CN115908479A - Brain tissue drift correction method and surgical navigation system - Google Patents
Brain tissue drift correction method and surgical navigation system Download PDFInfo
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- CN115908479A CN115908479A CN202210436752.7A CN202210436752A CN115908479A CN 115908479 A CN115908479 A CN 115908479A CN 202210436752 A CN202210436752 A CN 202210436752A CN 115908479 A CN115908479 A CN 115908479A
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Abstract
The invention discloses a brain tissue drift correction method and an operation navigation system, wherein the method comprises the following steps: acquiring a second point cloud of the brain tissue after the tissue drift based on the structured light; based on a matching algorithm, carrying out non-rigid registration on at least partial region of the second point cloud and at least partial region of the first point cloud to obtain a non-rigid matching relation corresponding to the at least partial region of the second point cloud before and after tissue drift; and correcting the first point cloud according to the non-rigid matching relation to obtain a third point cloud. According to the method, the second point cloud of the brain tissue after the tissue drift is quickly, conveniently and massively obtained by using the structured light, the second point cloud is matched with the first point cloud before the tissue drift, a corresponding non-rigid matching relation is accurately obtained, and the first point cloud is quickly and accurately corrected based on the non-rigid matching relation.
Description
Technical Field
The invention relates to the field of medical image processing, in particular to a brain tissue drift correction method, a brain tissue drift correction device, a surgical navigation system, electronic equipment, a computer-readable storage medium and a program product.
Background
In the existing brain surgery, a complete scan is generally performed on the brain of a patient before an operation to establish a three-dimensional model in advance, then, during the operation, the position of a surgical instrument is tracked through a surgical navigation system, and the position of the surgical instrument and the condition of peripheral brain tissues are displayed in the three-dimensional model in real time to help a doctor observe the operation process and guide subsequent actions. However, in the process of performing the operation, when an external protection of the brain tissue (such as dura mater and the like) is opened, the brain tissue may have position change and shape change (i.e., tissue drift) due to gravity, cerebrospinal fluid loss and other factors, so that the three-dimensional structure of the brain tissue in the operation has a larger morphological difference from the three-dimensional model established before the operation, and the three-dimensional model established before the operation and the operation plan cannot continuously and accurately guide the position of the scalpel.
In order to solve the problem, the prior art further presents some correction means, such as establishing parameter simulation of each tissue in advance, and correcting the model according to experimental data, but the method has low precision, large individual difference between people and general actual effect; another method is to acquire tissue spatial information during operation, such as Magnetic Resonance Imaging (MRI), but this method consumes a lot of time, greatly prolongs the operation, is not beneficial to the patient, and needs to configure expensive MRI equipment in the operating room, and has few hospitals.
Therefore, a method for correcting a point cloud model conveniently and at low cost is needed, so that accurate surgical navigation and positioning can be provided conveniently.
Disclosure of Invention
In view of the above technical problems in the prior art, the present invention provides a brain tissue drift correction method, device, surgical navigation system, electronic device, computer readable storage medium and program product, so as to quickly correct the corresponding model during the brain tissue deformation in the operation conveniently and at low cost.
In a first aspect, the present invention provides a method for correcting brain tissue drift, comprising:
acquiring a second point cloud of the brain tissue after the tissue drift based on the structured light;
based on a matching algorithm, carrying out non-rigid registration on at least partial region of the second point cloud and at least partial region of the first point cloud to obtain a non-rigid matching relation corresponding to at least partial region of the second point cloud before and after tissue drift; the first point cloud is point cloud data constructed on the brain tissue before tissue drift according to the medical image;
and correcting the first point cloud according to the non-rigid matching relation to obtain a third point cloud.
Optionally, in the method for correcting brain tissue drift provided by the present invention, the non-rigid registration of at least a partial region of the second point cloud with at least a partial region of the first point cloud based on a matching algorithm to obtain a corresponding non-rigid matching relationship before and after the tissue drift of at least a partial region of the second point cloud, includes:
carrying out coarse registration on at least partial region of the second point cloud and the first point cloud to obtain a rigid transformation matrix;
and on the basis of the rough registration, performing fine registration on at least partial region of the second point cloud and at least partial region corresponding to the first point cloud to obtain the non-rigid matching relationship.
Optionally, in the method for correcting brain tissue drift provided by the present invention, at least a partial region corresponding to the first point cloud is obtained as follows:
transforming at least partial area of the second point cloud by using the rigid transformation matrix, and determining an overlapping area of the transformed second point cloud and the first point cloud;
and cutting the first point cloud according to the overlapping area to obtain at least partial corresponding area in the first point cloud.
Optionally, in the method for correcting brain tissue drift provided by the present invention, at least a part of the area of the second point cloud is manually sketched, and/or the area of interest in the second point cloud is automatically extracted based on the point cloud characteristics.
Optionally, in the method for correcting brain tissue drift provided by the present invention, after the correcting the first point cloud according to the non-rigid matching relationship to obtain a third point cloud, the method further includes:
acquiring a coordinate conversion relation between a patient coordinate system and a structured light coordinate system where the second point cloud is located;
and combining the rigid transformation matrix to obtain a conversion relation between the third point cloud and the patient coordinate system.
Optionally, in the method for correcting brain tissue drift provided by the present invention, after the first point cloud is corrected according to the non-rigid matching relationship to obtain a third point cloud, the method further includes:
a verification step for determining whether the third point cloud satisfies an accuracy.
Optionally, the verifying step comprises:
and acquiring coordinate data of at least one point in the second point cloud, and determining whether the third point cloud meets the accuracy or not by combining the corresponding coordinate data of the at least one point in the third point cloud.
Optionally, the verifying step comprises:
transforming the second point cloud according to the rigid transformation matrix to obtain a fourth point cloud;
and determining whether the third point cloud meets the accuracy according to the coordinates of each corresponding point in the fourth point cloud and the third point cloud.
