CN112562073A - Relation model of deep brain tumor and white matter fiber bundle and preparation method thereof - Google Patents
Relation model of deep brain tumor and white matter fiber bundle and preparation method thereof Download PDFInfo
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Abstract
The invention provides a relation model of deep brain tumor and white matter fiber tract and a preparation method thereof, and a 3D high-resolution scanning image of the deep brain tumor, the adjacent white matter fiber tract and the brain parenchyma is obtained through an MR multi-mode imaging sequence. And importing the obtained original image data into modeling post-processing software, carrying out image fusion, segmentation and reconstruction on the deep brain tumor, internal hemorrhage, peripheral edema, adjacent functional fiber bundles and brain parenchymal structures to obtain a three-dimensional anatomical model of the deep brain tumor, and importing a color 3D printer to print an individualized accurate medical 3D printing solid model aiming at the brain tumor. Accurately positioning the brain tumor and the white matter fiber tracts adjacent to the brain tumor, the corticospinal tract, the corpus callosum and the like, and the front-back, up-down spatial relationship of the brain parenchyma. The occurrence probability of postoperative vision and motor dysfunction is reduced, and the requirements of accurate medical evaluation and operation are met. The device is used for preoperative planning and surgical simulation and is used as a precise medical teaching and training mold, and the medical training and teaching effect is improved.
Description
Technical Field
The invention belongs to the technical field of medical models, and particularly relates to a relation model of deep brain tumor and white matter fiber tracts and a preparation method thereof.
Background
The traditional microscopic operation of deep brain tumor has low total resection rate and high disability rate, permanent neurological dysfunction is easy to cause after the operation, and how to safely and accurately resect the tumor to the maximum extent is very important. Deep brain tumors are mostly seen in glioma, metastatic tumor, lymphoma and the like, and because the tumors are located in deep brain parenchyma and deep cortex of functional areas, the operation risk is very high; how to accurately, safely, quickly and completely remove tumors always troubles neurosurgeons.
In clinical practice, the subcutaneous conduction pathway-brain white matter fiber bundle has individual differences in traveling, the brain white matter fibers around the tumor are in the degrees of pushing, squeezing and breaking, the three-dimensional relationship between the tumor and the nerve fibers is visually limited, clinically, different neurosurgeons and medical students have different spatial imagination and are difficult to understand the three-dimensional space of the structure of the operation area only by means of a two-dimensional layer, and the like, so that the accurate preoperative evaluation of deep brain tumors by an operator, the accurate implementation of tumor excision in the operation, and effective medical surgical training and medical teaching practice face certain difficulties and challenges.
Disclosure of Invention
The invention aims to solve the problem of providing a relation model of deep brain tumor and white matter fiber tracts and a preparation method thereof, in particular to a 3D printing accurate medical model for constructing the relation between the deep brain tumor and the nerve fiber tracts based on an MR multi-mode imaging sequence, effectively evaluating the front and back of the related deep brain tumor and the adjacent functional fiber tracts individually, the relation between the upper space and the lower space improves the accurate preoperative understanding of doctors on complex anatomical structures of an operation area, identifies high-risk areas and predicts potential complications before operation, effectively protects the adjacent functional fiber bundles in the operation, removes tumors in the maximum range at the same time, effectively reduces the postoperative nerve function disability rate of brain patients after the adjacent functional fiber bundles are damaged, increases the operation confidence of surgeons, reduces unnecessary exploration time in the operation, can also be used for accurate medical teaching models and surgical operation training practices, and increases the medical teaching effect.
In order to solve the technical problems, the invention adopts the technical scheme that: a relation model of deep brain tumor and white matter fiber bundles comprises a deep brain tumor model and an adjacent white matter fiber bundle model, wherein the deep brain tumor model, an internal hemorrhage model, a peripheral edema model, and adjacent functional fiber bundle models and a brain parenchyma model are printed and prepared into a relation entity model of the deep brain tumor and the white matter fiber bundles after flexible photosensitive resins with different colors are loaded on the model components through a color 3D printer, and the front-back, upper-lower spatial relation of the deep brain tumor, the adjacent white matter fiber bundles and the brain parenchyma is accurately positioned.
Furthermore, the invention also provides a method for preparing the relation model of the brain deep tumor and the white matter fiber bundle, which is used for preparing the brain deep tumor model and comprises the following steps,
s1: acquiring an image by adopting an MR multi-mode scanning sequence;
s2: digital modeling of deep brain tumors based on MRI multi-modality images;
s3: preparing the deep brain tumor model using a 3D printer.
