A method, apparatus, storage medium and electronic device for collision prediction

Abstract

The specification discloses a collision prediction method, device, storage medium and electronic equipment, comprising: acquiring an environment image of an environment area where a medical imaging device is located, and determining a three-dimensional model of the environment area. The motion trajectory of the medical imaging device and the position of interest of the to-be-collected area are determined, and the area that can be reached by the moving part in the process that the medical imaging device moves to the position of interest according to the motion trajectory is determined as a first area. The position and posture of the moving part when the medical imaging device collects medical images of the position of interest from different angles are determined, the area that can be reached by the moving part is determined as a second area according to the determined position and posture of the moving part. The first area and the second area are taken as a detection area. The coincidence of the detection area and the three-dimensional model of the environment area is determined to obtain a collision prediction result, so as to avoid collision between the medical imaging device and objects or personnel in the surrounding environment in subsequent interventional surgery.

Description

TECHNICAL FIELD

[0001] The present specification relates to the technical field of computer technology, and particularly relates to a collision prediction method and device, a storage medium and an electronic device. BACKGROUND

[0002] With the development of science and technology, medical imaging equipment is applied more and more widely. Among them, medical imaging equipment includes Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and other equipment.

[0003] In an interventional operation, an operator needs to move and rotate the medical imaging equipment, so that the medical imaging collected by the medical imaging equipment can cover the required scanning area. However, in this process, the operator needs to pay attention to the movement of the medical imaging equipment at all times to avoid collision between the medical imaging equipment and objects or personnel in the surrounding environment. When it is found that there is a collision risk, the operator needs to stop moving or rotating the medical imaging equipment and adjust the movement trajectory of the medical imaging equipment.

[0004] Therefore, how to predict the collision of the medical imaging equipment is a problem to be solved. SUMMARY

[0005] The present specification provides a collision prediction method, device, storage medium and electronic device to partially solve the above problems existing in the prior art.

[0006] The present specification adopts the following technical solutions:

[0007] The present specification provides a collision prediction method, and the medical imaging equipment includes a moving part. The method comprises:

[0008] Obtaining an environment image of an environment area where the medical imaging equipment is located, and determining a three-dimensional model of the environment area according to the environment image;

[0009] Determining a movement trajectory of the medical imaging equipment and a position of interest of a to-be-collected area;

[0010] According to the three-dimensional model of the medical imaging equipment and the movement trajectory, determining a region that can be reached by the moving part in the process that the medical imaging equipment moves to the position of interest according to the movement trajectory as a first region;

[0011] determine positions and poses of the moving part when the medical imaging device collects medical images of the position of interest from different angles, and determine a region reachable by the moving part as a second region according to the determined positions and poses of the moving part;

[0012] take the first region and the second region as a detection region;

[0013] determine a collision prediction result according to coincidence of the detection region and a three-dimensional model of the environment region.

[0014] Optionally, the moving part includes a mechanical arm and a medical image collection unit;

[0015] determine a region reachable by the moving part as a first region according to the three-dimensional model of the medical imaging device and the motion trajectory, and specifically include:

[0016] perform voxel division on the three-dimensional model of the environment region to obtain each voxel region;

[0017] determine each voxel region passed through by the mechanical arm and the medical image collection unit when the moving part moves according to the motion trajectory as a first region according to the three-dimensional model of the medical imaging device and the motion trajectory.

[0018] Optionally, determine positions and poses of the moving part when the medical imaging device collects medical images of the position of interest from different angles, and determine a region reachable by the moving part as a second region according to the determined positions and poses of the moving part, and specifically include:

[0019] determine each pose combination of the mechanical arm and the medical image collection unit when the medical image collection unit collects medical images of the position of interest from different angles;

[0020] take the voxel region where the mechanical arm and the medical image collection unit corresponding to each pose combination are located as a second region.

[0021] Optionally, determine a collision prediction result according to coincidence of the detection region and a three-dimensional model of the environment region, and specifically include:

[0022] determine a voxel region where an obstacle in the environment region is located in each voxel region as a collision region;

[0023] determine whether there is a coincidence of the detection region and the collision region;

[0024] If yes, it is determined that the medical imaging device will collide with the obstacle in the environment region;

[0025] If no, it is determined that the medical imaging device will not collide.

