CN103978789A - Head medical model quick forming method based on 3D printing - Google Patents
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Abstract
The invention provides a head medical model quick forming method based on 3D printing. According to the method, CT/MR multimode medical images are used, a three-dimensional model is quickly established for head tissue/organs, and a 3D printing method is used for carrying out quick forming on the three-dimensional model. The method comprises the steps that (1) a multimode image registration technology is used, and the CT/MR images are registered into a unified space coordinate system; (2) according to medical information provided by the CT/MR images, different kinds of head tissue/organs are extracted; (3) the three-dimensional model is established for the extracted tissue/organs; and (4) the three-dimensional model is subjected to layering layer by layer, cross section data after layering are obtained, and 3D printing is carried out according to the cross section data. According to the CT/MR images, the head tissue/organs can be subjected to quick and accurate modeling, the manufacturing speed and the accuracy of a head medical model can be effectively improved, and a customized and personalized head medical model can be provided.
Description
Technical field
The present invention relates to a kind of design and fabrication technology of head medicine model, especially relate to a kind of medical model quick molding method of printing based on 3D.
Background technology
3D prints as an innovative technology, taking three-dimensional modeling data as basis, uses powdery metal or the plastics etc. can jointing material, carrys out constructed object by the mode of successively printing.3D prints has explicit costs advantage compared with traditional manufacture: without designing mould, needn't introduce production line.Meanwhile, the speed of 3D printing and making model is fast, and single material object manufacture expense is low, and is obtaining application in industrial rapid shaping, the fields such as manufacture and medical artifucial limb making that design a model.In recent years, the fast development of 3D printing technique attracted wide attention, and caused considerable influence, was a new trend of development of manufacturing.Along with the continuous decline of desktop 3D printer and printed material price, 3D printing technique strides forward to practical direction.3D prints can, for medical model manufacture provides the new approaches that are different from traditional manufacture, can improve the manufacture efficiency of model, and customizable and personalized medical model are provided.
Summary of the invention
The invention provides a kind of medical model quick molding method of printing based on 3D.Accurate Segmentation based on multi-modality medical image and registration, effectively utilize the information of CT image and MR image, threedimensional model is set up in skin of head, skull, grey matter, white matter, cerebrospinal fluid and brain Different brain region, the method of printing by 3D, manufacture accurately and fast head medicine model, and customizable, personalized model can be provided, solve that the traditional medicine modelling cycle is long, precision is low, the shortcoming of customizable not.
In order to solve the problems of the technologies described above, realize above-mentioned purpose, the present invention is achieved through the following technical solutions:
A head medicine model quick molding method of printing based on 3D, is characterized in that comprising following steps:
Multimodal medical image registration, the tissue/organ Accurate Segmentation based on multi-modality images, three-dimension modeling and the 3D of tissue/organ print, wherein, described multi-modality medical image, the image that comprises following any one mode or any combination: computer tomography (CT) image and magnetic resonance imaging (MR) image, or arbitrary combination of the image obtaining derived from the imaging technique of CT and the image that obtains derived from the imaging technique of MR.
The described imaging technique derived from CT, comprises following any one imaging technique: Perfusion CT imaging technique, function CT imaging technique and computed tomography angiography.
The described imaging technique derived from MR, comprises following any one imaging technique: perfusion MR imaging technique, functional MR imaging technique, dispersion tensor MR imaging technique and magnetic resonance angiography.
Described multimodal medical image registration step comprises:
(I) construct or select reference picture, and extracting the relevant information of multi-modality images;
(II) similarity measure of construct image;
(III) maximize similarity measure, calculate the motion vector of image;
(IV) coordinate space to reference picture by the spatial alternation at multi-modality images place;
Wherein said reference picture is one of them in multi-modality images subject to registration, or reference picture is the template image of standard, or by the template image of some width image configuration, or the priori structural images of being constructed by multi-modality images, or by healthy human body gather and high precision image;
The relevant information of wherein said multi-modality images, any combination that comprises following any or several information:
The gray scale of image pixel, image gradient, image local gradient direction, cross-correlation coefficient, mutual information (or normalized mutual information or conditional mutual information or mutual information of comprising spatial context content), Local Entropy of Image, Laplce's Neighborhood Graph and certain self-similarities, and improvement based on above information;
Wherein said spatial alternation is following any: rigid transformation, affine transformation, elastic registration or non-rigid transformation, and any combination of above-mentioned conversion on different images yardstick.
