Color assignment when merging image data sets

Abstract

Medical image data processing system for merging image data sets, comprising a processor designed to perform the following steps: a) Retrieving an initial image data set that describes an initial multidimensional image of a patient's anatomy composed of a multitude of pixels, where each pixel of the initial image is assigned an initial intensity value; b) Retrieving a second image data set that describes a second multidimensional image of the patient's anatomy composed of a large number of pixels, where each pixel of the second image is assigned a second intensity value; c) Determining registration data that describes a positional registration between the first image data set and the second image data set; d) Determining a third image dataset based on the first image dataset, the second image dataset and the registration data, which describes a third multidimensional image of the patient's anatomy composed of a multitude of image points, wherein the image points of the third image are assigned the first and second intensity values ​​of the positionally corresponding image points of the first and second images, respectively, and wherein for at least one, in particular for each, image point of the third image - the brightness value increases with increasing first intensity value and the color saturation increases with increasing second intensity value; - the brightness level increases with increasing second intensity value, at least when the first intensity value is low.

Description

TECHNICAL FIELD OF THE INVENTION

[0001] The present invention relates to a medical image processing system for merging medical image data sets, a corresponding computer program product and a computer-readable storage medium on which such a computer program product is stored. TECHNICAL BACKGROUND

[0002] In order to combine several image data sets, for example those created using different imaging techniques, into a common visual representation, it is a well-known approach to assign the signal intensities or values ​​recorded pixel by pixel from the respective image data sets to different coordinates in the color space of the common representation. For example, in the HSV color space of the common representation, the signal value of a first image data set can be assigned to the "color saturation" coordinate, and the signal value of a second image data set can be assigned to the "brightness value" coordinate. Known methods make use of a linear or proportional relationship between the signal intensity and the color space coordinate. For example, an original signal value of 25%, 50% or 75% of the maximum signal value is assigned a coordinate value in the common representation, and in the HSV color space, this is assigned a color saturation or a brightness value of 25%, 50% or 75%.75% of the corresponding maximum coordinate value is displayed. However, a low signal value assigned to brightness results in an equally low brightness value in the final image representation. For the affected pixels, it is therefore not possible to reproduce the signal value assigned to color saturation in a way that is visible to the viewer, let alone distinguishable. In other words, the pixels appear very dark or even black, regardless of the color saturation or the assigned signal value.

[0003] It is the object of the present invention to remedy the disadvantages described above and to provide an improved color assignment when merging image data sets into a common representation.

[0004] The invention can be used in the context of any surgical or therapeutic treatment of a patient, including its planning, for example in computer-assisted surgery (CAS) and / or in conjunction with medical technology systems such as Buzz®, a product of Brainlab AG.

[0005] Aspects of the present invention, examples, exemplary elements, and their embodiments are disclosed below. Various exemplary embodiments of the invention can be combined according to the invention, as far as this is technically expedient and feasible. BRIEF DESCRIPTION OF THE INVENTION

[0006] The following is a brief description of the specific features of the present invention. This should not be construed as limiting the invention to the features described in this section or to any combination of features.

[0007] The system disclosed here comprises at least one processor or at least one computer with at least one processor, which is generally designed to combine a plurality of multidimensional image data sets, for example those created using different imaging methods and positionally registered with one another, into a common pictorial representation in such a way that the signal intensities or values ​​recorded pixel by pixel from the respective image data sets are, in principle, assigned to different coordinates in the color space of the common representation, but in addition there is also a dependency of at least individual coordinate values ​​on the signal intensities or values ​​from another image data set. GENERAL DESCRIPTION

[0008] In this section, the general features of the present invention are described by way of example with reference to possible embodiments of the invention.

