CN112786161A - In-vivo motion detection and analysis method, device, equipment and medium - Google Patents

In-vivo motion detection and analysis method, device, equipment and medium Download PDF

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CN112786161A
CN112786161A CN202110167250.4A CN202110167250A CN112786161A CN 112786161 A CN112786161 A CN 112786161A CN 202110167250 A CN202110167250 A CN 202110167250A CN 112786161 A CN112786161 A CN 112786161A
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data
motion
vivo
dimensional model
determining
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钱蕾
欧阳钧
王镱凝
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Southern Medical University
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Southern Medical University
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    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/187—Segmentation; Edge detection involving region growing; involving region merging; involving connected component labelling
    • 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
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/20—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
    • 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
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10088—Magnetic resonance imaging [MRI]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30008—Bone

Abstract

The invention discloses a method, a device, equipment and a medium for detecting and analyzing in-vivo motion, wherein the method comprises the following steps: performing three-dimensional reconstruction on the in-vivo object to obtain three-dimensional model data; acquiring motion trail data of the in-vivo object; determining a dynamic rotation matrix according to the three-dimensional model data and the motion trail data; moving the three-dimensional model of the on-body object to an initial point of a motion trail; running the three-dimensional model of the in-vivo object according to a motion track; calculating a target distance between the intra-articular surfaces and the surfaces of the in-vivo object; and determining the dynamic position information of the bones in the joints of the in-vivo object according to the target distance. The invention reduces the cost, is easy to implement and can be widely applied to the technical field of medical data processing.

Description

In-vivo motion detection and analysis method, device, equipment and medium
Technical Field
The invention relates to the technical field of medical data processing, in particular to a method, a device, equipment and a medium for detecting and analyzing in-vivo motion.
Background
With the improvement of the living standard of the whole people, the requirement for exercise health is more and more strong, and the exercise medicine is developed. Diagnosis of motor impairment is often a combination of patient performance, clinical physical examination, and imaging diagnosis. However, because the individual difference is large, the clinical physical examination is difficult to repeat the movement process, only the examination of a special body position is needed, and the efficiency of the imaging examination for identifying non-acute inflammation is not high, the condition that the diagnosis of a doctor is abnormal but the patient mainly complains about the discomfort often occurs. Therefore, the diagnosis of sports medicine needs to be more suitable for the evaluation of the sports state. Therefore, the need to introduce research methods of motion biomechanics into the clinic to aid diagnosis is not negligible. For example, gait analysis has introduced clinical diagnostics, which as an efficient diagnostic technique has helped analyze many lower limb patient problems. However, more outward-expressing diseases, such as cerebral palsy gait, equinovarus gait, etc., are observed in gait. The impact in the joints related to the anatomical shape and movement is not typically manifested in gait, the non-acute phase imaging is not specific, and the clinical examination is often in a resting state and is difficult to find. Although these diseases do not show obvious clinical signs, patients continue to complain of joint discomfort. The disease is large in population, but is often ignored clinically, and the reason is that no effective diagnosis means exists clinically. Therefore, it is important to establish techniques that can snoop on the changes in motion within the joint, which are known in the industry as in vivo motion analysis techniques. The human body is a dynamic balance life body, from the viewpoint of movement, the movement of a person in a movement state is a basic form of human body function evaluation, and a static state is only one time node in a series of time strings, so that the performance of the human body in the movement state needs to be evaluated in order to comprehensively know the movement of the person and judge diseases and diseases after healing. Therefore, it is desired to diagnose and evaluate sports injuries and diseases by in vivo sports analysis. The dynamic motion analysis is feasible for research with an inanimate object, but it is often difficult to support ethical support for the motion analysis of a living body due to invasive procedures. This also makes the analysis of the movement of the living body slow, and therefore, how to analyze the moving state of the living body is a big difficulty in the field of sports medicine research.