[0026] Optionally, the method further comprises:

[0027] When the collision prediction result is collision, a target region is determined as an overlapping region of the detection region and the three-dimensional model of the environment region;

[0028] The target region is displayed in the three-dimensional model of the environment region to the operator, so that the operator adjusts the position of the obstacle according to the target region.

[0029] Optionally, after the three-dimensional model of the environment region is determined, the method further comprises:

[0030] The three-dimensional model of the medical imaging device is segmented from the three-dimensional model of the environment region to obtain the three-dimensional model of the environment region without the medical imaging device.

[0031] Optionally, the environment image of the environment region where the medical imaging device is located is acquired, and specifically comprises:

[0032] An image collected by a pre-fixed image collection device is acquired;

[0033] From the acquired image, the environment image of the environment region where the medical imaging device is located is determined.

[0034] Optionally, the motion trajectory of the medical imaging device and the position of interest of the to-be-collected region are determined, and specifically comprises:

[0035] Each to-be-collected region is displayed to the operator;

[0036] In response to an operation instruction sent by the operator, the to-be-collected region selected by the operator is determined from each to-be-collected region;

[0037] According to the position of interest of the to-be-collected region selected by the operator, the motion trajectory of the medical imaging device corresponding to the position of interest is determined.

[0038] The present specification provides a computer readable storage medium, the storage medium stores a computer program, and the computer program is executed by a processor to realize the above-mentioned collision prediction method.

[0039] The specification provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the above-mentioned collision prediction method when executing the program.

[0040] The above-mentioned at least one technical solution adopted by the specification can achieve the following beneficial effects:

[0041] In the collision detection method provided by the specification, the environment image of the environment area where the medical imaging device is located is first acquired, and the three-dimensional model of the environment area is determined according to the environment image. Then, the motion trajectory of the medical imaging device and the position of interest of the to-be-collected area are determined. Then, according to the three-dimensional model of the medical imaging device and the motion trajectory, the area that the moving part can reach in the process that the medical imaging device moves to the position of interest according to the motion trajectory is determined as the first area. According to the three-dimensional model of the medical imaging device, the position and posture of the moving part when the medical imaging device collects the medical image of the position of interest from different angles are determined, and according to the determined position and posture of the moving part, the area that the moving part can reach is determined as the second area. Then, the first area and the second area are taken as the detection area. Then, according to the coincidence of the detection area and the three-dimensional model of the environment area, the collision prediction result is determined.

[0042] As can be seen from the above method, when the collision prediction is performed, the environment image of the environment area where the medical imaging device is located is first acquired, and the three-dimensional model of the environment area is determined according to the environment image. Then, the motion trajectory of the medical imaging device and the position of interest of the to-be-collected area are determined. Then, according to the three-dimensional model of the medical imaging device and the motion trajectory, the area that the moving part can reach in the process that the medical imaging device moves to the position of interest according to the motion trajectory is determined as the first area. And, according to the three-dimensional model of the medical imaging device, the position and posture of the moving part when the medical imaging device collects the medical image of the position of interest from different angles are determined, and according to the determined position and posture of the moving part, the area that the moving part can reach is determined as the second area. Then, the first area and the second area are taken as the detection area. Then, by the coincidence of the detection area and the three-dimensional model of the environment area, the collision prediction result of the medical imaging device is determined, and the collision prediction result of the medical imaging device is determined in advance, so as to avoid the collision between the medical imaging device and the objects or personnel in the surrounding environment when the medical imaging device is moved or rotated in the subsequent interventional surgery, and reduce the risk of surgery. BRIEF DESCRIPTION OF DRAWINGS

[0043] The accompanying drawings described herein are used to provide further understanding of the specification, and form a part of the specification. The illustrative embodiments of the specification and their descriptions serve to explain the specification, and do not constitute an improper limitation on the specification. In the drawings:

[0044] FIG. 1 is a schematic diagram of a flowchart of a method for collision prediction according to an embodiment of the present specification;

[0045] Figure 1 FIG. 1 is a schematic diagram of a flowchart of a method for collision prediction according to an embodiment of the present specification;

[0046] Figure 2 FIG. 1 is a schematic diagram of a flowchart of a method for collision prediction according to an embodiment of the present specification;

[0047] Figure 3 FIG. 1 is a schematic diagram of a flowchart of a method for collision prediction according to an embodiment of the present specification;

[0048] Figure 4 FIG. 1 is a schematic diagram of a flowchart of a method for collision prediction according to an embodiment of the present specification; Figure 1 DETAILED DESCRIPTION

[0049] In order to make the purposes, technical solutions and advantages of the present specification clearer, the technical solutions of the present specification will be described below in conjunction with the specific embodiments of the present specification and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present specification, rather than all the embodiments. Based on the embodiments in the present specification, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present specification.