[0009] In general, the above-mentioned purpose of the invention is achieved by providing, in a first aspect, a system, for example a medical technology system, comprising at least one processor configured to perform the following steps: a) retrieving a first image data set describing a first multidimensional image of a patient's anatomy composed of a plurality of pixels, each pixel of the first image being assigned a first intensity value; b) retrieving a second image data set describing a second multi-dimensional image of the patient's anatomy composed of a plurality of pixels, each pixel of the second image being assigned a second intensity value; c) determining registration data describing a positional registration between the first image data set and the second image data set; d) Determining a third image data set based on the first image data set, the second image data set and the registration data, which describes a third multi-dimensional image of the patient's anatomy constructed from a plurality of pixels, wherein the pixels of the third image are assigned the first and the second intensity value of the positionally corresponding pixels of the first and the second image, respectively, and wherein for at least one, in particular for each pixel of the third image - the brightness value increases with increasing first intensity value and the color saturation increases with increasing second intensity value; - the brightness value increases with increasing second intensity value, at least when the first intensity value is low.

[0010] In other words, the system according to the invention initially retrieves two or more image data sets, all of which depict the same patient anatomy. At least one, and in particular all, of the image data sets can contain or be a 2D image or a 3D image of the patient's anatomy. In particular, it is possible for one or more image data sets to be a 2D image, and one or more further image data sets to be a 3D image of the patient's anatomy. After the individual image data sets have been positionally registered to one another in a known manner, i.e., a mutual assignment of the images of one and the same real structures reproduced in the data sets is possible, the images reproduced using the data sets can be combined to form a common representation.

[0011] As described in the introduction, the joint representation is based on assigning the signal intensities or values ​​captured pixel by pixel from the respective image data sets to different coordinates in the color space of the joint representation. The present invention is described here using a so-called HSV color space, whose coordinates are defined by a "hue," a "saturation," and a "value." However, the invention could equally well be applied to a representation in other color spaces, for example, an HSL, HSI, or HSB color space.

[0012] Furthermore, it should be noted that the assignment of the acquired signal or intensity values ​​to the color space coordinates of the common representation described here can be performed for at least one, several, or in particular for all pixels of at least one image data set, for example, the image data set with the smallest extension or dimensionality. The term "pixel" is used here as a synonym for "pixel" or "voxel."

[0013] In the present invention, the brightness value of the joint representation depends in an already known, for example proportional, manner on the signal or intensity value obtained from the first image data set, and the color saturation depends on the signal or intensity value obtained from the second image data set.

[0014] In addition, according to the invention, at least in the case of a low signal or intensity value from the first image data set, there is a dependency of the brightness value on the signal or intensity value from the second image data set. The values ​​of the coordinates in the color space resulting from the dependencies described here can, for example, result from an addition of the individual coordinate values ​​resulting from the respective signal or intensity values. In this way, it is possible to reproduce the signal or intensity value from the second image data set in the joint representation across its value range in a visually distinguishable manner. The viewer therefore has the option of distinguishing between a high and a low second signal or intensity value associated with the color saturation in the joint representation and with a low first signal or intensity value associated with the brightness value.In previous approaches to a joint representation in the HSV color space, this was not possible due to the dark image caused by the consistently low brightness value.

[0015] According to a specific embodiment, the brightness value increases with increasing second intensity value, at least at a first intensity value of less than 75%, in particular of less than 50%, in particular of less than 25% of the maximum first intensity value.

[0016] Since visualizing the differences in the second signal values ​​(corresponding primarily to "color saturation") is only necessary for low first signal values ​​(corresponding primarily to "brightness value"), the inventive dependence of the brightness value on the second signal value can be omitted for higher first signal values. In particular, this dependence can be reduced, for example, gradually reduced, as the first signal value increases.

[0017] In a further embodiment, the brightness value can increase with increasing second intensity value, the lower the first intensity value is.

[0018] Conversely, the brightness value may increase less with the second intensity value, the higher the first intensity value. For example, at the maximum first intensity value, the brightness value may assume a constant, particularly maximum, value independent of the second intensity value.

[0019] According to a further embodiment, the brightness value at a minimum second intensity value can increase linearly with increasing first intensity value, in particular directly proportional to the first intensity value.

[0020] Beyond the above-described additional dependence of the brightness value, according to a further preferred embodiment, the color saturation in the joint display can also exhibit an additional dependence, i.e., not only increasing with increasing second intensity value as in known approaches, but also increasing more strongly the lower the first intensity value. Conversely, the increase in color saturation can weaken the higher the first intensity value. With a maximum first intensity value, the color saturation can thus increase linearly with increasing second intensity value, in particular directly proportional to the second intensity value.

