Mobile ct and intraoperative mobile ct positioning navigation method based on laser guidance

Abstract

The application discloses a mobile CT and a laser-guided intraoperative mobile CT positioning and navigation method, and the method comprises the following steps: a laser emitting device projects a laser line on the ground, the relative pose of the mobile CT relative to the laser line is calculated, and the distance of the mobile CT and the steering angular velocity of the chassis are controlled based on the relative pose. The application makes full use of the high brightness and high coherence characteristics of the laser line, effectively reduces the dependence on traditional positioning marks, and significantly reduces the complexity and cost of the system. In addition, the application also specially considers the application requirements in complex medical scenes, and ensures the reliability and accuracy in variable environments. Therefore, the application provides a new and efficient solution for the precise positioning and navigation of the intraoperative mobile CT platform.

Description

Technical Field

[0001] This invention relates to the field of precision positioning of mobile CT platforms, and more particularly to an intraoperative mobile CT positioning and navigation method based on laser guidance. Background Technology

[0002] Mobile CT, also known as intraoperative CT, bedside CT, or portable CT, is small, lightweight, and easily moved, allowing patients to receive CT scans at their bedside. It is particularly suitable for emergency, critically ill, and mobility-impaired patients. Furthermore, it provides precise intraoperative navigation, enabling surgeons to perform CT scans simultaneously with surgery, even in cases of severe traumatic brain injury or multiple injuries. Traditional CT scans require a mobile bed to transport the patient to the scanning area, but this poses significant risks due to limitations imposed by clinical surgical conditions and patient treatment status. Because CT scanners are large and difficult to transport, patients in remote areas and those with limited mobility cannot undergo CT examinations. The core technology in mobile CT development is the development of a high-precision, highly stable mobile chassis, with an autonomous navigation solution suitable for complex medical scenarios being a key technology for this chassis.

[0003] Mobile CT has broad market prospects and medical demand. However, existing mobile CT scanners still require manual operation to move the machine, which not only increases labor costs but also makes it difficult to guarantee reliability and stability during surgery. Summary of the Invention

[0004] This invention proposes a mobile CT and a laser-guided intraoperative mobile CT positioning and navigation method, which makes full use of the high brightness and high coherence characteristics of laser lines, effectively reduces the dependence on traditional positioning markers, and significantly reduces the complexity and cost of the system.

[0005] Firstly, a laser-guided intraoperative mobile CT positioning and navigation method is proposed, comprising: a laser emitting device projecting a laser line on the ground, calculating the relative pose of the mobile CT relative to the laser line, and controlling the distance the mobile CT travels and the steering angular velocity of the chassis based on the relative pose.

[0006] In some examples, the laser emitter projects intersecting longitudinal and transverse laser lines on the ground. The parameters of the relative pose include the angular deviation between the heading of the moving CT and the longitudinal laser line, the lateral distance deviation from the heading to the longitudinal laser line, and the forward distance from the heading to the intersection of the longitudinal and transverse laser lines.

[0007] In some examples, the chassis steering angular velocity is controlled so that the angular deviation and lateral distance are zero.

[0008] In some examples, the distance the moving CT scanner travels forward / backward relative to the operating table is controlled so that the forward distance reaches a preset limit.

[0009] In some examples, the weighted PD method is used to fuse the parameters of the relative pose to obtain the steering angular velocity of the chassis.

[0010] In some examples, methods for calculating the relative pose parameters of a moving CT relative to a laser line include: defining a coordinate system comprising a camera coordinate system C, a spatial auxiliary coordinate system S, and a world coordinate system W; capturing an image of the laser line using a camera rigidly connected to the chassis; extracting the laser line from the image to obtain its image coordinates; obtaining the camera's spatial attitude angle using data from the chassis IMU; and calculating the angular deviation, lateral distance deviation, and forward distance in the defined coordinate system using the camera's spatial attitude angle and the laser line's image coordinates.

[0011] In some examples, the X-axis of the camera coordinate system C and the spatial auxiliary coordinate system S coincide, and the origin of both is the camera optical center; the world coordinate system W has the laser line intersection point as its origin, and its Y-axis coincides with the longitudinal laser line; the camera height H is the Z-coordinate of the origin of the camera coordinate system C in the world coordinate system; the chassis forward direction coincides with the Z-axis of the spatial auxiliary coordinate system.

