CN114566272A - Joint registration method, device, equipment and storage medium based on dynamic weight optimization - Google Patents

Joint registration method, device, equipment and storage medium based on dynamic weight optimization Download PDF

Info

Publication number
CN114566272A
CN114566272A CN202111631065.2A CN202111631065A CN114566272A CN 114566272 A CN114566272 A CN 114566272A CN 202111631065 A CN202111631065 A CN 202111631065A CN 114566272 A CN114566272 A CN 114566272A
Authority
CN
China
Prior art keywords
point
point set
coordinate system
weight
joint registration
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202111631065.2A
Other languages
Chinese (zh)
Inventor
杜思傲
戚翔尔
翟方文
赵龙飞
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hangzhou Jianjia Robot Co ltd
Original Assignee
Hangzhou Jianjia Robot Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hangzhou Jianjia Robot Co ltd filed Critical Hangzhou Jianjia Robot Co ltd
Priority to CN202111631065.2A priority Critical patent/CN114566272A/en
Publication of CN114566272A publication Critical patent/CN114566272A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Mathematical Physics (AREA)
  • Medical Informatics (AREA)
  • General Physics & Mathematics (AREA)
  • Public Health (AREA)
  • Pure & Applied Mathematics (AREA)
  • Biomedical Technology (AREA)
  • Primary Health Care (AREA)
  • Epidemiology (AREA)
  • Computational Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Databases & Information Systems (AREA)
  • Computing Systems (AREA)
  • Algebra (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Pathology (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Radiology & Medical Imaging (AREA)
  • Prostheses (AREA)

Abstract

The invention relates to the technical field of medical treatment, in particular to a joint registration method, a device, equipment and a storage medium based on dynamic weight optimization. The method comprises the following steps: acquiring the initial conversion relation between the tracker and a femur coordinate system in joint registration; designing an improved method for acquiring the conversion relation; dynamic adjustment of the weight is realized through the iteration process of the nearest point; and outputting a final femur coordinate system and a conversion matrix of the tracker coordinate system. The method for obtaining the conversion matrix by SVD decomposition is improved, the ICP iterative process is optimized by using a dynamic weight value adjusting mode, the influence of deviation points generated by medical image reconstruction errors, intraoperative sampling point errors and the like on the registration result in the joint registration process can be effectively controlled, and therefore the error of joint registration is reduced.