Optionally, in the method for correcting brain tissue drift provided by the present invention, the method further includes:
and under the condition that the third point cloud does not meet the accuracy, acquiring the second point cloud of the brain tissue after the tissue drift based on the structured light again until the third point cloud is obtained.
Optionally, in a method for correcting brain tissue drift provided by the present invention, the acquiring a second point cloud of brain tissue after tissue drift based on structured light includes:
and carrying out image acquisition on the brain tissue after the structured light pattern is projected by using a structured light spot cloud collector, and acquiring second point cloud of the brain tissue according to the acquired image data.
Optionally, in the method for correcting brain tissue drift provided by the present invention, the structural light point cloud collector is an infrared point cloud collector, a visible light point cloud collector, or a fluorescent point cloud collector.
In a second aspect, the present invention provides a brain tissue drift correction device, comprising:
the structured light acquisition module is used for acquiring a second point cloud of the brain tissue after the tissue drift based on the structured light;
the point cloud matching module is used for carrying out non-rigid registration on at least part of the area of the second point cloud and at least part of the area corresponding to the first point cloud based on a matching algorithm to obtain a non-rigid matching relation corresponding to at least part of the area of the second point cloud before and after tissue drift; the first point cloud is point cloud data constructed on the brain tissue before tissue drift according to the medical image;
and the correction module is used for correcting the first point cloud according to the non-rigid matching relation to obtain a third point cloud.
Optionally, in the apparatus for correcting brain tissue drift provided by the present invention, the point cloud matching module includes:
the first matching module is used for carrying out rough registration on at least part of area of the second point cloud and the first point cloud to obtain a rigid transformation matrix;
and the second matching module is used for carrying out fine registration on at least partial region of the second point cloud and at least partial region corresponding to the first point cloud on the basis of the coarse registration to obtain the non-rigid matching relationship.
Optionally, in the apparatus for correcting brain tissue drift provided by the present invention, the second matching module includes:
the first sub-module is used for transforming at least partial area of the second point cloud by using the rigid transformation matrix and determining an overlapping area of the transformed second point cloud and the first point cloud;
and the second sub-module is used for cutting the first point cloud according to the overlapping area to obtain at least part of corresponding area in the first point cloud.
Optionally, in the brain tissue drift correction device provided by the present invention, at least a part of the area of the second point cloud is manually sketched, and/or the area of interest in the second point cloud is automatically extracted based on the point cloud characteristics.
Optionally, the device for correcting brain tissue drift provided by the present invention further comprises:
the acquisition module is used for acquiring a coordinate conversion relation between a patient coordinate system and a structured light coordinate system where the second point cloud is located;
and the conversion module is used for combining the rigid transformation matrix to obtain the conversion relation between the third point cloud and the patient coordinate system.
Optionally, in the apparatus for correcting brain tissue drift provided by the present invention, further comprising:
a verification module to determine whether the third point cloud satisfies an accuracy.
Optionally, the verification module comprises:
and the first verification unit is used for acquiring coordinate data of at least one point in the second point cloud and determining whether the third point cloud meets the accuracy or not by combining the corresponding coordinate data of the at least one point in the third point cloud.
Optionally, the verification module comprises:
the second verification unit is used for transforming the second point cloud according to the rigid transformation matrix to obtain a fourth point cloud;
and the third verification unit is used for determining whether the third point cloud meets the accuracy according to the coordinates of each corresponding point in the fourth point cloud and the third point cloud.
Optionally, the device for correcting brain tissue drift provided by the present invention further comprises:
and the iteration module is used for executing the second point cloud of the brain tissue after the tissue drift based on the structured light again under the condition that the third point cloud does not meet the accuracy until the third point cloud meeting the accuracy is obtained.
Optionally, in the device for correcting brain tissue drift provided by the present invention, the structured light collection module is configured to use a structured light spot cloud collector to collect an image of the brain tissue after the structured light pattern is projected, and obtain a second point cloud of the brain tissue according to the collected image data.
Optionally, in the device for correcting brain tissue drift, the structured light spot cloud collector is an infrared point cloud collector, a visible light spot cloud collector, or a fluorescent point cloud collector.
In a third aspect, the invention provides a surgical navigation system comprising a brain tissue drift correction apparatus as described above.
Further, the surgical navigation system may include: the device comprises a host, a display device, input and output equipment (a mouse, a keyboard, a pedal, a touch screen and the like), a tracking device (such as an infrared tracking device, including an infrared emission structure and an infrared camera), a structure light spot cloud collector, wherein the host is provided with the brain tissue drift correction device, and the structure light spot cloud collector is provided with a positioning structure which can be tracked by the tracking device, such as a reference frame comprising three or more infrared reflective balls.
Optionally, the surgical navigation system further comprises a robotic arm for positioning.
In a fourth aspect, the present invention provides a surgical robotic system comprising a brain tissue drift correction apparatus as described above. Further, the surgical navigation system may include: the device comprises a host, a display device, input and output equipment (a mouse, a keyboard, a pedal, a touch screen and the like), a mechanical arm and a structural light spot cloud collector, wherein the host is internally provided with the brain tissue drift correction device, the structural light spot cloud collector can be connected to the tail end of the mechanical arm, the position of the structural light spot cloud collector in a mechanical arm coordinate system is obtained through the pose of the mechanical arm, and the coordinate conversion from the structural light spot cloud collector coordinate system to the mechanical arm coordinate system is realized.
Optionally, the surgical robotic system may further comprise a tracking device; such as an infrared tracking device, including an infrared emitting structure and an infrared camera.
In a fifth aspect, the present invention provides an electronic device, comprising:
a memory and one or more processors;
wherein the memory is communicatively coupled to the one or more processors and has stored therein instructions executable by the one or more processors, the instructions, when executed by the one or more processors, operable by the electronic device to implement the methods as provided above.
In a sixth aspect, the present invention provides a computer-readable storage medium having stored thereon computer-executable instructions that, when executed by a computing device, may be used to implement the methods as provided above.
