VR-based ct room operation virtual training method and system

Abstract

The present application relates to the technical field of data analysis, and particularly relates to a CT room operation virtual training method and system based on VR. The method comprises the following steps: acquiring a device model and a patient model in a current CT training operation; analyzing a relative position relationship between the device model and the patient model in an initial position to obtain an initial abnormal value; analyzing posture changes of multiple limb parts of the patient model before and after a limb adjustment operation to obtain multiple posture abnormality indexes; according to the initial abnormal value, analyzing changes in overlapping areas between the multiple limb parts of the patient model and the device model in a checking process of the current CT training operation to obtain multiple position abnormality indexes; and according to the multiple posture abnormality indexes and the multiple position abnormality indexes, obtaining an operation evaluation result of the current CT training operation. The present application can improve the evaluation accuracy of operation abnormal conditions of CT training operations.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data analysis, in particular to a CT room operation virtual training method and system based on VR. BACKGROUND

[0002] The CT room operation virtual training based on virtual reality (VR) has significant advantages. First, it can simulate real surgery scenarios through a virtual environment, providing a safe and risk-free training platform to avoid potential risks in actual operations. Second, through immersive 3D interactive experiences, it can accelerate the skill mastery of training personnel, shorten the learning cycle, and quickly improve operational proficiency. In addition, real-time feedback and personalized guidance based on operation results (such as AI-assisted evaluation), combined with repeatable training and remote collaboration functions, can significantly reduce training costs and optimize resource allocation.

[0003] In the prior art, the depth of a digital person (referring to a patient) penetrating the sidewall of a CT device is often analyzed to evaluate the degree of operation errors of training personnel performing CT training operations. This evaluation method considers only a single dimension and cannot effectively identify operation errors when training personnel perform CT training operations, resulting in poor training effectiveness of actual CT training operations.

[0004] That is, the prior art has poor accuracy in evaluating abnormal operation situations of CT training operations based on VR. SUMMARY

[0005] To solve the technical problem of poor accuracy in evaluating abnormal operation situations of CT training operations based on VR in the prior art, the present application aims to provide a CT room operation virtual training method and system based on VR, and the technical solutions adopted are as follows:

[0006] In a first aspect, one embodiment of the present application provides a CT room operation virtual training method based on VR, which comprises:

[0007] Obtaining a device model and a patient model in a current CT training operation, wherein the device model is used to represent a device for CT examination, and the patient model is used to represent a patient for CT examination;

[0008] Analyzing the relative position relationship between the device model and the patient model in the initial position to obtain an initial abnormal value;

[0009] Analyzing the posture changes of multiple limb parts of the patient model before and after a limb adjustment operation to obtain multiple posture abnormality indexes, wherein the limb adjustment operation is used to reduce the depth of the limb parts of the patient model penetrating into the device model;

[0010] According to the initial abnormal value, a plurality of position abnormal indexes of a plurality of limb parts of the patient model in a current CT training operation are analyzed to obtain a plurality of position abnormal indexes;

[0011] According to the plurality of posture abnormal indexes and the plurality of position abnormal indexes, an operation evaluation result of the current CT training operation is obtained.

[0012] In an embodiment, the relative position relationship between the device model and the patient model in the initial position is analyzed to obtain an initial abnormal value, comprising:

[0013] A deviation angle between a placement center line of the device model and a model center line of the patient model in the initial position is analyzed to obtain a deviation angle, wherein the placement center line is a center line of a examination bed of the device model, and the examination bed is used to place the patient model;

[0014] A shortest distance between a trunk part of the patient model in the initial position and an edge of the examination bed is calculated to obtain a distance minimum value;

[0015] According to the deviation angle and the distance minimum value, the initial abnormal value is obtained.

[0016] In an embodiment, the initial abnormal value is obtained according to the deviation angle and the distance minimum value, comprising:

[0017] A ratio of the deviation angle and the distance minimum value is calculated to obtain a position deviation value;

[0018] A ratio of a projection area and a reference area is calculated to obtain an occupation proportion, wherein the projection area is an area of a projection of the patient model when lying on the examination bed in a reference area, the reference area is a total area of the reference area, and the reference area is a plane area corresponding to the examination bed;

[0019] A product of the position deviation value and the occupation proportion is calculated to obtain the initial abnormal value.

[0020] In an embodiment, the posture abnormal indexes of the plurality of limb parts of the patient model before and after the limb adjustment operation are analyzed to obtain a plurality of posture abnormal indexes, comprising:

[0021] A position change of a target part before and after the limb adjustment operation is analyzed to obtain position change information of the target part, wherein the target part is any one of the plurality of limb parts of the patient model;

[0022] A change of a depth of the target part penetrating into the device model before and after the limb adjustment operation is analyzed to obtain depth change information of the target part;

[0023] According to the position change information and the depth change information of the target part, an attitude abnormality index of the target part is obtained.

[0024] In an embodiment, the position change information of the target part before and after the limb adjustment operation is obtained by analyzing the position change of the target part.

[0025] The position change information of the target part includes a plurality of point movement distances of the target part, and the target data points are data points supporting the limb adjustment operation in a plurality of data points constituting the target part.

[0026] The depth change information of the target part before and after the limb adjustment operation is obtained by analyzing the depth change of the target part penetrating into the device model.

[0027] The depth change information of the target part includes a plurality of depth difference values of the target part, and the depth change of the plurality of target data points penetrating into the device model before and after the limb adjustment operation is analyzed to obtain the plurality of depth difference values.

[0028] The attitude abnormality index of the target part is obtained according to the position change information and the depth change information of the target part.

[0029] In the plurality of depth difference values of the target part, the maximum depth difference value is determined as the limit depth difference value of the target part.

[0030] The sum value of the plurality of point movement distances of the target part is calculated to obtain the total movement distance value of the target part.

[0031] The limit depth difference value and the total movement distance value of the target part are calculated to obtain the attitude abnormality index of the target part.

[0032] In an embodiment, the plurality of position abnormality indexes are obtained by analyzing the overlap area change between the plurality of limb parts of the patient model and the device model during the examination process of the current CT training operation according to the initial abnormality value.