Description

Joint registration method, device, equipment and storage medium based on dynamic weight optimization
Technical Field
The invention relates to the technical field of medical treatment, in particular to a joint registration method, a device, equipment and a storage medium based on dynamic weight optimization.
Background
With the rapid development of computer-assisted surgery technology, surgical navigation systems are widely used in surgical operations. In the conventional surgical navigation system, a tracker of a three-dimensional positioning device is usually fixed on a hard tissue such as a bone to track the position and posture of the bone, for example, in a joint replacement surgery, in the surgical navigation system, a tracking array of the three-dimensional positioning device needs to be fixed with the bone, a probe is used to collect characteristic points on the surface of the bone, the characteristic points are matched with a bone model reconstructed from preoperative medical images, and the relative position relationship between the bone and the tracker is obtained.
Conventional joint registration uses an Iterative Closest Point algorithm (ICP algorithm for short), probe acquisition points are mapped to a tracker coordinate system, and are matched with a Point cloud reconstructed from a medical image, and a conversion relation between the tracker coordinate system and a bone coordinate system is obtained through adjacent Point iteration.
The invention content is as follows:
in order to solve the problems in the background art, the invention provides a joint registration method, a joint registration device and a joint registration storage medium based on dynamic weight optimization, wherein the dynamic weight is used for optimizing an ICP (inductively coupled plasma) iterative process, so that the influence of a deviation point on a registration result is reduced.
In a first aspect, the present invention provides a joint registration method based on dynamic weight optimization, including:
s101, acquiring a conversion relation between an initial tracker and a femur coordinate system in joint registration;
s102, designing an improved method for acquiring a conversion relation;
s103, realizing dynamic adjustment of the weight through the iteration process of the nearest neighbor point;
and S104, outputting a final conversion matrix of the femur coordinate system and the tracker coordinate system.
Preferably, the joint registration is a femoral knee registration in a knee surgery.
Further, the obtaining of the initial tracker-to-femur coordinate system conversion relationship in the joint registration includes:
obtaining point cloud F { F of femur from reconstructed model of medical image1,f2,...fn-the point cloud belongs to a dense point cloud, typically in a number of the order of one hundred thousand;
m guide point sets C { C) designed in advance in the femoral point cloud F1,c2,...cmA point set C is a subset of a point set F;
a doctor collects the characteristic points of the bone surface through the guide points, and the collected characteristic points are close to the guide points;
projecting data acquired by the probe to a tracker coordinate system to obtain a point set P;
calculating the initial conversion relation between the tracker and the femur coordinate system
Figure BDA0003441008100000021
Further, the improved method for obtaining the conversion relation is a weighted SVD decomposition method.
Further, the method for designing a weighted SVD decomposition obtaining transformation relation includes:
inputting a weight list omega, recentering the point set by taking the point set A and the point set B as examples, and acquiring a new point set A'iAnd BB'iThe calculation formula is as follows:
Figure BDA0003441008100000022
A′i＝{PA i-μA}，B′i＝{PB i-μB}
in the formula, muAAnd muBRespectively, the centralized values of the point set A and the point set B, N is the number of the point set points, PAAnd PBRespectively points in point set A and point set BAnd (5) coordinate values i are ith points in the point set.
Will click set A'iAnd Point set B'iMultiplying each point by the corresponding weight in the weight list, adjusting the ratio of each point in the process of solving conversion, and the calculation formula is as follows:
A′i＝A′i*ωi，B′i＝B′i*ωi
calculating a covariance matrix H of the point sets, carrying out SVD on the H to obtain a rotation matrix R and a transfer matrix t among the point sets, wherein the calculation formula is as follows:
Figure BDA0003441008100000031
[U,S,V]＝SVD(H)，R＝V*UT，t＝-R*μA+μB
in the formula, U, S and V are values after SVD decomposition.
A-to-B conversion matrix can be constructed by rotating matrix R and transition matrix t
Figure BDA0003441008100000032
Thus, an improved method for obtaining a transformation relation is obtained
Figure BDA0003441008100000033