In a seventh aspect, the present invention provides a computer program product comprising a computer program stored on a computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, are operable to carry out the methods as provided above.
The brain tissue drift correction method, the brain tissue drift correction device, the surgical navigation system, the electronic equipment, the computer-readable storage medium and the program product have the advantages that the method and the device are not limited to the following:
1. the method comprises the steps of rapidly, conveniently and massively acquiring second point clouds of brain tissues after tissue drift by using structured light, matching the second point clouds with first point clouds before the tissue drift, accurately obtaining corresponding non-rigid matching relations before and after the tissue drift, and rapidly and accurately correcting the first point clouds based on the non-rigid matching relations;
2. through the transformation matrix from coarse registration to rigidity, a good initial position is provided for the calculation of the non-rigid matching relation;
3. the first point cloud is segmented based on the rough registration, and a part of the first point cloud is obtained for fine registration, so that the calculation amount is reduced, and the registration accuracy is improved;
4. through the verification step, closed-loop feedback verification is provided, and the possibility of errors is further reduced.
Drawings
The features and advantages of the present invention will be more clearly understood by reference to the accompanying drawings, which are illustrative and not to be construed as limiting the invention in any way, and in which:
FIG. 1 is a schematic flow chart of a method for correcting brain tissue drift according to the present invention;
FIG. 2 is a schematic structural diagram of a device for correcting brain tissue drift according to the present invention;
fig. 3 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
In the following detailed description, numerous specific details of the invention are set forth by way of examples in order to provide a thorough understanding of the relevant disclosure. It will be apparent, however, to one skilled in the art that the present invention may be practiced without these specific details. It should be understood that the use of "system," "device," "unit" and/or "module" terminology herein is a method for distinguishing between different components, elements, portions or assemblies at different levels of sequential arrangement. However, these terms may be replaced by other expressions if they can achieve the same purpose.
It will be understood that when a device, unit or module is referred to as being "on … …," connected to "or" coupled to "another device, unit or module, it can be directly on, connected or coupled to or in communication with the other device, unit or module, or intervening devices, units or modules may be present, unless the context clearly dictates otherwise. For example, as used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of the present invention. As used in the specification and claims of this application, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" are intended to cover the expressly identified features, integers, steps, operations, elements, and/or components, but do not constitute an exclusive list of such features, integers, steps, operations, elements, and/or components.
These and other features and characteristics of the present invention, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will be better understood by reference to the following description and drawings, which form a part of this specification. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the invention. It will be understood that the figures are not drawn to scale.
Various block diagrams are used in the present invention to illustrate various variations of embodiments according to the present invention. It should be understood that the foregoing and following configurations are not intended to limit the present invention. The protection scope of the invention is subject to the claims.
The existing operation navigation system cannot effectively solve the problem that the precision of the pre-built three-dimensional digital model is influenced by tissue deformation in the operation, and particularly, due to the fact that brain tissues are large in size and uneven in structure, the existing ditch return characteristic mark point mode cannot achieve a good correction effect. In view of this, the embodiment of the present invention provides a method for correcting tissue drift of a three-dimensional model, which improves consistency of the three-dimensional digital model through two times of registration and correction of internal and external dual tissue marker points, thereby effectively solving the problem of the three-dimensional digital model drift caused by brain tissue deformation in the operation.
In a first aspect, the present invention provides a method for correcting brain tissue drift, and fig. 1 is a schematic flow chart of the method for correcting brain tissue drift provided by the present invention, as shown in fig. 1, the method includes:
s101, collecting a second point cloud of brain tissue after tissue drift based on structured light;
specifically, the structured light is a system structure composed of a projector and a camera, and after the projector projects a pattern subjected to specific coding onto the surface of an object, the camera acquires an image and is used for calculating three-dimensional data. The change of the coding pattern caused by the space shape of the object can calculate the information of the position, the depth and the like of the object according to the degree of the pattern, and further the three-dimensional space of the object to be imaged is restored. Utilize image acquisition equipment to carry out image acquisition to the brain tissue after the tissue drift that has projected the structured light pattern to generate the second point cloud of the brain tissue after the tissue drift in view of the above, compare with probe, laser acquisition few point, structured light can acquire a large amount of point clouds fast for accurately rectify brain tissue deformation, compare in MRI also need not to add expensive, occupy the MRI equipment in great space, also avoid causing radiation damage to doctor, patient.
S102, based on a matching algorithm, carrying out non-rigid registration on at least partial region of the second point cloud and at least partial region of the first point cloud to obtain a non-rigid matching relation corresponding to at least partial region of the second point cloud before and after tissue drift; the first point cloud is point cloud data constructed on the brain tissue before tissue drift according to the medical image;
specifically, the first point cloud is point cloud data constructed on the brain tissue before the tissue drift based on a medical image before the operation, such as a CT image, a magnetic resonance image, an ultrasound image, and the like. The first point cloud may be point cloud data constructed based on a single type of medical image, or point cloud data constructed based on multi-modal fusion of a plurality of types of medical images. The point cloud data can assist doctors to know the position and the structure of the focus and provide information support for surgical path planning. It should be noted that the medical image captures information of the entire brain tissue, and accordingly, the constructed first point cloud includes not only the contour point cloud of the brain tissue but also an internal point cloud of the brain tissue (for example, a focus inside the brain tissue, a deep brain blood vessel, a nerve fiber bundle, and the like).