[0050] The embodiments of the present specification provide a method, device, storage medium and electronic device for collision detection, which will be described in detail below in conjunction with the drawings.

[0051] Figure 1 FIG. 1 is a schematic diagram of a flowchart of a method for collision prediction according to an embodiment of the present specification, Figure 1 The medical imaging device in the method shown includes a moving part, and specifically includes the following steps:

[0052] S100: Obtain an environment image of an environment region where the medical imaging device is located, and determine a three-dimensional model of the environment region according to the environment image.

[0053] ​In the specification, the device for performing collision prediction can acquire an environment image of an environment region where the medical imaging device is located, and determine a three-dimensional model of the environment region according to the environment image. The device for performing collision prediction can be a server, or an electronic device such as a desktop computer, a notebook computer, etc. For ease of description, the method of collision prediction provided in the specification will be described below by taking the server as the execution subject. The medical imaging device can be a computed tomography (CT) device, a magnetic resonance imaging (MRI) device, etc. The medical imaging device can include a moving part, and can also include a fixed part such as a base of the medical imaging device. The environment region is a region where the medical imaging device is located, which can be an operating room for performing an interventional surgery, or any region where the medical imaging device is located when scanning, which is not specifically limited in the specification. The environment image is an image acquired by an image acquisition device in advance. The image acquisition device can be an RGB-D camera, which can be fixed on a wall surface of the environment region where the medical imaging device is located, and can acquire the environment image of the environment region at a pre-fixed angle.

[0054] Specifically, the server can acquire an image acquired by the pre-fixed image acquisition device, and determine the environment image of the environment region where the medical imaging device is located from the acquired image. Then, the three-dimensional model of the environment region is obtained by performing three-dimensional reconstruction according to the environment image. The three-dimensional model of the environment region includes a three-dimensional model of the medical imaging device and a three-dimensional model of the environment around the medical imaging device, such as a three-dimensional model of an operator, an operating table, and a surgical instrument around the medical imaging device. The three-dimensional reconstruction according to the environment image can be performed by any existing means, which is not specifically limited in the specification.

[0055] S102: Determine the motion trajectory of the medical imaging device and the position of interest of the region to be acquired.

[0056] The server can determine a motion trajectory of the medical imaging device and a location of interest of the region to be acquired, wherein the motion trajectory of the medical imaging device can be a pre-set motion trajectory, i.e., a motion trajectory from an initial position of the medical imaging device to the location of interest of the region to be acquired. The region to be acquired is a region required to be acquired by the medical imaging device, i.e., a region corresponding to the medical image, such as a brain region. The location of interest is a lesion location, i.e., a position required to be acquired by the medical imaging device, which is a lesion position that must be contained in the medical image acquired by the medical imaging device when acquiring the region to be acquired, such as a brain stem in the brain. The location of interest can be at any position in the medical image (i.e., the region to be acquired), such as the center of the medical image (i.e., the center of the region to be acquired), which is not limited in the specification, and the medical image only needs to contain the location of interest.

[0057] Specifically, the server can display each region to be acquired to the operator, determine the region to be acquired selected by the operator from each region to be acquired in response to an operation instruction sent by the operator. Then, according to the location of interest of the region to be acquired selected by the operator, determine the motion trajectory of the medical imaging device corresponding to the location of interest. Wherein each location of interest corresponds to at least one pre-set motion trajectory of the medical imaging device from the initial position to the location of interest of the region to be acquired. Therefore, when determining the motion trajectory of the medical imaging device corresponding to the location of interest according to the location of interest of the region to be acquired selected by the operator, the server can determine each motion trajectory of the medical imaging device corresponding to the pre-set location of interest according to the location of interest of the region to be acquired selected by the operator, and select a motion trajectory from each motion trajectory as the motion trajectory of the medical imaging device. Of course, when selecting a motion trajectory from each motion trajectory as the motion trajectory of the medical imaging device, the server can randomly select a motion trajectory from each motion trajectory as the motion trajectory of the medical imaging device. The server can also display each motion trajectory to the operator and determine the motion trajectory selected by the operator as the motion trajectory of the medical imaging device in response to the selection designation of the operator.