[0021] According to a further embodiment, the color saturation can assume a constant, in particular minimal, value at a minimum second intensity value, independently of the first intensity value.

[0022] According to a further embodiment, at a maximum second intensity value, the brightness value can increase with increasing first intensity value and / or the color saturation can decrease with increasing first intensity value. Thus, a counter-directional progression of the brightness value on the one hand and the color saturation on the other hand is achieved, which, at a maximum first signal value, improves the optical distinguishability of the second signal value across its value range.

[0023] According to a further embodiment, the first and / or second image data set describes a three-dimensional image of the patient's anatomy, which was created in particular using a CT or MRI imaging method. Likewise, the first and / or second image data set can describe a two-dimensional image of the patient's anatomy, which was created in particular using an ultrasound or radiography imaging method or reconstructed from a three-dimensional image data set, in particular from a CT or MRI image data set. In a common 2D representation, the individual image data sets can be brought into positional agreement across the surface by registration. In a common 3D representation, the individual image data sets can be brought into positional agreement in all three dimensions by registration.When a 2D image dataset is registered with a 3D image dataset, the former can display a so-called cross-sectional image of the 3D image dataset in a common 3D representation.

[0024] In a second aspect, the invention is directed to a computer program product comprising instructions which, when the program is executed by at least one computer, cause the at least one computer to carry out the steps according to the first aspect. The invention may alternatively or additionally relate to a (physical, e.g. electrical, e.g. technically generated) signal wave, e.g. a digital signal wave, such as an electromagnetic carrier wave, carrying information representing the program, e.g. the aforementioned program, which e.g. comprises code means suitable for carrying out individual or all steps of the method according to the first aspect. In one example, the signal wave is a data carrier signal carrying the aforementioned computer program.A computer program stored on a disk is a data file, and when the file is read out and transferred, it becomes a data stream, for example in the form of a (physical, for example electrical, for example technically generated) signal. The signal can be realized as a signal wave, e.g. as the electromagnetic carrier wave described here. The signal, e.g. the signal wave, is transmitted, for example, via a computer network, e.g. LAN, WLAN, WAN, mobile network, e.g. the Internet. For example, the signal, e.g. the signal wave, is designed for transmission by means of optical or acoustic data transmission. The invention according to the second aspect can therefore alternatively or additionally relate to a data stream that represents the aforementioned program, ie comprises the program.

[0025] In a third aspect, the invention is directed to a computer-readable storage medium on which the program product according to the second aspect is stored. The program storage medium is, for example, non-volatile.