[0033] The area abnormality value of the target part is obtained by analyzing the area change of the overlap area between the target part and the device model during the examination process of the current CT training operation, and the target part is any one of the plurality of limb parts of the patient model.

[0034] analyzing a depth change of an overlapping area between the target part and the device model in an examination process of the current CT training operation to obtain a depth abnormal value of the target part;

[0035] obtaining a position abnormal index of the target part according to the initial abnormal value, the area abnormal value of the target part and the depth abnormal value of the target part.

[0036] In one embodiment, the analysis of the area change of the overlapping area between the target part and the device model in the examination process of the current CT training operation to obtain the area abnormal value of the target part comprises:

[0037] obtaining a plurality of overlapping areas corresponding to the target part, wherein the plurality of overlapping areas and a plurality of examination time points in an examination time period correspond to each other one by one, the overlapping area is an area of an overlapping area between the target part and the device model at a corresponding examination time point, and the examination time period is a time period corresponding to an examination process of the current CT training operation;

[0038] analyzing an area difference between adjacent two overlapping areas in the plurality of overlapping areas corresponding to the target part in the examination time period to obtain a plurality of area difference values corresponding to the target part, wherein the area difference value is a difference value between the corresponding overlapping area of the target part at the corresponding examination time point and the overlapping area of the target part at the previous examination time point;

[0039] counting a number of the area difference values greater than 0 in the plurality of area difference values corresponding to the target part to obtain the area abnormal value of the target part;

[0040] the analysis of the depth change of the overlapping area between the target part and the device model in the examination process of the current CT training operation to obtain the depth abnormal value of the target part comprises:

[0041] obtaining a plurality of overlapping depths corresponding to the target part, wherein the plurality of overlapping depths and a plurality of examination time points in an examination time period correspond to each other one by one, the overlapping depth is a depth of an overlapping area of the target part penetrating into the device model at a corresponding examination time point, and the examination time period is a time period corresponding to an examination process of the current CT training operation;

[0042] analyzing a depth difference between adjacent two overlapping depths in the plurality of overlapping depths corresponding to the target part in the examination time period to obtain a plurality of depth difference values corresponding to the target part, wherein the depth difference value is a difference value between the corresponding depth area of the target part at the corresponding examination time point and the depth area of the target part at the previous examination time point;

[0043] The maximum depth difference value is determined as the depth abnormal value of the target site corresponding to the plurality of depth difference values.

[0044] In one embodiment, the operation evaluation result of the current CT training operation is obtained according to the plurality of posture abnormal indexes and the plurality of position abnormal indexes, including:

[0045] A plurality of posture tolerances are obtained, wherein the plurality of posture tolerances and the plurality of limb sites of the patient model are in one-to-one correspondence, and the posture tolerance is used to represent the maximum adjustment degree of the corresponding limb site in the limb adjustment operation.

[0046] A plurality of limb abnormality coefficients are obtained according to the plurality of posture tolerances, wherein the sum of the limb abnormality coefficient and the corresponding posture tolerance is 1.

[0047] A plurality of limb abnormal values are obtained according to the plurality of limb abnormality coefficients, the plurality of posture abnormal indexes and the plurality of position abnormal indexes.

[0048] The sum of the plurality of limb abnormal values is calculated to obtain an operation abnormal value.

[0049] The operation evaluation result of the current CT training operation is generated according to the operation abnormal value.

[0050] In one embodiment, the plurality of posture tolerances are obtained, including:

[0051] The total number of a plurality of target data points included in a target site is counted to obtain the movable point number of the target site, wherein the target site is any one of the plurality of limb sites of the patient model, and the target data point is a data point supporting the limb adjustment operation among a plurality of data points constituting the target site.

[0052] The ratio of the movable point number of the target site to the volume of the target site is calculated to obtain the first value of the target site.

[0053] The product of the first value of the target site and the second value of the target site is calculated to obtain the posture tolerance of the target site, wherein the second value of the target site is the maximum point movement limit distance among a plurality of point movement limit distances corresponding to the plurality of target data points.

[0054] In a second aspect, another embodiment of the present application provides a CT room operation virtual training system based on VR, including:

[0055] An acquisition module is configured to acquire a device model and a patient model in a current CT training operation, wherein the device model is used to represent a device for CT examination, and the patient model is used to represent a patient for CT examination;

[0056] An initial analysis module is configured to analyze a relative position relationship between the device model and the patient model in an initial position to obtain an initial abnormal value;

[0057] A posture analysis module is configured to analyze posture changes of a plurality of limb parts of the patient model before and after a limb adjustment operation to obtain a plurality of posture abnormality indexes, wherein the limb adjustment operation is used to reduce the depth of the limb parts of the patient model penetrating into the device model;

[0058] A position analysis module is configured to analyze changes in overlapping areas between the plurality of limb parts of the patient model and the device model in an examination process of the current CT training operation according to the initial abnormal value to obtain a plurality of position abnormality indexes;

[0059] An operation evaluation module is configured to obtain an operation evaluation result of the current CT training operation according to the plurality of posture abnormality indexes and the plurality of position abnormality indexes.

[0060] In a third aspect, a further embodiment of the present application provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor, and when the computer program is executed by the processor, the steps of the method in the first aspect are implemented.

[0061] In a fourth aspect, a further embodiment of the present application provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method in the first aspect are implemented.

[0062] The present application has the following beneficial effects:

[0063] The application obtains the device model and the patient model in the current CT training operation, analyzes the relative position relationship between the device model and the patient model in the initial position, evaluates the position deviation degree of the patient model in the placement stage, analyzes the posture change of the multiple limb parts of the patient model before and after the limb adjustment operation, and effectively distinguishes the severity of the deep penetration of the patient model into the device model, so as to accurately evaluate the posture abnormality degree of the patient model in the limb adjustment stage; further, the application analyzes the overlap area change between the multiple limb parts of the patient model and the device model in the examination process of the current CT training operation, determines the conflict degree between the multiple limb parts of the patient model and the device model when the operation is abnormal, and then comprehensively and accurately evaluates the severity of the position conflict between the patient model and the device model in the examination, so as to perform the operation evaluation of the current CT training operation, and improve the evaluation accuracy of the operation abnormality of the CT training operation based on VR. BRIEF DESCRIPTION OF DRAWINGS

[0064] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, and the advantages thereof, below briefly introduces the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only show some embodiments of the present application, and for those skilled in the art, other drawings can be obtained from these drawings without any creative effort.

