Puncture path planning method for peritoneal puncture drainage of severe acute pancreatitis

Abstract

The present application relates to the technical field of puncture path planning, in particular to a puncture path planning method for severe acute pancreatitis abdominal puncture drainage. First, a three-dimensional model of the abdominal cavity is constructed by fusing CT images and MRI images and tissue regions (organ regions, blood vessel regions, lesion regions) are segmented, and candidate paths are generated based on target positions and preset body surface entry points. Then, risk coefficients are calculated based on the geometric characteristics (angle, length, and proximity to tissue regions) of the candidate paths, and the preliminary optimal paths are screened. Further, the puncture body position and the direction of the gravity component are determined in combination with the sagittal / coronal position relationship, the tissue density distribution is analyzed, and the tissue deformation interference index is quantified. Finally, the risk coefficients and the deformation index are fused to realize the re-screening of the paths, determine the optimal and alternative paths, so as to effectively avoid key tissue structures and reduce the risk of intraoperative displacement, and improve the puncture accuracy and stability.

Description

Technical Field

[0001] This invention relates to the field of puncture path planning technology, and specifically to a puncture path planning method for paracentesis drainage in severe acute pancreatitis. Background Technology

[0002] Severe acute pancreatitis (SAP) is an acute abdominal condition characterized by rapid onset, rapid progression, numerous complications, and high mortality. Patients often experience necrosis of the pancreas and surrounding tissues, fluid retention, abdominal infection, and abdominal abscess. Paracentesis is currently one of the most important clinical treatments, effectively reducing intra-abdominal pressure, draining ascites and necrotic material, and alleviating abdominal infection, thereby improving patient prognosis. Therefore, determining the puncture path quickly, accurately, and safely, avoiding damage to major blood vessels, intestines, and vital organs, while ensuring the effectiveness of the puncture, is a crucial clinical challenge.

[0003] Current technologies typically rely on ultrasound or CT imaging to guide doctors in performing puncture procedures. This means that clinicians use ultrasound or CT scans to obtain information about the lesions in the patient's abdominal cavity and the structures of adjacent important tissue areas, and combine this information with imaging data to assist in selecting the puncture path. However, this method relies heavily on empirical puncture based on anatomical structures. In actual application, adjustments to the patient's puncture position can alter the distribution of tissues in the abdominal cavity and the direction of gravity. As a result, tissues along the puncture path will undergo deformations such as mutual pushing. If the influence of these factors is ignored when selecting the puncture path, the position of the puncture path will deviate from the actual tissue location, reducing the accuracy of path planning and seriously affecting the puncture effect. Summary of the Invention

[0004] To address the technical problem that adjustments to the patient's position during puncture can alter the distribution of intra-abdominal tissues and the direction of gravity, causing deformation such as mutual pushing between tissues along the puncture path, and that ignoring these factors when selecting the puncture path can lead to a deviation between the puncture path and the actual tissue location, reducing the accuracy of path planning and severely impacting the puncture effect, this invention aims to provide a puncture path planning method for paracentesis drainage in severe acute pancreatitis. The specific technical solution adopted is as follows:

[0005] CT and MRI images of the patient's abdominal cavity were acquired, segmented, and used to determine tissue regions and construct a three-dimensional model of the patient's abdominal cavity.

[0006] Based on the preset surface entry points, target puncture points, and tissue area distribution, multiple candidate puncture paths are generated; the positional relationship between the candidate puncture paths and the patient's surface cutting plane, the length of the candidate puncture paths, and the distribution characteristics of the puncture paths and tissue areas are analyzed to determine the risk coefficient for screening several preliminary preferred paths from all candidate puncture paths.

[0007] Based on the positional relationship between the preliminary optimized path and the sagittal and coronal planes of the three-dimensional abdominal cavity model, the puncture position and the direction of the gravity component of the path under the puncture position are determined; under any preliminary optimized path, based on the direction of the gravity component of the path, the distribution density characteristics of the tissue region are analyzed, and the tissue deformation interference index of the preliminary optimized path is determined.

[0008] By combining the tissue deformation interference index with the risk coefficient of the preliminary preferred path, the optimal puncture path and alternative puncture paths are determined among all the preliminary preferred paths.

[0009] Furthermore, the method for obtaining the candidate puncture path includes:

[0010] The line connecting each preset surface entry point to the target puncture point is used as the initial puncture path.

[0011] For any initial puncture path, spatial intersection detection is performed between the initial puncture path and all tissue regions in the 3D model of the abdominal cavity. If an intersection point exists, the initial puncture path is a restricted path.

[0012] Among all initial puncture paths, all initial puncture paths that are not restricted areas are selected as candidate puncture paths.

[0013] Furthermore, the method for obtaining the risk coefficient includes:

[0014] The cosine of the angle between each candidate puncture path and the patient's surface cutting plane is used as the puncture angle risk factor.

[0015] The length of each candidate puncture path is used as a puncture distance risk factor;

[0016] On each candidate puncture path, the spatial Euclidean distance between each voxel point and the nearest tissue region voxel point is used as a distance factor. The sum of the distance factors corresponding to all voxel points on each candidate puncture path is negatively correlated and normalized, and then used as the puncture structure risk factor for each candidate puncture path.

