A method, system and device for combined multi-color imaging of CT and MRI

Abstract

The present invention relates to a method, system and device for combined multi-color imaging of CT and MRI; including: obtaining CT images and MRI images of the same part; selecting preset tissues as positioning marks; determining the lesion to be identified based on the CT images and MRI images; respectively obtaining the relative signal intensity ratio of the lesion to be identified and the positioning mark in the CT image and the MRI image; constructing an intensity correction model based on the relative signal intensity ratio; selecting primary color channels, assigning different primary color channels to CT and MRI sequences, adjusting the primary color signal intensity based on the intensity correction model, and synthesizing multi-color imaging. The present invention provides for the first time a method for combined multi-color imaging of human body electronic computer tomography and nuclear magnetic resonance, which performs relative quantitative comparison of lesion imaging intensity on the basis of achieving lesion co-localization, provides multi-color imaging with higher resolution, and provides new solutions and means for clinical diagnosis and identification of lesion tissues.

Description

Technical Field

[0001] The present invention relates to the field of medical imaging technology, and in particular to a method, system and equipment for combined CT and MRI multi-color imaging. Background Art

[0002] Human body computer tomography, commonly referred to as CT (Computed tomography), is a medical imaging diagnostic technology that combines X-rays and computer technology. CT scans can provide high-resolution tomographic images of the internal structure of the human body and have unique advantages in detecting acute brain hemorrhage, skull fractures and intracranial tumors.

[0003] Magnetic resonance imaging (MRI) is another medical imaging technique that uses the magnetic resonance phenomenon of hydrogen nuclei in human tissue to obtain images. MRI can provide clear images with high contrast and does not require the use of electron-ionizing radiation or contrast agents. MRI can accurately measure the relaxation time T1 and T2 sequences of atomic nuclei, reflecting the chemical structure information in human tissue. The combination of T1 and T2 sequences can be used to distinguish different tissue types, such as gray matter and white matter in brain tissue, as well as for early diagnosis of brain tissue necrosis, malignant tumors and neurodegenerative diseases. MRI sequences refer to the use of different pulse techniques to obtain the contrast and image characteristics of different tissues. In addition to T1 and T2, there are also commonly used fluid attenuated inversion recovery (FLAIR), diffusion weighted imaging (DWI) and susceptibility weighted imaging (SWI).

[0004] For the same detection site, CT and MRI present different image features due to different imaging mechanisms. When performing imaging examinations for cerebrovascular diseases, CT has advantages in identifying ultra-early hemorrhagic stroke, DWI sequences have advantages in identifying ultra-early ischemic stroke, and SWI has advantages in identifying old cerebral microhemorrhages. When presenting images, the above-mentioned examinations provide multiple grayscale images according to their respective brightness standards and size scales, and the reader needs to roughly locate the lesions in multiple images and roughly judge the brightness differences based on his or her subjective judgment. Therefore, according to objective positioning marks, the imaging space and relative signal intensity of CT and different MRI sequences can be corrected to avoid the influence of subjective factors of the reader; secondly, the primary color channel is given, and the characteristics of CT and MRI are comprehensively presented in a color image on one image, which can reduce the error of the reader's subjective comprehensive judgment; compared with grayscale imaging and single detection, color images can provide a higher level of lesion resolution. At present, there are no reports on methods and equipment for combined multi-color imaging of CT and MRI. Summary of the invention

[0005] The purpose of the present invention is to provide a method, system and device for combined multi-color imaging of CT and MRI to solve the problems existing in the above-mentioned prior art.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A method for combined multi-color imaging of CT and MRI, comprising:

[0008] Obtain CT images and MRI images of the same part;

[0009] Select the preset organization as the positioning mark;

[0010] Determining a lesion to be identified based on the CT image and the MRI image;

[0011] Respectively obtaining the relative signal intensity ratios of the lesion to be identified and the positioning marker in the CT image and the MRI image;

[0012] constructing an intensity correction model based on the relative signal intensity ratio;

[0013] Select the primary color channel, assign different primary color channels to the CT and MRI sequences, adjust the primary color signal intensity based on the intensity correction model, and synthesize multi-color imaging. The primary color channel can select the red-green-blue three-primary color mode, the cyan-magenta-yellow-black four-primary color mode or other modes, and each primary color channel is assigned based on the final imaging effect as the standard.