Further, the dynamic adjustment of the weight value through the iterative process of the nearest neighbor point includes:
passing the point set P through a conversion relation
Figure BDA0003441008100000034
Projecting the point cloud F to a coordinate system to obtain a point set PFQuery PFConstructing a nearest point set B { B } of each point in the point cloud F and the nearest point1,b2,...bm}；
Inputting the point set P, the point set B and the weight list omega, and outputting a new conversion relation by using the method of claim 5
Figure BDA0003441008100000035
Calculating PFThe ith point and the corresponding nearest point biDistance dst ofiAnd calculating the mean of all the distances
Figure BDA0003441008100000036
If dstiGreater than dstmeanThen ω will beiMinus the weight variation value omegaframe(ii) a If dstiIs less than dstmeanThen ω will beiAdding the weight variation value omegaframeWeight ωiAdjusting the set maximum value ωmaxAnd minimum value ωmin；
Updating the transformation matrix after each iteration
Figure BDA0003441008100000037
And a weight list omega.
Further, the outputting a final femoral coordinate system and tracker coordinate system transformation matrix includes:
will convert the relationship
Figure BDA0003441008100000038
As the initial condition of iterative optimization, bringing the initial condition into the iterative process of design;
setting a minimum and maximum value of the number of iterations, intermediate the minimum and maximum number of iterations, if the matrix is transformed
Figure BDA0003441008100000039
If the matrix difference from the last iteration output is less than a threshold value, the iteration stop condition is considered to be reached, the iteration process is ended in advance, and the latest conversion matrix is output
Figure BDA0003441008100000041
As a final result; if the upper limit of the iteration times is reached, the iteration process is ended, and the dead loop is avoided.
In a second aspect, the present invention further provides a joint registration apparatus based on dynamic weight optimization, including:
the data acquisition module is used for acquiring a point cloud F and a guide point set C of the femur from a model reconstructed from a medical image and projecting data acquired by the probe to a tracker coordinate system to acquire a point set P;
a calculation module for calculating an improved conversion relation according to the point set P, the point set C and the weight list omega of the corresponding points
Figure BDA0003441008100000042
The iteration module is used for realizing dynamic adjustment of the weight through the iteration process of the nearest point;
and the output module outputs the final femur coordinate system and the transformation matrix of the tracker coordinate system.
In a third aspect, the present invention further provides a joint registration device based on dynamic weight optimization, where the device includes: the dynamic weight optimization-based joint registration method is characterized by comprising a memory, a processor and computer program instructions stored in the memory and capable of running on the processor, wherein the processor is used for executing the computer program instructions stored in the memory so as to realize the dynamic weight optimization-based joint registration method.
In a fourth aspect, the present invention further provides a joint storage medium based on dynamic weight optimization, where the computer storage medium stores computer program instructions, and the computer program instructions, when executed by a processor, implement the joint registration method based on dynamic weight optimization.
In a fifth aspect, the joint registration method of the present invention is applied to a femoral knee joint, but not limited to the femoral knee joint, and is applicable to medical image registration of hard tissues of a human body.
The method takes the joint registration as an example, improves the method for obtaining the conversion matrix by SVD decomposition, optimizes the ICP iterative process by using a mode of dynamically adjusting the weight, and can effectively control the influence of deviation points generated by medical image reconstruction errors, intraoperative sampling point errors and the like on the registration result in the joint registration process, thereby reducing the error of the joint registration. The invention is equally applicable to procedures that also use the rigidity of hard tissue to match the point cloud reconstructed from the medical image, such as spinal surgery, oral surgery, extracranial surgery, and the like.
Drawings
Features, advantages and technical effects of exemplary embodiments of the present invention will be described below with reference to the accompanying drawings.
FIG. 1 is a schematic flow chart of a dynamic weight optimization-based joint registration method provided in the present invention;