[0017] The normalized value of the product of the puncture angle risk factor, puncture distance risk factor, and puncture structure risk factor for each candidate puncture path is used as the risk coefficient for each candidate puncture path.

[0018] Further, determining the puncture position and the direction of the gravity component of the path in the puncture position includes:

[0019] Among all the preliminary preferred paths, the preliminary preferred path with the lowest risk coefficient is selected as the target puncture path;

[0020] The intersection line of the sagittal and coronal planes of the three-dimensional abdominal cavity model is used as the central axis;

[0021] Draw a perpendicular line from the central axis through the preset surface entry point corresponding to the target puncture path. Use the angle between the perpendicular line and the sagittal plane as the angle deviation factor. When the angle deviation factor is less than the preset deviation angle, the patient's puncture position is supine or prone, and the direction of the gravity component of the path is vertically downward.

[0022] When the angle deviation factor is greater than the preset deviation angle, the normalized value of the difference between the angle deviation factor and the preset deviation angle is used as the puncture position rotation coefficient.

[0023] The product of the angle between the vertical line and the coronal plane and the body rotation coefficient is used as the rotation estimation angle;

[0024] The patient is rotated to the affected or healthy side by the estimated rotation angle to obtain the puncture position, and the direction of the vertical downward direction after the estimated rotation angle is taken as the direction of the gravity component of the path of the patient in this puncture position.

[0025] Furthermore, the method for obtaining the tissue deformation interference index includes:

[0026] Select any tissue region as the test region, obtain the maximum two-dimensional cross-section of the test region in the direction of the gravity component of the path, analyze the density characteristics of the tissue region through which the maximum two-dimensional cross-section passes, and determine the gravity compression coefficient of the test region.

[0027] Analyze the distribution characteristics of tissue regions in the vicinity of the area to be tested to determine the local spatial density of the area to be tested;

[0028] The normalized value of the product of the gravitational compression coefficient and the local spatial density of the area to be tested is used as the tissue deformation coefficient of the area to be tested.

[0029] For any preliminary selection path, the sum of the tissue deformation coefficients of the tissue regions to which each voxel point belongs on the preliminary selection path is normalized and used as the tissue deformation interference index of the preliminary selection path.

[0030] Furthermore, the method for obtaining the gravitational compression coefficient includes:

[0031] The mean HU value of all voxel points in each tissue region is used as the density coefficient of each tissue region, and the normalized value of the product of the number of voxel points in each tissue region and the density coefficient is used as the quality coefficient of each tissue region.

[0032] Obtain the maximum two-dimensional cross-section of the area to be tested in the direction of the gravity component of the path. Take all tissue areas that the maximum two-dimensional cross-section passes through in the opposite direction of the gravity component of the path as the target area. Take the normalized sum of the mass coefficients of all target areas as the gravity compression coefficient of the area to be tested.

[0033] Furthermore, the method for obtaining the local spatial density includes:

[0034] The local spatial density of the test region is obtained by negatively correlating and normalizing the volume of the smallest bounding volume of the region to be tested and all tissue regions intersecting with the test region.

[0035] Further, determining the optimal puncture path and alternative puncture paths among all preliminary preferred paths includes:

[0036] The normalized value of the product of the tissue deformation interference index and the risk coefficient for each preliminary selected path is used as the puncture risk index for each preliminary selected path.

[0037] The preliminary preferred path corresponding to the minimum puncture risk index is taken as the optimal puncture path, and the remaining preliminary preferred paths are taken as alternative puncture paths.

[0038] Furthermore, the method for obtaining the tissue region includes:

[0039] The patient's abdominal CT and MRI images are used as inputs to a pre-trained neural network to output tissue regions, wherein the tissue regions include at least organ regions, blood vessel regions, and lesion regions.

[0040] Furthermore, the method for obtaining the preliminary preferred path includes:

[0041] Among all candidate puncture paths, those with a risk coefficient less than a preset risk threshold are selected as the preliminary optimal paths.

[0042] The present invention has the following beneficial effects:

[0043] First, CT and MRI images of the patient's abdominal cavity were acquired and segmented. After identifying the tissue regions, these images were integrated to construct a three-dimensional model of the abdominal cavity, visually displaying the complex anatomical relationships. This helps in identifying abdominal structures and generating multiple feasible candidate puncture paths. Since SAP abscesses are often located near the posterior aspect of organs or the retroperitoneum, the feasibility of the puncture path is crucial to ensure it effectively avoids important structures and reaches the target point. Furthermore, the length of the puncture path directly affects the controllability of the needle insertion path. Therefore, the risk coefficient of the candidate puncture paths was calculated based on these characteristics to select a preliminary preferred path from all candidates. The impact of gravity on tissue regions varies depending on the patient's puncture position. Therefore, the possible puncture positions of the patient under the preliminary preferred path were assessed by analyzing the positional relationship between the sagittal and coronal planes and the preliminary preferred path. The direction of the gravity component of the path under this puncture position was calculated, which helps in subsequent analysis of the risk of path deviation due to tissue displacement during the procedure. Further, based on the direction of the gravity component of the path, the distribution density characteristics of the tissue region were tracked to quantify the compression intensity of the surrounding tissue region on each preliminary preferred path, introducing a tissue deformation interference index. Finally, the tissue deformation interference index of the initially selected path is combined with the risk coefficient to achieve a re-screening of the initially selected path, determine the optimal puncture path and alternative puncture paths, which helps to avoid high deformation risk areas in advance, ensure that the puncture path accurately avoids key tissue areas and maintains stability, and improves path stability. Attached Figure Description

[0044] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 A flowchart illustrating a puncture path planning method for paracentesis drainage in severe acute pancreatitis, provided in one embodiment of the present invention.