[0014] Optionally, based on the corrected CT image and MRI image, determining the lesion to be identified also includes:

[0015] The sizes and positions of the CT image and the MRI image are corrected based on the positioning markers.

[0016] Optionally, the first relative signal intensity ratio between the lesion to be identified and the positioning marker in the CT image is:

[0017] C'=C1 / C2

[0018] Wherein, C' is the first relative signal intensity ratio, C1 is the signal intensity of the lesion to be identified in the CT image, and C2 is the signal intensity of the positioning marker in the CT image;

[0019] The second relative signal intensity ratio between the lesion to be identified and the positioning marker in the MRI image is:

[0020] M'=M1 / M2

[0021] Wherein, M' is the second relative signal intensity ratio, M1 is the signal intensity of the lesion to be identified in the MRI image, and M2 is the signal intensity of the positioning marker in the MRI image.

[0022] Optionally, based on the relative signal intensity ratio, constructing an intensity correction model comprises:

[0023] The first relative signal intensity ratio and the second relative signal intensity ratio are subjected to linear regression processing to construct the intensity correction model.

[0024] Optionally, the intensity correction model is:

[0025] C=aM+b

[0026] Among them, C is the relative signal ratio of CT imaging intensity, M is the relative signal ratio of MRI imaging intensity, a is the custom correction coefficient, and b is the custom parameter. A database is built based on clinical trial data or historical literature data, and the a and b parameters of the fitting line are calculated through data model fitting.

[0027] A system for combined multi-color imaging of CT and MRI, used to implement the combined multi-color imaging method, the system comprising: an acquisition module, a communication module and a processing module;

[0028] The acquisition module is used to acquire CT images and MRI images of the same part;

[0029] The communication module is used to transmit the acquired CT image and MRI image to the processing module;

[0030] The processing module is used to assign different primary color channels to the CT image and the MRI image to synthesize multi-color imaging.

[0031] Optionally, the processing module includes: a positioning unit, a proposed identification unit, a signal strength acquisition unit, a construction unit and a synthesis unit;

[0032] The positioning unit is used to select a preset tissue as a positioning mark;

[0033] The proposed identification unit is used to determine the lesion to be identified based on the CT image and the MRI image;

[0034] The signal intensity acquisition unit is used to respectively acquire the relative signal intensity ratios of the lesion to be identified and the positioning marker in the CT image and the MRI image;

[0035] The construction unit constructs an intensity correction model based on the relative signal intensity ratio;

[0036] The synthesis unit selects primary color channels, assigns CT and MRI sequences to different primary color channels, adjusts primary color signal intensity based on the intensity correction model, and synthesizes multi-color imaging.

[0037] Optionally, the signal strength acquisition unit includes: a first signal strength acquisition subunit and a second signal strength acquisition subunit;

[0038] The first signal intensity acquisition subunit is used to acquire a first relative signal intensity ratio between the lesion to be identified and the positioning marker in the CT image:

[0039] C'=C1 / C2

[0040] Wherein, C' is the first relative signal intensity ratio, C1 is the signal intensity of the lesion to be identified in the CT image, and C2 is the signal intensity of the positioning marker in the CT image;

[0041] The second signal intensity acquisition subunit is used to acquire a second relative signal intensity ratio between the lesion to be identified and the positioning marker in the MRI image:

[0042] M'=M1 / M2

[0043] Wherein, M' is the second relative signal intensity ratio, M1 is the signal intensity of the lesion to be identified in the MRI image, and M2 is the signal intensity of the positioning marker in the MRI image.