FIG. 2 is a schematic illustration of the deviation point of an embodiment of the invention from the bone surface;
FIG. 3 is a schematic structural diagram of a joint registration apparatus based on dynamic weight optimization according to the present invention;
fig. 4 is a schematic structural diagram of a computing device provided in the present invention.
Detailed Description
Features and exemplary embodiments of various aspects of the present disclosure will be described in detail below, and in order to make objects, technical solutions and advantages of the present disclosure more apparent, the present disclosure will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are intended to be illustrative only and are not intended to be limiting of the disclosure. It will be apparent to one skilled in the art that the present disclosure may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present disclosure by illustrating examples of the present disclosure.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
For a better understanding of the present invention, a connecting device according to an embodiment of the present invention will be described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a joint registration method based on dynamic weight optimization according to the present invention.
As shown in fig. 1, the present invention provides a joint registration method based on dynamic weight optimization, which includes:
s101, acquiring a conversion relation between an initial tracker and a femur coordinate system in joint registration;
s102, designing an improved method for acquiring a conversion relation;
s103, realizing dynamic adjustment of the weight through the iteration process of the nearest neighbor point;
and S104, outputting a final conversion matrix of the femur coordinate system and the tracker coordinate system.
Illustratively, the joint registration is a femoral knee registration in a knee surgery.
Further, the acquiring a transformation relationship between the initial tracker in the joint registration and the femoral coordinate system in S101 includes:
obtaining point cloud F { F of femur from reconstructed model of medical image1,f2,...fn-the point cloud belongs to a dense point cloud, typically in a number of the order of one hundred thousand;
m guide point sets C { C) designed in advance in the femoral point cloud F1,c2,...cmA point set C is a subset of a point set F;
a doctor collects the characteristic points of the bone surface through the guide points, and the collected characteristic points are close to the guide points;
projecting data acquired by the probe to a tracker coordinate system to obtain a point set P;
calculating the initial conversion relation between the tracker and the femur coordinate system
Figure BDA0003441008100000061
Further, the improved method for obtaining the conversion relation in S102 is a weighted SVD decomposition method.
Further, the method for designing a weighted SVD decomposition obtaining transformation relation in S102 includes:
inputting a weight list omega, recentering the point set by taking the point set A and the point set B as examples, and acquiring a new point set A'iAnd B'iSpecifically, it can be calculated by the following formula:
Figure BDA0003441008100000062
A′i＝{PA i-μA}，B′i＝{PB i-μB}
in the formula, muAAnd muBRespectively, the centralized values of the point set A and the point set B, N is the number of the point set points, PAAnd PBThe coordinate values of points in the point set A and the point set B are respectively, and i is the ith point in the point set.
Will click set A'iAnd Point set B'iEach point in the table is multiplied by a corresponding weight in the weight list, and the proportion of each point in the solving and converting process is adjusted, specifically, the proportion can be calculated by the following formula:
A′i＝A′i*ωi，B′i＝B′i*ωi
calculating a covariance matrix H of the point sets, performing SVD on the H to obtain a rotation matrix R and a transfer matrix t between the point sets, and specifically, calculating by the following formula:
Figure BDA0003441008100000071
[U,S,V]＝SVD(H)，R＝V*UT，t＝-R*μA+μB
in the formula, U, S and V are values after SVD decomposition.
A-to-B conversion matrix can be constructed by rotating matrix R and transition matrix t
Figure BDA0003441008100000072
Thus, an improved method for obtaining a transformation relation is obtained
Figure BDA0003441008100000073
Further, in S103, the dynamic adjustment of the weight value through the iterative process of the nearest neighbor point includes:
passing the point set P through a conversion relation
Figure BDA0003441008100000074
Projecting the point cloud F to a coordinate system to obtain a point set PFQuery P, as shown in FIG. 3FConstructing a nearest point set B { B } of each point in the point cloud F and the nearest point1,b2,...bm}；