Researchers have taken a variety of approaches to in vivo motion analysis of living bodies, and current research approaches on dynamic biomechanics include three major categories: one is a finite element modeling and analysis method which has been developed to a great extent at present, and the method is to establish a static grid model, perform boundary constraint, contact setting and loading of load or motion on the static model by using a mathematical method, simulate the grid model in a mode of approaching real motion, and analyze relevant mechanical parameters by using the grid model. The other is that the image ultrasonic equipment is used for collecting data of different nodes in a motion state, for example, CT or MRI is used for collecting images of a certain state in the motion, and then the quasi-static data is used for reasoning and analyzing, so that the aim of pushing the nodes to the motion process is fulfilled; ultrasonic technology is also used for analyzing the motion state, but due to the limitation of the ultrasonic technology, information with deep anatomical positions is difficult to obtain. The third category is the collection of long-term dynamic data using intra-articular monitoring devices, but this technique is usually accompanied by surgical sensor placement, is costly, and is suitable for exploratory studies on small samples, not detection means.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method, an apparatus, a device, and a medium for detecting and analyzing an in-vivo movement, which are low in cost and easy to implement.
One aspect of the present invention provides a method for detecting and analyzing an in-vivo movement, including:
performing three-dimensional reconstruction on the in-vivo object to obtain three-dimensional model data;
acquiring motion trail data of the in-vivo object;
determining a dynamic rotation matrix according to the three-dimensional model data and the motion trail data;
moving the three-dimensional model of the on-body object to an initial point of a motion trail;
running the three-dimensional model of the in-vivo object according to a motion track;
calculating a target distance between the intra-articular surfaces and the surfaces of the in-vivo object;
and determining the dynamic position information of the bones in the joints of the in-vivo object according to the target distance.
Preferably, the three-dimensional reconstruction of the in-vivo object to obtain three-dimensional model data includes:
and performing three-dimensional reconstruction on the in-vivo object through the imaging DICOM data to determine three-dimensional model data.
Preferably, the acquiring motion trajectory data of the in-vivo object includes:
determining body surface landmark points of the in-vivo object;
acquiring space motion coordinate data of the body surface mark points of the in-vivo object in the motion process;
and determining the motion trail data of the in-vivo object according to the space motion coordinate data.
Preferably, the determining the motion trajectory data of the on-body object according to the spatial motion coordinate data includes:
acquiring first coordinate data of the on-body object before movement;
acquiring second coordinate data of the on-body object after movement;
determining motion trail data of the in-vivo object according to the first coordinate data and the second coordinate data;
wherein, the expression of the motion trail data is:
[x1，y1，z1]＝R×[x0，y0，z0]+T
wherein [ x0, y0, z0] represents first coordinate data; r represents a rotation matrix; [ x1, y1, z1] represents second coordinate data; t denotes a translation matrix.
Preferably, the calculating a target distance between the intra-articular face and the face of the in-vivo object comprises:
acquiring three coordinate points of each triangular patch of the three-dimensional model;
calculating the nearest distance of the triangular patch according to the three coordinate points;
and determining the minimum distance between different bone surfaces in the joint according to the nearest distance of the triangular patch.
Another aspect of the embodiments of the present invention provides an apparatus for detecting and analyzing an in-vivo movement, including:
the three-dimensional reconstruction module is used for performing three-dimensional reconstruction on the in-vivo object to obtain three-dimensional model data;
the track acquisition module is used for acquiring motion track data of the in-vivo object;
the matrix determining module is used for determining a dynamic rotation matrix according to the three-dimensional model data and the motion trail data;
the initialization module is used for moving the three-dimensional model of the on-body object to an initial point of a motion track;
the operation module is used for operating the three-dimensional model of the in-vivo object according to a motion track;
a calculation module for calculating a target distance between the intra-articular surface and the intra-articular surface of the on-body object;
and the analysis module is used for determining the dynamic position information of the bones in the joints of the in-vivo object according to the target distance.
Another aspect of the embodiments of the present invention provides an electronic device, including a processor and a memory;
the memory is used for storing programs;
the processor executes the program to implement the method as described above.
Another aspect of the embodiments of the present invention provides a computer-readable storage medium storing a program, the program being executed by a processor to implement the method as described above.
The embodiment of the invention also discloses a computer program product or a computer program, which comprises computer instructions, and the computer instructions are stored in a computer readable storage medium. The computer instructions may be read by a processor of a computer device from a computer-readable storage medium, and the computer instructions executed by the processor cause the computer device to perform the foregoing method.