[0044] Optionally, the intensity correction model is:

[0045] C=aM+b

[0046] Among them, C is the relative signal ratio of CT imaging intensity, M is the relative signal ratio of MRI imaging intensity, a is the custom correction coefficient, and b is the custom parameter. A database is built based on clinical trial data or historical literature data, and the a and b parameters of the fitting line are calculated through data model fitting.

[0047] An electronic device comprises: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the method of CT and MRI combined multi-color imaging is implemented.

[0048] The beneficial effects of the present invention are:

[0049] The present invention fills the gap of the previous lack of combined color imaging of MRI and CT, adjusts the CT or MRI imaging intensity according to the target tissue or lesion, so that the imaging difference between CT and MRI is comparable; and gives the primary color channel a synthesized color image, so that the imaging difference between CT and MRI can achieve a comprehensive presentation of positioning and quantification, which can provide a new solution and equipment for lesion identification in clinical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0051] Figure 1 A schematic diagram of a method for establishing a CT and MRI combined multi-color image according to an embodiment of the present invention;

[0052] Figure 2 This is a schematic diagram of the structure of a CT and MRI combined multi-color imaging device according to an embodiment of the present invention;

[0053] Figure 3 This is a flow chart of a CT and MRI combined multi-color image according to an embodiment of the present invention;

[0054] Figure 4 The original CT and MRI images are used to calculate the model formula in the modeling example of the embodiment of the present invention;

[0055] Figure 5 CT and MRI images used for positioning and size correction in the modeling example of the embodiment of the present invention;

[0056] Figure 6 The CT and MRI images after positioning and size correction in the modeling example of the embodiment of the present invention;

[0057] Figure 7Collect image intensity for selecting skull tissue (box) in CT and MRI images in the modeling example of the embodiment of the present invention;

[0058] Figure 8 Selecting brain white matter tissue (oval frame) in CT and MRI images in the modeling example of the embodiment of the present invention to collect image intensity;

[0059] Fig. 9 The CT and MRI images are after image intensity correction in the modeling example of the embodiment of the present invention;

[0060] Fig.10 is a calculation model formula for distinguishing brain white matter according to the modeling data fitted in Table 1 in an embodiment of the present invention;

[0061] Fig.11 In the modeling example of the embodiment of the present invention, the CT, MRI-T2, and MRI-T1 after the correction of the intensity are assigned to the primary color channel;

[0062] Fig.12 Generate a CT and MRI combined multi-color image in a modeling example of an embodiment of the present invention;

[0063] Fig.13 The CT and MRI original images are used to distinguish and image another part of the brain tissue in the imaging example of the embodiment of the present invention;

[0064] Fig.14 The CT and MRI images after positioning and size correction in the imaging example of the embodiment of the present invention;

[0065] Fig.15 CT and MRI images after intensity correction in the imaging example of the embodiment of the present invention;

[0066] Fig.16 It is a joint multi-color image after being assigned primary color channels in the imaging example of the embodiment of the present invention;

[0067] Fig.17 A joint multi-color image after being assigned primary color channels in another part imaging example of an embodiment of the present invention;

[0068] Fig.18 This is a joint multi-color image after being assigned primary color channels in another part imaging example of an embodiment of the present invention. DETAILED DESCRIPTION

[0069] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0070] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0071] This embodiment proposes a method for combined CT and MRI multi-color imaging, including:

[0072] Obtain CT images and MRI images of the same part;

[0073] Select the preset organization as the positioning mark;

[0074] Specifically, in this embodiment, the selection criteria of the preset tissue is set as that the imaging difference of the tissue in CT and MRI is small and easy to locate; such as bone tissue.

[0075] In this embodiment, a specific tissue imaging marker is selected as a positioning marker;

[0076] Based on CT and MRI images, determine the lesions to be identified;

[0077] Obtaining the relative signal intensity ratios of the lesion to be identified and the positioning marker in the CT image and the MRI image respectively;

[0078] In this embodiment, the relative signal intensity ratio C of the lesion C1 to be identified in CT and the positioning marker C2 is obtained according to clinical trial data or literature historical data, that is, C=C1 / C2;

[0079] As above, the relative signal intensity ratio M between the lesion to be identified M1 and the positioning marker M2 in the specific MRI sequence is obtained, that is, M=M1 / M2.