[0026] A further aspect of the invention, not claimed here, relates to a computer-implemented method for merging image data sets. The method is, for example, a medical method. The method according to this aspect comprises executing the following exemplary steps, which are executed by the at least one processor, on at least one processor of at least one computer (for example, at least one computer that is part of a navigation system). The following list defines examples of such a method: 1. Medical image data processing method for merging image data sets, comprising the following steps: a) retrieving a first image data set describing a first multidimensional image of a patient's anatomy composed of a plurality of pixels, each pixel of the first image being assigned a first intensity value; b) retrieving a second image data set describing a second multi-dimensional image of the patient's anatomy composed of a plurality of pixels, each pixel of the second image being assigned a second intensity value; c) determining registration data describing a positional registration between the first image data set and the second image data set; d) Determining a third image data set based on the first image data set, the second image data set and the registration data, which describes a third multi-dimensional image of the patient's anatomy constructed from a plurality of pixels, wherein the pixels of the third image are assigned the first and the second intensity value of the positionally corresponding pixels of the first and the second image, respectively, and wherein for at least one, in particular for each pixel of the third image - the brightness value increases with increasing first intensity value and the color saturation increases with increasing second intensity value; - the brightness value increases with increasing second intensity value, at least when the first intensity value is low. 2. Method according to example 1, wherein the brightness value increases with increasing second intensity value at least at a first intensity value of less than 75%, in particular of less than 50%, in particular of less than 25% of the maximum first intensity value. 3. Method according to one of examples 1 and 2, wherein the brightness value increases more the lower the first intensity value is. 4. Method according to one of examples 1 to 3, wherein the brightness value assumes a constant, in particular maximum, value at the maximum first intensity value independently of the second intensity value. 5. Method according to one of examples 1 to 4, wherein the brightness value at a minimum second intensity value increases linearly with increasing first intensity value, in particular directly proportional to the first intensity value. 6. The method according to any one of examples 1 to 5, wherein the color saturation increases more the lower the first intensity value is. 7. Method according to one of examples 1 to 6, wherein the color saturation assumes a constant, in particular minimum, value at a minimum second intensity value independently of the first intensity value. 8. Method according to one of examples 1 to 7, wherein the color saturation at the maximum first intensity value increases linearly with increasing second intensity value, in particular directly proportional to the second intensity value. 9. Method according to one of examples 1 to 8, wherein at a maximum second intensity value the brightness value increases with increasing first intensity value and / or the color saturation decreases with increasing first intensity value. 10. The method according to any one of examples 1 to 9, wherein the first and / or the second image data set describes a three-dimensional image of the patient's anatomy, which was created in particular by means of a CT or MRI imaging method. 11. The method according to any one of examples 1 to 9, wherein the first and / or the second image data set describes a two-dimensional image of the patient's anatomy, which image was created in particular by means of an ultrasound or radiography imaging method or was reconstructed from a three-dimensional image data set, in particular from a CT or MRI image data set. DEFINITIONS Retrieving or capturing data

[0027] The terms “retrieving data” and “acquiring data,” used synonymously herein, encompass, for example (in the context of a computer-implemented method), the scenario in which the data is ascertained by the system according to the invention or a corresponding computer-implemented method or program. Ascertaining data encompasses, for example, measuring physical quantities and converting the measured values ​​into data, for example digital data, and / or calculating (and, for example, outputting) the data by means of a computer, for example in the context of the method according to the invention. A “determining” step, as described herein, comprises or consists, for example, of issuing a command to carry out the determination described herein.The step comprises or consists, for example, in issuing a command to cause a computer, for example a remote computer, for example a remote server, for example in the cloud, to perform the determination. Alternatively or additionally, a step of “determining” as described herein comprises or consists, for example, in receiving the data resulting from the determination described herein, for example, in receiving the resulting data from the remote computer, for example from the remote computer that was caused to perform the determination. The meaning of the term “data acquisition” also includes, for example, the scenario in which the data is received or retrieved by the computer-implemented method or program (e.g.Input to the program), for example from another program, a previous method step or a data storage medium, for example for further processing by the computer-implemented method or program. The generation of the data to be acquired can, but does not have to, be part of the method according to the invention. The expression “acquiring data” can therefore also mean, for example, waiting to receive data and / or receiving the data. The received data can, for example, be fed in via an interface. The expression “data acquisition” can also mean that the computer-implemented method or program performs steps to (actively) receive or retrieve the data from a data source, for example a data storage medium (such as ROM, RAM, a database, a hard disk, etc.), or via the interface (for example from another computer or a network).The data acquired by the described method, apparatus, or system may be acquired from a database located in a data storage device connected to a computer for transferring data between the database and the computer, e.g., from the database to the computer. The computer acquires the data to use as input for data retrieval steps. The retrieved data may be output to the same or another database for storage for later use. The database, or the database used to perform the disclosed method, may be located on a network data storage device or a network server (e.g., a cloud data storage device or a cloud server) or a local data storage device (e.g., a mass storage device operatively connected to at least one computer executing the disclosed method).The data can be made "ready for use" by an additional step before the acquisition step. In this additional step, the data is generated in order to be acquired. The data is, for example, detected or acquired (e.g., by an analysis device). Alternatively or additionally, the data is input after the additional step, for example via interfaces. The generated data can, for example, be input (e.g., into the computer). According to the additional step (which precedes the acquisition step), the data can also be made available by performing the additional step of storing the data in a data carrier (such as ROM, RAM, CD, and / or hard disk), so that it is ready for use within the framework of the method or program according to the invention. The step of "acquiring data" can therefore also consist of instructing a device to obtain and / or provide the data to be acquired.In particular, the data acquisition step is not an invasive step that would constitute a significant physical intervention in the body, the performance of which requires professional medical expertise and, even when performed with the necessary professional care and skill, entails a significant health risk. In particular, the data acquisition step, e.g., data determination, does not involve a surgical step and, in particular, does not involve a step of surgical or therapeutic treatment of a human or animal body. In order to distinguish the various data used in the present method, the data are referred to as "XY data" and the like and are defined with reference to the information they describe, which is then preferably referred to as "XY information" and the like. registration