[0046] Figure 2 A schematic diagram of an abdominal CT image provided for one embodiment of the present invention;

[0047] Figure 3 This is a flowchart illustrating a method for obtaining tissue deformation interference indicators according to an embodiment of the present invention. Detailed Implementation

[0048] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a puncture path planning method for peritoneal puncture and drainage in severe acute pancreatitis proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0049] 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 invention pertains.

[0050] The following description, in conjunction with the accompanying drawings, details a specific scheme for a puncture path planning method for paracentesis drainage in severe acute pancreatitis provided by the present invention.

[0051] Please see Figure 1 The diagram illustrates a method for puncture path planning in paracentesis for severe acute pancreatitis, according to an embodiment of the present invention. The method includes the following steps:

[0052] Step S1: Obtain CT and MRI images of the patient's abdominal cavity, segment them, determine the tissue regions, and use them to construct a three-dimensional model of the patient's abdominal cavity.

[0053] Patients with severe acute pancreatitis often develop large areas of necrosis, fluid accumulation, or abscesses within the abdominal cavity. These lesions are often irregular in shape, poorly defined, and may contain air bubbles, hematomas, or free fluid. They are frequently adjacent to or even compress vital organs (such as the spleen, stomach, intestines, kidneys, and liver) as well as critical blood vessels such as the abdominal aorta, superior mesenteric artery, and portal vein. The complexity of the anatomical relationships of the lesions and the individual variability of patients necessitate that puncture path planning be based on comprehensive, accurate, and objective spatial information of the abdominal cavity structure to effectively avoid high-risk areas and ensure the safety and effectiveness of puncture drainage. Therefore, it is necessary to construct a three-dimensional model of the patient's abdominal cavity.

[0054] First, acquire three-phase enhanced abdominal CT images of the patient (arterial phase, venous phase, and delayed phase). The scanning parameters are: tube voltage 120kV, slice thickness 1mm, and reconstruction matrix 512×512. Save the CT images in DICOM format, saving the grayscale values ​​(HU values) and spatial coordinates. Please refer to [link to relevant documentation]. Figure 2 The illustration shows a schematic diagram of an abdominal CT image according to one embodiment of the present invention. T2WI (high fluid signal) and DWI (high necrotic area signal) are preferred for acquiring abdominal MRI images of the patient to enhance the ability to identify necrotic liquefaction areas, hidden abscesses, and soft tissue boundaries, compensating for the disadvantages of CT. The images are saved in DICOM format.

[0055] Using the spatial coordinates of the CT image corresponding to the venous phase as the spatial reference coordinate system, all other CT images and MRI images are aligned to this CT image: the image to be registered is overlapped with the reference CT image (the CT image corresponding to the venous phase), with the initial position being the default or coarsely estimated value. Rigid and flexible registration methods based on mutual information are used to align the coordinate systems of other images to be registered to the coordinate system of the reference CT image, ensuring that different modal data correspond within the same spatial location.

[0056] Abdominal lesions typically present as low-density areas (CT value below 30 HU), with indistinct and irregular boundaries of the abscess cavity. Necrotic tissue may contain pneumothorax and hematoma, and these lesions are adjacent to nearby intestines, spleen, stomach, and major blood vessels, resulting in complex anatomical relationships. These lesions and structures show little grayscale difference and unclear boundaries on CT scans. Manual segmentation is extremely time-consuming, subjective, and lacks repeatability, easily leading to path planning deviations that could traverse vital organs or blood vessels, posing a puncture risk. Therefore, in this embodiment of the invention, a deep learning segmentation method can be utilized to fully mine grayscale, texture, and spatial context features for segmentation processing of CT and MRI images.

[0057] Training the neural network: A large amount of abdominal CT images and MRI T2WI and MRI DWI image data were acquired through the Internet, and manually labeled abscesses, necrotic areas, organs, and blood vessels were used as supervision signals. A 3D nnU-Net multimodal segmentation network was constructed. The network structure is as follows: the registered CT, T2WI, and DWI images are used as multi-channel inputs. The multimodal inputs are extracted layer by layer through a combination of multi-layer 3D convolution + instance normalization + LeakyReLU activation units. After each downsampling, the number of channels is doubled and the feature map size is halved to gradually aggregate global spatial context information. At the end of the encoder, the output feature maps of the CT, T2WI, and DWI encoders are fused and uniformly encoded to the decoder input using feature concatenation. The fused features are upsampled by multi-layer deconvolution to restore spatial resolution layer by layer. At the same time, skip connections are made with the corresponding layer feature maps of the encoder to preserve multi-scale details. The number of channels is mapped to the number of categories through the last 1×1×1 convolution. The loss function employs a joint optimization strategy of Dice loss and multi-class cross-entropy loss; the Adam optimizer is used, with an initial learning rate of... The multimodal segmentation network is trained using a cosine annealing strategy with a batch size of 1 and a training cycle of 500-1000 epochs.