[0080] Based on the relative signal intensity ratio, an intensity correction model is constructed;

[0081] In this embodiment, according to the relative signal strength of the CT and MRI specific sequences determined above, the linear regression method is used to analyze the correlation between the CT imaging intensity C and the MRI imaging intensity M, a calculation model between C and M is constructed, and the custom correction coefficient a and the custom parameter b are calculated.

[0082] The primary color channels are selected, different primary color channels are assigned to CT and MRI sequences, the primary color signal intensity is adjusted based on the intensity correction model, and multi-color imaging is synthesized.

[0083] In this embodiment, the custom correction coefficient a and the custom parameter b are used to adjust the imaging intensity of the target CT and MRI, so that the objective relative signal intensity of the lesion can be obtained, and the CT and MRI characteristics are integrated to perform objective evaluation and present color images.

[0084] The primary color channels can select the red-green-blue three-primary color mode, the cyan-magenta-yellow-black four-primary color mode or other modes, and each primary color channel is given the final imaging effect as a standard.

[0085] Furthermore, based on the corrected CT image and MRI image, before determining the lesion to be identified, the following steps are also included:

[0086] The size and position of the CT image and the MRI image are corrected based on the positioning markers.

[0087] Specifically, in this embodiment, the image position and size are corrected according to the positioning mark and matching, so as to reduce the influence of the scale and imaging differences between CT and MRI on the lesion positioning;

[0088] Furthermore, the intensity correction model includes:

[0089] C=aM+b

[0090] Where C is the relative signal ratio of CT imaging intensity, M is the relative signal ratio of MRI imaging intensity, a is the custom correction coefficient, and b is the custom parameter. A database is built based on clinical trial data or historical literature data, and the a and b parameters of the fitting line are calculated through data model fitting.

[0091] The present invention can solve the problems of correcting imaging space, correcting signal intensity, and multi-color imaging. By selecting positioning markers to construct a correction formula for CT and MRI imaging intensity, a custom correction coefficient can be selected according to the characteristics of the lesion, thereby achieving the purpose of distinguishing and identifying the lesion in the multi-color image, thereby providing an objective method and solution for objectively and comprehensively presenting the lesion characteristics of CT and MRI in clinical medicine.

[0092] This embodiment also provides a system for combined multi-color imaging of CT and MRI, the system comprising: an acquisition module, a communication module and a processing module;

[0093] An acquisition module, used for acquiring CT images and MRI images of the same part;

[0094] A communication module, used for transmitting the acquired CT images and MRI images to the processing module;

[0095] The processing module is used to assign different primary color channels to CT images and MRI images to synthesize multi-color imaging.

[0096] Further, the processing module includes: a positioning unit, a proposed identification unit, a signal strength acquisition unit, a construction unit and a synthesis unit;

[0097] A positioning unit, used for selecting a preset tissue as a positioning mark;

[0098] A proposed identification unit, used for determining a lesion to be identified based on the CT image and the MRI image;

[0099] A signal intensity acquisition unit, used to respectively acquire the relative signal intensity ratios of the lesion to be identified and the positioning marker in the CT image and the MRI image;

[0100] A construction unit is used to construct an intensity correction model based on the relative signal intensity ratio;

[0101] The synthesis unit is used to select primary color channels, assign CT and MRI sequences to different primary color channels, adjust the primary color signal intensity based on the intensity correction model, and synthesize multi-color imaging.