Inputting the point set P, the point set B and the weight list omega, and outputting a new conversion relation by using the method of claim 5
Figure BDA0003441008100000075
Calculating PFThe ith point and the corresponding nearest point biDistance dst ofiAnd calculating the mean of all the distances
Figure BDA0003441008100000076
If dstiGreater than dstmeanThen ω will beiMinus the weight variation value omegaframe(ii) a If dstiIs less than dstmeanThen ω will beiAdding the weight variation value omegaframeWeight value ωiAdjusting the set maximum value ωmaxAnd minimum value ωmin；
Updating a transformation matrix after each iteration
Figure BDA0003441008100000081
And a weight list omega.
Further, the outputting a final femoral coordinate system and tracker coordinate system transformation matrix in S104 includes:
will convert the relationship
Figure BDA0003441008100000082
As the initial condition of iterative optimization, bringing the initial condition into the iterative process of design;
setting a minimum and maximum value of the number of iterations, intermediate the minimum and maximum number of iterations, if the matrix is transformed
Figure BDA0003441008100000083
If the matrix difference from the last iteration output is less than a threshold value, the iteration stop condition is considered to be reached, the iteration process is ended in advance, and the latest conversion matrix is output
Figure BDA0003441008100000084
As a final result; if the upper limit of the iteration times is reached, the iteration process is ended, and the dead loop is avoided.
Fig. 3 is a schematic structural diagram of a joint registration device based on dynamic weight optimization according to the present invention. As shown in fig. 3, the present invention further provides a joint registration apparatus based on dynamic weight optimization, which includes: the device comprises a data acquisition module 201, a calculation module 202, an iteration module 203 and an output module 204.
The data acquisition module 201 is configured to acquire a point cloud F of a femur, a guide point set C from a model of medical image reconstruction, and project data acquired by a probe to a tracker coordinate system to acquire a point set P;
the calculating module 202 is configured to calculate an improved conversion relationship according to the point set P, the point set C, and the weight list ω of the corresponding point
Figure BDA0003441008100000085
The iteration module 203 is used for dynamically adjusting the weight through the iteration process of the nearest neighbor point;
the output module 204 is used for outputting the final femur coordinate system and the transformation matrix of the tracker coordinate system.
Each module/unit in the apparatus shown in fig. 3 has a function of implementing each step in fig. 1, and can achieve the corresponding technical effect, and for brevity, the description is not repeated here.
As shown in fig. 4, the present invention further provides a joint registration device based on dynamic weight optimization. The apparatus may include a processor 301 and a memory 302 having stored computer program instructions.
Specifically, the processor 301 may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement the present invention.
Memory 302 may include mass storage for data or instructions. By way of example, and not limitation, memory may include a Hard Disk Drive (HDD), floppy Disk Drive, flash memory, optical Disk, magneto-optical Disk, magnetic tape, or Universal Serial Bus (USB) Drive or a combination of two or more of these.
In one example, the memory 302 may include removable or non-removable (or fixed) media, or memory is non-volatile solid-state memory. The memory may be internal or external to the integrated gateway disaster recovery device.
In one example, the Memory 302 may be a Read Only Memory (ROM). In one example, the ROM may be mask-programmed ROM, Programmable ROM (PROM), Erasable PROM (EPROM), Electrically Erasable PROM (EEPROM), Electrically Alterable ROM (EAROM), or flash memory, or a combination of two or more of these.
In one example, memory 302 may include Read Only Memory (ROM), Random Access Memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical/tangible memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software comprising computer-executable instructions and when the software is executed (e.g., by one or more processors), it is operable to perform operations described with reference to the methods according to an aspect of the present disclosure.
The processor 301 reads and executes the computer program instructions stored in the memory 302 to implement the method/steps in the embodiment shown in fig. 1, and achieve the corresponding technical effects achieved by the embodiment shown in fig. 2 and fig. 3 executing the method/steps, which are not described herein again for brevity.