[0065] Figure 1 A schematic flow chart of a CT room operation virtual training method based on VR provided by an embodiment of the present application;

[0066] Figure 2 A structural schematic diagram of a CT room operation virtual training system based on VR provided by an embodiment of the present application;

[0067] Figure 3 A structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0068] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined application purposes, the specific embodiments, structures, features and effects of a CT room operation virtual training method and system based on VR according to the present application are described in detail as follows by combining with the drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0069] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0070] The application provides a CT room operation virtual training method and system based on VR.

[0071] The application provides a CT room operation virtual training method based on VR. Figure 1 The application provides a CT room operation virtual training method based on VR.

[0072] Step S1, obtaining a device model and a patient model in a current CT training operation.

[0073] The device model is used to represent a device for CT examination, and the patient model is used to represent a patient for CT examination.

[0074] The target user performs multiple CT training operations in a three-dimensional virtual scene (such as a virtual CT examination room) constructed based on VR technology, and the current CT training operation is a CT training operation corresponding to the current time in the multiple CT training operations, and the target user is any user (such as a medical student) performing CT training operations.

[0075] The device model is a virtual model of a CT examination instrument fixedly placed in the three-dimensional virtual scene, and the device model includes an examination bed for placing the patient model and a ring-shaped examination member (for emitting X-rays and receiving X-rays passing through the patient's body and converting them into electrical signals) for performing CT examination. The examination bed can move along the axis direction of the ring-shaped examination member. When the patient model is placed on the examination bed by the target user and the placement position is adjusted (at this time, the patient model is in an initial position), the examination bed moves along the axis direction of the ring-shaped examination member and enters the internal area surrounded by the ring-shaped examination member until it reaches a predetermined position. Then the ring-shaped examination member starts, and after a certain period of time (such as 2 minutes) of X-ray emission and reception processing, the ring-shaped examination member is closed, and the examination bed moves back to the initial position along the axis direction of the ring-shaped examination member. Thus, one CT training operation is completed. In each CT training operation, the target user needs to place the patient model on the examination bed and adjust the placement state of the patient model (such as adjusting the patient model from lying on one side to lying flat) so that the patient model can smoothly complete the CT examination process and avoid interference with the device model (such as avoiding the patient model and the device model partially overlapping).

[0076] It should be understood that when the examination bed is in the predetermined position, the part to be examined (such as the chest cavity) of the patient model on the examination bed is in the internal area surrounded by the ring-shaped examination member.

[0077] Step S2, analyze the relative position relationship between the device model and the patient model in the initial position to obtain an initial abnormal value.

[0078] In this step, by analyzing the relative position relationship between the device model and the patient model in the initial position, the initial state of the patient model after being placed on the examination bed by the target user is determined, and then the rationality (or abnormality) of the interaction between the patient model and the device model based on the initial state (i.e., the process of the CT examination) is analyzed, thereby completing the static position analysis of the patient model and providing a reference for the subsequent dynamic position analysis of the patient model (i.e., the analysis of the change of the overlapping area between the patient model and the device model for the multiple limb parts of the patient model in the current CT training operation during the examination process).

[0079] Further, the analysis of the relative position relationship between the device model and the patient model in the initial position to obtain an initial abnormal value comprises:

[0080] analyzing the deviation between the placement center line of the device model and the model center line of the patient model in the initial position to obtain a deviation angle, wherein the placement center line is the center line of the examination bed of the device model, and the examination bed is used to place the patient model;

[0081] calculating the shortest distance between the trunk part of the patient model in the initial position and the edge of the examination bed to obtain a distance minimum value;

[0082] obtaining the initial abnormal value according to the deviation angle and the distance minimum value.

[0083] In the present application, the model center line of the patient model in the initial position can be understood as the center line of the spine of the patient model in the initial position.

[0084] Based on the above settings, the relative position relationship between the device model and the patient model in the initial position is comprehensively considered from the aspects of angle and distance to obtain a more accurate initial abnormal value.

[0085] Wherein, the greater the deviation angle, the more serious the deviation of the model center line of the patient model in the initial position from the placement center line of the device model, the more likely the placement position of the patient model is abnormal, and the higher the initial abnormal value; vice versa.

[0086] Similarly, the smaller the distance minimum value, the closer the trunk part of the patient model in the initial position to the edge of the examination bed, the greater the probability that the limb part of the patient model in the initial position exceeds the edge of the examination bed and interferes with the annular examination part, the more likely the placement position of the patient model is abnormal, and the higher the initial abnormal value; vice versa.

[0087] Further, the initial abnormality value is obtained according to the deviation angle and the distance minimum value, including:

[0088] calculating a ratio of the deviation angle and the distance minimum value to obtain a position deviation value;

[0089] calculating a ratio of a projection area and a reference area to obtain an occupancy ratio, wherein the projection area is an area of a projection of the patient model when lying on the examination bed in a reference region, the reference area is a total area of the reference region, and the reference region is a planar region corresponding to the examination bed;

[0090] calculating a product of the position deviation value and the occupancy ratio to obtain the initial abnormality value.

[0091] In the above arrangement, the occupancy ratio is calculated to quantitatively represent the occupancy of the patient model after being placed on the examination bed, and the relative position relationship between the device model and the patient model in the initial position is accurately evaluated in combination with the volume of the patient model and the angle deviation and the minimum distance obtained by the foregoing analysis, so that the initial abnormality value calculated is more accurate and reliable.

[0092] The higher the occupancy ratio is, the larger the volume of the patient model is, the larger the occupancy of the patient model when lying on the examination bed is, the smaller the adjustable range when moving with the examination bed is, the easier the placement position is to be abnormal, and the higher the initial abnormality value is.