During operation, when an external protector (such as dura mater and the like) of brain tissue is opened, the brain tissue may have a position change and a shape change (i.e., a tissue drift) due to gravity, cerebrospinal fluid loss and other factors, that is, the actual position and shape of the brain tissue further generate an error from the first point cloud established before the operation, and the operation path planned before the operation may no longer be suitable for the brain tissue after the tissue drift. At this time, based on a matching algorithm, at least one part of the second point cloud is matched with at least one part of the area of the first point cloud to obtain a non-rigid matching relation, and the non-rigid matching relation is used for correcting the first point cloud according to a matching result. At least a portion of the second point cloud may be point cloud data corresponding to all or a portion of a surface area of the brain tissue exposed intraoperatively. And importing the first point cloud and the second point cloud into the same coordinate system, and performing non-rigid matching on at least one part of the second point cloud and at least one part of the area of the first point cloud by using a matching algorithm to obtain a corresponding non-rigid matching relation of the at least one part of the area of the second point cloud before and after brain tissue drift. The non-rigid matching means that the two point cloud data reach the maximum contact ratio under a certain constraint condition by using modes such as translation, rotation, expansion, affine, transmission, polynomial and the like; constraints such as brain tissue elastomechanics, critical point deformation distance constraints, etc.
The matching algorithm may be a non-rigid matching algorithm, or may be a combination of a rigid matching algorithm and a non-rigid matching algorithm (rigid matching may be understood as a special case of non-rigid matching), a rigid matching algorithm such as an iterative closest point algorithm (ICP algorithm) and the like, a non-rigid matching algorithm such as a robust point matching algorithm (RPM algorithm) and the like, and is not limited herein.
S103, correcting the first point cloud according to the non-rigid matching relation to obtain a third point cloud.
Specifically, the non-rigid matching relationship describes shape transformation before and after brain tissue drift, and the first point cloud is corrected by using the non-rigid matching relationship to obtain a third point cloud of the brain tissue after the tissue drift. It should be noted that, the correcting the first point cloud may be to correct a local part of the first point cloud according to a non-rigid matching relationship, for example, correct at least a part of the area of the first point cloud, and then join with another area of the first point cloud through edge processing; the correction of the first point cloud may also be a correction of the first point cloud as a whole, for example, a correction of the whole of the first point cloud directly according to a non-rigid matching relationship, for example, a correction of the whole of the first point cloud in combination with an influence factor (e.g., the farther the influence factor is from the at least partial region of the first point cloud, the smaller the influence factor is, and the weaker the corresponding drift is), and for example, a correction of the whole of the first point cloud in combination with a boundary condition (e.g., in the first point cloud, the drift of the skull base brain tissue is limited, and for example, the drift degree of the deep blood vessel is limited).
According to the embodiment, the second point cloud of the brain tissue after the tissue drift is obtained rapidly, conveniently and massively by using the structured light, the second point cloud is matched with the first point cloud before the tissue drift, the non-rigid matching relation corresponding to the first point cloud before and after the tissue drift is accurately obtained, and the first point cloud is rapidly and accurately corrected based on the non-rigid matching relation.
Based on any one of the embodiments, in one embodiment, the non-rigid registration of at least a partial region of the second point cloud with at least a partial region of the first point cloud based on a matching algorithm to obtain a non-rigid matching relationship corresponding to at least a partial region of the second point cloud before and after tissue drift includes:
carrying out coarse registration on at least partial area of the second point cloud and the first point cloud to obtain a rigid transformation matrix;
and on the basis of the rough registration, performing fine registration on at least partial region of the second point cloud and at least partial region corresponding to the first point cloud to obtain the non-rigid matching relationship.
Specifically, at least a partial region of the second point cloud is a whole or partial three-dimensional surface region exposed by the brain tissue after the tissue drift acquired by structured light, which represents the actual three-dimensional surface morphology of the brain tissue after the drift, and the at least partial region of the second point cloud is roughly registered with the first point cloud to obtain a rigid transformation matrix. Only rigid transformation is adopted in the coarse registration process, namely, the shape of the at least partial region in the second point cloud is not changed, so that the at least partial region and the first point cloud reach the maximum coincidence degree. It can be understood that the rigid transformation matrix utilizes translation and rotation to enable the two point cloud data to reach the maximum coincidence degree, so that a better initial position is provided for precise registration, and the precise registration efficiency is improved. Further, the patient registration is a process of corresponding the real space coordinates of the patient to a three-dimensional patient model established based on the medical image, for example, before operation, the patient needs to be registered, and the craniotomy position is determined by combining with the preoperative planned surgical path, so as to accelerate the efficiency of the coarse registration process and improve the accuracy of the coarse registration process in the embodiment, the registration relationship between the real space coordinates of the patient and the three-dimensional patient model (the first point cloud can be obtained based on the three-dimensional model) in the patient registration process can be used as a rigid transformation matrix of the coarse registration.
And then, on the basis of the rough registration, carrying out further fine registration on the at least partial region and the first point cloud, namely, enabling the partial region and the corresponding at least partial region in the first point cloud to achieve the maximum contact ratio by using translation, rotation, expansion, contraction, affine, transmission, polynomial and the like.
It can be understood that at least a partial region of the second point cloud and the first point cloud may also be directly subjected to non-rigid registration to obtain a non-rigid matching relationship between the at least a partial region of the second point cloud and the first point cloud, and the matching method is suitable for fast registration.
In the embodiment, a rigid transformation matrix is obtained through rough registration, and at least partial area of the second point cloud and the first point cloud are subjected to rapid preliminary registration; on the basis of rough registration, at least partial region of the second point cloud and at least partial region corresponding to the first point cloud are subjected to high-precision registration through precise registration, and matching precision is improved.
Based on any one of the above embodiments, in an embodiment, at least a partial region corresponding to the first point cloud is obtained by:
transforming at least partial area of the second point cloud by using the rigid transformation matrix, and determining an overlapping area of the transformed second point cloud and the first point cloud;
and cutting the first point cloud according to the overlapping area to obtain at least partial corresponding area in the first point cloud.