[0058] In the specification, since each region to be acquired can correspond to at least one location of interest, when determining the motion trajectory of the medical imaging device corresponding to the location of interest according to the location of interest of the region to be acquired selected by the operator, the server can first determine each location of interest corresponding to the region to be acquired selected by the operator and display it to the operator. Then, in response to the selection operation of the operator, determine the location of interest selected by the operator and determine the motion trajectory of the medical imaging device corresponding to the selected location of interest according to the location of interest selected by the operator.

[0059] S104: According to the three-dimensional model of the medical imaging device and the motion trajectory, determine the area that the moving part can reach in the process that the medical imaging device moves to the interested position according to the motion trajectory as a first area.

[0060] In order to avoid the medical imaging device colliding with objects or personnel in the surrounding environment when collecting medical images, the server can determine all the areas that the medical imaging device can reach in the process from the initial position to the interested position according to the three-dimensional model of the medical imaging device and the motion trajectory. Subsequently, the collision prediction result of the medical imaging device can be determined according to the determined area.

[0061] Based on this, the server can determine the area that the moving part can reach in the process that the medical imaging device moves to the interested position according to the motion trajectory as a first area according to the three-dimensional model of the medical imaging device and the motion trajectory. Specifically, the server can perform voxel division on the three-dimensional model of the environment area to obtain each voxel area. Then, according to the three-dimensional model of the medical imaging device and the motion trajectory, the server determines the voxel area that the moving part passes through when the moving part moves according to the motion trajectory as the first area. Wherein, when performing voxel division on the three-dimensional model of the environment area to obtain each voxel area, the server can perform voxel division on the three-dimensional model of the environment area according to a specified size to obtain each voxel area.

[0062] S106: According to the three-dimensional model of the medical imaging device, determine the position and attitude of the moving part when the medical imaging device collects medical images of the interested position from different angles, and determine the area that the moving part can reach as a second area according to the determined position and attitude of the moving part.

[0063] In this specification, when the moving part of the medical imaging device moves to the interested position according to the motion trajectory, the medical imaging device can collect medical images of the interested position at different angles. When the collection angle is different, the position and attitude of the moving part are also different. In addition, when the collection angle is the same, the position and attitude of the moving part may also be different.

[0064] Based on this, in order to ensure that the moving part of the medical imaging device does not have a collision risk when collecting medical images of the interested position at any angle, the server can determine all the areas that the moving part can reach when the medical imaging device collects medical images of the interested position from different angles. Therefore, the server can determine the position and attitude of the moving part when the medical imaging device collects medical images of the interested position from different angles according to the three-dimensional model of the medical imaging device, and determine the area that the moving part can reach as a second area according to the determined position and attitude of the moving part.

[0065] Specifically, the server can determine, according to the three-dimensional model of the medical imaging device, combinations of positions and postures of the moving part when the medical imaging device collects medical images of the position of interest from different angles. Then, a voxel region where the moving part in each combination is located is taken as the second region.

[0066] S108: Take the first region and the second region as detection regions.

[0067] The server can take the first region and the second region as detection regions. The first region is all regions that can be reached by the moving part of the medical imaging device during movement of the moving part along the motion trajectory, and the second region is all regions that can be reached by the moving part when the moving part collects medical images of the position of interest from different angles. Taking the first region and the second region as detection regions makes it possible to determine a collision prediction result according to the detection regions in subsequent processes.

[0068] S110: Determine a collision prediction result according to whether the detection regions coincide with the three-dimensional model of the environment region.