[0093] Exemplarily, if the current CT training operation is set as the jth CT training operation in a plurality of CT training operations performed by the target user, the initial abnormality value of the jth CT training operation can be represented as:

[0094]

[0095] wherein, represents the occupancy ratio corresponding to the jth CT training operation, represents the deviation angle corresponding to the jth CT training operation, represents the distance minimum value corresponding to the jth CT training operation.

[0096] In step S3, the posture change of the plurality of limb parts of the patient model before and after the limb adjustment operation is analyzed to obtain a plurality of posture abnormality indexes.

[0097] The limb adjustment operation is used to reduce the depth of the limb part of the patient model penetrating into the device model.

[0098] ​The limb part of the patient model has a certain position adjustment capability to simulate the case that the limb part (such as a limb) of the human body can be flexibly adjusted within a certain range. After the target user places the patient model on the examination bed, the target user can perform a limb adjustment operation to fold the limb part of the patient model near the torso part of the patient model during the movement of the patient model to the predetermined position with the examination bed, so as to reduce the interference degree (i.e., reduce the depth and area of the limb part of the patient model penetrating into the device model) when the limb part of the patient model interferes with (i.e., collides with) the annular examination part of the device model.

[0099] In the above arrangement, the severity of the patient model penetrating into the device model is effectively distinguished by analyzing the posture change of the plurality of limb parts of the patient model before and after the limb adjustment operation, the posture abnormality degree of the patient model in the limb adjustment stage is accurately evaluated, and the operation evaluation result finally output is more accurate.

[0100] Further, the analysis of the posture change of the plurality of limb parts of the patient model before and after the limb adjustment operation obtains a plurality of posture abnormality indexes, including:

[0101] The position change of the target part before and after the limb adjustment operation is analyzed to obtain position change information of the target part, wherein the target part is any one of the plurality of limb parts of the patient model;

[0102] The change of the depth of the target part penetrating into the device model before and after the limb adjustment operation is analyzed to obtain depth change information of the target part;

[0103] The posture abnormality index of the target part is obtained according to the position change information and the depth change information of the target part.

[0104] In the above arrangement, the position change and the penetration depth change of each limb part before and after the limb adjustment operation are analyzed to accurately quantify the posture abnormality degree of the patient model in the limb adjustment stage.

[0105] Further, the analysis of the position change of the target part before and after the limb adjustment operation obtains the position change information of the target part, including:

[0106] The position change of a plurality of target data points included in the target part before and after the limb adjustment operation is analyzed to obtain a plurality of point movement distances of the target part, wherein the position change information of the target part includes the plurality of point movement distances of the target part, and the target data point is a data point supporting the limb adjustment operation among a plurality of data points constituting the target part.

[0107] In the above process, by analyzing the positional changes of multiple target data points included in the target part before and after the limb adjustment operation, the positional changes of the target part before and after the limb adjustment operation are comprehensively evaluated from the fine-grained dimension of data points, making the obtained positional change information of the target part more accurate.

[0108] The multiple points of the target part correspond one-to-one with the multiple target data points included in the target part. The point movement distance is the distance between the point position of the corresponding target data point before the limb adjustment operation and the point position after the limb adjustment operation. The point position of the target data point can be understood as the position of the projection point of the target data point on the plane corresponding to the reference area.

[0109] The data points among the multiple data points of the target part that support limb adjustment operations can be understood as: "Adaptive Movable Points" among the multiple data points of the target part, whose positions can be automatically adjusted according to real-time input (such as user operations, physics engine calculations, and environmental data).

[0110] The analysis of the change in the depth of the target part penetrating the device model before and after the limb adjustment operation, to obtain the depth change information of the target part, includes:

[0111] The changes in the depth of the multiple target data points penetrating the device model before and after the limb adjustment operation are analyzed to obtain multiple depth differences of the target part. The depth change information of the target part includes multiple depth differences of the target part.

[0112] In the above process, by analyzing the depth changes of multiple target data points included in the target area before and after the limb adjustment operation, the depth changes of the target area before and after the limb adjustment operation are comprehensively evaluated from the fine-grained dimension of data points, making the obtained depth change information of the target area more accurate.

[0113] In this context, multiple depth differences of the target area correspond one-to-one with multiple target data points included in the target area, and the depth difference represents the depth at which the corresponding target data point penetrates into the device model after the limb adjustment operation. and the depth of penetration into the device model The difference, the depth of the target data point penetrating into the device model can be understood as the shortest distance between the projection point of the target data point in the plane corresponding to the reference area and the device boundary area, and the device boundary area can be understood as the projection area of ​​the inner sidewall of the annular inspection piece in the plane corresponding to the reference area.

[0114] The step of obtaining the attitude anomaly index of the target part based on the position change information and depth change information of the target part includes:

[0115] Among the plurality of depth difference values of the target site, the maximum depth difference value is determined as a limit depth difference value of the target site;

[0116] The sum value of the plurality of point movement distances of the target site is calculated to obtain a total movement distance value of the target site;

[0117] The limit depth difference value and the total movement distance value of the target site are calculated to obtain a posture abnormality index of the target site.

[0118] Exemplarily, if the current CT training operation is set as the jth CT training operation of a plurality of CT training operations performed by the target user, the posture abnormality index of the kth limb site of the plurality of limb sites of the patient model may be expressed as:

[0119]

[0120] wherein, is the limit depth difference value of the kth limb site, is the point movement distance of the pth target data point in the kth limb site, is the total number of the plurality of target data points included in the kth limb site, may be understood as the total movement distance value of the kth limb site.

[0121] In an application, after determining the posture abnormality index of each limb site of the plurality of limb sites of the patient model based on the above process, the maximum and minimum normalization method can be used to normalize the plurality of posture abnormality indexes (the normalized value range is (0, 1)) to eliminate the difference in values, facilitate subsequent use, and further improve the accuracy of the final operation evaluation result.