Specifically, at least a partial region of the second point cloud corresponds to all or a partial three-dimensional surface region of the brain tissue exposed after the patient tissue drifts, represents the actual three-dimensional surface morphology of the brain tissue after the drift, at least a partial region of the second point cloud is transformed by using a rigid transformation matrix (i.e., corresponds to a coarse registration process), an overlapping region with the first point cloud is determined on the basis of the coarse registration, the first point cloud is cut according to the overlapping region, and at least a corresponding partial region in the first point cloud is determined. More specifically, the at least partial region of the first point cloud may be enclosed with a bounding box, and then an overlapping region with the first point cloud may be determined from the bounding box. The overlapping area can be an area directly determined according to the intersection of the second point cloud after coarse registration and the first point cloud, or an area determined after a preset distance is reduced outwards or inwards on the basis of the intersection area.
In the embodiment, at least a partial region of the second point cloud is rapidly and preliminarily matched with the first point cloud according to the rigid transformation matrix, and the corresponding at least partial region of the at least partial region in the first point cloud is accurately determined, so that the accuracy of the subsequent fine registration process is improved.
Based on any of the above embodiments, in an embodiment, at least a part of the area of the second point cloud is manually outlined, and/or the area of interest in the second point cloud is automatically extracted based on the point cloud features.
Specifically, at least a partial region of the second point cloud may be a region of interest determined in the second point cloud by manual drawing (for example, by using a mouse, a stylus, or the like); the region of interest in the second point cloud may also be automatically extracted based on the point cloud features, for example, the region of interest is extracted based on color features (color differences of a treatment towel, a scalp and brain tissue), the region of interest is extracted based on texture features (texture differences of brain tissue and non-operative regions), and the region of interest is extracted based on gray scale features as the at least partial region; the method can also be a combined mode, namely, the region of interest of the second point cloud is preliminarily and automatically extracted based on the point cloud characteristics, then manual delineation input is detected, and the preliminarily determined region of interest is corrected for one or more times according to the manual delineation input to obtain a final region of interest as the at least partial region.
The embodiment efficiently and accurately determines at least part of the area in the second point cloud.
Based on any of the above embodiments, in an embodiment, after the correcting the first point cloud according to the non-rigid matching relationship based on the structured light to obtain a third point cloud, the method further includes:
acquiring a coordinate conversion relation between a patient coordinate system and a structured light coordinate system where the second point cloud is located;
and combining the rigid transformation matrix to obtain a conversion relation between the third point cloud and the patient coordinate system.
Specifically, after the corrected third point cloud is obtained, the third point cloud may also be corresponding to a spatial coordinate system where the patient is located, so as to guide the surgical procedure. The coordinate transformation relation between the patient coordinate system and the structured light coordinate system where the second point cloud is located can be obtained in advance, the third point cloud is transformed to the structured light coordinate system where the second point cloud is located according to the rigid transformation matrix in the surgical navigation process, and then the transformation relation between the patient coordinate system and the structured light coordinate system where the second point cloud is located is combined, so that the transformation relation between the third point cloud and the patient coordinate system can be established.
The coordinate conversion relation between the patient coordinate system and the structured light coordinate system where the second point cloud is located can be obtained based on the mechanical arm, for example, the structured light point cloud collection device is installed on the mechanical arm, the coordinate conversion relation is calibrated before the structured light point cloud collection device and the mechanical arm leave factory, and in an operation, the coordinate relation between the mechanical arm coordinate system and the patient coordinate system is rigidly fixed, so that the structured light point cloud collector connected with the tail end of the mechanical arm is driven by the mechanical arm to collect point clouds at different positions, and the coordinate conversion relation between the patient coordinate system and the structured light coordinate system where the second point cloud is located can be obtained through calculation; the coordinate transformation relationship between the patient coordinate system and the structured light coordinate system where the second point cloud is located may also be determined by a tracking device, for example, a reference frame (or an identification point) is fixed on the structured light spot cloud acquisition device, the coordinate transformation relationship is calibrated before the structured light spot cloud acquisition device and the reference frame leave a factory, a reference frame (or an identification point) is also fixed at a relevant position (such as a hospital bed) of the patient or the patient, the tracking device (such as an infrared tracking system, including an infrared emission structure and an infrared camera) is utilized to obtain coordinate data of the point cloud acquisition device and patient coordinate data through the reference frame, and the coordinate transformation relationship between the patient coordinate system and the structured light coordinate system where the second point cloud is located is determined according to the coordinate data.
In the embodiment, the coordinate conversion relation from the third point cloud to the patient coordinate system is determined, so that the real space coordinate information of the brain tissue after the tissue drift is convenient to know, and accurate information support is provided for the operation.
Based on any of the above embodiments, in an embodiment, after the correcting the first point cloud according to the non-rigid matching relationship to obtain a third point cloud, the method further includes:
a verification step for determining whether the third point cloud satisfies an accuracy.
In the embodiment, whether the third point cloud meets the accuracy is verified, so that the precision of the third point cloud is guaranteed, and accurate technical support is provided for the operation process.
Based on any one of the above embodiments, in one embodiment, the verifying step includes:
and acquiring coordinate data of at least one point in the second point cloud, and determining whether the third point cloud meets the accuracy or not by combining the corresponding coordinate data of the at least one point in the third point cloud.
Specifically, after the third point cloud is obtained, the drift correction effect of the third point cloud is further verified, and partial feature points, such as (manually or automatically) a blood vessel branch node, a brain tissue sulcus point, and the like, may be selected from the second point cloud, and the coordinates of the feature points after the feature points correspond to the third point cloud are determined according to the rigid transformation matrix, and finally, the accuracy of the feature points is determined according to the coordinate data difference between the feature points and the coordinates of the corresponding feature points in the third point cloud. The accuracy may be determined based on a maximum deviation distance, an average deviation distance, a weighted deviation distance, and the like.
In this embodiment, the accuracy of the corrected third point cloud is ensured through the verification step.
Based on any one of the above embodiments, in one embodiment, the verifying step includes:
transforming the second point cloud according to the rigid transformation matrix to obtain a fourth point cloud;
and determining whether the third point cloud meets the accuracy according to the coordinates of each corresponding point in the fourth point cloud and the third point cloud.