[0058] Then, the current patient's multimodal imaging data (CT images and MRI images) are input into a pre-trained neural network, which can then segment the data to obtain tissue regions, including at least organ regions, blood vessel regions, and lesion regions.

[0059] Finally, the label maps of the multimodal joint segmentation results are stacked along the z-axis, and the surface mesh of the tissue region is extracted using the Marching Cubes algorithm to generate a standard STL format three-dimensional model, thereby completing the construction of the patient's abdominal cavity three-dimensional model.

[0060] In this embodiment of the invention, the collection and acquisition of patient personal data are authorized by the relevant users, and the process does not violate relevant laws and regulations, nor does it violate public order and good morals.

[0061] Step S2: Based on the preset surface entry point, target puncture point, and distribution of tissue area, generate multiple candidate puncture paths; analyze the positional relationship between the candidate puncture path and the patient's surface cutting plane, the length of the candidate puncture path, and the distribution characteristics of the puncture path and tissue area, and determine the risk coefficient to screen out several preliminary preferred paths from all candidate puncture paths.

[0062] After completing the multimodal joint segmentation and three-dimensional model reconstruction of the abdominal lesions and important structures, a safe and feasible puncture path needs to be formulated based on the patient's three-dimensional abdominal model. Due to the complexity of the abdominal cavity structure, the variety of lesion morphology, and the frequent occurrence of anatomical variations, and because serious complications can easily occur if the puncture path is obstructed or important structures are damaged, it is necessary to systematically plan and quantitatively evaluate the puncture path before the operation to ensure the safety and convenience of the puncture operation.

[0063] First, multiple preset surface access points can be constructed on the patient's body surface, and multiple candidate puncture paths can be generated based on the distribution of the target puncture point and the tissue area.

[0064] Preferably, in one embodiment of the present invention, the method for obtaining candidate puncture paths includes:

[0065] The puncture needle and guide needle for abdominal paracentesis are usually designed in a straight line. Although the operation path is not strictly straight during clinical operation, it generally tends to be close to a straight line. Especially under the constraints of body position, respiratory movement and instrument guidance, the straight path has the best needle control and positioning stability. Therefore, the path generation is also mainly in the form of a straight line: along the abdominal wall surface outside the lesion area, the surface entry points are preset according to the standard anatomical safety area, and the points are evenly distributed with a spacing of 5-10 mm to ensure that all possible entry areas are covered as much as possible; each preset surface entry point is connected to the target puncture point in the lesion area to generate a three-dimensional straight line puncture path. The path is expressed in the form of a standard vector, and parameters such as the starting point coordinates and the ending point coordinates are recorded, thereby obtaining multiple initial puncture paths.

[0066] For puncture paths, blood vessels, vital organs, and other tissue structures should be avoided. Therefore, for any initial puncture path, spatial intersection detection is performed between the initial puncture path and all tissue regions in the three-dimensional abdominal cavity model. If an intersection point exists, the initial puncture path is a restricted path and is eliminated. Among all initial puncture paths, all non-restricted initial puncture paths are selected as candidate puncture paths.

[0067] Due to extensive pancreatic parenchymal necrosis, abscess formation, and large accumulation of peritoneal effusion, SAP patients often experience disordered abdominal anatomy, organ displacement, and vascular structure variations, making puncture path planning more complex than in routine cases. However, candidate puncture paths are only screened through simple intersection tests, failing to consider the potential risks during the puncture process. Therefore, further analysis of the puncture paths in the candidate path set is necessary.

[0068] In actual puncture procedures, excessively long puncture paths are easily affected by patient breathing and changes in body position, increasing the risk of complications. Conversely, shorter puncture paths offer better controllability of the needle insertion path and smaller positioning errors, reducing needle deviation caused by fluid drift in the peritoneal cavity or intestinal movement. Furthermore, the puncture angle directly affects the smoothness of needle insertion and the effectiveness of pus drainage during the procedure. In addition, SAP cavities are often located near the posterior aspect of organs or the retroperitoneum. Whether the path can effectively avoid important structures and reach the target point in a straight line is an important prerequisite for ensuring the feasibility of puncture. Therefore, by integrating the aforementioned characteristics, we analyze candidate puncture paths to determine their risk coefficients and select several preliminary preferred paths from all candidate puncture paths.

[0069] Preferably, in one embodiment of the present invention, the method for obtaining the risk coefficient of candidate puncture paths and the process of screening preliminary preferred paths include:

[0070] When the lesion area is deep, using a smaller puncture angle requires increasing the length of the puncture path to meet the depth requirements, which may lead to decreased needle stability and significantly increase the risk of damage to adjacent tissues. Therefore, the smaller the puncture angle, the greater the risk. Thus, the cosine of the angle (puncture angle) between each candidate puncture path and the patient's surface cutting plane (the surface cutting plane at the preset surface entry point corresponding to the candidate puncture path) is used as the puncture angle risk factor. The smaller the angle, the larger the cosine value, and the greater the puncture angle risk factor. The angle is set to... .