Further, the S1 employs an MR multi-modality image sequence to acquire deep brain tumor and internal hemorrhage, peripheral edema, white matter fiber tract and brain parenchyma image to achieve an optimal signal-to-noise ratio for segmentation and extraction of different structures.
Further, the S1 includes the following steps,
s11: acquiring enhanced tumor parenchyma by using an enhanced T1WI or optimized magnetization prepared fast gradient echo sequence;
s12: extracting edema around the tumor and internal cystic change by adopting a liquid attenuation inversion recovery sequence;
s13: collecting internal bleeding of lesions by adopting an SWI sequence;
s14: dividing important white matter fiber bundles around the tumor by adopting diffusion tensor imaging and image post-processing;
s15: and a rapid gradient echo sequence and a variable inversion angle rapid spin echo sequence are adopted to segment brain, cerebellum and brainstem structures.
Further, the S2 includes the following steps,
s21: segmenting a white matter fiber bundle structure around a tumor by adopting diffusion tensor imaging sequence fiber bundle tracking imaging, importing the obtained diffusion tensor imaging image into an MR diffusion tensor imaging software package of an image post-processing workstation, carrying out corresponding white matter fiber bundle tracking, and storing the white matter fiber bundle tracking image into an operable DICOM format recognizable image;
s22: acquiring an enhanced brain tumor parenchyma part for a high-signal deep brain tumor by using an enhanced T1WI or optimized magnetization prepared fast gradient echo sequence;
s23: selecting a reasonable threshold using a fluid attenuated inversion recovery sequence to extract the peritumoral edema and internal cystic changes that are manifested as long T2 signals;
s24: selecting a reasonable threshold value by adopting an SWI sequence or manually sketching and extracting a bleeding part in the tumor which is displayed as an obvious low signal, manually correcting a detail outline to be accurate, and using a magic brush tool to remove the mark of a non-target area;
s25: adopting a rapid gradient echo sequence and a variable inversion angle rapid spin echo sequence to finely adjust a reasonable threshold value to segment brain, cerebellum and brainstem structures, manually correcting detail contours to enable the detail contours to be accurate, and using a magic brush tool to remove marks of non-target areas;
s26: after the deep brain tumor, internal hemorrhage, peripheral edema, adjacent subcortical functional fiber bundles and brain parenchymal structure are segmented, fusion of all image components is carried out, and the obtained image file is converted into an STL format file for 3D printing.
Further, the white matter fiber bundles include, but are not limited to, corticospinal tracts, cortical nuclear tracts, optic tracts, corpus callosum, and arcus fibers.
Further, the S2 further includes the following steps,
s27: performing transparentization treatment on brain parenchyma and edema around the tumor to be transparentized by adopting blender software, setting colors, and exporting an obj format file with colors.
Further, the S3 includes the following steps,
s31: pouring the reconstructed STL file into 3D printing equipment;
s32: and setting printing parameters, and loading printing materials to manufacture a brain deep tumor solid model.
Further, the printing parameters are that the printing thickness is 28 micrometers, the printing precision is less than or equal to 200 micrometers, the ambient temperature is 18-26 ℃, and the Shore hardness is 30.
Furthermore, the printing material is flexible photosensitive resin, tumor entities are arranged, different color photosensitive resin materials are adopted near different fiber bundles, and brain deep tumor and adjacent fiber bundle models are printed in an materialized mode.
The invention has the advantages and positive effects that:
the method is based on the MR multi-mode imaging sequence, an integrated 3D printing accurate medical model of the relation between the deep brain tumor and the functional fiber bundles is constructed, preoperative three-dimensional visualization of a complex deep brain tumor patient, a compression site and a compression degree is facilitated, and the relation between the deep brain tumor and peripheral edema, internal bleeding and adjacent subcortical functional fiber bundles is effectively displayed. For example, the visual tract responsible for visual conduction, the descending motion conduction cone tract-corticospinal tract, the cortical nucleus tract, the combined fiber corpus callosum and other white matter fiber tracts improve the precise preoperative understanding of doctors on the complex anatomical structures in the operation area, identify high-risk areas and predict potential complications, are beneficial to preoperative operation planning and simulated intervention, effectively reduce the postoperative nerve function disability rate of brain patients after the injury of adjacent functional fiber tracts while removing tumors to the maximum extent, increase the operation confidence of surgeons, reduce unnecessary exploration time courses in the operation, reduce the operation complications, and can also be used for medical teaching and surgical operation training practices and increase the effectiveness of medical teaching effects.
