Simulation-based target resection region determination method and apparatus

Abstract

The application relates to a simulation-based target resection region determination method and device. The method comprises the following steps: obtaining a heart simulation model and corresponding blood flow field information; determining a first target region in an interventricular septum model according to a left heart lumen model and the blood flow field information; performing stress calculation according to the interventricular septum model to determine a second target region in the interventricular septum model; determining an initial target region according to the first target region and the second target region; determining a thickness range of the initial target region at a time point of the end-systole according to the interventricular septum model; determining a target resection thickness according to a preset basic thickness and the thickness range; and determining a first target resection region according to the heart simulation model, the initial target region, the target resection thickness and the blood flow field information. The method can accurately determine a myocardial tissue region required to be resected.

Description

Simulation-based method and apparatus for determining the target resection region Technical Field

[0001] This application relates to the field of medical simulation technology, and in particular to a method and apparatus for determining the target resection area based on simulation. Background Technology

[0002] Hypertrophic cardiomyopathy (HCM) is a pathological heart disease caused by thickening of the myocardium. It usually leads to left ventricular outflow tract obstruction, severely affecting cardiac function and even causing life-threatening conditions. Treatment typically involves removing the thickened myocardial tissue to restore normal blood flow to the heart. In traditional techniques, staff usually determine the area of ​​myocardial tissue to be removed based on their experience and imaging data.

[0003] In traditional techniques, it is impossible for staff to determine in advance the area of ​​myocardial tissue to be removed and its impact on the restoration of cardiac blood flow. In practice, the myocardial tissue needs to be removed step by step while monitoring the condition of the heart before proceeding to the next step. The duration of the operation depends on the surgeon's experience, especially in cases of occult obstruction hypertrophic cardiomyopathy, where accurately determining the area of ​​myocardial tissue to be removed is even more challenging.

[0004] Therefore, there is an urgent need for a method that can accurately determine the area of ​​myocardial tissue that needs to be removed. Summary of the Invention

[0005] Therefore, it is necessary to provide a simulation-based method and apparatus for determining the target resection area that can accurately identify the area of ​​myocardial tissue to be resected, in order to address the above-mentioned technical problems.

[0006] In a first aspect, this application provides a simulation-based method for determining a target resection region. The method includes: acquiring a cardiac simulation model and corresponding blood flow field information; the cardiac simulation model includes a left cardiac cavity model and a ventricular septum model; the blood flow field information includes blood flow field information of the left cardiac cavity; determining a first target region in the ventricular septum model based on the left cardiac cavity model and the blood flow field information; performing stress calculations based on the ventricular septum model to determine a second target region in the ventricular septum model; determining an initial target region based on the first target region and the second target region; determining the thickness range of the initial target region at end-systole based on the ventricular septum model; determining a target resection thickness based on a preset baseline thickness and the thickness range; and determining a first target resection region based on the cardiac simulation model, the initial target region, the target resection thickness, and the blood flow field information.

[0007] In one embodiment, acquiring the cardiac simulation model and the corresponding blood flow field information includes:

[0008] Acquire a 4D image of the target heart and a first simulation boundary condition; establish a simulation model of the target heart based on the 4D image, and determine the heart simulation model; the heart simulation model includes a left ventricular cavity model and a ventricular septum model; perform fluid dynamics simulation calculations based on the first simulation boundary condition and the left ventricular cavity model to determine the blood flow field information corresponding to the left ventricular cavity model.

[0009] In one embodiment, determining the first target region in the interventricular septum model based on the left ventricular lumen model and the corresponding blood flow field information includes: determining the obstruction region in the left ventricular lumen model based on a preset blood flow field information threshold range, the left ventricular lumen model, and the blood flow field information; and determining the first target region in the interventricular septum model based on the correspondence between the left ventricular lumen model and the interventricular septum model in the cardiac simulation model and the obstruction region.

[0010] In one embodiment, the ventricular septum model includes multiple volume elements, and each volume element includes multiple nodes. The step of performing stress calculations based on the ventricular septum model to determine a second target region within the ventricular septum model includes: determining the coordinates of multiple nodes of the target volume element at the initial stage of systole and at the end of systole based on the ventricular septum model; determining a first distance matrix between each node and other nodes of the same volume element at the initial stage of systole based on the coordinates of the multiple nodes of the target volume element at the end of systole; determining a second distance matrix between each node and other nodes of the same volume element at the end of systole based on the coordinates of the multiple nodes of the target volume element at the end of systole; constructing a displacement gradient tensor of the volume element based on the first and second distance matrices, and determining the strain tensor matrix corresponding to the target volume element; determining the stress tensor matrix corresponding to the target volume element based on a preset tensor transformation relationship and the strain tensor matrix corresponding to the target volume element; and, based on the stress tensor matrices corresponding to each volume element and a preset stress threshold, defining the region formed by volume elements whose equivalent stress in the stress tensor is greater than the preset stress threshold as the second target region.

[0011] In one embodiment, determining the initial target region based on the first target region and the second target region includes: taking the union of the first target region and the second target region as the initial target region.

[0012] In one embodiment, determining the target cut-off thickness based on the preset base thickness and the thickness range includes: subtracting the base thickness from the upper and lower limits of the thickness range to determine the target thickness range; and using the median value of the target thickness range as the target cut-off thickness.