[0102] Further, the signal strength acquisition unit includes: a first signal strength acquisition subunit and a second signal strength acquisition subunit;

[0103] The first signal intensity acquisition subunit is used to acquire the first relative signal intensity ratio between the lesion to be identified and the positioning marker in the CT image:

[0104] C'=C1 / C2

[0105] Wherein, C' is the first relative signal intensity ratio, C1 is the signal intensity of the lesion to be identified in the CT image, and C2 is the signal intensity of the positioning marker in the CT image;

[0106] The second signal intensity acquisition subunit is used to acquire a second relative signal intensity ratio between the lesion to be identified and the positioning marker in the MRI image:

[0107] M'=M1 / M2

[0108] Wherein, M' is the second relative signal intensity ratio, M1 is the signal intensity of the lesion to be identified in the MRI image, and M2 is the signal intensity of the positioning marker in the MRI image.

[0109] Further,

[0110] The intensity correction model is:

[0111] C=aM+b

[0112] Among them, C is the relative signal ratio of CT imaging intensity, M is the relative signal ratio of MRI imaging intensity, a is the custom correction coefficient, and b is the custom parameter. A database is built based on clinical trial data or historical literature data, and the a and b parameters of the fitting line are calculated through data model fitting.

[0113] This embodiment also proposes an electronic device, which includes: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, a method for combined CT and MRI multi-color imaging is implemented.

[0114] Since the identification of the nature and scope of tissue types and lesions is mainly based on the differences in tissue composition, whether an imaging method and system is conducive to the identification of tissue types and lesions can be judged by its ability to present tissue differences. It is known to those skilled in the art that CT imaging identification relies on the contrast differences of different tissues under X-rays, and MRI imaging identification relies on the contrast differences of different tissues under specific pulse technology in a magnetic field. It is known to those skilled in the art that MRI and CT have different imaging principles, standards and equipment, and each has advantages in the ability to identify different tissues. At present, there is no imaging method and system that can integrate the differences in MRI and CT imaging for the identification of tissue types and lesions.

[0115] In the specific implementation of this embodiment, a specific positioning marker is used as a reference for imaging signal correction. It is known to those skilled in the art that there are imaging features of specific tissues that are easily identified in certain sequences of CT and MRI. Such specific tissues are used as positioning markers, and are used as correction standards for spatial size and position, that is, the positioning marker tissue should be corrected to the same spatial size and position in certain sequence imaging of CT and MRI. Secondly, in CT or MRI, the imaging intensity of the positioning marker tissue is used as a reference value to calculate the ratio of the imaging intensity of other tissues relative to the marked tissue; based on the relative ratio of the imaging intensity of the positioning marker tissue in CT and MRI, the conversion of the imaging difference of other tissues between CT and MRI can be realized. Finally, after the correction of spatial size, position and imaging intensity, CT and MRI are assigned to primary color channels, and the primary color intensity of the channel is assigned according to the gray imaging intensity. The comprehensive image of multiple primary color channels can include MRI and CT imaging features, so as to realize the comprehensive presentation of tissue types and lesion characteristics in color images.

[0116] The following takes human head brain tissue imaging as an example to illustrate the detailed implementation process of this embodiment. Figure 3 , including:

[0117] 1. Use GESYTEC 3000I CT equipment and SIGNA TM Architect 3.0T MRI equipment; MRI sequences include T1, T2, FLAIR and OXA3, etc., CT windows include bone window and soft tissue window, etc.; collect human head and brain tissue imaging images. Figure 4 .

[0118] 2. Further, the CT and MRI image information obtained by the above method is transmitted to the memory through the communication interface of the CT and MRI combined color imaging device, such as Figure 2The structure diagram of the CT and MRI combined color imaging device is shown. The device includes a communication interface 301, a processor 302, a memory 303, a display 304, and a power supply. Specifically, the communication interface 301 may include an interface for communicating with other devices. The interface may be a wired interface, a wireless interface, or a combination thereof. The communication interface 301 is used to receive the image measurement information of the user sent by the external CT and MRI devices.

[0119] 3. The processor 302 may be a central processing unit (CPU), and the memory 303 may include a random-access memory (RAM) and may also include a non-volatile memory.