[0122] Step S4, according to the initial abnormal value, analyze the change of the overlapping area between the plurality of limb sites of the patient model and the device model in the examination process of the current CT training operation, to obtain a plurality of position abnormality indexes.

[0123] It is emphasized that the analysis of the change of the overlapping area between the plurality of limb sites of the patient model and the device model in the examination process of the current CT training operation is carried out after the limb adjustment operation of the plurality of limb sites of the patient model is completed.

[0124] In the examination process, the time period can be understood as the time period from when the body adjustment operation of the plurality of limb parts of the patient model is completed to when the examination bed moves to the predetermined position (or from when the body adjustment operation of the plurality of limb parts of the patient model is completed to a predetermined time period, which can be 2 minutes).

[0125] Only static position analysis of the patient model cannot effectively evaluate the severity of the collision between the patient model and the device model under the current placement operation, and based on the above setting, by analyzing the dynamic position of the patient model in the examination process, the severity of the collision between the patient model and the device model under the current placement operation can be effectively evaluated, so as to output more accurate operation evaluation results.

[0126] Further, the analysis of the change of the overlapping area between the plurality of limb parts of the patient model and the device model in the examination process of the current CT training operation according to the initial abnormal value obtains a plurality of position abnormal indexes, including:

[0127] The area change of the overlapping area between the target part and the device model in the examination process of the current CT training operation is analyzed to obtain an area abnormal value of the target part, wherein the target part is any one of the plurality of limb parts of the patient model;

[0128] The depth change of the overlapping area between the target part and the device model in the examination process of the current CT training operation is analyzed to obtain a depth abnormal value of the target part;

[0129] According to the initial abnormal value, the area abnormal value of the target part and the depth abnormal value of the target part, a position abnormal index of the target part is obtained.

[0130] In the examination process, the factors affecting the degree of collision between the patient model and the device model (i.e., the degree of model overlap) mainly include: the initial position of the patient model before entering the enclosed area of the annular examination part of the device model (represented by the initial abnormal value), the area of the overlapping area between the patient model and the annular examination part (represented by the area abnormal value), and the depth of the overlapping area between the patient model and the annular examination part in the annular examination part (represented by the depth abnormal value).

[0131] Wherein, the more intense the area change is, the larger the corresponding area abnormal value is, the more intense the degree of collision between the patient model and the device model is, and the larger the finally calculated position abnormal index is, and vice versa.

[0132] Similarly, the more severe the depth change, the greater the corresponding depth abnormal value, the more severe the collision between the patient model and the device model, and the greater the finally calculated position abnormality index, and vice versa.

[0133] The greater the initial abnormal value, the more unreasonable the initial position of the patient model, the more severe the collision between the patient model and the device model, and the greater the finally calculated position abnormality index, and vice versa.

[0134] Based on this, in the above process, the position abnormality index of the corresponding limb part is determined by comprehensively considering the three main factors affecting the collision degree, which can more accurately evaluate the collision degree between the corresponding limb part and the device model.

[0135] It should be noted that the greater the position abnormality index of a certain limb part, the more unreasonable the placement of the patient model at that limb part in the current CT training operation, that is, in addition to outputting the overall operation evaluation result for the current CT training operation, the placement of each limb part can also be reasonably evaluated and improvement suggestions can be given based on the position abnormality index of each limb part, so as to help the target user to identify and correct the operation problem more quickly.

[0136] For example, if the current CT training operation is set as the jth CT training operation in the multiple CT training operations performed by the target user, the position abnormality index of the kth limb part of the multiple limb parts of the patient model can be represented as:

[0137]

[0138] wherein, represents the initial abnormal value of the jth CT training operation, represents the area abnormal value of the kth limb part, represents the depth abnormal value of the kth limb part.

[0139] Specifically, the area change of the analysis target part in the overlapping area with the device model in the examination process of the current CT training operation is obtained to obtain the area abnormal value of the target part, which includes:

[0140] Obtain a plurality of overlapping areas corresponding to the target part, wherein the plurality of overlapping areas and a plurality of examination time points in an examination time period one-to-one correspond, the overlapping area is the area of the overlapping area between the target part and the device model at the corresponding examination time point, and the examination time period is the time period corresponding to the examination process of the current CT training operation;

[0141] ​In the plurality of overlapping areas corresponding to the target part in the examination time period, analyze the area difference between adjacent two overlapping areas to obtain a plurality of area difference values corresponding to the target part, wherein the area difference value is the difference between the overlapping area corresponding to the target part at the corresponding examination time and the overlapping area corresponding to the target part at the previous examination time;

[0142] In the plurality of area difference values corresponding to the target part, count the number of area difference values greater than 0 to obtain the area abnormal value of the target part;

[0143] The analysis of the depth change of the overlapping area between the target part and the device model in the examination process of the current CT training operation obtains the depth abnormal value of the target part, comprising:

[0144] Obtain a plurality of overlapping depths corresponding to the target part, wherein the plurality of overlapping depths and the plurality of examination times in the examination time period correspond one-to-one, the overlapping depth is the depth of the overlapping area between the target part and the device model at the corresponding examination time, and the examination time period is the time period corresponding to the examination process of the current CT training operation;

[0145] In the plurality of overlapping depths corresponding to the target part in the examination time period, analyze the depth difference between adjacent two overlapping depths to obtain a plurality of depth difference values corresponding to the target part, wherein the depth difference value is the difference between the depth area corresponding to the target part at the corresponding examination time and the depth area corresponding to the target part at the previous examination time;

[0146] In the plurality of depth difference values corresponding to the target part, determine the maximum depth difference value as the depth abnormal value of the target part.

[0147] The plurality of examination times in the examination time period are equally spaced in the time domain. For example, if the total length of the examination time period is set to 2 minutes, the time interval between adjacent two examination times is 1 second, and the total number of the plurality of examination times in the examination time period is 120.

[0148] In the above process, by counting the number of area difference values greater than 0 in the plurality of area difference values corresponding to the target part, that is, counting the time of overlapping area growth, the threshold setting error that may be introduced by the threshold comparison method can be avoided, and the area abnormal value of the target part can be more accurately and conveniently quantified.