[0012] In some examples, the method for obtaining the image coordinates of the laser line includes: For the first frame of the image, the edges of the light stripe are first extracted using the Canny edge detection algorithm, and then the laser line is detected by Hough transform to obtain the initial value of the laser line's direction. For subsequent frames, the initial value of the direction calculated in the previous frame is used as the initial value of the current direction. Near the detected laser line, points with gray values ​​greater than a threshold are found. For each point with a gray value greater than the threshold, the boundary of the laser line is located in the image based on its normal direction. Based on the distance between the boundaries, the width of the laser line on each normal is calculated to obtain the width distribution of the laser line. Within the width range of the laser line on each normal, the Steger algorithm is applied to extract the center point of the laser line. The center point of the laser line is classified using the random sampling consensus algorithm to extract the point sets of the vertical and horizontal laser lines respectively, while excluding outliers. The separated point sets are fitted to obtain the coordinates of the vertical and horizontal laser lines on the image.

[0013] Secondly, a mobile CT method is proposed, which utilizes the laser-guided intraoperative mobile CT positioning and navigation method for navigation.

[0014] The beneficial effects of this invention include: (1) The high brightness and high coherence of the laser ensure the system's anti-interference ability in complex medical scenarios. It can still operate stably and maintain accuracy even under drastic changes in lighting and when surgical instruments significantly obstruct the line of sight, demonstrating excellent robustness; (2) The laser-guided solution enables autonomous navigation of the mobile CT, eliminating the need for hand-held propulsion and freeing up the hands of medical staff; (3) Using only a visual sensor, an inertial measurement unit (IMU), and a laser emitting device, the mobile CT platform achieves precise positioning and navigation relative to the operating table with low equipment and computational costs. Attached Figure Description

[0015] Figure 1 The present invention provides a flowchart of a mobile CT positioning and navigation method based on laser lines.

[0016] Figure 2 This is a diagram of a coordinate system.

[0017] Figure 3 This is a schematic diagram of the relative pose of the moving CT and the laser line.

[0018] Figure 4 This is a schematic diagram illustrating the classification of motion states of a mobile CT scanner relative to a laser line.

[0019] Figure 5 This is a schematic diagram of the deployment scenarios for mobile CT scanners.

[0020] Explanation of reference numerals in the attached figures: 1. Camera coordinate system C; 2. Spatial auxiliary coordinate system S; 3. Laser line captured in the image; 4. Moving direction of the mobile CT; 5. World coordinate system W; 6. Lateral laser line; 7. Longitudinal laser line; 8. Angle deviation; 9. Lateral distance; 10. Forward distance; 11. Laser intersection point; 12. Laser emitting device; 13. Operating table; 14. CT scanner; 15. Mobile CT load-bearing chassis for deploying the program of the intraoperative mobile CT positioning and navigation method and system of the present invention. Detailed Implementation

[0021] This invention provides an embodiment of a mobile CT positioning and navigation method / system based on laser lines. Figure 1 This is a flowchart of the method / system. The basic idea of ​​the method / system is: to project two intersecting laser lines onto the ground using a laser emitting device fixed under the operating table, to capture images 3 of these two laser lines using a camera, to extract the laser lines from image 3, to calculate the relative pose of the moving CT relative to the laser lines, and to control the forward distance of the moving CT and the steering angular velocity of the chassis based on the relative pose.

[0022] like Figure 2As shown: This invention defines three spatial coordinate systems, namely the camera coordinate system C(1), the spatial auxiliary coordinate system S(2), and the world coordinate system W(5). The X-axis of the camera coordinate system and the spatial auxiliary coordinate system coincide, and the origin of both is the camera optical center; the world coordinate system has the laser line intersection point 11 as the origin, and the Y-axis coincides with the longitudinal laser line 7; the camera height H is the Z coordinate of the origin of the camera coordinate system in the world coordinate system.

[0023] The laser-line-based mobile CT positioning and navigation method / system includes the following key steps: laser line extraction based on the improved Steger algorithm, actual pose calculation relative to the laser line, and autonomous navigation. The detailed steps and principles of the method will be explained below.

[0024] S1. Laser line extraction.

[0025] For the first frame, the edges of the light stripe are first extracted using the Canny edge detection algorithm, and then the laser line is detected using Hough transform, obtaining the initial value of the laser line's direction. For subsequent frames, the initial value of the direction calculated in the previous frame is used as the initial value of the current direction. Points with high grayscale values ​​are found near the detected laser line and used as initial processing targets. For each high grayscale point, the boundary position of the laser line is located in the image based on the normal direction of that point (obtained through gradient calculation), thus determining the width of the laser line in the normal direction. Based on the distance between the boundaries, the width of the laser line on each normal is calculated, obtaining the width distribution of the laser line. Within the width range of the laser line on each normal, the Steger algorithm is applied to extract the center point of the laser line, obtaining a more accurate laser line position. The Random Sample Consensus (RANSAC) algorithm is used to classify the laser center points, extracting point sets for both vertical and horizontal laser lines. RANSAC's robustness effectively excludes outliers (points that do not conform to the linear model). The separated point sets are then fitted to obtain the coordinates of the laser line in the image.