[0069] The server can determine a collision prediction result according to whether the detection regions coincide with the three-dimensional model of the environment region. Specifically, the server can determine, in each voxel region, a voxel region where an obstacle in the environment region is located as a collision region. It is determined whether the detection regions coincide with the collision regions. If yes, it is determined that the medical imaging device will collide with the obstacle in the environment region. If no, it is determined that the medical imaging device will not collide. When determining whether the detection regions coincide with the collision regions, the server can determine whether the detection regions coincide with the collision regions according to a distance between the detection regions and the collision regions (i.e., a distance between a voxel region corresponding to the detection regions and a voxel region corresponding to the collision regions). When the distance is less than a preset threshold, it is determined that the detection regions coincide with the collision regions. When the distance is not less than the preset threshold, it is determined that the detection regions do not coincide with the collision regions.

[0070] In addition, since the detection regions and the collision regions in the same voxel region may only occupy a part of the voxel region, and the detection regions and the collision regions may not coincide in the voxel region, for example, as shown in Figure 2 , Figure 2 is a plan view of a voxel region provided in this specification, Figure 2 region O is a plan view of a voxel region, region A is a plan view of a detection region, and region B is a plan view of a collision region, Figure 2The region A and the region B in the region O, but both only occupy a part of the region O, and there is no overlapping region of the region A and the region B in the region O. Therefore, when determining whether the detection region and the collision region overlap, the server can determine the distance between the detection region and the collision region (i.e., the distance between the voxel region corresponding to the detection region and the voxel region corresponding to the collision region), and determine whether the determined distance is less than the preset threshold. If yes, the voxel region with the distance less than the threshold is regarded as a detection region, and it is determined whether the detection region and the collision region overlap in the detection region. Otherwise, it is determined that the detection region and the collision region do not overlap.

[0071] When the determined distance is less than the preset threshold, it is determined that the detection region and the collision region have a possible overlapping region (i.e., the detection region). However, the detection region and the collision region may only occupy a part of the possible overlapping region, and the detection region and the collision region may not overlap in the possible overlapping region. Therefore, the server can further determine whether the detection region and the collision region overlap in the possible overlapping region (i.e., the detection region). Specifically, the server can determine the position of the detection region and the position of the collision region in the detection region, and determine the distance between the detection region and the collision region in the detection region as a detection distance according to the determined position of the detection region and the position of the collision region. Then, it is determined whether the detection distance is less than the preset threshold. If yes, it is determined that the detection region and the collision region overlap in the detection region. Otherwise, it is determined that the detection region and the collision region do not overlap in the detection region.

[0072] As can be seen from the above method, in the method, when performing collision prediction, the server can first acquire an environment image of an environment region where the medical imaging device is located, and determine a three-dimensional model of the environment region according to the environment image. Then, the motion trajectory of the medical imaging device and the position of interest of the to-be-collected region are determined. Then, according to the three-dimensional model of the medical imaging device and the motion trajectory, a region that can be reached by the moving part during the movement of the medical imaging device to the position of interest according to the motion trajectory is determined as a first region. In addition, according to the three-dimensional model of the medical imaging device, the position and posture of the moving part when the medical imaging device collects a medical image of the position of interest from different angles are determined, and according to the determined position and posture of the moving part, a region that can be reached by the moving part is determined as a second region. Then, the first region and the second region are taken as detection regions. Then, the collision prediction result of the medical imaging device is determined by determining the overlapping of the detection regions and the three-dimensional model of the environment region. In this way, the collision prediction result of the medical imaging device is determined in advance, so as to avoid the collision between the medical imaging device and the objects or persons in the surrounding environment when the medical imaging device is moved or rotated in the subsequent interventional surgery, and the risk of surgery is reduced.

[0073] In the present specification, the moving part can include the mechanical arm and the medical image acquisition unit, so in the step S104, the server can voxelize the three-dimensional model of the environment region to obtain each voxel region. Then, according to the three-dimensional model of the medical imaging device and the motion trajectory, the server determines each voxel region through which the mechanical arm and the medical image acquisition unit pass when the moving part moves according to the motion trajectory, as the first region. In addition, in the step S106, the server determines the position and pose of the moving part when the medical imaging device acquires medical images of the position of interest from different angles, and according to the determined position and pose of the moving part, determines the region that can be reached by the moving part as the second region. Then, the server can determine each pose combination of the mechanical arm and the medical image acquisition unit when the medical image acquisition unit acquires medical images of the position of interest from different angles. Then, the voxel region in which the mechanical arm and the medical image acquisition unit corresponding to each pose combination are located is determined as the second region.