The tissue/organ Accurate Segmentation step of described multi-modality medical image comprises:
For the processing of MR image, specifically refer to the image (hereinafter to be referred as MR image) obtaining to MR image or derived from the imaging technique of MR and be handled as follows:
(I) image that pretreatment MR image or the imaging technique derived from MR obtain, obtains pretreated image;
(II) remove the tissue such as skull and cerebellum in MR image or the image that obtains derived from the imaging technique of MR;
(III) cut apart regions such as obtaining grey matter, white matter, cerebrospinal fluid and background, and calculate the thickness of the tissues such as grey matter;
(IV) cut apart the area-of-interest in brain;
For the processing of CT image, specifically refer to the image (hereinafter to be referred as CT image) obtaining to CT image or derived from the imaging technique of CT, be handled as follows:
(I) image that pretreatment CT image or the imaging technique derived from CT obtain, removes picture noise and intensity profile inhomogeneities;
(II) cut apart the skull tissue of head;
(III) cut apart the skin of head;
(IV) cut apart and obtain overall brain tissue.
For adopting MR image segmentation algorithm in the treatment step (III) of MR image, it is characterized in that: use multilevel diversity method to cut apart grey matter, white matter and cerebrospinal fluid; For adopting non-linear symmetrical registration Algorithm to cut apart the area-of-interest of brain in the treatment step (IV) of MR image, it is characterized in that: described non-linear symmetrical registration Algorithm, can be divided into brain region 90Ge Nao district.
The three-dimension modeling step of described tissue/organ comprises:
(I) result of cutting apart based on image, the triangle mesh curved surface on each tissue/organ surface can be accurately described in generation;
(II) according to the information of multi-modality medical image and priori anatomical knowledge, generate the information of each tissue/organ, and append in grid surface;
(III) threedimensional model is converted to successively cross-sectional data;
Wherein said triangle mesh curved surface, is characterized in that: the file format of storing this triangle mesh curved surface is STL, or 3MF, or self-align triangle mesh curved surface and other additional informations of comprising;
The information of wherein said appended to grid surface, can comprise following content: the density of the tissue/organ that grid surface is corresponding, the thickness of curved surface, the filling rate of curved surface institute enclosing region and the color of curved surface representative tissue/organ;
Wherein said successively cross-sectional data ground thickness is 0.1 millimeter ~ 3 millimeters;
Generate the successively cross-sectional data described in it, spendable software is Cura, or Slicr, or netfabb, or Skeinforge, or the software of the SDK exploitation providing based on Windows8.1 and later release.
Described 3D Method of printing, comprises following steps:
(I) select suitable 3D printed material for different tissue/organ;
(II) additional information according to claim 13, arranges the parameter that 3D prints;
(III) carry out 3D print procedure;
3D printed material described in it, it is characterized in that: print the material of skull and be calcium phosphate or calcium phosphate biological ceramic or composite material or the PLA of phosphoric acid calcium or the mixture that contains PLA or acrylonitrile-butadiene-styrene copolymer or the mixture containing acrylonitrile-butadiene-styrene copolymer, the material of printing skin and brain tissue is silica gel or rubber or gelatin or industrial starch or the mixture that contains above-mentioned any or several material.
Described 3D printed material, comprising: the material that 3D prints and the solid colour of tissue/organ or comparatively approaching; Or can be according to different tissues (organ), or according to the Different brain region in brain, can select the material of different colours.
In described 3D print procedure, the bed thickness that 3D prints is 0.1 millimeter ~ 3 millimeters.
Above-mentioned explanation is only the general introduction of technical solution of the present invention, in order to better understand technological means of the present invention, and can be implemented according to the content of description, below with preferred embodiment of the present invention and coordinate accompanying drawing to be described in detail as follows.The specific embodiment of the present invention is provided in detail by following examples and accompanying drawing thereof.
Brief description of the drawings
Accompanying drawing described herein is used to provide a further understanding of the present invention, forms the application's a part, and schematic description and description of the present invention is used for explaining the present invention, does not form inappropriate limitation of the present invention.In the accompanying drawings:
Fig. 1 is the flow chart of steps of the head medicine model quick molding method of printing based on 3D provided by the invention;
The flow chart of steps of the CT/MR multimodal medical image registration that Fig. 2 provides for one embodiment of the invention;
The MR image that Fig. 3 provides for one embodiment of the invention is cut apart the flow chart of steps of brain tissue;
The flow chart of steps of the brain three-dimension modeling that Fig. 4 provides for one embodiment of the invention;
The 3D that Fig. 5 provides for one embodiment of the invention prints the flow chart of steps of brain tissue.
Detailed description of the invention
Below in conjunction with drawings and Examples, technology implementation process of the present invention is described further.
A head medicine model quick molding method of printing based on 3D, the implementation step of the method is as described below.
1. gather image:
Gather Cranial Computed Tomography image and the MR image of same human body.