In one embodiment, the computing device may also include a communication interface 303 and a bus 304. As shown in fig. 4, the processor 301, the memory 302, and the communication interface 303 are connected via a bus 304 to perform communication with each other.
The communication interface 303 is mainly used for implementing communication between modules, apparatuses, units and/or devices in the present invention.
Bus 304 includes hardware, software, or both to couple the components of the online data traffic billing device to each other. By way of example, and not limitation, a Bus may include an Accelerated Graphics Port (AGP) or other Graphics Bus, an Enhanced Industry Standard Architecture (EISA) Bus, a Front-Side Bus (Front Side Bus, FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) Bus, an infiniband interconnect, a Low Pin Count (LPC) Bus, a memory Bus, a Micro Channel Architecture (MCA) Bus, a Peripheral Component Interconnect (PCI) Bus, a PCI-Express (PCI-X) Bus, a Serial Advanced Technology Attachment (SATA) Bus, a video electronics standards association local (VLB) Bus, or other suitable Bus or a combination of two or more of these. A bus may include one or more buses, where appropriate. Although this disclosure describes and illustrates a particular bus, this disclosure contemplates any suitable bus or interconnect.
In addition, the invention also provides a computer storage medium to realize the method by combining the joint registration method based on dynamic weight optimization in the embodiment. The computer storage medium has stored thereon computer program instructions, which when executed by the processor 301, implement any of the above-described embodiments of a dynamic weight optimization-based joint registration method.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer-readable storage medium may be, for example but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The method takes the joint registration as an example, improves the method for obtaining the conversion matrix by SVD decomposition, optimizes the ICP iterative process by using a mode of dynamically adjusting the weight, and can effectively control the influence of deviation points generated by medical image reconstruction errors, intraoperative sampling point errors and the like on the registration result in the joint registration process, thereby reducing the error of the joint registration. The invention is equally applicable to procedures that also use the rigidity of hard tissue to match the point cloud reconstructed from the medical image, such as spinal surgery, oral surgery, extracranial surgery, and the like.
It is to be understood that this disclosure is not limited to the particular configurations and processes described above and shown in the drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present disclosure are not limited to the specific steps described and illustrated, and those skilled in the art may make various changes, modifications, and additions or change the order between the steps after comprehending the spirit of the present disclosure.
The functional blocks shown in the above-described structural block diagrams may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic Circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the present disclosure are the programs or code segments used to perform the required tasks. Computer program code for carrying out operations for aspects of the present invention may be written by those skilled in the relevant art in one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, or a combination thereof, as well as conventional procedural programming languages, such as the "C" programming language or similar programming languages. Further programs or code segments may be stored in a machine-readable medium or transmitted by data signals carried in a carrier wave over transmission media or communication links. A machine-readable medium may include any medium that can store or transfer information. Examples of a machine-readable medium include electronic circuits, semiconductor memory devices, ROM, flash memory, Erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, Radio Frequency (RF) links, and so forth.
Aspects of the present disclosure are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based computer instructions which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
As described above, only the specific embodiments of the present disclosure are provided, and it can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the module and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. It should be understood that the scope of the present disclosure is not limited thereto, and any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope of the present disclosure, and these modifications or substitutions should be covered within the scope of the present disclosure.