[0149] Correspondingly, by analyzing the depth difference between two adjacent overlapping depths in the corresponding multiple overlapping depths of the target site within the examination period, and determining the maximum depth difference value as the depth abnormal value of the target site, the numerical interference introduced by the extreme value can be avoided, and the depth mutation degree in the overlapping process of the patient model and the device model can be accurately represented by the maximum difference value of the depth, that is, a more accurate depth abnormal value is obtained.

[0150] Step S5, obtaining an operation evaluation result of the current CT training operation according to the plurality of posture abnormal indices and the plurality of position abnormal indices.

[0151] In the above process, the posture abnormalities and position abnormalities exhibited by the plurality of limb parts of the patient model during the examination process are comprehensively analyzed, which can comprehensively evaluate the current CT training operation from multiple angles, so that the finally output operation evaluation result is more accurate and effective.

[0152] The operation evaluation result of the current CT training operation obtained according to the plurality of posture abnormal indices and the plurality of position abnormal indices comprises:

[0153] Obtaining a plurality of posture tolerances, wherein the plurality of posture tolerances and the plurality of limb parts of the patient model are one-to-one corresponding, and the posture tolerance is used to represent the maximum adjustment degree of the corresponding limb part during the limb adjustment operation;

[0154] According to the plurality of posture tolerances, a plurality of limb abnormality coefficients are obtained, wherein the sum of the limb abnormality coefficient and the corresponding posture tolerance is 1;

[0155] According to the plurality of limb abnormality coefficients, the plurality of posture abnormal indices and the plurality of position abnormal indices, a plurality of limb abnormal values are obtained;

[0156] The sum of the plurality of limb abnormal values is calculated to obtain an operation abnormal value;

[0157] According to the operation abnormal value, an operation evaluation result of the current CT training operation is generated.

[0158] The operation abnormal value is used to represent the operation abnormality degree of the current CT training operation. The greater the value, the higher the operation abnormality degree of the current CT training operation, and vice versa.

[0159] For example, if the current CT training operation is set as the jth CT training operation of the target user in a plurality of CT training operations, the limb abnormal value of the kth limb part of the plurality of limb parts of the patient model can be represented as: which can be represented as:

[0160]

[0161] wherein, represents a limb abnormality coefficient of the kth limb part, represents a position abnormality index of the kth limb part after normalization processing (processing is completed by a maximum minimum normalization method, and the value range after normalization is (0, 1)), a posture abnormality index of the kth limb part after normalization processing.

[0162] The maximum adjustment degree of different limb parts during the limb adjustment operation is different, so in the above process, the posture tolerance is introduced to distinguish the maximum adjustment degree of different limb parts during the limb adjustment operation, so that the limb abnormality value calculated by each limb part is more accurate.

[0163] Specifically, the plurality of posture tolerances are obtained, comprising:

[0164] The total number of a plurality of target data points included in a target part is counted to obtain a movable point number of the target part, wherein the target part is any one of a plurality of limb parts of the patient model, and the target data point is a data point supporting the limb adjustment operation among a plurality of data points constituting the target part.

[0165] The ratio of the movable point number of the target part to the volume of the target part is calculated to obtain a first value of the target part.

[0166] The product of the first value of the target part and a second value of the target part is calculated to obtain the posture tolerance of the target part, wherein the second value of the target part is the maximum point movement limit distance among a plurality of point movement limit distances corresponding to the plurality of target data points.

[0167] The larger the volume of the limb part is, the more likely the limb part is to collide with the device model, and the more difficult the position adjustment of the limb part is, and vice versa.

[0168] The more the movable point number of the limb part is, the larger the maximum point movement limit distance among the plurality of target data points is, indicating that the flexibility of the limb part is higher, and the position adjustment of the limb part is more difficult, and vice versa.

[0169] Exemplarily, if the current CT training operation is set as the jth CT training operation in a plurality of CT training operations performed by the target user, the posture tolerance of the kth limb part of the plurality of limb parts of the patient model can be represented as:

[0170]

[0171] wherein,​ a second value representing a k-th limb part, a volume of the k-th limb part, a number of movable points of the k-th limb part.

[0172] In some embodiments, the process of generating an operation evaluation result of the current CT training operation according to the operation abnormal value can be:

[0173] normalizing the operation abnormal value to obtain an operation abnormal normalized value;

[0174] summarizing the operation order corresponding to the current CT training operation, the operation user identity (i.e. the identity of the target user, such as the name, age, etc. of the target user), the operation abnormal normalized value, the plurality of posture abnormal indices and the plurality of position abnormal indices to generate the operation evaluation result.

[0175] In the application, the target user can record each CT training operation and the corresponding operation abnormal normalized value in multiple CT training operations, and display them in table form for the target user and other users (such as the target user is a medical student in school, and the other users can be the teaching teacher) to observe the training situation of the target user.

[0176] In addition, in each CT training operation, the part where the device model and the patient model overlap can also be highlighted to help the target user quickly find the part of the patient injury caused by the current CT training operation process, so that the target user can correct the unreasonable action in time in the subsequent operation.

[0177] In summary, the present application obtains the device model and the patient model in the current CT training operation, and analyzes the relative position relationship between the device model and the patient model in the initial position to evaluate the position deviation degree of the patient model in the placement stage, and then analyzes the posture change of the plurality of limb parts of the patient model before and after the limb adjustment operation to adapt to the situation that the limbs of the patient can be adjusted in a certain range, effectively distinguishes the severity of the deep penetration of the patient model into the device model, and accurately evaluates the posture abnormality degree of the patient model in the limb adjustment stage. Further analyze the change of the overlapping area between the plurality of limb parts of the patient model and the device model in the examination process of the current CT training operation to determine the conflict degree between each limb part of the patient model and the device model when the operation is abnormal, and then comprehensively and accurately evaluate the severity of the position conflict that may occur between the patient model and the device model during the examination, and accordingly evaluate the operation of the current CT training operation. The operation abnormality of the CT training operation based on VR can improve the evaluation accuracy of the operation abnormality of the CT training operation based on VR.