2. pretreatment MR image:
A) utilization is redirected with resampling algorithm MR image is normalized, and obtains the image of 256 × 256 × 256 sizes;
B) utilize heterogeneity gray correction algorithm to carry out gray correction to MR image.
3. CT image and MR image rigid registration:
A) obtain the information such as CT/MR image size, resolution ratio, origin and gray scale exponent number;
B) using MR image as with reference to image, using CT image as floating image, interpolation CT image, the resolution adjustment of CT image is consistent with MR image, the origin of coordinates of alignment CT/MR image;
C) use the characteristic information structure similarity functions such as gradation of image;
D) maximize similarity function, calculate displacement, spin matrix and the scaling amount of CT image in X/Y/Z direction;
E) interpolation CT image, calculates the MR image after registration.
4. cut apart CT image:
A) window width/window position of CT is set, binary image, obtains the region at skin, skull and brain tissue place;
B) the initial value position using the result of binaryzation as Level Set Models;
C) use multilevel set algorithm to cut apart Cranial Computed Tomography image, cut apart the surfaces externally and internally that obtains skin and skull simultaneously, cut apart the outer surface that obtains brain tissue;
5. cut apart MR image
A) utilize Marching Cubes algorithm to carry out three-dimensional surface rebuilding to brain MR image, come smooth three-dimensional surface and obtain the summit of proportional spacing by space smoothing power, then the driving force based on gradation of image is distinguished brain tissue and non-brain tissue, then drive summit to shift to real brain tissue border by brain probability graph, finally obtain having removed the brain tissue of skull;
B) adopt FLIRT algorithm and Demons algorithm Registration of MR image and MNI brain map, obtained removing the brain region of cerebellum;
C) adopt local gray level constraint and cortex thickness to retrain the method that multilevel collection is cut apart, be partitioned into grey matter, white matter and target context in brain region;
D) use non-linear symmetrical registration Algorithm Registration of MR image and MNI brain map, thereby obtain the nonlinear transformation of MNI brain map to MR image.
6. generate the threedimensional model of tissue (organ)
A), according to the segmentation result of CT image, for skin of head and skull generate double-deck three-dimensional grid curved surface, wherein, the internal layer curved surface of skin is the outer curved surface of skull;
B) according to the segmentation result of MR image, for skin of head, skull, ectocinerea are set up double-deck three-dimensional grid curved surface;
C) set up the three-dimensional grid curved surface of white matter, set up the three-dimensional grid curved surface in 90Ge Nao district;
D) smooth three-dimensional grid surface;
E) thickness of calculating skin of head, skull and grey matter;
F), according to CT image and MR image information, medical science priori and segmentation result, be the information such as three-dimensional grid curved surface annex curved surface thickness, density, filling rate and color.
7. 3D prints:
A) three-dimensional surface model is carried out to slicing treatment, slice thickness is set, threedimensional model is converted to the cross-sectional data of layering.
B) select calcium phosphate as skull printed material, select medical gelatin to print the soft tissue of skin and brain;
C) arrange 3D print bed thickness be 1 millimeter;
D) arrange after the parameter such as print speed, feeding speed, the online 3D printer of host computer prints.
The foregoing is only the preferred embodiments of the present invention, be not limited to the present invention, for a person skilled in the art, the present invention can have various modifications and variations.Within the spirit and principles in the present invention all, any amendment of doing, be equal to replacement, improvement etc., within all should being included in protection scope of the present invention.
Claims (10)
1. the head medicine model quick molding method of printing based on 3D, it is characterized in that comprising following steps: multimodal medical image registration, the tissue/organ Accurate Segmentation based on multi-modality images, three-dimension modeling and the 3D of tissue/organ print, wherein, described multi-modality medical image, the image that comprises following any one mode or any combination: CT image and MR image, or arbitrary combination of the image obtaining derived from the imaging technique of CT and the image that obtains derived from the imaging technique of MR.
2. the head medicine model quick molding method of printing based on 3D according to claim 1, it is characterized in that: the described imaging technique derived from CT, comprises following any one imaging technique: Perfusion CT imaging technique, function CT imaging technique and computed tomography angiography.
3. the head medicine model quick molding method of printing based on 3D according to claim 1, it is characterized in that: the described imaging technique derived from MR, comprise following any one imaging technique: perfusion MR imaging technique, functional MR imaging technique, dispersion tensor MR imaging technique and magnetic resonance angiography.