Claims (11)

1. A joint registration method based on dynamic weight optimization is characterized by comprising the following steps:
s101, acquiring a conversion relation between an initial tracker and a femur coordinate system in joint registration;
s102, designing an improved method for acquiring a conversion relation;
s103, realizing dynamic adjustment of the weight through an iteration process of the nearest neighbor point;
s104 outputs the final femoral coordinate system and the transformation matrix of the tracker coordinate system.
2. The method of claim 1, wherein the joint is registered as a femoral knee joint during knee surgery.
3. The method according to claim 1, wherein the step S101 of obtaining a transformation relationship between an initial tracker and a femoral coordinate system in joint registration comprises:
obtaining point cloud F { F) of femur from model reconstructed from medical image1,f2,...fn-the point cloud belongs to a dense point cloud, typically in a number of the order of one hundred thousand;
m guide point sets C { C) designed in advance in the femoral point cloud F1,c2,...cmA point set C is a subset of a point set F;
a doctor collects the characteristic points of the bone surface through the guide points, and the collected characteristic points are close to the guide points;
projecting data acquired by the probe to a tracker coordinate system to obtain a point set P;
calculating the initial conversion relation between the tracker and the femur coordinate system
Figure FDA0003441008090000011
4. The dynamic weight optimization-based joint registration method of claim 1, wherein the S102 designs an improved method for obtaining the transformation relationship as a weighted SVD decomposition method.
5. The joint registration method based on dynamic weight optimization according to claim 1 or 4, wherein the S102 designs a method for obtaining a conversion relationship by weighted SVD decomposition, which comprises:
inputting a weight list omega, recentering the point set by taking the point set A and the point set B as examples, and acquiring a new point set A'iAnd B'iThe calculation formula is as follows:
Figure FDA0003441008090000012
A′i＝{PA i-μA}，B′i＝{PB i-μB}
in the formula, muAAnd muBRespectively, the centralized values of the point set A and the point set B, N is the number of the point set points, PAAnd PBThe coordinate values of points in the point set A and the point set B are respectively, and i is the ith point in the point set.
Will click set A'iAnd Point set B'iMultiplying each point by the corresponding weight in the weight list, adjusting the ratio of each point in the process of solving conversion, and the calculation formula is as follows:
A′i＝A′i*ωi，B′i＝B′i*ωi
calculating a covariance matrix H of the point sets, carrying out SVD on the H to obtain a rotation matrix R and a transfer matrix t among the point sets, wherein the calculation formula is as follows:
Figure FDA0003441008090000021
[U,S,V]＝SVD(H)，R＝V*UT，t＝-R*μA+μB
wherein U, S and V are values after SVD decomposition
A-to-B conversion matrix can be constructed by rotating matrix R and transition matrix t
Figure FDA0003441008090000022
Thus, an improved method for obtaining a transformation relation is obtained
Figure FDA0003441008090000023
6. The joint registration method based on dynamic weight optimization of claim 1, wherein S103 implements dynamic adjustment of the weight through an iterative process of nearest neighbor points, comprising:
passing the point set P through a conversion relation
Figure FDA0003441008090000024
Projecting the point cloud F to a coordinate system to obtain a point set PFQuery PFConstructing a nearest point set B { B } of each point in the point cloud F and the nearest point1,b2,...bm}；
Inputting the point set P, point set B and weight list omega, and outputting a new rotation relation by using the method of claim 5
Figure FDA0003441008090000025
Calculating PFThe ith point and the corresponding nearest point biDistance dst ofiAnd calculating the mean of all the distances
Figure FDA0003441008090000026
If dstiGreater than dstmeanThen ω will beiMinus the weight variation value omegaframe(ii) a If dstiIs less than dstmeanThen ω will beiAdding the weight variation value omegaframeWeight value ωiAdjusting the set maximum value ωmaxAnd minimum value ωmin；
Updating the transformation matrix after each iteration
Figure FDA0003441008090000027
And a weight list omega.
7. The method for joint registration based on dynamic weight optimization of claim 1, wherein the step S104 outputs a final transformation matrix between the femur coordinate system and the tracker coordinate system, comprising:
will convert the relationship
Figure FDA0003441008090000031
As the initial condition of iterative optimization, bringing the initial condition into the iterative process of design;
setting a minimum and maximum value of the number of iterations, intermediate the minimum and maximum number of iterations, if the matrix is transformed
Figure FDA0003441008090000032
The matrix difference from the last iteration output is less than a threshold,considering that the iteration stop condition is reached, ending the iteration process in advance and outputting the latest conversion matrix
Figure FDA0003441008090000033
As a final result; if the upper limit of the iteration times is reached, the iteration process is ended, and the dead loop is avoided.
8. A joint registration device based on dynamic weight optimization is characterized by comprising:
the data acquisition module is used for acquiring a point cloud F and a guide point set C of the femur from a model reconstructed from a medical image and projecting data acquired by the probe to a tracker coordinate system to acquire a point set P;
a calculation module for calculating an improved conversion relation according to the point set P, the point set C and the weight list omega of the corresponding points
Figure FDA0003441008090000034
The iteration module is used for realizing dynamic adjustment of the weight through the iteration process of the nearest point;
and the output module outputs the final femur coordinate system and the transformation matrix of the tracker coordinate system.
9. A joint registration device based on dynamic weight optimization is characterized in that,
the apparatus comprises: a memory, a processor, and computer program instructions stored in the memory and executable on the processor, wherein the processor is configured to execute the computer program instructions stored in the memory to implement the dynamic weight optimization-based joint registration method according to any one of claims 1 to 7.
10. A joint storage medium based on dynamic weight optimization is characterized in that,
the computer storage medium having stored thereon computer program instructions which, when executed by a processor, implement the dynamic weight optimization-based joint registration method of any of claims 1 to 7.
11. An application of the joint registration method based on dynamic weight optimization according to claims 1-7, wherein the joint registration method is applied to knee joints of femur, but not limited to knee joints of femur, and medical image registration of hard tissues of human body.
CN202111631065.2A 2021-12-28 2021-12-28 Joint registration method, device, equipment and storage medium based on dynamic weight optimization Pending CN114566272A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111631065.2A CN114566272A (en) 2021-12-28 2021-12-28 Joint registration method, device, equipment and storage medium based on dynamic weight optimization