[0178] The application provides a CT room operation virtual training system based on VR, please refer to Figure 2 , which shows a structural schematic diagram of a CT room operation virtual training system 200 based on VR provided by an embodiment of the application, and the system comprises:

[0179] An acquisition module 201 is configured to acquire a device model and a patient model in a current CT training operation, wherein the device model is used to represent a device for CT examination, and the patient model is used to represent a patient for CT examination;

[0180] An initial analysis module 202 is configured to analyze a relative position relationship between the device model and the patient model in an initial position to obtain an initial abnormal value;

[0181] A posture analysis module 203 is configured to analyze posture changes of a plurality of limb parts of the patient model before and after a limb adjustment operation to obtain a plurality of posture abnormality indexes, wherein the limb adjustment operation is used to reduce the depth of the limb parts of the patient model penetrating into the device model;

[0182] A position analysis module 204 is configured to analyze the changes of overlapping areas between the plurality of limb parts of the patient model and the device model in a CT examination process of the current CT training operation according to the initial abnormal value to obtain a plurality of position abnormality indexes;

[0183] An operation evaluation module 205 is configured to obtain an operation evaluation result of the current CT training operation according to the plurality of posture abnormality indexes and the plurality of position abnormality indexes.

[0184] It should be noted that the system provided in the above embodiment is only exemplified by the division of the above functional modules, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, the CT room operation virtual training system based on VR and the CT room operation virtual training method based on VR provided in the above embodiment belong to the same concept, and the specific implementation process is described in detail in the method embodiment, which will not be repeated here.

[0185] The embodiment of the application further provides an electronic device. Please refer to Figure 3 , the electronic device can include a processor 301, a memory 302, and a program 3021 stored in the memory 302 and executable on the processor 301.

[0186] When the program 3021 is executed by the processor 301, it can implement Figure 1 any step in the corresponding method embodiment and achieve the same beneficial effects, which will not be repeated here.

[0187] Those skilled in the art can understand that all or part of the steps of the method of the above-mentioned embodiments can be completed by relevant hardware through program instructions. The program can be stored in a readable medium.

[0188] The embodiment of the present application further provides a readable storage medium, wherein the readable storage medium stores a computer program, and the computer program can realize the above-mentioned method when the computer program is executed by a processor. Figure 1 Any step in the corresponding method embodiment can achieve the same technical effects, and the same technical effects can be achieved. To avoid repetition, it will not be described here.

[0189] The computer readable storage medium of the embodiment of the present application can adopt any combination of one or more computer readable media. The computer readable medium can be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium may, for example, be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination thereof. More specific examples (non-exhaustive list) of the computer readable storage medium include: 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or apparatus.

[0190] The computer readable signal medium can include a data signal propagating in a baseband or as part of a carrier wave, in which the computer readable program code is carried. Such a propagated data signal can take a variety of forms, including but not limited to electro-magnetic, optical, or any suitable combination thereof. The computer readable signal medium can also be any computer readable medium that can be used to carry or store computer readable program code except the computer readable storage media that can be distinguished from the computer readable program code, which can be used by or in conjunction with an instruction execution system, device or apparatus.

[0191] The program code contained in the storage medium can be transmitted by any suitable medium, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination thereof.

[0192] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0193] The embodiment of the present application further provides a computer program product, which, when running on a computer, enables the computer to execute the above related steps to realize the VR-based CT room operation virtual training method provided by the above embodiment.

[0194] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0195] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment mainly describes the difference from other embodiments.

Claims

1. A VR-based virtual training method for CT room operation, characterized in that, The method includes: Obtain the device model and patient model in the current CT training operation, wherein the device model is used to represent the device performing the CT examination, and the patient model is used to represent the patient performing the CT examination; Analyze the relative positional relationship between the device model and the patient model in their initial positions to obtain initial outliers; The posture changes of multiple limb parts of the patient model before and after the limb adjustment operation are analyzed to obtain multiple posture abnormality indices. The limb adjustment operation is used to reduce the depth of the patient model's limb parts penetrating into the device model. Based on the initial outlier, the changes in the overlapping area between multiple limb parts of the patient model and the device model during the current CT training operation are analyzed to obtain multiple positional anomaly indices. Based on the multiple posture anomaly indices and the multiple position anomaly indices, the operational evaluation result of the current CT training operation is obtained; The relative positional relationship between the analysis device model and the patient model at the initial position is used to obtain initial outliers, including: The deviation between the placement centerline of the device model and the model centerline of the patient model in its initial position is analyzed to obtain the deviation angle, wherein the placement centerline is the centerline of the examination bed of the device model, and the examination bed is used to place the patient model; Calculate the shortest distance between the torso of the patient model in its initial position and the edge of the examination bed to obtain the minimum distance value; The initial outlier value is obtained based on the deviation angle and the minimum distance. The analysis of the patient model involved postural changes in multiple limb parts before and after limb adjustment operations, yielding several postural abnormality indices, including: Analyze the positional changes of the target area before and after the limb adjustment operation to obtain the positional change information of the target area, wherein the target area is any one of the multiple limb areas of the patient model; The depth change of the target part into the device model before and after the limb adjustment operation is analyzed to obtain the depth change information of the target part; Based on the position change information and depth change information of the target part, the attitude anomaly index of the target part is obtained; Based on the initial outlier, the changes in the overlap area between multiple limb parts of the patient model and the device model during the current CT training operation are analyzed to obtain multiple location anomaly indices, including: The area change of the overlapping area between the target site and the equipment model is analyzed during the examination of the current CT training operation to obtain the area anomaly value of the target site, wherein the target site is any one of the multiple limb sites of the patient model. The depth changes of the overlapping area between the target body and the equipment model are analyzed during the examination of the current CT training operation to obtain the depth anomaly value of the target body. The location anomaly index of the target location is obtained based on the initial anomaly value, the area anomaly value of the target location, and the depth anomaly value of the target location.