Specifically, the second point cloud can be transformed according to the rigid transformation matrix to obtain a fourth point cloud, the correction deviation of the third point cloud can be judged by comparing the fourth point cloud with the third point cloud, the deviation distance between the fourth point cloud and each corresponding point of the third point cloud is calculated, and then whether the third point cloud meets the accuracy or not can be judged according to the parameters such as the maximum deviation distance, the average deviation distance, the weighted deviation distance and the abnormal point proportion of the deviation distances of all the points. Furthermore, the points in the third point cloud with different accuracy intervals can be displayed on the display device in different colors, so that the accuracy of the third point cloud can be conveniently and visually known.
In this embodiment, the accuracy of the corrected third point cloud is ensured through the verification step.
Based on any of the above embodiments, in an embodiment, the method further includes:
and under the condition that the third point cloud does not meet the accuracy, acquiring the second point cloud of the brain tissue after the tissue drift based on the structured light again until the third point cloud is obtained.
In the embodiment, under the condition that the third point cloud does not meet the accuracy, the second point cloud is obtained again and the first point cloud is corrected, so that the precision of the third point cloud is guaranteed, and accurate technical support is provided for the operation process.
In one embodiment, based on any of the above embodiments, the acquiring a second point cloud of brain tissue after tissue drift based on structured light includes:
and carrying out image acquisition on the brain tissue after the structured light pattern is projected by using a structured light spot cloud collector, and acquiring second point cloud of the brain tissue according to the acquired image data.
Specifically, in the operation, a structured light projection device is used for projecting a coded structured light pattern to the brain tissue after the tissue drift, a structured light spot cloud collector is used for carrying out image acquisition on the brain tissue after the structured light pattern is projected, the acquired image data is processed to obtain second point cloud data of the brain tissue, and the data processing can comprise filtering, gray level processing, depth information calculation and the like.
According to the embodiment, a large number of point clouds can be rapidly collected through the structured light, second point cloud data can be accurately calculated, and a non-rigid matching relation can be rapidly and accurately determined by combining a non-rigid registration process.
Based on any one of the above embodiments, in an embodiment, the structured light point cloud collector is an infrared point cloud collector, a visible light point cloud collector, or a fluorescent point cloud collector.
Specifically, the second point cloud may be collected based on infrared structured light or based on structured light in a visible light frequency band, and accordingly, the structured light point cloud collector may be an infrared point cloud collector or a visible light point cloud collector. In addition, the second point cloud can be acquired based on fluorescence imaging, the fluorescent substance is excited by specific external energy (such as high-energy rays of laser and the like) to cause the electron orbit of the fluorescent substance to jump to the high-energy orbit, and energy is released in the process of returning to the ground state to generate a detectable fluorescence signal. Specifically, for the projection device corresponding to the fluorescence point cloud collector, only one projection device may be used to project the structured light to the drifted brain tissue (for example, the brain blood vessel contains the fluorescent substance) containing the fluorescent substance, wherein the projected structured light covers the excitation wavelength band corresponding to the fluorescent substance, one light source may be used to project the excitation light to the drifted brain tissue containing the fluorescent substance, and another projector may be used to project the structured light to the drifted brain tissue. For the fluorescent point cloud collection device, only one image collection device can be used to collect the structured light pattern of the brain tissue with fluorescence for generating the second point cloud, wherein the collection light wave band corresponding to the image collection device needs to cover the fluorescence wave band. Furthermore, one image acquisition device may be used to acquire the fluorescence image, another image acquisition device may be used to acquire the structured light pattern of the brain tissue, and the fluorescence pattern and the structured light pattern may be combined to generate the second point cloud, which is not limited herein.
The light spot cloud collector with various structures provided in the embodiment meets the differentiation requirements of users.
In a second aspect, the present invention provides a brain tissue drift correction device, and fig. 2 is a schematic structural diagram of the brain tissue drift correction device provided by the present invention, as shown in fig. 2, the device includes:
a structured light collection module 210 for collecting a second point cloud of brain tissue after tissue drift based on structured light;
the point cloud matching module 220 is configured to perform non-rigid registration on at least a partial region of the second point cloud and at least a partial region of the first point cloud based on a matching algorithm to obtain a corresponding non-rigid matching relationship before and after tissue drift of at least a partial region of the second point cloud; the first point cloud is point cloud data constructed on the brain tissue before tissue drift according to the medical image;
and the correcting module 230 is configured to correct the first point cloud according to the non-rigid matching relationship to obtain a third point cloud.
According to the embodiment, the second point cloud of the brain tissue after the tissue drift is obtained rapidly, conveniently and massively by using the structured light, the second point cloud is matched with the first point cloud before the tissue drift, the non-rigid matching relation corresponding to the first point cloud before and after the tissue drift is accurately obtained, and the first point cloud is rapidly and accurately corrected based on the non-rigid matching relation.
Based on any of the above embodiments, in one embodiment, the point cloud matching module 220 includes:
the first matching module is used for carrying out rough registration on at least part of the area of the second point cloud and the first point cloud to obtain a rigid transformation matrix;
and the second matching module is used for carrying out fine registration on at least partial region of the second point cloud and at least partial region corresponding to the first point cloud on the basis of the coarse registration to obtain the non-rigid matching relationship.
In the embodiment, a rigid transformation matrix is obtained through rough registration, and at least partial area of the second point cloud and the first point cloud are subjected to rapid preliminary registration; on the basis of rough registration, at least partial region of the second point cloud and at least partial region corresponding to the first point cloud are subjected to high-precision registration through precise registration, and matching precision is improved.
Based on any one of the above embodiments, in an embodiment, the second matching module includes:
the first submodule is used for transforming at least part of the area of the second point cloud by using the rigid transformation matrix and determining an overlapping area of the transformed second point cloud and the first point cloud;
and the second sub-module is used for cutting the first point cloud according to the overlapping area to obtain at least part of corresponding area in the first point cloud.