[0120] 4. Figure 2 Possible hardware forms of the CT and MRI combined color imaging device shown are smart terminals, wearable devices, etc.

[0121] 5. Memory 303 is used to store programs and universal intensity correction formulas for CT and MRI combined color imaging. It can also store historical data such as user's imaging reference values, processed data and final imaging.

[0122] 6. Processor 302, used to call the program stored in the memory to execute the joint color imaging process.

[0123] 7. Display 304, used to display the user's joint color imaging results.

[0124] 8. Combine the following Figure 1-3 The specific implementation process of this application is described.

[0125] 9. The skull was selected as a positioning marker because it has similar imaging features in CT and MRI T1 sequences.

[0126] 10. MRI and CT have different imaging principles, standards, and equipment, so the size of the skull imaging space presented is different. Figure 4 , 5 .

[0127] 11. According to the standard that the imaging space size of skull positioning markers should be consistent in MRI and CT, calibrate the image space size of MRI and CT. Figure 6 .

[0128] 12. Based on the condition that the imaging difference of the positioning marker in CT and MRI is small and easy to locate, the skull is selected as the positioning marker, and the collection area for calculating the skull imaging intensity is selected in the CT and MRI images; based on the condition that the imaging difference of the target area in CT and MRI is large and easy to identify, the target area is further selected. Figure 7 .

[0129] 13. Select the collection area for calculating the white matter imaging intensity in CT and MRI images. Figure 8 and Fig. 9 .

[0130] 14. Adjust the image intensity in CT or MRI images based on the observer's assessment of the CT or MRI image intensity parameters for optimal white matter resolution.

[0131] 15. Calculate the relative intensity ratio C of the white matter C1 and the skull C2 in CT, that is, C = C1 / C2; similarly, calculate the relative intensity ratio M of the white matter M1 and the skull M2 in the MRI-T1 sequence, that is, M = M1 / M2; select two or more different parts and repeat the calculation to obtain the a value and b value; collect the C, M, a and b values ​​in the head MRI and CT of multiple different individuals through the above method, and they have the following relationship, see Table 1.

[0132] Table 1 C and M values ​​collected from CT and MRI images in the modeling example

[0133]

[0134]

[0135] After data fitting, see Fig.10 ; The best calculation model formula for distinguishing brain white matter C = aM + b is C = 0.3585M-0.1313, R 2 =0.9391; where C is the CT imaging intensity, M is the MRI imaging intensity, a is the custom correction coefficient 0.3585, b is the custom parameter 0.1313, R 2 Equal to the square of the correlation coefficient.

[0136] 16. MRI-T2 is assigned a red primary color channel, MRI-T1 is assigned a blue primary color channel, and a lightly corrected CT is assigned a green primary color channel. The channel selection criteria are set by yourself; the brightness of the above primary color channels is set based on the gray depth of MRI or CT. Fig.11 .

[0137] 17. According to the standard that the size of skull positioning markers in MRI and CT should coincide, the images of the red, blue and green primary color channels are corrected to ensure that the skull positioning markers in the three channels are completely matched and coincident; finally, CT and MRI combined multi-color imaging is generated. Fig.12 .

[0138] Based on this, the present invention also proposes a method and device for identifying brain white matter by CT and MRI combined multi-color imaging.

[0139] 16. Use CT equipment and MRI equipment; MRI sequences include T1 and T2, and the CT window is a soft tissue window; collect brain tissue imaging images of another part of the skull. Fig.13 .

[0140] 17. Further, the CT and MRI image information obtained by the above method is transmitted to the memory through the communication interface of the CT and MRI combined color imaging device, such as Figure 2 Shown is a schematic diagram of the structure of a CT and MRI combined color imaging device.

[0141] 18. The processor 302 may be a central processing unit, and the memory 303 may include a random access memory and may also include a non-volatile memory.

[0142] 19. Memory 303 is used to store programs and universal intensity correction formulas for CT and MRI combined color imaging. It can also store historical data such as user's imaging reference values, processed data and final imaging.