[0028] The n-dimensional image of a body is registered when the spatial location of each point of an actual object in a space, e.g., a body part in an operating room, is assigned to an image data point of an image (CT, MR, etc.) stored in a navigation system. Image registration

[0029] Image registration involves converting various data sets into a coordinate system. The data can be multiple photographs and / or data from different sensors, at different times, or from different angles. It is used in computer vision, medical imaging, and in the compilation and analysis of images and data from satellites. Registration is necessary to compare or integrate the data obtained from these different measurements. Analytical devices

[0030] In order to determine the position of body parts to be treated, analytical devices such as X-ray machines, CT machines or MRI machines are used to produce analytical images (such as X-ray images or MRI images) of the body. Analytical devices are used, for example, to carry out medical imaging procedures. Analytical devices use, for example, medical imaging procedures and are, for example, devices for analyzing a patient's body, for example using waves and / or radiation and / or energy beams, for example electromagnetic waves and / or radiation, ultrasonic waves and / or particle beams. Analytical devices are, for example, devices that produce images (for example two- or three-dimensional images) of the patient's body (and, for example, internal structures and / or anatomical parts of the patient's body) by analyzing the body. Analytical devices are used, for example,used in medical diagnostics, e.g., radiology. However, it can be difficult to identify the body part to be treated in the analysis image. For example, it may be easier to identify an indicator body part that correlates with changes in the position of the treated body part and, for example, the movement of the treated body part. Tracking an indicator body part thus allows tracking of a movement of the treated body part based on a known correlation between the changes in position (e.g., movements) of the indicator body part and the changes in position (e.g., movements) of the treated body part. Alternatively, or in addition to tracking indicator body parts, markers (which can be used as indicators and are therefore referred to as "marker indicators") can be tracked using marker capture devices.The position of the marker indicators has a known (predetermined) correlation with (e.g., a fixed relative position relative to) the position of indicator structures (such as the chest wall, e.g., true ribs or false ribs, or the diaphragm or intestinal walls, etc.) that change their position, e.g., due to vital movements. Imaging techniques

[0031] In medicine, imaging techniques (also called imaging methods and / or medical imaging techniques) are used to generate image data (e.g., two- or three-dimensional image data) of anatomical structures (such as soft tissue, bones, organs, etc.) of the human body. The term "medical imaging techniques" refers to (preferably device-assisted) imaging techniques (e.g., so-called medical imaging modalities and / or radiological imaging techniques), such as computed tomography (CT) and cone beam computed tomography (CBCT, e.g., volumetric CBCT), X-ray tomography, magnetic resonance imaging (MRI), conventional X-ray, sonography and / or ultrasound examinations, and positron emission tomography. The medical imaging techniques are performed, for example, by the analysis devices.Examples of medical imaging modalities used by medical imaging technologies include: X-ray radiography, magnetic resonance imaging, medical ultrasound, endoscopy, elastography, tactile imaging, thermography, medical photography, and nuclear medicine functional imaging techniques such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT), as mentioned in Wikipedia. The image data generated in this way is also referred to as "medical image data." In device-assisted imaging technologies, for example, analysis devices are used to generate the image data. The imaging technologies are used, for example, in medical diagnostics to analyze the anatomical body to generate images described by the image data. The imaging technologies are also used, for example, to detect pathological changes in the human body.However, some of the changes in the anatomical structure, such as the pathological changes in the structures (tissue), may not be detectable and visible, for example, in the images produced by imaging techniques. A tumor is an example of a change in an anatomical structure. If the tumor grows, it can be said to be an expanded anatomical structure. This expanded anatomical structure may not be detectable; for example, only part of the expanded anatomical structure may be detectable. Primary / high-grade brain tumors, for example, are usually visible on MRI scans when contrast agents are used to infiltrate the tumor. MRI scans are an example of an imaging technique. In MRI scans of such brain tumors, the signal enhancement in the MRI images (due to the contrast agents penetrating the tumor) is considered to be a solid tumor mass.The tumor is therefore detectable and, for example, visible in the image produced by the imaging method. It is estimated that, in addition to these tumors, which are referred to as "enhancing" tumors, approximately 10% of brain tumors are not detectable on a scan and, for example, are not visible to a user viewing the images produced by the imaging procedure. SHORT DESCRIPTION OF THE FIGURES