[0071] The longer the puncture path, the worse the path controllability, and the higher the risk. Therefore, the length of each candidate puncture path is used as the puncture distance risk factor.

[0072] Then, the distance between each puncture path and the tissue region is analyzed to assess the potential risk of organ damage. For each candidate puncture path, the spatial Euclidean distance between each voxel point and the nearest voxel point in the tissue region is used as a distance factor. A larger distance factor indicates a lower proximity and therefore higher safety. Therefore, the sum of the distance factors corresponding to all voxel points on each candidate puncture path is negatively correlated and normalized to correct the logical relationship, resulting in a puncture structure risk factor for each candidate puncture path. A larger risk factor indicates a higher probability of organ damage from that candidate puncture path. This negative correlation mapping and normalization can be performed using the formula... ,in, Let x represent an exponential function with the natural constant e as the base, and let x represent the independent variable.

[0073] Finally, the normalized value of the product of the puncture angle risk factor, puncture distance risk factor, and puncture structure risk factor for each candidate puncture path is used as the risk coefficient for each candidate puncture path. Based on the aforementioned logical analysis, it is known that the larger the risk coefficient, the higher the probability of intraoperative accidental injury to the corresponding candidate puncture path. Therefore, among all candidate puncture paths, those with risk coefficients less than a preset risk threshold are selected as the preliminary optimal paths. Normalization is a technique well-known to those skilled in the art, and the choice of normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0074] It should be noted that the preset risk threshold in this embodiment of the invention is 0.5, and the specific value can be adjusted according to the implementation scenario, which is not limited here.

[0075] Step S3: Based on the positional relationship between the preliminary optimized path and the sagittal and coronal planes of the three-dimensional abdominal cavity model, determine the puncture position and the direction of the gravity component of the path under the puncture position; under any preliminary optimized path, based on the direction of the gravity component of the path, analyze the distribution density characteristics of the tissue region and determine the tissue deformation interference index of the preliminary optimized path.

[0076] During actual puncture procedures, changes in the patient's position can cause a redistribution of abdominal contents due to gravity. Generally, if the puncture path is roughly perpendicular to the ground (i.e., the angle with the direction of gravity is close to 0° or 180°), the fluid in the abscess cavity will accumulate along the path under the pull of gravity, resulting in smooth drainage. However, if the path is close to a horizontal angle (90°) with the direction of gravity, the lesion will shift downwards due to gravity, potentially deviating from the original path and causing the needle tip to deviate from the target. In actual puncture procedures, it is often difficult to strictly adhere to the preoperative plan to achieve the ideal angle adjustment, especially for procedures involving the dorsal or anterior abdominal wall. During the puncture procedure, the patient is often placed in a supine or prone position to ensure stability and controllability. However, in these positions, it is often difficult to keep the puncture path perfectly parallel to the direction of gravity. In contrast, when the entry point on the body surface is significantly tilted in the sagittal plane (an anatomical plane that divides the human body into left and right parts), the surgeon often arranges for the patient to adopt a slightly lateral tilt position. In this position, the patient can usually make limited adjustments to their position during the procedure to align the puncture path with the direction of gravity as much as possible, thereby improving drainage patency and path stability. Therefore, in this embodiment of the invention, the range of possible puncture positions for the patient under the initially preferred path can be evaluated, and the direction of the gravity component of the path under these positions can be calculated.

[0077] Preferably, in one embodiment of the present invention, determining the puncture position and the direction of the gravity component of the path in the puncture position includes:

[0078] Among all the preliminary optimized paths, the preliminary optimized path with the lowest risk coefficient is used as the benchmark to ensure that the subsequent analysis process can be carried out based on the current optimal path. Therefore, the preliminary optimized path with the lowest risk coefficient is taken as the target puncture path.

[0079] Using the intersection line of the sagittal plane (an anatomical term that divides the human body into left and right parts) and the coronal plane (an anatomical term that divides the human body into anterior and posterior parts) of the three-dimensional abdominal cavity model as the central axis, and taking the central axis as a reference, a unified analysis standard can be achieved.

[0080] Then, a perpendicular line is drawn from the preset surface entry point corresponding to the target puncture path to the midline. The angle between this perpendicular line and the sagittal plane is used as the angle deviation factor. The smaller the angle deviation factor, the closer the target puncture path is to the sagittal plane direction. To improve drainage efficiency and considering the ease of patient positioning, the patient can be placed in a supine or prone position. Therefore, when the angle deviation factor is less than the preset deviation angle, the patient's position is supine or prone. In this case, the direction of the gravity component of the path is vertically downward. The preset deviation angle is set to... .