[0026] Straight line direction initial value acquisition.

[0027] For the first frame, Canny edge detection is performed, and straight lines are detected using Hough transform. At most two intersecting laser lines are detected, and the normals n1 and n2 of the two lines are recorded as initial values ​​for the laser line normals. If it is not the first frame, the laser line normals from the previous frame are used as the initial values ​​for the current frame.

[0028] Laser line width calculation.

[0029] For each laser line, after obtaining the initial value of the line equation, iterate through the pixels near the line. For the pixel p with a gray level greater than the threshold T, search for the boundary along the normal n direction on both sides and obtain the width of the light stripe at the point p. Store the point p and the width value together in the array m_Pair of type pair.

[0030] Adaptive Gaussian filtering.

[0031] Iterate through the elements in m_Pair, and for each point, perform a local Gaussian filter on the corresponding pixel in the original image. Following the derivation of the Steger algorithm, ... The filtered image aft_filter is obtained by using the Gaussian kernel size.

[0032] Steger algorithm extracts laser center point.

[0033] For each point in the image traversed by the aft_filter, and for points I(u, v) > T that are not edge points, the Hesslan matrix is ​​first calculated using the following formula:

[0034]

[0035] in,

[0036]

[0037] u and v are the row and column coordinates of the pixel, respectively, and I(u, v) is the gray-level distribution function.

[0038] Then calculate H. (u，v) The eigenvalues ​​and the eigenvector e = (e_i) with the largest absolute value of the eigenvalues u e v ), (e u e v Let (u, v) be the feature vector at (u, v) in the image. The pixel with the strongest light intensity along this direction is found, i.e., the point on the laser center line. Note: The feature vector with the largest absolute value represents the direction of the fastest change in light intensity, which can be used to find the pixel on the center line of the light stripe image.

[0039] Then, the light intensity distribution function around the current pixel is constructed using a second-order Taylor expansion:

[0040]

[0041] Where: e is the feature vector at (u, v) in the image, [I u I v ] represents the value of the first derivative, and t is an unknown parameter.

[0042] Finally, taking the derivative with respect to t, the point where the derivative is 0 is the location of the strongest light intensity (extreme value), which is also the center point of the laser beam.

[0043]

[0044] If t·e u and t·e v If all values ​​are less than 0.5, it means the extreme point is located within the current pixel. The nearby light intensity distribution function is applicable, (u0+t·e u v0+t·e v The coordinates of the laser center point are 0.

[0045] After traversing the entire process, the coordinate set of the laser center point, LaserPoints, is obtained.

[0046] RANSAC separates point set.

[0047] Let the linear model be y = kx + b, and the number of iterations be T. In each iteration, two points (x1, y1) and (x2, y2) are randomly selected from LaserPoints, and the linear parameters k and b are calculated based on these two points:

[0048]

[0049] For each point P in LaserPoints i Calculate the distance d from it to the line y = kx + b. i And determine whether it is less than the threshold d. thresh If so, then P is considered to be true. i If a property belongs to this line, it does not; otherwise, it does not. Choose properties whose distance is less than d. thresh The set of points is LinePoints, the set of line points obtained in the current iteration. Record the number of LinePoints obtained so far; if this number is the largest so far, update the parameters k of the best-fit line. best and b best and the optimal set of line points (LinePoints) best .

[0050] If a straight line is extracted, then exclude the set of points representing that straight line, LinePoints, from LaserPoints. best and LinePoints best As LinePoints1, the previous step is then repeated in the remaining point set to obtain the point set LinePoints2 for the second line.

[0051] Straight line fitting.

[0052] The two separated point sets, LinePoints1 and LinePoints2, are fitted with least squares to obtain the equations of the two laser lines on the image. The one with the larger absolute slope value that is greater than the empirical value is the longitudinal laser line used as a navigation path reference.

[0053] S2. The detailed steps for calculating the actual pose of the laser line are as follows:

[0054] Spatial pose angle extraction.