The embodiment of the invention carries out three-dimensional reconstruction on the in-vivo object to obtain three-dimensional model data; acquiring motion trail data of the in-vivo object; determining a dynamic rotation matrix according to the three-dimensional model data and the motion trail data; moving the three-dimensional model of the on-body object to an initial point of a motion trail; running the three-dimensional model of the in-vivo object according to a motion track; calculating a target distance between the intra-articular surfaces and the surfaces of the in-vivo object; according to the target distance, the intra-articular bone dynamic position information of the in-vivo object is determined, and the embodiment of the invention has the advantages of reducing cost and being easy to implement.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flowchart illustrating the overall steps of an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In view of the problems in the prior art, an embodiment of the present invention provides an in-vivo movement detection and analysis method, as shown in fig. 1, the method includes the following steps:
performing three-dimensional reconstruction on the in-vivo object to obtain three-dimensional model data;
acquiring motion trail data of the in-vivo object;
determining a dynamic rotation matrix according to the three-dimensional model data and the motion trail data;
moving the three-dimensional model of the on-body object to an initial point of a motion trail;
running the three-dimensional model of the in-vivo object according to a motion track;
calculating a target distance between the intra-articular surfaces and the surfaces of the in-vivo object;
and determining the dynamic position information of the bones in the joints of the in-vivo object according to the target distance.
Preferably, the three-dimensional reconstruction of the in-vivo object to obtain three-dimensional model data includes:
and performing three-dimensional reconstruction on the in-vivo object through the imaging DICOM data to determine three-dimensional model data.
Preferably, the acquiring motion trajectory data of the in-vivo object includes:
determining body surface landmark points of the in-vivo object;
acquiring space motion coordinate data of the body surface mark points of the in-vivo object in the motion process;
and determining the motion trail data of the in-vivo object according to the space motion coordinate data.
Preferably, the determining the motion trajectory data of the on-body object according to the spatial motion coordinate data includes:
acquiring first coordinate data of the on-body object before movement;
acquiring second coordinate data of the on-body object after movement;
determining motion trail data of the in-vivo object according to the first coordinate data and the second coordinate data;
wherein, the expression of the motion trail data is:
[x1，y1，z1]＝R×[x0，y0，z0]+T
wherein [ x0, y0, z0] represents first coordinate data; r represents a rotation matrix; [ x1, y1, z1] represents second coordinate data; t denotes a translation matrix.
Preferably, the calculating a target distance between the intra-articular face and the face of the in-vivo object comprises:
acquiring three coordinate points of each triangular patch of the three-dimensional model;
calculating the nearest distance of the triangular patch according to the three coordinate points;
and determining the minimum distance between different bone surfaces in the joint according to the nearest distance of the triangular patch.
The following describes in detail the specific implementation process of the in vivo movement detection and analysis method of the present invention, taking the cod liver oil capsule as a marking point to mark the pelvis and the femur respectively as an example:
a, carrying out fish liver oil capsule sticking on the body surface apophysis of a patient, and marking pelvis and thighbone;
b, MRI scanning, wherein the scanning range is from the upper part of the iliac spine of the pelvis to the lower part of the lesser trochanter of the femur, and continuous images of TSE and FSE weighted by T1 are derived, and the derived data are Dicom format data;
c, importing the Dicom data into Mimics software, and performing three-dimensional reconstruction to obtain three-dimensional models of pelvis and femur;
the three-dimensional model data in the embodiment of the invention mainly refers to stl and other format three-dimensional model data obtained by three-dimensional reconstruction of imaging DICOM data.
D, a patient wears a mark ball, walks in a large step, records Marker dynamic space coordinates of the ball in the walking process by using a Qualisys motion capture system, and the data recording frequency is 200 frames;
the motion trajectory data of the embodiment of the invention refers to the space motion coordinate data (X, Y, Z) of the body surface mark points obtained by motion capture, and the data capture frequency is more than 60 Hz.
E, deriving spatial motion data (each point has X, Y and Z point information which changes along with time) through gait analysis software, namely three-dimensional coordinate dynamic data of each Marker;
wherein, assuming the initial points of the model are x0, y0, z0, after the movement, x1, y1, z1 are obtained,
then the following relations can be satisfied between x1, y1, z1 and x0, y0, z 0:
[x1，y1，z1]＝R*[x0，y0，z0]+T
where R represents a rotation matrix and T represents a translation matrix.
