 60 comprises a scan unit 61, which serves to acquire raw data RD from an examination area of ​​a patient. The scan unit can, for example, be based on the principle of computed tomography. The raw data RD acquired by this scan unit 61 are transmitted to a control unit 62, which is used both for controlling the scan unit 61 and for evaluating the raw data RD and reconstructing image data BD. The generated image data BD are transmitted to a visualization device 50, which displays the image in the FIG 5The visualization device 50 has the setup shown. It can also be controlled via an input unit 64 to transmit, for example, position data Pos_HC, Pos_HF of segments to be moved in the image data BD to the visualization device 50. The visualization data or visualized volume data VD generated by the visualization device 50 are transmitted to an image display device 63, which includes, for example, a monitor with which the visualized data VD can be displayed graphically.

[0059] Finally, it should be noted once again that the methods and devices described above are merely preferred embodiments of the invention and that the invention can be varied by a person skilled in the art without departing from the scope of the invention, insofar as it is defined by the claims. For the sake of completeness, it should also be noted that the use of the indefinite articles "a" or "an" does not preclude the possibility that the features in question may be present multiple times. Likewise, the term "unit" does not preclude the possibility that it consists of several components, which may also be spatially distributed.

Claims

1. Method for the visualisation of medical image data (BD) as volume data (VD), having the steps: - acquiring the medical image data (BD) having a number of voxels, - producing a 3D mask (MD) by segmenting the image data (BD) and classifying the segmented regions (SG) into predefined classes, - storing the image data (BD) and the mask data (MD) in two separate 3D texture files, wherein the mask data (MD) has labels which assign a specific segmented volume element (SG) to each voxel, wherein the segmented volume element (SG) corresponds to a segmented region (SG), - calculating a translation vector (Vec_tr) which describes the displacement of a segmented volume element (SG) between an original position (pos_HF) and a destination position (pos_HC), - producing a pictorial display of the image data (BD) by applying a raycasting method to the stored image data (BD), - performing a displacement by the translation vector (Vec_tr) of a segmented volume element (SG) in the pictorial display so that - for a scanning position (pos) a position (pos_tr) displaced by the translation vector (Vec_tr) is scanned and, if the mask data (MD) at the displaced position (pos_tr) has the label of the segmented volume element (SG) to be displaced, the image data (BD) at the displaced position (pos_tr) is assigned to the scanning position (pos), wherein the segmented volume element (SG) is made transparent at the original position (pos_HF).

2. Method according to the preceding claim, wherein - a predefined distance factor (dist) is predefined between the segmented volume element (SG) to be displaced and the remaining volume, and - the translation vector (Vec_tr) is derived from the product of the difference between the predefined distance factor (dist) and the value 1, and a difference between the position (pos_SC) of the centre of the segmented volume element to be displaced (SG) and a current position (pos_HC) of an input medium.

3. Method according to one of the preceding claims, wherein segmented volume elements (SG) lying close to the input medium are displaced by a greater extent than segmented volume elements (SG) lying further away from the input medium.

4. Visualisation entity (50) for visualising medical image data (BD) as volume data (VD), having: - an acquisition unit (51) for acquiring the medical image data (BD) having a number of voxels, - a mask production unit (52) for producing a 3D mask (MD) by segmenting the image data (BD) and classifying the segmented regions (SG) into predefined classes, - a storage unit (DB) for storing the image data (BD) and the mask data (MD) in two separate 3D texture files, wherein the mask data (MD) has labels which assign a specific segmented volume element (SG) to each voxel, wherein the segmented volume element (SG) corresponds to a segmented region (SG), - a vector production unit (53) for calculating a translation vector (Vec_tr) which describes the displacement of a segmented volume element (SG) between an original position (pos_HF) and a destination position (pos_HC), - a translation unit (54) for the displacement by the translation vector (Vec_tr) of the segmented volume element (SG) in the pictorial display, - a visualisation unit (55) for producing a pictorial display (VD) of the image data (BD) by applying a raycasting method to the stored image data (BD) wherein for a scanning position (pos) a position (pos_tr) displaced by the translation vector (Vec_tr) is scanned, and if the mask data (MD) at the displaced position (pos_tr) has the label of the segment volume element (SG) to be displaced, the image data (BD) at the displaced position (pos_tr) is assigned to the scanning position (pos), wherein the segmented volume element (SG) is made transparent at the original position (pos_HF).

5. Medical imaging system (60), having: - a visualisation entity (50) according to claim 4, - an image recording unit (61, 62) for producing image data (BD) from an examination region of an object to be examined.

6. Medical imaging system according to claim 5, wherein the image recording unit has a scanning unit of one of the following types: - a CT scanning unit, - an MR scanning unit, - a tomosynthesis unit.

7. Computer program which can be loaded directly into a storage unit of a medical imaging system (60), with program sections for executing all steps of a method according to one of the method claims when the computer program is executed in the medical imaging system (60).

8. Computer-readable medium, on which are stored program sections that can be executed by a computer unit, in order to execute all steps of the method according to one of the method claims when the program sections are executed by the computer unit.