[0055] like Figure 3 As shown, the relative pose of the mobile CT and the laser line can be represented by three parameters: angular deviation 8, lateral distance (also known as lateral distance deviation) 9, and forward distance 10. Since the combination of the camera and the mobile CT chassis can be considered a rigid body, the changes in the tilt and pitch angles of the mobile CT chassis directly reflect the corresponding angular changes of the camera. This angular change is determined by… Figure 1 The IMU data frames corresponding to the video frames entering the algorithm are directly extracted.

[0056] Spatial coordinate conversion.

[0057] The spatial attitude angle of the camera θ gives the transformation matrix from the camera coordinate system C to the auxiliary spatial coordinate system S, specifically:

[0058]

[0059] θ represents the camera's roll and pitch angles, respectively.

[0060] Take any two points P from the laser line extracted in step S1. i (i = 0, 1), transformed into homogeneous terms, we get:

[0061]

[0062] (x i y i ) is the image coordinate of point i on the laser line.

[0063] Transform it to the auxiliary spatial coordinate system S:

[0064] P Si =RK -1 P i (i = 0, 1)

[0065] Where K is the camera intrinsic parameter. Specifically, it can be represented as:

[0066]

[0067] (x Si y Si , z Si ) is the coordinate of point i in the auxiliary spatial coordinate system.

[0068] The expression for the angle deviation to be determined is:

[0069]

[0070] in,

[0071] z S0 z S1 The coordinates of the two points i (i = 0, 1) on the Z-axis of the auxiliary spatial coordinate system are x and x. S0 x S1 These are the coordinates of the two points i (i = 0, 1) on the X-axis of the auxiliary spatial coordinate system.

[0072] Scale recovery.

[0073] The above steps utilize the direction of the longitudinal laser line, V S It's a vector without a true scale; it only provides the angle between the chassis heading and the laser line, but not the lateral offset of the moving CT relative to the laser line. Therefore, a constraint with a true scale needs to be added. Specifically, this constraint is:

[0074] y S0 =y S1 =H

[0075] Among them, y S0 y S1 H represents the coordinates of the two points i (i = 0, 1) on the Y-axis of the auxiliary spatial coordinate system, and H is the camera height, which is also the ordinate of the laser line in the auxiliary spatial coordinate system.

[0076] After adding this constraint, the intercept of the longitudinal laser line 7 with the X-axis in the auxiliary spatial coordinate system is the lateral distance deviation 9 between the moving CT and the laser line.

[0077]

[0078] -b S k is the intercept of the longitudinal laser line 7 with the X-axis. S The slope of the longitudinal laser line 7.

[0079] Additionally, if the transverse laser lines are extracted, then the laser intersection point P on the image is... c Transform to an auxiliary spatial coordinate system using the same method, and obtain The forward distance 10 from intersection 11 to the moving CT can be obtained:

[0080]

[0081] (x SC y SC , z SC ) is the intersection point P c Coordinates in an auxiliary spatial coordinate system.

[0082] S3. Autonomous Navigation Solution:

[0083] Based on the pose parameters obtained in step S2, a weighted PD method is designed to control the steering speed of the mobile CT chassis, achieving precise intraoperative navigation. Specifically, the target values ​​for both the angular deviation yaw and the lateral distance deviation d are set to 0.

[0084] like Figure 4 As shown on the left, the motion states of the mobile CT chassis are categorized into four types, and the navigation target is to reach... Figure 4 For the right St state, the PD expression is designed as follows:

[0085]

[0086] Where: K p It is proportional gain, K d It is the differential gain, e yaw It is the normalized angle deviation, e d It is the lateral distance deviation, μ yaw μd and μd are the weighting coefficients for angular error and lateral distance deviation, respectively, and the output value ω is the angular velocity.

[0087] Additionally, the forward distance D is used to control the distance the moving CT travels relative to the operating table, and a stopping strategy for the moving CT is formulated by setting a pre-defined limit.

[0088] The present invention also provides an embodiment of a mobile CT scanner. The mobile CT scanner includes a laser emitting device, a camera, an IMU, a chassis, and a control system. The laser emitting device projects two intersecting laser lines onto the ground. The camera and IMU are fixed to the chassis. The camera captures images of these laser lines. The IMU measures the spatial attitude angle of the camera / chassis. The control system is configured to: extract the image coordinates of the laser lines according to the methods described in steps S1, S2, and S3 above; calculate the actual pose of the mobile CT scanner relative to the laser lines; and control the movement of the mobile CT scanner.

[0089] The control system includes a processor and a memory. The memory stores non-transitory computer-readable instructions (e.g., one or more computer program modules). The processor executes the non-transitory computer-readable instructions, which, when executed by the processor, can perform one or more steps in the laser-line-based mobile CT positioning and navigation method. The memory and processor can be interconnected via a bus system and / or other forms of connection.