[0032] The invention is described below with reference to the accompanying figures. These provide further background information and illustrate specific embodiments of the invention. However, the scope of the invention is not limited to the specific features disclosed in connection with the figures. They show: Fig. 1 a joint representation of a patient's anatomy created from two image data sets by means of the image data processing system according to the invention Fig. 2 a function used in known methods for assigning individual signal values ​​to HSV color space coordinates Fig. 3 3D representation of an assignment of individual signal values ​​to HSV color space coordinates used in known methods Fig. 4 a result of the assignment Fig. 3 resulting color chart Fig. 5 a further illustration of an assignment of individual signal values ​​to HSV color space coordinates used in known methods Fig. 6 3D representation of an assignment of individual signal values ​​to HSV color space coordinates used according to the present invention Fig. 7 a further illustration of an assignment of individual signal values ​​to HSV color space coordinates used according to the present invention Fig. 8 a result of the assignment Fig. 6 resulting color chart PREFERRED EMBODIMENTS

[0033] Conventional color maps in the conventional approaches to merging image data sets described at the beginning, for example “Uwe Pietrzyk, Karl Herholz, Alexander Schuster, Hans-Martin v. Stockhausen, Helmut Lucht, Wolf-Dieter Heiss. Clinical applications of registration and fusion of multimodality brain images from PET, SPECT, CT, and MRI. European Journal of Radiology 1995; 174-182”, result from simple linear functions in two-dimensional space, as in Fig. 2. With such a linear function applied to the color saturation or brightness in an HSV color model, a Fig. 4 The color map shown was created using two signal intensities.

[0034] For example, the signal intensity 1 obtained from a first image data set is used to model the brightness value and the signal intensity 2 obtained from a second image data set is used to model the color saturation according to the Fig. 2 is used. Combined and plotted over both signal intensities, color saturation and brightness form the Fig. displayed 3D surfaces.

[0035] From the value pair of color saturation and brightness value, which results for each combination of signal intensity 1 and signal intensity 2, the corresponding RGB value can be determined and used to create the Fig. 4. The right-hand columns of the color map show almost no difference in color impression. The colors are therefore not unique to a cell or a specific signal intensity pair and thus indistinguishable for the observer. Only with increasing signal intensity 1 (movement to the columns further to the left) do the vertical and horizontal color gradients increase and the cells can be better distinguished. The resolution of the color map is partially poor. The observer's intuitive assignment of a color to a signal intensity pair is therefore impaired.

[0036] The Fig. Figure 5 shows the color saturation and brightness values ​​for some intensity variations to explain their influence on these results. See, for example, Fig. 5C: Signal intensity 1 has the value 0 (point P1), signal intensity 2 increases from 0 to 100% (point P4). This progression is represented by the rightmost column in the color map. Fig. 4. Along the entire path from P1 to P4, the brightness value remains at 0, since signal intensity 1 is at 0%. The increasing color saturation introduced by signal intensity 2 cannot affect the resulting black color. The entire column of rightmost cells is therefore black.