[0081] Conversely, when the angle deviation factor is greater than the preset deviation angle, it indicates a greater deviation between the target puncture path and the sagittal plane. Therefore, the patient's puncture position should be adjusted to a lateral decubitus position. Furthermore, a larger angle deviation factor results in a more complete deviation of the puncture position from the standard lateral decubitus position, making it easier to maintain. Therefore, the normalized value of the difference between the angle deviation factor and the preset deviation angle is used as the puncture position rotation coefficient. A larger rotation coefficient makes it easier for the patient to maintain the rotational position and achieve the ideal puncture position. The normalization method here can be achieved using the formula... ,in, Indicates the angle deviation factor. This indicates the preset deviation angle.

[0082] In the most standard lateral decubitus puncture position, the coronal plane is parallel to the direction of gravity. Therefore, the rotational characteristics of the patient's puncture position can be represented by the direction of the coronal plane: calculate the angle between the aforementioned vertical line and the coronal plane. This angle represents the degree of inclination of the target puncture path relative to the coronal plane. Then, multiply this angle by the rotation coefficient as the rotation estimate angle. The rotation estimate angle is the angle by which the patient needs to rotate towards the affected or healthy side. This allows us to obtain the patient's puncture position in the lateral decubitus position so that the puncture path can be close to the direction of gravity. At the same time, the direction of the vertical downward direction after shifting the rotation estimate angle is taken as the direction of the gravity component of the patient's path in this puncture position.

[0083] The direction of the gravity component of the puncture path is used to quantify the spatial angle between the initially selected path and the gravity direction in a static position. It reflects the directional difference between the puncture path and the gravity vector. In actual puncture procedures, due to the influence of gravity, intra-abdominal structures may shift position under gravitational traction or deform under pressure from surrounding tissues, thus altering their spatial relationship with the puncture path. To further improve the accuracy of safety assessment of the puncture path under actual puncture position conditions, it is necessary to analyze the distribution density characteristics of the tissue regions traversed by the initially selected path based on the direction of the gravity component of the path. This allows for the measurement of the tissue deformation interference index for each initially selected path, reflecting the compression effect between tissue structures.

[0084] Preferably, in one embodiment of the present invention, the method for obtaining the tissue deformation interference index includes:

[0085] Please see Figure 3 The diagram illustrates a method flowchart for obtaining tissue deformation interference indicators according to an embodiment of the present invention. The method includes the following steps:

[0086] Step S301: Select any tissue region as the test region, obtain the maximum two-dimensional cross-section of the test region in the direction of the path gravity component, analyze the density characteristics of the tissue region through which the maximum two-dimensional cross-section passes, and determine the gravity compression coefficient of the test region.

[0087] The greater the mass density of an tissue region, the greater the influence of gravity on it. Therefore, for a given tissue region, if the mass of the tissue region in the opposite direction of the path gravity component is greater, the pressure on that tissue region will be greater.

[0088] First, obtain the quality coefficient of each tissue region: The quality of a tissue region can be approximated by the HU value. In CT detection, the HU value can represent density characteristics. The larger the value, the higher the density. Therefore, the mean of the HU values ​​of all voxels in each tissue region is used as the density coefficient of each tissue region. Then, the normalized value of the product of the number of voxels in each tissue region and the density coefficient is used as the quality coefficient of each tissue region. The larger the quality coefficient, the greater the compressive effect that tissue region will have on other tissue regions under the action of gravity.

[0089] Select any tissue region as the test region, and then obtain the maximum two-dimensional cross-section of the test region along the path of the gravity component. Taking the test region as the starting position, all tissue regions that the maximum two-dimensional cross-section of the test region passes through in the opposite direction of the path of the gravity component are taken as target regions. The sum of the mass coefficients of all target regions is normalized and used as the gravity compression coefficient of the test region. The larger the gravity compression coefficient of the test region, the stronger the compression exerted on the test region by other tissue regions under the action of gravity, and the more likely the test region is to undergo tissue deformation. Normalization is a technique well known to those skilled in the art. The choice of normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0090] Step S302: Analyze the distribution characteristics of tissue regions in the vicinity of the area to be tested to determine the local spatial density of the area to be tested.

[0091] Furthermore, whether the local anatomical space of the test area is tightly bounded by other tissue regions is also an important factor in assessing the degree of tissue deformation. Therefore, the volume of the smallest bounding volume of the test area and all adjacent (directly adjacent, i.e., intersecting) tissue regions is obtained. The smaller this volume, the tighter the space of the test area is bounded by other tissue regions, and the easier it is to form "spatial competition". Therefore, the negative correlation mapping and normalization of this volume is used as the local spatial density. The larger the local spatial density, the higher the density of other structures in the local space of the test area, and the higher the degree of tissue deformation. The negative correlation mapping and normalization here can be performed using the formula... ,in, Let x represent an exponential function with the natural constant e as the base, and let x represent the independent variable.

[0092] Step S303: Combine the gravity compression coefficient and local spatial density of the area to be tested to obtain the tissue deformation coefficient of the area to be tested.