In the embodiment, at least a partial region of the second point cloud is rapidly and preliminarily matched with the first point cloud according to the rigid transformation matrix, and at least a partial region corresponding to the at least partial region in the first point cloud is accurately determined, so that the accuracy of a subsequent fine registration process is improved.
Based on any of the above embodiments, in an embodiment, at least a part of the area of the second point cloud is manually outlined, and/or the area of interest in the second point cloud is automatically extracted based on the point cloud features.
The embodiment efficiently and accurately determines at least part of the area in the second point cloud.
Based on any of the above embodiments, in one embodiment, the apparatus further comprises:
the acquisition module is used for acquiring a coordinate conversion relation between a patient coordinate system and a structured light coordinate system where the second point cloud is located;
and the conversion module is used for combining the rigid transformation matrix to obtain the conversion relation between the third point cloud and the patient coordinate system.
In the embodiment, the coordinate conversion relation from the third point cloud to the patient coordinate system is determined, so that the real space coordinate information of the brain tissue after the tissue drift is convenient to know, and accurate information support is provided for the operation.
Based on any of the above embodiments, in an embodiment, the apparatus further includes:
a verification module to determine whether the third point cloud satisfies an accuracy.
In the embodiment, whether the third point cloud meets the accuracy is verified, so that the precision of the third point cloud is guaranteed, and accurate technical support is provided for the operation process.
Based on any one of the above embodiments, in one embodiment, the verification module includes:
and the first verification unit is used for acquiring coordinate data of at least one point in the second point cloud and determining whether the third point cloud meets the accuracy or not by combining the corresponding coordinate data of the at least one point in the third point cloud.
In this embodiment, the accuracy of the corrected third point cloud is ensured through the verification step.
Based on any one of the above embodiments, in one embodiment, the verification module includes:
the second verification unit is used for transforming the second point cloud according to the rigid transformation matrix to obtain a fourth point cloud;
and the third verification unit is used for determining whether the third point cloud meets the accuracy according to the coordinates of each corresponding point in the fourth point cloud and the third point cloud.
In this embodiment, the accuracy of the corrected third point cloud is ensured through the verification step.
Based on any of the above embodiments, in an embodiment, the apparatus further includes:
and the iteration module is used for executing the step of collecting the second point cloud of the brain tissue after the tissue drift based on the structured light again under the condition that the third point cloud does not meet the accuracy until the third point cloud is obtained.
In the embodiment, under the condition that the third point cloud does not meet the accuracy, the second point cloud is obtained again and the first point cloud is corrected, so that the precision of the third point cloud is guaranteed, and accurate technical support is provided for the operation process.
Based on any one of the above embodiments, in an embodiment, the structured light acquisition module is configured to perform image acquisition on the brain tissue after the structured light pattern is projected by using a structured light point cloud collector, and acquire a second point cloud of the brain tissue according to acquired image data.
According to the embodiment, a large number of point clouds can be rapidly collected through the structured light, second point cloud data can be accurately calculated, and a non-rigid matching relation can be rapidly and accurately determined by combining a non-rigid registration process.
Based on any of the above embodiments, in an embodiment, the structured light point cloud collector is an infrared point cloud collector, a visible light point cloud collector, or a fluorescent point cloud collector.
The light spot cloud collector with various structures provided in the embodiment meets the differentiation requirements of users.
In a third aspect, the present invention provides a surgical navigation system comprising the brain tissue drift correction apparatus as described above.
Specifically, the corrected third point cloud is obtained by using the brain tissue drift correction device, and the surgical navigation system further comprises a tracking device which is used for tracking the positions of surgical instruments and the auxiliary positioning device and guiding the surgical process by combining the third point cloud.
In a fourth aspect, the present invention provides an electronic device, and fig. 3 is a schematic structural diagram of the electronic device, and as shown in fig. 3, the electronic device includes:
a memory 330 and one or more processors 310;
wherein the memory 330 is communicatively coupled to the one or more processors 310, the memory 330 stores therein program instructions 332 executable by the one or more processors 310, and the program instructions 332 are executed by the one or more processors 310 to cause the one or more processors 310 to perform the steps of the above-described method embodiments. Further, the electronic device 300 may also interact with external devices through the communication interface 320.
In a fifth aspect, the present invention provides a computer-readable storage medium having stored thereon computer-executable instructions, which, when executed by a computing device, may be used to implement the respective brain tissue drift correction methods as provided above.
In a sixth aspect, the present invention also provides a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the computer to carry out the above-mentioned methods of providing brain tissue drift correction.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and modules may refer to the corresponding descriptions in the foregoing method and/or apparatus embodiments, and are not described herein again.
While the subject matter described herein is provided in the general context of computer systems that execute in conjunction with the execution of an operating system and application programs on the computer system, those skilled in the art will recognize that other implementations may also be performed in combination with other types of program modules. Generally, program modules include routines, programs, components, data structures, and other types of structures that perform particular tasks or implement particular abstract data types. Those skilled in the art will appreciate that the subject matter described herein may be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, and the like, as well as distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.
Those of ordinary skill in the art will appreciate that the various illustrative elements and method steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to perform all or part of the steps of the method according to the embodiments of the present invention. Such computer-readable storage media include physical volatile and nonvolatile, removable and non-removable media implemented in any manner or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Computer-readable storage media specifically include, but are not limited to, a USB flash drive, a removable hard drive, a Read-Only Memory (ROM), a Random Access Memory (RAM), an erasable programmable Read-Only Memory (EPROM), an electrically erasable programmable Read-Only Memory (EEPROM), flash Memory or other solid state Memory technology, a CD-ROM, a Digital Versatile Disk (DVD), an HD-DVD, a Blu-Ray or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer.