And F, importing the reconstructed bone three-dimensional model and dynamic motion data into Matlab software, executing the in-vivo motion detection and analysis method, obtaining the moving bone three-dimensional model after operation, and analyzing the ischial femoral gap in the motion process by using the dynamic three-dimensional model.
Specifically, the distance calculation between the inner surface and the surface of the joint according to the embodiment of the present invention is obtained based on the stl model calculation, each triangular patch of the stl model has three coordinate points, and the closest distance between the points of the coordinate points of all the triangular patches of the two bones forming the joint, that is, the closest distance of the triangular patch, is calculated to represent the minimum distance between the bone surface and the bone surface in the joint. The dynamic position relationship is exemplified by ischial femoral impact syndrome, and we focus on the dynamic distance between ischia and lesser trochanter of femur.
In summary, the embodiment of the present invention employs site labeling, utilizes cod-liver oil capsule as a labeling point to label the pelvis and the femur respectively, and the MRI scanner is philips nmr 1.5T, and has a scanning range from the upper edge of the pelvis to the lower edge of the lesser trochanter of the femur, and a scanning layer thickness of 2 mm. Successive image derivations of the TSE and FSE weighted by T1 were selected in the MRI scanner workstation, respectively, with the derivations data in Dicom format. Importing the Dicom data into the Mimics software, adjusting the gray level of the 'threshold segmentation' (threshold) to be a bone window, and performing image segmentation; selecting a lumbar vertebra bony structure by using region growing (region growing), and segmenting the bony structure and other structures; the missing structures are filled up or the redundant Masks are deleted layer by using an 'Edit Masks', and different sections of lumbar vertebrae are segmented by the tool. And finally obtaining a three-dimensional reconstruction model of the pelvis and the femur. And (3) carrying out large-step walking by the volunteer wearing the mark ball, recording the space coordinates of the mark points in the walking process by using a Qualissis motion capture system, wherein the data recording frequency is 200 frames, and deriving space motion data, namely three-dimensional coordinate dynamic data of each Marker point by gait analysis software. And importing the reconstructed bone three-dimensional model and dynamic motion data into Matlab software, executing the in-vivo motion detection and analysis method, obtaining a moving bone three-dimensional model, analyzing the change of the ischial femoral gap in the motion process by using the dynamic three-dimensional model, and initially establishing a dynamic hip joint model.
It will be appreciated that this method of the present embodiment is applicable to all joints and the present invention is illustrated only in the context of a hip joint.
Compared with the prior art, the method can be used for fitting the real motion trajectory of the three-dimensional bony model, establishing a motion-in-vivo motion detection method, obtaining the three-dimensional motion model through the iconography data and the gait data, and analyzing and calculating the kinematics data in the joint by using the model, so that the diagnosis of the joint related diseases is facilitated. The method is established on the basis of conventional imaging examination and gait examination, a new method for fusing the algorithm is established, the purpose of in-vivo motion analysis can be achieved, the method is simple, convenient and feasible, and a new detection scheme is provided for motion medical treatment.
The embodiment of the invention also provides an in-vivo motion detection and analysis device, which comprises:
the three-dimensional reconstruction module is used for performing three-dimensional reconstruction on the in-vivo object to obtain three-dimensional model data;
the track acquisition module is used for acquiring motion track data of the in-vivo object;
the matrix determining module is used for determining a dynamic rotation matrix according to the three-dimensional model data and the motion trail data;
the initialization module is used for moving the three-dimensional model of the on-body object to an initial point of a motion track;
the operation module is used for operating the three-dimensional model of the in-vivo object according to a motion track;
a calculation module for calculating a target distance between the intra-articular surface and the intra-articular surface of the on-body object;
and the analysis module is used for determining the dynamic position information of the bones in the joints of the in-vivo object according to the target distance.
The embodiment of the invention also provides the electronic equipment, which comprises a processor and a memory;
the memory is used for storing programs;
the processor executes the program to implement the method as described above.