[0143] 20. Processor 302, used to call the program stored in the memory to execute the joint color imaging process.

[0144] 21. Display 304, used to display the user's joint color imaging results.

[0145] 22. The skull is selected as a positioning marker because it has similar imaging features in CT and MRI T1 sequences.

[0146] 23. MRI and CT have different imaging principles, standards and equipment, and the size of the skull imaging space presented is different. Fig.13 .

[0147] 24. According to the standard that the imaging space size of skull positioning markers should be consistent in MRI and CT, calibrate the image space size of MRI and CT. Fig.14 .

[0148] 25. Using skull positioning markers as the standard, select the collection area for calculating skull imaging intensity in CT and MRI images. First, preliminarily select the target area, select the stable signal area in the preliminarily selected area, and then finely select the target area.

[0149] 26. Classification, select the collection area for calculating the white matter imaging intensity in CT and MRI images.

[0150] 27. According to the calculation model formula of brain white matter C = 0.3585M-0.1313, the CT imaging intensity is determined according to the MRI value. Fig.15 .

[0151] 28. MRI-T2 is assigned a red primary color channel, MRI-T1 is assigned a blue primary color channel, and slightly corrected CT is assigned a green primary color channel. The channel selection criteria are set by yourself; the brightness of the above primary color channels is set based on the gray depth of MRI or CT.

[0152] 29. According to the standard that the size of skull positioning marks in MRI and CT should coincide, the images of the red, blue and green primary color channels are corrected to ensure that the skull positioning marks in the three channels are completely coincident; finally, CT and MRI combined multi-color imaging is generated to identify the boundary transition zone of gray and white matter in the brain. Fig.16 .

[0153] 30. For other parts of brain tissue, the same as steps 16-29, the CT tissue window and MRI T1 and Flari sequences are also used to generate joint color imaging results, such as Fig.17 and Fig.18 .

[0154] This embodiment has discovered for the first time a method and device for identifying the nature and scope of tissue types and lesions by integrating MRI and CT imaging features. Therefore, based on clinical trial data or literature historical data, the characteristics of the target tissue or lesion in MRI specific sequence and CT imaging are determined, and the relative imaging intensity ratio of the target tissue or lesion in the MRI specific sequence and CT is calculated, and the relationship between the intensity ratio of the target tissue or lesion in the MRI specific sequence and CT is obtained, and a calculation model for conversion between MRI and CT is constructed. Based on this, the basis for the integration of MRI and CT imaging features is realized. Secondly, different primary color channels are given to MRI specific sequences and CT, and the imaging characteristics of the target tissue or lesion are comprehensively presented through color images. It can be seen that this embodiment develops a method and device for combined multi-color imaging of CT and MRI, which is of great significance for the comprehensive advantages of MRI and CT imaging for the identification of tissue types and lesions. Therefore, this embodiment fills the gap of the lack of combined color imaging of MRI and CT in the past. This technology adjusts the CT or MRI imaging intensity according to the target tissue or lesion, so that the imaging difference between CT and MRI is comparable; and the primary color channel is given to synthesize the color image, so that the imaging difference between CT and MRI can be comprehensively presented in terms of positioning and quantification, which can provide a new solution and device for lesion identification for clinical applications.

[0155] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for combined multi-color imaging of CT and MRI, characterized in that: include: Obtain CT images and MRI images of the same part; Select the preset organization as the positioning mark; Determining a lesion to be identified based on the CT image and the MRI image; Respectively obtaining the relative signal intensity ratios of the lesion to be identified and the positioning marker in the CT image and the MRI image; constructing an intensity correction model based on the relative signal intensity ratio; The primary color channels are selected, the CT and MRI sequences are assigned to different primary color channels, the primary color signal intensity is adjusted based on the intensity correction model, and multi-color imaging is synthesized.