[0037] If the signal intensity 2 at P4 is increased to 100%, Fig. Figure 5A shows a further progression toward P3 with increasing signal intensity 1. It represents the top row of the color map. The color saturation remains at a constant value, and the color change is initiated only by the increase in signal intensity 2. The constant color saturation within the row does not contribute to the color changes in that row. This effect also applies to every other row of cells. Each row has the same color saturation in its cells. Only the increasing brightness value makes a difference and determines the color change in each row. As a result, the rows show more distinguishable cell colors on the left side of the color map.

[0038] A similar effect occurs with the columns. For each column, the cells have the same brightness value. Only the increasing color saturation makes the difference and enables color changes within a column. However, since a lower value weakens the visible effect of saturation changes, columns on the right side show weaker visible color gradients than those on the left side (see figure). Fig. 5B and Fig. 5C for the same color saturation, but different brightness gradients). With both effects on the rows and columns, the color gradients are unbalanced.

[0039] The present invention addresses the deficiencies described above. It represents an improvement over the color maps currently in use. It aims to overcome the deficiencies related to the poor balance of color impression and color gradients, the impaired resolution, and the impaired intuitive assignment of colors to specific signal intensity pairs.

[0040] The most important improvements are changes to the color saturation and brightness functions. The wedge-shaped gradients for the conventional color saturation and brightness gradients used in Fig. 3, changes will be made as described in Fig. 6. The color saturation and brightness gradients are set off from the conventional gradients (see blue isolines in Fig. which characterize the surfaces of the 3D functions). The Fig. Figure 7 shows the gradients of color saturation and brightness on specific paths. From a comparison of the color chart from the Fig. 8 with the conventional color chart from the Fig. 4 the differences discussed below arise:

[0041] First, the brightness and color saturation at P4 are increased compared to the linear progression described above. This results in the upward trend of color saturation on the way from P1 to P4 (rightmost column in the color chart and Fig. 7C). A new upward trend is introduced for the brightness value. This enables color visibility and a stronger color gradient in the rightmost column of the color map with increasing signal intensity 2, which is completely missing in the conventional color map (compare Fig. 4 and Fig. 8).

[0042] Secondly, the trends of color saturation and brightness are opposite between P3 and P4 (see Fig. 7A). The color saturation increases on the path from P3 to P4, and the brightness decreases on the path from P4 to P3 (top row of a color chart). These changes in color saturation and brightness amplify the color differences in the top row of a color chart (compare Fig. 4 and Fig. 8).

[0043] Third, chroma and brightness form smooth surfaces for all points formed by the grid of signal intensities 1 and 2, corresponding to the trends defined above for the paths between P1 and P4 and P3 and P4 (rightmost column and top row of the color map). Chroma is a monotonically decreasing function at signal intensity 1 and a monotonically increasing function at signal intensity 2. The brightness is monotonically increasing at both signal intensities. This results in a monotonic increase across the entire column and row of the color map in either a positive or negative direction for chroma and brightness.

[0044] Fourthly, the projections of the isolines of color saturation and brightness intersect at each point of the grid (see Fig. 6). This ensures that the resulting color is unique for every combination of signal intensities. Fig. 4 on the left, the conventional color chart shows redundant colors in the rightmost column, while the inventive color chart consists of Fig. 8 on the left side shows a total of unique colors.

[0045] Fifth, the smooth surfaces for color saturation and brightness can be easily adjusted to the desired visual color perception. Starting from a rather coarse grid of signal intensities, the color saturation and brightness parameters can be interpolated for finer grids using various methods.

[0046] Compared to the conventional color chart from the Fig. 4 shows the color chart according to the invention from the Fig. 8 offers the following advantages: The color map according to the invention exhibits a much better balance of color gradients and resolution, as well as a wider range of color saturation variation. In every area of ​​the map, the colors in neighboring cells can be distinguished equally well. A color is always clearly assigned to a cell or a specific signal intensity pair. This makes it easier for the viewer to intuitively assign a color to a signal intensity pair.