[0093] Based on the analysis in the preceding steps, it is known that the gravitational compression coefficient and local spatial density of the test area are positively correlated with the degree of tissue deformation. Therefore, the normalized product of the gravitational compression coefficient and local spatial density of the test area is used as the tissue deformation coefficient of the test area. The larger the tissue deformation coefficient, the greater the degree of deformation of the test area under the current puncture position. Normalization is a technique well-known to those skilled in the art. The normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0094] Step S304: For any preliminary optimization path, the tissue deformation coefficients of the tissue regions traversed by all voxel points on the preliminary optimization path are combined to determine the tissue deformation interference index of the preliminary optimization path.

[0095] The aforementioned three sub-steps quantify the degree of deformation that may occur in each tissue region. In this sub-step, the tissue deformation interference index corresponding to each preliminary optimization path can be calculated based on these sub-steps.

[0096] For any preliminary optimal path, the sum of the tissue deformation coefficients of the tissue regions to which each voxel point belongs on the preliminary optimal path is normalized and used as the tissue deformation interference index for that preliminary optimal path. This index reflects the comprehensive deformation risk of the preliminary optimal path, and the larger the value, the greater the risk, and the lower the probability of it being the final optimal puncture path. Normalization is a technique well known to those skilled in the art, and the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0097] Step S4: Combine the tissue deformation interference index of the preliminary preferred path with the risk coefficient to determine the optimal puncture path and alternative puncture paths among all the preliminary preferred paths.

[0098] In step S2, the risk coefficient of the preliminary optimized path is obtained for static assessment of the probability of intraoperative accidental damage to the preliminary optimized path. In step S3, the risk probability of dynamic tissue deformation that each preliminary optimized path may have under the puncture position is quantified. Therefore, by combining the two, a more comprehensive and complete index can be obtained, so that the intraoperative risk of the final optimal puncture path is minimized, effectively improving the accuracy of path planning and ensuring the puncture effect.

[0099] Preferably, in one embodiment of the present invention, determining the optimal puncture path and alternative puncture paths among all preliminary preferred paths includes:

[0100] Based on the foregoing analysis, both the tissue deformation interference index and the risk coefficient are positively correlated with the probability of intraoperative risks associated with the initially selected puncture path. Therefore, the normalized product of the tissue deformation interference index and the risk coefficient for each initially selected path is used as the puncture risk index for that path. The larger the value, the lower the probability of it being the optimal puncture path. Normalization is a technique well-known to those skilled in the art, and the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0101] Therefore, the preliminary optimal path corresponding to the minimum puncture risk index is taken as the optimal puncture path, and the remaining preliminary optimal paths are taken as alternative puncture paths.

[0102] In summary, we first acquired and segmented CT and MRI images of the patient's abdominal cavity. After identifying the tissue regions, we integrated these images to construct a three-dimensional model of the abdominal cavity. This model visually displays the complex anatomical relationships, aiding in the identification of abdominal structures and the generation of multiple feasible candidate puncture paths. Since SAP abscesses are often located posterior to organs or in the retroperitoneum, the feasibility of the puncture path is paramount, as it effectively avoids important structures and directly reaches the target. Furthermore, the length of the puncture path directly affects the controllability of the needle insertion path. Therefore, we calculated the risk coefficient of candidate puncture paths based on these characteristics to select a preliminary optimal path from all candidates. The impact of gravity on tissue regions varies depending on the patient's puncture position. Therefore, we assessed the possible puncture positions the patient might take under the preliminary optimal path by analyzing the positional relationship between the sagittal and coronal planes and the preliminary optimal path, and calculated the direction of the gravity component of the path under this puncture position. This helps in subsequent analysis of the risk of path deviation due to tissue displacement during the procedure. Furthermore, based on the direction of the gravity component of the path, the distribution density characteristics of the tissue region are tracked, the compression intensity of the surrounding tissue region on each initially selected path is quantified, and a tissue deformation interference index is introduced. Finally, the tissue deformation interference index of the initially selected path is combined with the risk coefficient to achieve a re-screening of the initially selected path, determine the optimal puncture path and alternative puncture paths, which helps to avoid high deformation risk areas in advance, ensure that the puncture path accurately avoids key tissue areas and maintains stability, and improves path stability.