Drawings
Fig. 1 to 6 are overall structural schematic diagrams of the embodiment of the present invention.
FIG. 7 is a process flow diagram of a method of making an embodiment of the invention.
In the figure:
1. optic beam, optic radiation model 2, normal side cone beam model 3, cone beam model for peritumoral push destruction
4. Peritumoral edema model 5, deep brain tumor model 6, and brain parenchyma model
7. A lacing model 8 and a corpus callosum combined fiber model.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Embodiments of the invention are further described below with reference to the accompanying drawings:
as shown in FIGS. 1-6, a relation model of deep brain tumor and white matter fiber tract comprises a deep brain tumor model 5 and an adjacent white matter fiber tract model, and for the deep brain tumor model 5 and an internal hemorrhage, peritumoral edema model 4, an adjacent functional fiber tract model and a brain parenchyma model 6, the specific white matter fiber tract models comprise a visual tract, a visual radiation model 1, a belt model 7, a cone tract model and a corpus callosum combined fiber model 8, wherein the cone tract model comprises a normal lateral cone tract model 2 and a cone tract model 3 damaged by peritumoral push. After the flexible photosensitive resin with different colors is loaded on each model part through a color 3D printer, the model parts are printed and prepared into a brain deep tumor and white matter fiber bundle relation solid model, and the front-back and up-down spatial relation of the brain deep tumor and the white matter fiber bundles and the brain parenchyma adjacent to the brain deep tumor is accurately positioned.
Different modalities of MR imaging can provide different imaging information from the aspects of tumor components, brain anatomy and functions, and comprehensively analyzes the relationship between the deep tumor in a functional area and surrounding structures, so that neurosurgeons can obtain more accurate judgment and make more reasonable diagnosis and treatment plans. The diffusion tensor imaging fiber bundle tracing technology can three-dimensionally reconstruct an important brain white matter fiber bundle projection path under the cortex, for example, a visual tract responsible for visual conduction, a descending motion conduction tract cone bundle-corticospinal tract, a cortical nucleus bundle, a combined fiber corpus callosum and other white matter fiber bundles, and the like, visually display whether the functional fiber bundle is invaded by a tumor body, and surround or push and shift the condition, accurately position the spatial relationship between the tumor and the subcortical functional fiber bundles such as the visual tract and the cone bundle, and reduce the occurrence probability of postoperative vision and motor dysfunction, which is particularly important for preoperatively optimally designing an operation approach, realizing the maximized excision of the tumor in a deep functional region, minimizing normal brain tissue injury and functional injury, and optimally restoring an accurate neurosurgery target after operation.
Specifically, as shown in FIG. 7, a method for preparing a relation model between a deep brain tumor and a white matter fiber tract, which is used for preparing the deep brain tumor model, comprises the following steps,
s1: the image acquisition is carried out by adopting an MR multi-mode scanning sequence, and preferably, the MR multi-mode image sequence is adopted to acquire deep brain tumors, internal hemorrhage, peripheral edema, white matter fiber tracts and brain parenchyma images to achieve the optimal signal-to-noise ratio for segmentation and extraction of different structures. Specifically, the present embodiment employs a siemens MR apparatus.
S11: acquiring enhanced tumor parenchyma by using an enhanced T1WI or optimized magnetization prepared fast gradient echo sequence and an MP-RAGE sequence;
s12: extracting edema around the tumor and cystic change inside the tumor by adopting a liquid attenuation inversion recovery sequence, namely a FLAIR sequence;
s13: collecting internal bleeding of lesions by adopting an SWI sequence;
s14: dividing important white matter fiber bundles around the tumor, such as cone bundles, visual bundles and the like, by adopting Diffusion Tensor Imaging (DTI) and image post-processing;
s15: the structure of brain, cerebellum, brainstem and the like is segmented by adopting a rapid gradient echo sequence, namely an MP-RAGE sequence and a variable inversion angle rapid spin echo sequence, namely a T2-SPACE sequence.
S2: based on digital modeling of deep brain tumor of MRI multi-modality image, loading the obtained MR high-resolution imaging sequence of the deep brain tumor into software such as Neuro 3D, 3Dprinter, 3Dslicer, VTK and the like of Siemens or Philips image post-processing workstations for image segmentation, calibration and fusion.