[0074] After the step S110, when the collision prediction result is that a collision occurs, the server determines the overlapping region of the detection region and the three-dimensional model of the environment region as the target region. Then, in the three-dimensional model of the environment region, the server displays the target region to the operator, so that the operator adjusts the position of the obstacle according to the target region. In the three-dimensional model of the environment region, the server can highlight the target region to the operator, so that the operator can subsequently adjust the position of the obstacle according to the highlighted target region.

[0075] In the present specification, since the three-dimensional model of the environment region includes the three-dimensional model of the medical imaging device, in order to avoid affecting the accuracy of the subsequent collision prediction result when determining the collision region, the server can delete the three-dimensional model of the medical imaging device included in the three-dimensional model of the environment region in advance. Therefore, after the step S100 of determining the three-dimensional model of the environment region, the server can also separate the three-dimensional model of the medical imaging device from the three-dimensional model of the environment region to obtain the three-dimensional model of the environment region that does not include the three-dimensional model of the medical imaging device.

[0076] Based on the same idea, the present specification also provides a corresponding collision detection device, as shown in Figure 3 .

[0077] Figure 3 A schematic diagram of a collision detection device provided by the present specification, the medical imaging device includes a moving part, specifically including:

[0078] The first determining module 200 is configured to acquire an environment image of an environment region where the medical imaging device is located, and determine a three-dimensional model of the environment region according to the environment image.

[0079] The second determining module 202 is configured to determine a motion trajectory of the medical imaging device and a position of interest of a region to be collected.

[0080] The first region module 204 is configured to determine, according to the three-dimensional model of the medical imaging device and the motion trajectory, a region that can be reached by the moving part in a process in which the medical imaging device moves to the position of interest according to the motion trajectory, as a first region.

[0081] The second region module 206 is configured to determine, according to the three-dimensional model of the medical imaging device, a position and a posture of the moving part when the medical imaging device collects a medical image of the position of interest from different angles, and determine, according to the determined position and posture of the moving part, a region that can be reached by the moving part, as a second region.

[0082] The detection region module 208 is configured to take the first region and the second region as a detection region.

[0083] The collision prediction module 210 is configured to determine a collision prediction result according to a coincidence of the detection region and the three-dimensional model of the environment region.

[0084] Optionally, the moving part includes a mechanical arm and a medical image collection unit.

[0085] The first region module 204 is specifically configured to perform voxel division on the three-dimensional model of the environment region to obtain each voxel region, and determine, according to the three-dimensional model of the medical imaging device and the motion trajectory, each voxel region through which the mechanical arm and the medical image collection unit pass when the moving part moves according to the motion trajectory, as a first region.

[0086] Optionally, the second region module 206 is specifically configured to determine each posture combination of the mechanical arm and the medical image collection unit when the medical image collection unit collects a medical image of the position of interest from different angles, and take a voxel region in which the mechanical arm and the medical image collection unit corresponding to each posture combination are located, as a second region.

[0087] Optionally, the collision prediction module 210 is specifically configured to: determine, in each voxel region, the voxel region where the obstacle in the environment region is located, as the collision region; determine whether there is a situation where the detection region and the collision region overlap; if so, determine that the medical imaging device will collide with the obstacle in the environment region; if not, determine that the medical imaging device will not collide.

[0088] Optionally, the device further includes:

[0089] The prompting module 212 is used to determine the overlapping area of ​​the three-dimensional models of the detection area and the environment area as the target area when the collision prediction result is that a collision has occurred; and to display the target area to the operator in the three-dimensional model of the environment area so that the operator can adjust the position of the obstacle according to the target area.

[0090] Optionally, after determining the three-dimensional model of the environmental region, the first determining module 200 is further configured to segment the three-dimensional model of the medical imaging device from the three-dimensional model of the environmental region to obtain a three-dimensional model of the environmental region that does not include the three-dimensional model of the medical imaging device.

[0091] Optionally, the first determining module 200 is specifically used to acquire images acquired by a pre-fixed image acquisition device; and to determine the environmental image of the area where the medical imaging device is located from the acquired images.