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111631065.2A CN114566272A (en) 2021-12-28 2021-12-28 Joint registration method, device, equipment and storage medium based on dynamic weight optimization

Publications (1)

Publication Number Publication Date
CN114566272A true CN114566272A (en) 2022-05-31

Family

ID=81712333

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111631065.2A Pending CN114566272A (en) 2021-12-28 2021-12-28 Joint registration method, device, equipment and storage medium based on dynamic weight optimization

Country Status (1)

Country Link
CN (1) CN114566272A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117372661A (en) * 2023-12-07 2024-01-09 华科精准（北京）医疗科技有限公司 Surgical navigation system, surgical robot system and registration method

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117372661A (en) * 2023-12-07 2024-01-09 华科精准（北京）医疗科技有限公司 Surgical navigation system, surgical robot system and registration method
CN117372661B (en) * 2023-12-07 2024-03-12 华科精准（北京）医疗科技有限公司 Surgical navigation system, surgical robot system and registration method

Similar Documents

Publication Publication Date Title
CN114566272A (en) Joint registration method, device, equipment and storage medium based on dynamic weight optimization
WO2022214105A1 (en) Orthopedic surgery registration apparatus, terminal device, and storage medium
CN116019548A (en) Knee joint replacement operation planning method, device and equipment based on X-ray image and storage medium
CN112382359B (en) Patient registration method and device, electronic equipment and computer readable medium
CN116747016A (en) Intelligent surgical robot navigation and positioning system and method
CN116630427B (en) Method and device for automatically positioning hip bone and femur in CT image
CN116898574B (en) Preoperative planning method, system and equipment for artificial intelligent knee joint ligament reconstruction
CN113570648A (en) Multi-bone image registration method, electronic device and medical navigation system
US10646281B2 (en) Device and method for determining an axis of a rigid body
CN116168801A (en) Method, device, equipment and storage medium for intelligently recommending prosthesis parameters in hip replacement surgery
CN114938995B (en) Pelvic registration system and medical device for use in hip replacement surgery
CN116725679B (en) Registration point determination and registration method, apparatus, device, medium and program product
CN115252047A (en) Femur distal end and anterior and posterior condyles measuring, positioning and osteotomy device for knee joint replacement
CN116523973A (en) Bone registration method and device
CN111053613B (en) Motion tracking method and device, mechanical arm and computer readable storage medium
CN117409050A (en) Post-processing method for hip joint registration and related equipment
CN117808851A (en) Joint registration result verification method and related equipment based on CT auxiliary view
US20240242342A1 (en) Apparatus and method for automated analysis of lower extremity image
CN117159144B (en) Navigation and positioning system and method for knee joint ligament reconstruction operation robot
CN117635671A (en) Registration method and related equipment based on three-dimensional reconstruction and gradient-free optimization
CN114343844A (en) Method and device for recovering registration data in joint surgery navigation system
CN117593315A (en) Image processing method, apparatus, device, medium, and computer program product
CN113643226B (en) Labeling method, labeling device, labeling equipment and labeling medium
CN116214511B (en) Outer limb control method, device, electronic equipment and readable storage medium
CN117462312A (en) Method and device for calculating and evaluating eccentricity and length difference after hip joint replacement operation

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
CB02 Change of applicant information
CB02 Change of applicant information

Address after: 310023 rooms 305 and 306, building 8, 181 Wuchang Avenue, Wuchang Street, Yuhang District, Hangzhou City, Zhejiang Province

Applicant after: Hangzhou Jianjia Medical Technology Co.,Ltd.

Address before: 310023 rooms 305 and 306, building 8, 181 Wuchang Avenue, Wuchang Street, Yuhang District, Hangzhou City, Zhejiang Province

Applicant before: HANGZHOU JIANJIA ROBOT Co.,Ltd.