[0103] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0104] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for planning the puncture path for paracentesis drainage in severe acute pancreatitis, characterized in that, The method includes: CT and MRI images of the patient's abdominal cavity were acquired, segmented, and used to determine tissue regions and construct a three-dimensional model of the patient's abdominal cavity. Based on the preset surface entry points, target puncture points, and tissue area distribution, multiple candidate puncture paths are generated; the positional relationship between the candidate puncture paths and the patient's surface cutting plane, the length of the candidate puncture paths, and the distribution characteristics of the puncture paths and tissue areas are analyzed to determine the risk coefficient for screening several preliminary preferred paths from all candidate puncture paths. Based on the positional relationship between the preliminary optimized path and the sagittal and coronal planes of the three-dimensional abdominal cavity model, the puncture position and the direction of the gravity component of the path under the puncture position are determined; under any preliminary optimized path, based on the direction of the gravity component of the path, the distribution density characteristics of the tissue region are analyzed, and the tissue deformation interference index of the preliminary optimized path is determined. By combining the tissue deformation interference index of the preliminary optimized path with the risk coefficient, the optimal puncture path and alternative puncture paths are determined among all the preliminary optimized paths. Determining the puncture position and the direction of the gravity component of the path in that position includes: selecting the preliminary preferred path with the lowest risk coefficient from all preliminary preferred paths as the target puncture path; using the intersection of the sagittal and coronal planes of the three-dimensional abdominal model as the central axis; drawing a perpendicular line to the central axis through the preset surface access point corresponding to the target puncture path, and using the angle between the perpendicular line and the sagittal plane as the angle deviation factor. When the angle deviation factor is less than the preset deviation angle, the patient's puncture position is supine or prone, and the direction of the gravity component of the path is vertically downward; when the angle deviation factor is greater than the preset deviation angle, the normalized value of the difference between the angle deviation factor and the preset deviation angle is used as the puncture position rotation coefficient; using the product of the angle between the perpendicular line and the coronal plane and the position rotation coefficient as the rotation estimation angle; rotating the patient towards the affected or healthy side by the rotation estimation angle to obtain the puncture position, and using the direction after shifting the vertically downward direction by the rotation estimation angle as the direction of the gravity component of the path in that puncture position; The method for obtaining the tissue deformation interference index includes: selecting a tissue region as the test region, obtaining the maximum two-dimensional cross-section of the test region along the path gravity component direction, analyzing the density characteristics of the tissue region traversed by the maximum two-dimensional cross-section, and determining the gravity compression coefficient of the test region; analyzing the distribution characteristics of the tissue regions in the vicinity of the test region and determining the local spatial density of the test region; normalizing the product of the gravity compression coefficient and the local spatial density corresponding to the test region as the tissue deformation coefficient of the test region; and for any preliminary optimized path, normalizing the sum of the tissue deformation coefficients of the tissue regions to which each voxel point belongs on the preliminary optimized path as the tissue deformation interference index of the preliminary optimized path.

2. The method for planning the puncture path for paracentesis drainage in severe acute pancreatitis according to claim 1, characterized in that, Methods for obtaining candidate puncture paths include: The line connecting each preset surface entry point to the target puncture point is used as the initial puncture path. For any initial puncture path, spatial intersection detection is performed between the initial puncture path and all tissue regions in the 3D model of the abdominal cavity. If an intersection point exists, the initial puncture path is a restricted path. Among all initial puncture paths, all initial puncture paths that are not restricted areas are selected as candidate puncture paths.

3. The method for planning the puncture path for paracentesis drainage in severe acute pancreatitis according to claim 1, characterized in that, Methods for obtaining risk coefficients include: The cosine of the angle between each candidate puncture path and the patient's surface cutting plane is used as the puncture angle risk factor. The length of each candidate puncture path is used as a puncture distance risk factor; On each candidate puncture path, the spatial Euclidean distance between each voxel point and the nearest tissue region voxel point is used as a distance factor. The sum of the distance factors corresponding to all voxel points on each candidate puncture path is negatively correlated and normalized, and then used as the puncture structure risk factor for each candidate puncture path. The normalized value of the product of the puncture angle risk factor, puncture distance risk factor, and puncture structure risk factor for each candidate puncture path is used as the risk coefficient for each candidate puncture path.

4. The method for planning the puncture path for paracentesis drainage in severe acute pancreatitis according to claim 1, characterized in that, Methods for obtaining the gravitational compression coefficient include: The mean HU value of all voxel points in each tissue region is used as the density coefficient of each tissue region, and the normalized value of the product of the number of voxel points and the density coefficient in each tissue region is used as the quality coefficient of each tissue region. Obtain the maximum two-dimensional cross-section of the area to be measured along the path gravity component direction. Take all tissue regions that the maximum two-dimensional cross-section passes through in the opposite direction of the path gravity component as the target area. The sum of the mass coefficients of all target areas is normalized and used as the gravity compression coefficient of the area to be measured.

5. The method for planning the puncture path for paracentesis drainage in severe acute pancreatitis according to claim 1, characterized in that, Methods for obtaining local spatial density include: The local spatial density of the test region is obtained by negatively correlating and normalizing the volume of the smallest bounding volume of the region to be tested and all tissue regions intersecting with the test region.

6. The method for planning the puncture path for paracentesis drainage in severe acute pancreatitis according to claim 1, characterized in that, The optimal puncture path and alternative puncture paths were determined from all preliminary preferred paths, including: The normalized value of the product of the tissue deformation interference index and the risk coefficient for each preliminary selected path is used as the puncture risk index for each preliminary selected path. The preliminary preferred path corresponding to the minimum puncture risk index is taken as the optimal puncture path, and the remaining preliminary preferred paths are taken as alternative puncture paths.

7. The method for planning the puncture path for paracentesis drainage in severe acute pancreatitis according to claim 1, characterized in that, Methods for obtaining organizational regions include: The patient's abdominal CT and MRI images are used as inputs to a pre-trained neural network, which outputs tissue regions, including at least organ regions, vascular regions, and lesion regions.

8. The method for planning the puncture path for paracentesis drainage in severe acute pancreatitis according to claim 1, characterized in that, Methods for obtaining the preliminary optimal path include: Among all candidate puncture paths, those with a risk coefficient less than a preset risk threshold are selected as the preliminary optimal paths.

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