[0092] Optionally, the second determining module 202 is specifically used to: display each area to be collected to the operator; in response to the operation command sent by the operator, determine the area to be collected selected by the operator from each area to be collected; and determine the motion trajectory of the medical imaging device corresponding to the location of interest of the area to be collected selected by the operator.

[0093] This specification also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figure 1 The collision prediction method shown.

[0094] This instruction manual also provides Figure 4 The diagram shows a schematic structural representation of the electronic device. Figure 4 At the hardware level, the electronic device includes a processor, internal bus, network interface, memory, and non-volatile memory, and may also include other hardware required for the business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then runs it to achieve the above-mentioned functions. Figure 1 The collision prediction method shown.

[0095] Of course, besides the software implementation, the present specification does not exclude other implementation manners, such as a logic device or a combination of software and hardware, and so on, that is, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or a logic device.

[0096] In the 1990s, it was quite obvious to distinguish whether an improvement in a technology was in hardware (e.g., improvement in circuit structures of diodes, transistors, switches, etc.) or in software (improvement in method flow). However, as technology has evolved, many improvements in method flow today can be considered as direct improvements in hardware circuit structures. Designers almost always obtain the corresponding hardware circuit structures by programming the improved method flow into hardware circuits. Therefore, it cannot be said that an improvement in a method flow cannot be implemented by hardware entity modules. For example, a programmable logic device (PLD) (e.g., a field programmable gate array (FPGA)) is an integrated circuit whose logic function is determined by user programming of the device. A digital system is "integrated" on a PLD by the designer programming it, rather than by asking a chip manufacturer to design and fabricate a custom integrated circuit chip. Moreover, instead of manually fabricating integrated circuit chips, this programming is now mostly implemented by "logic compiler" software, which is similar to software compilers used in program development, and the original code to be compiled is written in a specific programming language, which is called a hardware description language (HDL), and there are many such languages, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc., and the most commonly used are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should be aware that, as long as the method flow is logically programmed in the above-mentioned hardware description languages and programmed into an integrated circuit, a hardware circuit implementing the logical method flow can be easily obtained.

[0097] The controller can be implemented in any suitable way, for example, the controller can take the form of a microprocessor or processor and a computer readable medium storing computer readable program code, such as software or firmware, executable by the (micro)processor, logic gates, switches, an application specific integrated circuit (ASIC), a programmable logic controller and an embedded microcontroller, examples of which include but are not limited to the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20 and Silicone Labs C8051F320, the memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also know that, in addition to being implemented in pure computer readable program code, the controller can equally well be implemented to perform the same functions using logic gates, switches, an application specific integrated circuit, a programmable logic controller and an embedded microcontroller, etc. by means of a logical programming of the method steps. The controller can thus be considered as a hardware component, and the means comprised therein for performing the various functions can be considered as structures within the hardware component. Alternatively, the means for performing the various functions can even be considered as both a software module implementing the method and a structure within the hardware component.

[0098] The systems, apparatuses, modules or units illustrated by the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0099] For the sake of description, the above apparatuses are described in various units by functions respectively. Of course, the functions of each unit can be implemented in one or more software and / or hardware in the implementation of the present specification.

[0100] Those skilled in the art will understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0101] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof.

[0102] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof.

[0103] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof. ​ one or more flowcharts and / or blocks in the flowcharts and / or combination thereof.

[0104] In one typical configuration, the computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0105] The memory can include non-persistent memory and / or volatile memory, such as random access memory (RAM) and / or cache memory, non-volatile memory, such as read-only memory (ROM), EPROM, and / or flash memory. The memory is an example of computer-readable media.

[0106] Computer-readable media includes permanent and non-permanent, movable and non-movable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer-readable media does not include transitory media such as modulated data signals and carriers.

[0107] It should also be noted that the terms "comprising", "containing", or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article or apparatus that comprises a list of elements does not only include those elements, but can also include other elements not expressly listed or inherent to such process, method, article or apparatus. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or apparatus that includes the element.

[0108] Those skilled in the art will appreciate that embodiments of the present specification can be provided as methods, systems or computer program products. Therefore, the present specification can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Moreover, the present specification can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0109] The present specification can be described in the general context of computer-executable instructions, such as program modules, executed by computers. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The present specification can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in both local and remote computer storage media including storage devices.