2. The VR-based virtual training method for CT room operation according to claim 1, characterized in that, The process of obtaining the initial outlier value based on the deviation angle and the minimum distance includes: Calculate the ratio of the deviation angle to the minimum distance to obtain the position deviation value; Calculate the ratio of the projected area to the reference area to obtain the occupancy ratio, wherein the projected area is the area of ​​the patient model projected onto the reference area when the patient model is lying flat on the examination bed, the reference area is the total area of ​​the reference area, and the reference area is the planar area corresponding to the examination bed; The product of the position deviation value and the occupancy ratio is calculated to obtain the initial anomaly value.

3. The VR-based virtual training method for CT room operation according to claim 1, characterized in that, The analysis of the target body part's positional changes before and after the limb adjustment operation yields positional change information for the target body part, including: The positional changes of multiple target data points included in the target part before and after the limb adjustment operation are analyzed to obtain the movement distance of multiple points of the target part. The positional change information of the target part includes the movement distance of multiple points of the target part, and the target data points are the data points that support the limb adjustment operation among the multiple data points constituting the target part. The analysis of the change in the depth of the target part penetrating the device model before and after the limb adjustment operation, to obtain the depth change information of the target part, includes: The changes in the depth of the multiple target data points penetrating the device model before and after the limb adjustment operation are analyzed to obtain multiple depth differences of the target part. The depth change information of the target part includes multiple depth differences of the target part. The step of obtaining the attitude anomaly index of the target part based on the position change information and depth change information of the target part includes: Among the multiple depth differences of the target location, the largest depth difference is determined as the limit depth difference of the target location; Calculate the sum of the movement distances of multiple points of the target part to obtain the total movement distance of the target part; The extreme depth difference and total movement distance of the target location are calculated to obtain the attitude anomaly index of the target location.

4. The VR-based virtual training method for CT room operation according to claim 1, characterized in that, The analysis examines the area change of the overlapping region between the target body and the equipment model during the current CT training operation, obtaining the area anomalies of the target body, including: Multiple overlapping areas corresponding to the target part are obtained, wherein the multiple overlapping areas correspond one-to-one with multiple inspection times within the inspection time period, the overlapping area is the area of ​​the overlapping region between the target part and the device model at the corresponding inspection time, and the inspection time period is the time period corresponding to the inspection process of the current CT training operation. Among the multiple overlapping areas corresponding to the target part during the inspection time period, the area difference between two adjacent overlapping areas is analyzed to obtain multiple area difference values ​​corresponding to the target part. The area difference value is the difference between the overlapping area corresponding to the target part at the corresponding inspection time and the overlapping area corresponding to the target part at the previous inspection time. Among the multiple area differences corresponding to the target location, the number of area differences greater than 0 is counted to obtain the area anomaly value of the target location; The analysis of the depth changes in the overlapping area between the target region and the equipment model during the current CT training operation to obtain the depth anomalies of the target region includes: Multiple overlap depths corresponding to the target part are obtained, wherein the multiple overlap depths correspond one-to-one with multiple examination times within the examination time period, the overlap depth is the depth of the overlapping area between the target part and the device model at the corresponding examination time into the device model, and the examination time period is the time period corresponding to the examination process of the current CT training operation. In the multiple overlapping depths corresponding to the target part during the inspection time period, the depth difference between two adjacent overlapping depths is analyzed to obtain multiple depth difference values ​​corresponding to the target part. The depth difference value is the difference between the depth area corresponding to the target part at the corresponding inspection time and the depth area corresponding to the target part at the previous inspection time. Among the multiple depth differences corresponding to the target location, the largest depth difference is determined as the depth anomaly value of the target location.

5. The VR-based virtual training method for CT room operation according to claim 1, characterized in that, The step of obtaining the operational evaluation result of the current CT training operation based on the plurality of pose anomaly indices and the plurality of position anomaly indices includes: Multiple posture tolerances are obtained, wherein the multiple posture tolerances correspond one-to-one with multiple limb parts of the patient model, and the posture tolerances are used to represent the maximum degree of adjustment of the corresponding limb parts during limb adjustment operations. Based on the multiple posture tolerances, multiple limb abnormality coefficients are obtained, wherein the sum of the limb abnormality coefficients and the corresponding posture tolerances is 1; Multiple limb abnormality values ​​are obtained based on multiple limb abnormality coefficients, multiple posture abnormality indices, and multiple position abnormality indices; Calculate the sum of the multiple limb abnormal values ​​to obtain the operational abnormal value; The operational evaluation result of the current CT training operation is generated based on the operational anomalies.

6. The VR-based virtual training method for CT room operation according to claim 5, characterized in that, The acquisition of multiple attitude tolerances includes: The total number of target data points included in the target part is counted to obtain the number of movable points of the target part. The target part is any one of the multiple limb parts of the patient model, and the target data points are the data points that support limb adjustment operations among the multiple data points constituting the target part. Calculate the ratio of the number of movable points of the target part to the volume of the target part to obtain the first value of the target part; The attitude tolerance of the target part is obtained by multiplying the first value and the second value of the target part, wherein the second value of the target part is the largest point movement limit distance among the multiple point movement limit distances corresponding to the multiple target data points.

7. A VR-based virtual training system for CT room operation, characterized in that, The system is used to implement the steps of the method according to any one of claims 1-6, and the system comprises: The acquisition module is used to acquire the device model and patient model in the current CT training operation, wherein the device model is used to represent the device performing the CT examination, and the patient model is used to represent the patient performing the CT examination. The initial analysis module is used to analyze the relative positional relationship between the device model and the patient model in its initial position to obtain initial outliers. The posture analysis module is used to analyze the posture changes of multiple limb parts of the patient model before and after the limb adjustment operation, and obtain multiple posture abnormality indices. The limb adjustment operation is used to reduce the depth of the patient model's limb parts penetrating into the device model. The position analysis module is used to analyze the changes in the overlapping area between multiple limb parts of the patient model and the device model during the examination process of the current CT training operation, based on the initial anomaly value, and to obtain multiple position anomaly indices. The operation evaluation module is used to obtain the operation evaluation result of the current CT training operation based on the multiple posture anomaly indices and the multiple position anomaly indices.

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