[0090] For example, a processor can be a central processing unit (CPU), a graphics processing unit (GPU), or other form of processing unit with data processing and / or program execution capabilities. For instance, a CPU can be based on x86 or ARM architectures. A processor can be a general-purpose processor or a special-purpose processor, and it can control other components in a computer to perform desired functions.

[0091] For example, memory can include any combination of one or more computer program products, which can include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory can include, for example, random access memory (RAM) and / or cache memory. Non-volatile memory can include, for example, read-only memory (ROM), hard disk, erasable programmable read-only memory (EPROM), compact optical disc read-only memory (CD-ROM), USB storage, flash memory, etc. One or more computer program modules can be stored on the computer-readable storage medium, and the processor can run one or more computer program modules to implement various functions of the computer.

Claims

1. A method for intraoperative mobile CT positioning navigation based on laser guidance, characterized in that, The application relates to a laser-guided intraoperative mobile CT positioning and navigation method. The laser emitting device projects intersecting longitudinal and transverse laser lines on the ground, the relative pose parameters include the angle deviation between the heading direction of the mobile CT and the longitudinal laser line, the lateral distance deviation of the heading direction to the longitudinal laser line and the forward distance of the heading direction to the intersection point of the longitudinal and transverse laser lines. wherein: is a proportional gain, is a derivative gain, is a normalized angle deviation, is a lateral distance deviation, and are weighting factors for the angle deviation and the lateral distance deviation, respectively, is a steering angle velocity of the chassis.

2. The laser guide based intraoperative mobile CT positioning and navigation method according to claim 1, characterized in that, The steering angle velocity of the chassis is controlled to make the angle deviation and the lateral distance deviation be 0.

3. The laser guide based intraoperative mobile CT positioning and navigation method according to claim 2, characterized in that, The forward distance is controlled to reach a preset limit value.

4. The laser guide based intraoperative mobile CT positioning and navigation method according to claim 2, wherein, The steering angle velocity of the chassis is obtained by fusing the relative pose parameters by using a weighted PD method.

5. The laser guide based intraoperative mobile CT positioning and navigation method according to claim 2, wherein, The relative pose parameter calculation method of the mobile CT relative to the laser line comprises the following steps:

6. The laser guide based intraoperative mobile CT positioning and navigation method according to claim 2, wherein, A camera rigidly connected to the chassis is used to capture the image of the laser line. Definition of coordinate systems comprising a camera coordinate system , a spatial auxiliary coordinate system and a world coordinate system ; The laser line is extracted from the image to obtain the image coordinates of the laser line. The space attitude angle of the camera is obtained through the data of the chassis IMU. The angle deviation, the lateral distance deviation and the forward distance are calculated in the defined coordinate system by using the space attitude angle of the camera and the image coordinates of the laser line. The image coordinate acquisition method of the laser line comprises the following steps:

7. The laser guide based intraoperative mobile CT positioning and navigation method according to claim 6, characterized in that, Camera coordinate system and spatial auxiliary coordinate system X axes coincide and the origins are both camera optical centers; world coordinate system with the laser line intersection point as the origin and the Y axis coinciding with the longitudinal laser line; camera height H is the Z coordinate of the origin in the world coordinate system the Z coordinate of the origin in the world coordinate system The chassis forward direction coincides with the spatial auxiliary coordinate system's axial direction.

8. The laser guide based intraoperative mobile CT positioning and navigation method according to claim 6, wherein, For the first frame of image, the edge of the light bar is extracted by using a Canny edge detection algorithm, then a Hough transform is performed to detect the laser line to obtain the direction initial value of the laser line, and for the subsequent frames, the direction initial value calculated in the previous frame is used as the current direction initial value. In the vicinity of the detected laser line, a point with a gray value greater than a threshold value is found. For each point with a gray value greater than the threshold value, the boundary of the laser line is located by searching in the image according to the normal direction of the point. The width of the laser line on each normal line is calculated according to the distance between the boundaries to obtain the width distribution of the laser line. In the width range of the laser line on each normal line, a Steger algorithm is applied to extract the center point of the laser line. The center points of the laser line are classified by using a random sample consensus algorithm to extract the point sets of the longitudinal and transverse laser lines respectively, and the outliers are excluded. The separated point sets are fitted to obtain the coordinates of the longitudinal and transverse laser lines in the image. The laser-guided intraoperative mobile CT positioning and navigation method is used for navigation.

9. A mobile CT, characterized by, ​

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