2. The method for combined CT and MRI multi-color imaging according to claim 1, characterized in that: Based on the corrected CT and MRI images, the following steps are also required before determining the lesion to be identified: The sizes and positions of the CT image and the MRI image are corrected based on the positioning markers.

3. The method of combined CT and MRI multi-color imaging according to claim 1, characterized in that: The first relative signal intensity ratio between the lesion to be identified and the positioning marker in the CT image is: C'=C1 / C2 Wherein, C' is the first relative signal intensity ratio, C1 is the signal intensity of the lesion to be identified in the CT image, and C2 is the signal intensity of the positioning marker in the CT image; The second relative signal intensity ratio between the lesion to be identified and the positioning marker in the MRI image is: M'=M1 / M2 Wherein, M' is the second relative signal intensity ratio, M1 is the signal intensity of the lesion to be identified in the MRI image, and M2 is the signal intensity of the positioning marker in the MRI image.

4. The method for combined CT and MRI multi-color imaging according to claim 3, characterized in that: Based on the relative signal intensity ratio, constructing the intensity correction model comprises: The first relative signal intensity ratio and the second relative signal intensity ratio are subjected to linear regression processing to construct the intensity correction model.

5. The method for combined CT and MRI multi-color imaging according to claim 4, characterized in that: The intensity correction model is: C=aM+b Among them, C is the relative signal ratio of CT imaging intensity, M is the relative signal ratio of MRI imaging intensity, a is the custom correction coefficient, and b is the custom parameter.

6. A system for combined multi-color imaging of CT and MRI, characterized in that: For implementing the method of joint multi-color imaging as claimed in any one of claims 1 to 5, the system comprises: an acquisition module, a communication module and a processing module; The acquisition module is used to acquire CT images and MRI images of the same part; The communication module is used to transmit the acquired CT image and MRI image to the processing module; The processing module is used to assign different primary color channels to the CT image and the MRI image to synthesize multi-color imaging.

7. The system for combined CT and MRI multi-color imaging according to claim 6, characterized in that: The processing module includes: a positioning unit, a proposed identification unit, a signal strength acquisition unit, a construction unit and a synthesis unit; The positioning unit is used to select a preset tissue as a positioning mark; The proposed identification unit is used to determine the lesion to be identified based on the CT image and the MRI image; The signal intensity acquisition unit is used to respectively acquire the relative signal intensity ratios of the lesion to be identified and the positioning marker in the CT image and the MRI image; The construction unit constructs an intensity correction model based on the relative signal intensity ratio; The synthesis unit selects primary color channels, assigns CT and MRI sequences to different primary color channels, adjusts primary color signal intensity based on the intensity correction model, and synthesizes multi-color imaging.

8. The system for combined CT and MRI multi-color imaging according to claim 7, characterized in that: The signal strength acquisition unit comprises: a first signal strength acquisition subunit and a second signal strength acquisition subunit; The first signal intensity acquisition subunit is used to acquire a first relative signal intensity ratio between the lesion to be identified and the positioning marker in the CT image: C'=C1 / C2 Wherein, C' is the first relative signal intensity ratio, C1 is the signal intensity of the lesion to be identified in the CT image, and C2 is the signal intensity of the positioning marker in the CT image; The second signal intensity acquisition subunit is used to acquire a second relative signal intensity ratio between the lesion to be identified and the positioning marker in the MRI image: M'=M1 / M2 Wherein, M' is the second relative signal intensity ratio, M1 is the signal intensity of the lesion to be identified in the MRI image, and M2 is the signal intensity of the positioning marker in the MRI image.

9. The system for combined CT and MRI multi-color imaging according to claim 6, characterized in that: The intensity correction model is: C=aM+b Among them, C is the relative signal ratio of CT imaging intensity, M is the relative signal ratio of MRI imaging intensity, a is the custom correction coefficient, and b is the custom parameter.

10. An electronic device, characterized in that: The device comprises: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the method for joint multi-color imaging as claimed in any one of claims 1 to 5 is implemented.

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