[0047] The additional dependencies, adaptations, and variations compared to conventional approaches discussed in the present invention can be represented and applied, for example, using simple mathematical functions. When merging more than two image data sets, their respective signals can be made distinguishable by assigning the signal values ​​from different data sets to different color values, for example, in the HSV color model. For other color models, the color space coordinates not yet assigned for the above-mentioned purposes can be used accordingly.

[0048] The rows and columns are in the Fig.1. The parameters represent a sample set and can be designed and defined differently for specific modalities or purposes. It should be noted that the concept can also be applied to tables with R, G, and B parameters when used for the RGB color model. The color mapping concept discussed here as an example is based on the parameters chroma, brightness, and chromaticity for the HSV color model. It can also be applied to other color models, such as HSL, HSI, or HSB color models. QUOTES CONTAINED IN THE DESCRIPTION

[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Cited non-patent literature

[0000] Uwe Pietrzyk, Karl Herholz, Alexander Schuster, Hans-Martin v. Stockhausen, Helmut Lucht, Wolf-Dieter Heiss. Clinical applications of registration and fusion of multimodality brain images from PET, SPECT, CT, and MRI. European Journal of Radiology 1995; 174-182

[0033]

Claims

[1] Medical image data processing system for merging image data sets, comprising a processor designed to carry out the following steps: a) retrieving a first image data set describing a first multidimensional image of a patient's anatomy composed of a plurality of pixels, each pixel of the first image being assigned a first intensity value; b) retrieving a second image data set describing a second multi-dimensional image of the patient's anatomy composed of a plurality of pixels, each pixel of the second image being assigned a second intensity value; c) determining registration data describing a positional registration between the first image data set and the second image data set; d) Determining a third image data set based on the first image data set, the second image data set and the registration data, which describes a third multi-dimensional image of the patient's anatomy constructed from a plurality of pixels, wherein the pixels of the third image are assigned the first and the second intensity value of the positionally corresponding pixels of the first and the second image, respectively, and wherein for at least one, in particular for each pixel of the third image - the brightness value increases with increasing first intensity value and the color saturation increases with increasing second intensity value; - the brightness value increases with increasing second intensity value, at least when the first intensity value is low. [2] Medical image data processing system according to claim 1, wherein the brightness value increases with increasing second intensity value at least at a first intensity value of less than 75%, in particular of less than 50%, in particular of less than 25% of the maximum first intensity value. [3] Medical image data processing system according to one of claims 1 and 2, wherein the brightness value increases more the lower the first intensity value is. [4] Medical image data processing system according to one of claims 1 to 3, wherein the brightness value assumes a constant, in particular maximum, value at the maximum first intensity value independently of the second intensity value. [5] Medical image data processing system according to one of claims 1 to 4, wherein the brightness value at a minimum second intensity value increases linearly with increasing first intensity value, in particular directly proportional to the first intensity value. [6] Medical image data processing system according to one of claims 1 to 5, wherein the color saturation increases more the lower the first intensity value is. [7] Medical image data processing system according to one of claims 1 to 6, wherein the color saturation assumes a constant, in particular minimum, value at a minimum second intensity value independently of the first intensity value. [8] Medical image data processing system according to one of claims 1 to 7, wherein the color saturation at the maximum first intensity value increases linearly with increasing second intensity value, in particular directly proportional to the second intensity value. [9] Medical image data processing system according to one of claims 1 to 8, wherein at a maximum second intensity value the brightness value increases with increasing first intensity value and / or the color saturation decreases with increasing first intensity value. [10] Medical image data processing system according to one of claims 1 to 9, wherein the first and / or the second image data set describes a three-dimensional image of the patient's anatomy, which was created in particular by means of a CT or MRI imaging method. [11] Medical image data processing system according to one of claims 1 to 9, wherein the first and / or the second image data set describes a two-dimensional image of the patient's anatomy, which was created in particular by means of an ultrasound or radiography imaging method or reconstructed from a three-dimensional image data set, in particular from a CT or MRI image data set. [12] A computer program product comprising instructions which, when executed by a computer, cause the computer to carry out the steps of any one of claims 1 to 11. [13] A computer-readable storage medium on which the computer program product according to claim 12 is stored.

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