In summary, the present disclosure provides a method, an apparatus, a surgical navigation system, an electronic device, a computer-readable storage medium, and a program product for correcting brain tissue drift, which utilize structured light to quickly, conveniently and massively acquire second point clouds of brain tissue after tissue drift, use the second point clouds to match with first point clouds before tissue drift, accurately obtain corresponding non-rigid matching relationships before and after tissue drift, and quickly and accurately correct the first point clouds based on the non-rigid matching relationships.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explaining the principles of the invention and are not to be construed as limiting the invention. Therefore, any modifications, equivalents, improvements and the like which are made without departing from the spirit and scope of the present invention shall be included in the protection scope of the present invention. Further, it is intended that the appended claims cover all such variations and modifications as fall within the scope and boundaries of the appended claims or the equivalents of such scope and boundaries.
Claims (18)
1. A method for correcting brain tissue drift, comprising:
acquiring a second point cloud of the brain tissue after the tissue drift based on the structured light;
based on a matching algorithm, carrying out non-rigid registration on at least partial region of the second point cloud and at least partial region of the first point cloud to obtain a non-rigid matching relation corresponding to at least partial region of the second point cloud before and after tissue drift; the first point cloud is point cloud data constructed on the brain tissue before tissue drift according to the medical image;
and correcting the first point cloud according to the non-rigid matching relation to obtain a third point cloud.
2. The method according to claim 1, wherein the non-rigid registration of at least a partial region of the second point cloud with at least a partial region of the first point cloud based on a matching algorithm to obtain a non-rigid matching relationship between at least partial regions of the second point cloud before and after the tissue drift, comprises:
carrying out coarse registration on at least partial region of the second point cloud and the first point cloud to obtain a rigid transformation matrix;
and on the basis of the rough registration, performing fine registration on at least partial region of the second point cloud and at least partial region corresponding to the first point cloud to obtain the non-rigid matching relationship.
3. The method of claim 2, wherein the corresponding at least partial region in the first point cloud is obtained by:
transforming at least partial area of the second point cloud by using the rigid transformation matrix, and determining an overlapping area of the transformed second point cloud and the first point cloud;
and cutting the first point cloud according to the overlapping area to obtain at least partial corresponding area in the first point cloud.
4. The method of claim 1, wherein at least a portion of the second point cloud is manually delineated and/or a region of interest in the second point cloud is automatically extracted based on point cloud characteristics.
5. The method of claim 2, wherein after the correcting the first point cloud according to the non-rigid matching relationship to obtain a third point cloud, the method further comprises:
acquiring a coordinate conversion relation between a patient coordinate system and a structured light coordinate system where the second point cloud is located;
and combining the rigid transformation matrix to obtain a conversion relation between the third point cloud and the patient coordinate system.
6. The method of claim 1, wherein after said correcting the first point cloud according to the non-rigid matching relationship to obtain a third point cloud, the method further comprises:
a verification step for determining whether the third point cloud satisfies an accuracy.
7. The method according to claim 1, wherein the acquiring a second point cloud of brain tissue after tissue drift based on structured light comprises:
and carrying out image acquisition on the brain tissue after the structured light pattern is projected by using a structured light spot cloud collector, and acquiring second point cloud of the brain tissue according to the acquired image data.
8. The method of claim 7, wherein the structure light point cloud collector is an infrared point cloud collector, a visible light point cloud collector, or a fluorescent point cloud collector.
9. A brain tissue drift correction device, comprising:
the structured light acquisition module is used for acquiring a second point cloud of the brain tissue after the tissue drift based on the structured light;
the point cloud matching module is used for carrying out non-rigid registration on at least part of the area of the second point cloud and at least part of the area of the first point cloud based on a matching algorithm to obtain a corresponding non-rigid matching relation of at least part of the area of the second point cloud before and after tissue drift; the first point cloud is point cloud data constructed on the brain tissue before tissue drift according to the medical image;
and the correction module is used for correcting the first point cloud according to the non-rigid matching relation to obtain a third point cloud.
10. The apparatus according to claim 9, wherein the point cloud matching module comprises:
the first matching module is used for carrying out rough registration on at least part of the area of the second point cloud and the first point cloud to obtain a rigid transformation matrix;
and the second matching module is used for carrying out fine registration on at least partial region of the second point cloud and at least partial region corresponding to the first point cloud on the basis of the coarse registration to obtain the non-rigid matching relationship.
11. The apparatus according to claim 10, wherein the second matching module comprises:
the first submodule is used for transforming at least part of the area of the second point cloud by using the rigid transformation matrix and determining an overlapping area of the transformed second point cloud and the first point cloud;
and the second sub-module is used for cutting the first point cloud according to the overlapping area to obtain at least part of corresponding area in the first point cloud.
12. The apparatus according to claim 10, further comprising:
the acquisition module is used for acquiring a coordinate conversion relation between a patient coordinate system and a structured light coordinate system where the second point cloud is located;
and the conversion module is used for combining the rigid transformation matrix to obtain the conversion relation between the third point cloud and the patient coordinate system.
13. The apparatus according to claim 9, wherein the structured light collection module is configured to use a structured light spot cloud collector to collect an image of the brain tissue after the structured light pattern is projected, and obtain the second point cloud of the brain tissue according to the collected image data.
14. A surgical navigation system comprising a brain tissue drift correction apparatus according to any one of claims 9 to 13.
15. A surgical robotic system comprising a brain tissue drift correction device according to any one of claims 9-13.
16. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements all or part of the steps of the method according to any one of claims 1 to 8 when executing the program.
17. A non-transitory computer-readable storage medium, on which a computer program is stored, wherein the computer program, when being executed by a processor, implements all or part of the steps of the brain tissue drift correction method according to any one of claims 1 to 8.
18. A computer program product comprising computer executable instructions for implementing all or part of the steps of the method of any one of claims 1 to 8 when executed.
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| CN116681697A (en) * | 2023-07-28 | 2023-09-01 | 无锡日联科技股份有限公司 | Cobalt removal measuring method and device for diamond compact and electronic equipment |
| CN116681697B (en) * | 2023-07-28 | 2023-10-20 | 无锡日联科技股份有限公司 | Cobalt removal measuring method and device for diamond compact and electronic equipment |
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