S21: adopting diffusion tensor imaging, namely, carrying out DTI (digital image acquisition) sequence fiber bundle tracking imaging to segment a white matter fiber bundle structure around a tumor, carrying out obtained diffusion tensor imaging, namely, leading the DTI image into a Siemens or Philips image post-processing workstation MR (magnetic resonance) diffusion tensor imaging software package, carrying out corresponding tracking on white matter fiber bundles such as a corticospinal bundle, a cortical nucleus bundle, a visual tract, a corpus callosum, an arched fiber and the like, and storing the images into an operable DICOM format recognizable image;
s22: the use of a rapid gradient echo sequence with enhanced T1WI or optimized magnetization preparation, the MP-RAGE sequence, was shown to collect an enhanced brain tumor parenchymal portion for high-signal deep brain tumors;
s23: selecting reasonable threshold using fluid attenuation inversion recovery sequence, i.e. FLAIR sequence, to extract the peritumoral edema and internal cystic changes fraction shown as long T2 signal;
s24: selecting a reasonable threshold value by adopting an SWI sequence or manually sketching and extracting a bleeding part in the tumor which is displayed as an obvious low signal, manually correcting a detail outline to be accurate, and using a magic brush tool to remove the mark of a non-target area;
s25: adopting an MPRAGE sequence and a variable inversion angle fast spin echo sequence, namely a T2-SPACE sequence to finely adjust reasonable threshold to segment structures such as a brain, a cerebellum, a brainstem and the like, manually correcting detail contours to enable the detail contours to be accurate, and using a magic brush tool to remove marks of non-target regions;
s26: after the deep brain tumor, internal hemorrhage, peripheral edema, adjacent subcortical functional fiber bundles and brain parenchymal structure are segmented, fusion of all image components is carried out, and the obtained image file is converted into an STL format file for 3D printing.
S27: performing transparentization treatment on brain parenchyma and edema around the tumor to be transparentized by adopting blend software, setting colors, and exporting an obj format file with colors, wherein if the hardness needs to be increased, a transparent layer can be coated outside.
S3: a 3D printer was used to prepare a deep brain tumor model.
S31: and pouring the reconstructed STL file into a 3D printing device.
S32: and setting printing parameters, and loading printing materials to manufacture a brain deep tumor solid model. Specifically, the printing parameters are as follows, the printing thickness is 28 microns, the printing precision is less than or equal to 200 microns, the ambient temperature is 18-26 ℃, the Shore hardness is 30, after the loaded printing material is flexible photosensitive resin, tumor entities and adjacent different fiber bundles are set to adopt different colors, the brain deep tumor and adjacent fiber bundle models are printed in an materialized mode, specifically, the colors can be red, blue, yellow, green, white and the like, and because part of blood vessels are too thin, transparent layer links are added to ensure the space continuity and the compression-resistant effect.
The invention has the advantages and positive effects that:
the method is based on the MR multi-mode imaging sequence, an integrated 3D printing accurate medical model of the relation between the deep brain tumor and the functional fiber bundles is constructed, preoperative three-dimensional visualization of a complex deep brain tumor patient, a compression site and a compression degree is facilitated, and the relation between the deep brain tumor and peripheral edema, internal bleeding and adjacent subcortical functional fiber bundles is effectively displayed. For example, the visual tract responsible for visual conduction, the descending motion conduction cone tract-corticospinal tract, the cortical nucleus tract, the combined fiber corpus callosum and other white matter fiber tracts improve the precise preoperative understanding of doctors on the complex anatomical structures in the operation area, identify high-risk areas and predict potential complications, are beneficial to preoperative operation planning and simulated intervention, effectively reduce the postoperative nerve function disability rate of brain patients after the injury of adjacent functional fiber tracts while removing tumors to the maximum extent, increase the operation confidence of surgeons, reduce unnecessary exploration time courses in the operation, reduce the operation complications, and can also be used for medical teaching and surgical operation training practices and increase the effectiveness of medical teaching effects.
While one embodiment of the present invention has been described in detail, the description is only a preferred embodiment of the present invention and should not be taken as limiting the scope of the invention. All equivalent changes and modifications made within the scope of the present invention shall fall within the scope of the present invention.
Claims (10)
1. A deep brain tumor and white matter fiber tract relation model is characterized in that: the method comprises the steps of loading flexible photosensitive resin with different colors into the brain deep tumor model, the internal hemorrhage model, the peripheral edema model, the functional fiber bundle model and the brain parenchyma model, printing and preparing the model parts into a brain deep tumor and white matter fiber bundle relation solid model after the flexible photosensitive resin with different colors is loaded by a color 3D printer, and accurately positioning the front-back and up-down spatial relation of the brain deep tumor, the white matter fiber bundles adjacent to the brain deep tumor model and the brain parenchyma.