[0110] The various embodiments described in this specification are described using a numbering of embodiments approach: these are each individually integrated contributions pertaining to different aspects of the description. For each embodiment, the description focuses on the differences from the other embodiments. In particular, the description of the system embodiments is relatively brief, as the system embodiments are largely analogous to the method embodiments. The relevant parts of the description of the method embodiments are referred to.

[0111] The above description is embodied in the form of embodiments only and is not intended to limit the present specification. The present specification can be variously changed and modified by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification should be included in the scope of the claims of the present specification.

Claims

1. A collision prediction method, characterized in that, The medical imaging equipment includes a moving part, and the method includes: Acquire environmental images of the area where the medical imaging equipment is located, and determine a three-dimensional model of the area based on the environmental images; Determine the motion trajectory of the medical imaging equipment and the location of interest in the area to be acquired; Based on the three-dimensional model of the medical imaging device and the motion trajectory, the area that the moving part can reach during the process of the medical imaging device moving to the position of interest according to the motion trajectory is determined as the first area; Based on the three-dimensional model of the medical imaging device, the position and posture of the moving part are determined when the medical imaging device acquires medical images of the location of interest from different angles. Based on the determined position and posture of the moving part, the area that the moving part can reach is determined as the second area. The first region and the second region are used as the detection regions; The collision prediction result is determined based on the overlap between the three-dimensional models of the detection area and the environmental area.

2. The method as described in claim 1, characterized in that, The moving parts include a robotic arm and a medical image acquisition unit; Based on the three-dimensional model of the medical imaging device and the motion trajectory, the area reachable by the moving part during the process of the medical imaging device moving to the position of interest according to the motion trajectory is determined as a first area, specifically including: The three-dimensional model of the environment region is divided into voxels to obtain each voxel region; Based on the three-dimensional model of the medical imaging device and the motion trajectory, the voxel regions traversed by the robotic arm and the medical image acquisition unit when the moving part moves according to the motion trajectory are determined as the first region.

3. The method as described in claim 2, characterized in that, When the medical imaging device acquires medical images of the location of interest from different angles, the position and orientation of the moving part are determined. Based on the determined position and orientation of the moving part, the area reachable by the moving part is determined as a second region, specifically including: Determine the various posture combinations of the robotic arm and the medical image acquisition unit when the medical image acquisition unit acquires medical images of the location of interest from different angles; The voxel region where the robotic arm and the medical image acquisition unit are located corresponding to each posture combination is designated as the second region.

4. The method as described in claim 3, characterized in that, Based on the overlap between the 3D models of the detection area and the environment area, the collision prediction result is determined, specifically including: In each voxel region, the voxel region where the obstacle in the environment region is located is determined as the collision region; Determine whether the detection area overlaps with the collision area; If so, it is determined that the medical imaging equipment will collide with an obstacle in the environmental area; If not, it is determined that the medical imaging equipment will not be involved in a collision.

5. The method as described in claim 1, characterized in that, The method further includes: When the collision prediction result indicates that a collision has occurred, the overlapping area of ​​the three-dimensional models of the detection area and the environment area is determined as the target area. In the three-dimensional model of the environment, the target area is displayed to the operator so that the operator can adjust the position of the obstacle according to the target area.

6. The method as described in claim 1, characterized in that, After determining the three-dimensional model of the environmental region, the method further includes: The three-dimensional model of the medical imaging device is segmented from the three-dimensional model of the environmental region to obtain a three-dimensional model of the environmental region that does not contain the three-dimensional model of the medical imaging device.

7. The method as described in claim 1, characterized in that, Acquire environmental images of the area where the medical imaging equipment is located, specifically including: Acquire images captured by a pre-fixed image acquisition device; From the acquired images, determine the environmental image of the area where the medical imaging equipment is located.

8. The method as described in claim 1, characterized in that, Determining the motion trajectory of the medical imaging equipment and the location of interest in the area to be acquired specifically includes: Display the areas to be collected to the operator; In response to the operation instructions sent by the operator, the area to be collected selected by the operator is determined from each area to be collected; Based on the location of interest in the area to be acquired selected by the operator, the motion trajectory of the medical imaging equipment corresponding to the location of interest is determined.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the method described in any one of claims 1 to 8.

10. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in any one of claims 1 to 8.

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