4. the head medicine model quick molding method of printing based on 3D according to claim 1, is characterized in that: described multimodal medical image registration step comprises:
(I) construct or select reference picture, and extracting the relevant information of multi-modality images;
(II) similarity measure of construct image;
(III) maximize similarity measure, calculate the motion vector of image;
(IV) coordinate space to reference picture by the spatial alternation at multi-modality images place;
Wherein said reference picture is one of them in multi-modality images subject to registration, or reference picture is the template image of standard, or by the template image of some width image configuration, or the priori structural images of being constructed by multi-modality images, or by healthy human body gather and high precision image;
The relevant information of wherein said multi-modality images, any combination that comprises following any or several information:
Gray scale, image gradient, image local gradient direction, cross-correlation coefficient, mutual information, Local Entropy of Image, Laplce's Neighborhood Graph and the certain self-similarities of image pixel, and improvement based on above information;
Wherein said spatial alternation is following any: rigid transformation, affine transformation, elastic registration or non-rigid transformation, and any combination of above-mentioned conversion on different images yardstick.
5. the head medicine model quick molding method of printing based on 3D according to claim 1, is characterized in that: the tissue/organ Accurate Segmentation step of described multi-modality medical image comprises:
For the processing of MR image, specifically refer to the image obtaining to MR image or derived from the imaging technique of MR and be handled as follows:
(I) image that pretreatment MR image or the imaging technique derived from MR obtain, obtains pretreated image;
(II) remove the tissue such as skull and cerebellum in MR image or the image that obtains derived from the imaging technique of MR;
(III) cut apart regions such as obtaining grey matter, white matter, cerebrospinal fluid and background, and calculate the thickness of the tissues such as grey matter;
(IV) cut apart the area-of-interest in brain;
For the processing of CT image, specifically refer to the image obtaining to CT image or derived from the imaging technique of CT and be handled as follows:
(I) image that pretreatment CT image or the imaging technique derived from CT obtain, removes picture noise and intensity profile inhomogeneities;
(II) cut apart the skull tissue of head;
(III) cut apart the skin of head;
(IV) cut apart and obtain overall brain tissue.
6. the head medicine model quick molding method of printing based on 3D according to claim 5, for adopting MR image segmentation algorithm in the treatment step (III) of MR image, is characterized in that: use multilevel diversity method to cut apart grey matter, white matter and cerebrospinal fluid; For adopting non-linear symmetrical registration Algorithm to cut apart the area-of-interest of brain in the treatment step (IV) of MR image, it is characterized in that: described non-linear symmetrical registration Algorithm, can be divided into brain region 90Ge Nao district.
7. the head medicine model quick molding method of printing based on 3D according to claim 1, is characterized in that: the three-dimension modeling step of described tissue/organ comprises:
(I) result of cutting apart based on image, the triangle mesh curved surface on each tissue/organ surface can be accurately described in generation;
(II) according to the information of multi-modality medical image and priori anatomical knowledge, generate the information of each tissue/organ, and append in grid surface;
(III) threedimensional model is converted to successively cross-sectional data;
Wherein said triangle mesh curved surface, is characterized in that: the file format of storing this triangle mesh curved surface is STL, or 3MF, or self-align triangle mesh curved surface and other additional informations of comprising;
The information of wherein said appended to grid surface, can comprise following content: the density of the tissue/organ that grid surface is corresponding, the thickness of curved surface, the filling rate of curved surface institute enclosing region and the color of curved surface representative tissue/organ;
Wherein said successively cross-sectional data ground thickness is 0.1 millimeter ~ 3 millimeters;
Generate the successively cross-sectional data described in it, spendable software is Cura, or Slicr, or netfabb, or Skeinforge, or the software of the SDK exploitation providing based on Windows8.1 and later release.
8. the head medicine model quick molding method of printing based on 3D according to claim 1, is characterized in that: described 3D Method of printing, comprises following steps:
(I) select suitable 3D printed material for different tissue/organ;
(II) additional information according to claim 13, arranges the parameter that 3D prints;
(III) carry out 3D print procedure;
3D printed material described in it, it is characterized in that: print the material of skull and be calcium phosphate or calcium phosphate biological ceramic or composite material or the PLA of phosphoric acid calcium or the mixture that contains PLA or acrylonitrile-butadiene-styrene copolymer or the mixture containing acrylonitrile-butadiene-styrene copolymer, the material of printing skin and brain tissue is silica gel or rubber or gelatin or industrial starch or the mixture that contains above-mentioned any or several material.
9. the head medicine model quick molding method of printing based on 3D according to claim 8, is characterized in that: described 3D printed material, comprising: the material that 3D prints and the solid colour of tissue/organ or comparatively approaching; Or can be according to different tissues (organ), or according to the Different brain region in brain, can select the material of different colours.
10. the head medicine model quick molding method of printing based on 3D according to claim 1, is characterized in that: in described 3D print procedure, the bed thickness that 3D prints is 0.1 millimeter ~ 3 millimeters.
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