An embodiment of the present invention further provides a computer-readable storage medium, where the storage medium stores a program, and the program is executed by a processor to implement the method described above.
The embodiment of the invention also discloses a computer program product or a computer program, which comprises computer instructions, and the computer instructions are stored in a computer readable storage medium. The computer instructions may be read by a processor of a computer device from a computer-readable storage medium, and the computer instructions executed by the processor cause the computer device to perform the foregoing method.
In alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flow charts of the present invention are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are performed independently.
Furthermore, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the described functions and/or features may be integrated in a single physical device and/or software module, or one or more functions and/or features may be implemented in a separate physical device or software module. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary for an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer, given the nature, function, and internal relationship of the modules. Accordingly, those skilled in the art can, using ordinary skill, practice the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the invention, which is defined by the appended claims and their full scope of equivalents.
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 execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (8)

1. An in-vivo motion detection and analysis method, comprising:
performing three-dimensional reconstruction on the in-vivo object to obtain three-dimensional model data;
acquiring motion trail data of the in-vivo object;
determining a dynamic rotation matrix according to the three-dimensional model data and the motion trail data;
moving the three-dimensional model of the on-body object to an initial point of a motion trail;
running the three-dimensional model of the in-vivo object according to a motion track;
calculating a target distance between the intra-articular surfaces and the surfaces of the in-vivo object;
and determining the dynamic position information of the bones in the joints of the in-vivo object according to the target distance.
2. The method for detecting and analyzing motion of an on-body as claimed in claim 1, wherein the three-dimensional reconstruction of the on-body object to obtain three-dimensional model data comprises:
and performing three-dimensional reconstruction on the in-vivo object through the imaging DICOM data to determine three-dimensional model data.
3. The method for detecting and analyzing the motion of the on-body according to claim 1, wherein the acquiring the motion trail data of the on-body object comprises:
determining body surface landmark points of the in-vivo object;
acquiring space motion coordinate data of the body surface mark points of the in-vivo object in the motion process;
and determining the motion trail data of the in-vivo object according to the space motion coordinate data.
4. The method for detecting and analyzing the motion of the on-body according to claim 3, wherein the determining the motion trajectory data of the on-body object according to the spatial motion coordinate data comprises:
acquiring first coordinate data of the on-body object before movement;
acquiring second coordinate data of the on-body object after movement;
determining motion trail data of the in-vivo object according to the first coordinate data and the second coordinate data;
wherein, the expression of the motion trail data is:
[x1，y1，z1]＝R×[x0，y0，z0]+T
wherein [ x0, y0, z0] represents first coordinate data; r represents a rotation matrix; [ x1, y1, z1] represents second coordinate data; t denotes a translation matrix.
5. The method of claim 1, wherein the calculating the target distance between the intra-articular surface and the intra-articular surface of the on-body object comprises:
acquiring three coordinate points of each triangular patch of the three-dimensional model;
calculating the nearest distance of the triangular patch according to the three coordinate points;
and determining the minimum distance between different bone surfaces in the joint according to the nearest distance of the triangular patch.
6. An apparatus for detecting and analyzing a motion of a body, comprising:
the three-dimensional reconstruction module is used for performing three-dimensional reconstruction on the in-vivo object to obtain three-dimensional model data;
the track acquisition module is used for acquiring motion track data of the in-vivo object;
the matrix determining module is used for determining a dynamic rotation matrix according to the three-dimensional model data and the motion trail data;
the initialization module is used for moving the three-dimensional model of the on-body object to an initial point of a motion track;
the operation module is used for operating the three-dimensional model of the in-vivo object according to a motion track;
a calculation module for calculating a target distance between the intra-articular surface and the intra-articular surface of the on-body object;
and the analysis module is used for determining the dynamic position information of the bones in the joints of the in-vivo object according to the target distance.
7. An electronic device comprising a processor and a memory;
the memory is used for storing programs;
the processor executing the program realizes the method according to any one of claims 1-5.
8. A computer-readable storage medium, characterized in that the storage medium stores a program, which is executed by a processor to implement the method according to any one of claims 1-5.
CN202110167250.4A 2021-02-07 2021-02-07 In-vivo motion detection and analysis method, device, equipment and medium Pending CN112786161A (en)

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