2. A method for preparing a relation model between a deep brain tumor and a white matter fiber tract, which is used for preparing the deep brain tumor model of claim 1, and is characterized in that: comprises the following steps of (a) carrying out,
s1: acquiring an image by adopting an MR multi-mode scanning sequence;
s2: digital modeling of deep brain tumors based on MRI multi-modality images;
s3: preparing the deep brain tumor model using a 3D printer.
3. The method for preparing the relation model of the deep brain tumor and the white matter fiber tract according to claim 2, wherein: the S1 employs MR multi-modality image sequences to acquire deep brain tumors and internal hemorrhage, peripheral edema, white matter tracts and brain parenchyma images to achieve optimal signal-to-noise ratio for segmentation and extraction of different structures.
4. The method for preparing the relation model of the deep brain tumor and the white matter fiber tract according to claim 3, wherein: the S1 includes the steps of,
s11: acquiring enhanced tumor parenchyma by using an enhanced T1WI or optimized magnetization prepared fast gradient echo sequence;
s12: extracting edema around the tumor and internal cystic change by adopting a liquid attenuation inversion recovery sequence;
s13: collecting internal bleeding of lesions by adopting an SWI sequence;
s14: dividing important white matter fiber bundles around the tumor by adopting diffusion tensor imaging and image post-processing;
s15: and a rapid gradient echo sequence and a variable inversion angle rapid spin echo sequence are adopted to segment brain, cerebellum and brainstem structures.
5. The method for preparing the relation model of the deep brain tumor and the white matter fiber tract according to any one of claims 2 to 4, wherein: the S2 includes the steps of,
s21: segmenting a white matter fiber bundle structure around a tumor by adopting diffusion tensor imaging sequence fiber bundle tracking imaging, importing the obtained diffusion tensor imaging image into an MR diffusion tensor imaging software package of an image post-processing workstation, carrying out corresponding white matter fiber bundle tracking, and storing the white matter fiber bundle tracking image into an operable DICOM format recognizable image;
s22: acquiring an enhanced brain tumor parenchyma part for a high-signal deep brain tumor by using an enhanced T1WI or optimized magnetization prepared fast gradient echo sequence;
s23: selecting a reasonable threshold using a fluid attenuated inversion recovery sequence to extract the peritumoral edema and internal cystic changes that are manifested as long T2 signals;
s24: selecting a reasonable threshold value by adopting an SWI sequence or manually sketching and extracting a bleeding part in the tumor which is displayed as an obvious low signal, manually correcting a detail outline to be accurate, and using a magic brush tool to remove the mark of a non-target area;
s25: adopting a rapid gradient echo sequence and a variable inversion angle rapid spin echo sequence to finely adjust a reasonable threshold value to segment brain, cerebellum and brainstem structures, manually correcting detail contours to enable the detail contours to be accurate, and using a magic brush tool to remove marks of non-target areas;
s26: after the deep brain tumor, internal hemorrhage, peripheral edema, adjacent subcortical functional fiber bundles and brain parenchymal structure are segmented, fusion of all image components is carried out, and the obtained image file is converted into an STL format file for 3D printing.
6. The method for preparing the relation model of the deep brain tumor and the white matter fiber tract according to claim 5, wherein: the white matter fiber bundles include, but are not limited to, corticospinal tracts, cortical nuclear tracts, optic tracts, corpus callosum, arcus fibers.
7. The method for preparing the relation model of the deep brain tumor and the white matter fiber tract according to claim 5, wherein: the S2 further includes the following steps,
s27: performing transparentization treatment on brain parenchyma and edema around the tumor to be transparentized by adopting blender software, setting colors, and exporting an obj format file with colors.
8. The method for preparing the relation model of the deep brain tumor and the white matter fiber tract according to claim 5, wherein: the S3 includes the steps of,
s31: pouring the reconstructed STL file into 3D printing equipment;
s32: and setting printing parameters, and loading printing materials to manufacture a brain deep tumor solid model.
9. The method for preparing the relation model of the deep brain tumor and the white matter fiber tract according to claim 8, wherein: the printing parameters are that the printing thickness is 28 micrometers, the printing precision is less than or equal to 200 micrometers, the ambient temperature is 18-26 ℃, and the Shore hardness is 30.
10. The method for preparing the relation model of the deep brain tumor and the white matter fiber tract according to claim 9, wherein: the printing material is flexible photosensitive resin, a tumor entity is arranged, different color photosensitive resin materials are adopted near different fiber bundles, and the brain deep tumor and the adjacent fiber bundle model are printed in an materialized mode.
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