[0013] In one embodiment, determining the first target resection area based on the cardiac simulation model, the initial target area, the target resection thickness, and the blood flow field information includes: projecting the initial target area onto the cardiac simulation model under a long-axis section view of the heart to determine a two-dimensional resection area; determining the blood flow field information corresponding to the obstruction area based on the obstruction area and the blood flow field information; determining the thickness adjustment coefficients corresponding to multiple two-dimensional sub-regions of the two-dimensional resection area in the long-axis direction based on a preset long-axis direction and the blood flow field information corresponding to the obstruction area; and determining the first target resection area based on the multiple thickness adjustment coefficients, the target resection thickness, and the two-dimensional resection area.

[0014] In one embodiment, determining the first target excision region based on the plurality of thickness adjustment coefficients, the target excision thickness, and the two-dimensional excision region includes: multiplying the plurality of thickness adjustment coefficients by the target excision thickness to determine the thickness of the plurality of two-dimensional sub-regions of the two-dimensional excision region in the long axis direction; and determining the first target excision region based on the thickness of the plurality of two-dimensional sub-regions of the two-dimensional excision region in the long axis direction and the two-dimensional excision region.

[0015] In one embodiment, the method further includes: updating the heart simulation model according to the first target resection region; obtaining second simulation boundary conditions; performing fluid dynamics simulation calculations based on the second simulation boundary conditions and the updated left heart cavity model to determine the blood flow field information corresponding to the updated left heart cavity model; if the blood flow field information corresponding to the updated left heart cavity model meets preset conditions, then the first target resection region is taken as the final resection region; if the blood flow field information corresponding to the updated left heart cavity model does not meet preset conditions, then determining a second target resection region based on the updated heart simulation model and the blood flow field information corresponding to the updated left heart cavity model, and updating the heart simulation model based on the second target resection region until the blood flow field information corresponding to the updated left heart cavity model meets preset conditions, and taking the first target resection region and at least one second target resection region as the final resection region.

[0016] Secondly, this application also provides a simulation-based device for determining the target excision region. The device includes:

[0017] The acquisition module is used to acquire a cardiac simulation model and corresponding blood flow field information; the cardiac simulation model includes a left cardiac cavity model and a ventricular septum model; the blood flow field information includes blood flow field information of the left cardiac cavity.

[0018] The first determining module is used to determine the first target region in the interventricular septum model based on the left heart cavity model and the blood flow field information;

[0019] The second determining module is used to perform stress calculations based on the interventricular septum model to determine the second target region in the interventricular septum model.

[0020] The third determining module is used to determine an initial target region based on the first target region and the second target region;

[0021] The fourth determining module is used to determine the thickness range of the initial target region at the end of systole based on the interventricular septum model.

[0022] The fifth determining module is used to determine the target cut-off thickness based on the preset base thickness and the thickness range.

[0023] The sixth determining module is used to determine the first target resection area based on the heart simulation model, the initial target area, the target resection thickness, and the blood flow field information.

[0024] The aforementioned simulation-based method and apparatus for determining the target resection area, after acquiring a cardiac simulation model and corresponding blood flow field information, determines a first target area in the interventricular septum model based on the left cardiac cavity model and the blood flow field information; performs stress calculations based on the interventricular septum model to determine a second target area in the interventricular septum model; then determines an initial target area based on the first and second target areas; next, determines the thickness range of the initial target area at end-systole based on the interventricular septum model; finally, determines the target resection thickness based on a preset baseline thickness and the thickness range; and determines the first target resection area based on the cardiac simulation model, the initial target area, the target resection thickness, and the blood flow field information, thereby accurately determining the area of ​​myocardial tissue to be resected. Attached Figure Description

[0025] Figure 1 shows the application environment of a simulation-based target excision region determination method in one embodiment;

[0026] Figure 2 is a flowchart illustrating a simulation-based method for determining the target excision region in one embodiment;

[0027] Figure 3 is a schematic diagram of the process for obtaining a cardiac simulation model and corresponding blood flow field information in one embodiment;

[0028] Figure 4 is a schematic diagram of a heart simulation model under a long-axis section view of the heart in one embodiment;

[0029] Figure 5 is a planar schematic diagram of the first target resection region in a cardiac simulation model in one embodiment;

[0030] Figure 6 is a three-dimensional schematic diagram of the first target resection region in a cardiac simulation model in one embodiment;

[0031] Figure 7 is a three-dimensional schematic diagram of the first target resection region in a cardiac simulation model in another embodiment;

[0032] Figure 8 is a three-dimensional schematic diagram of the final resected area in a cardiac simulation model in one embodiment;

[0033] Figure 9 is a structural block diagram of a simulation-based target excision region determination device in one embodiment;

[0034] Figure 10 is an internal structure diagram of a computer device in one embodiment;

[0035] Figure 11 is a flowchart illustrating a simulation-based method for determining the target excision region in another embodiment;

[0036] Figure 12 is a schematic diagram of a ventricular septum model in one embodiment. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0038] Hypertrophic cardiomyopathy (HCM) is a pathological heart disease caused by thickening of the myocardium. It often leads to obstruction of the left ventricular outflow tract, severely affecting cardiac function and even endangering life. Septal myectomy typically restores normal blood flow by removing the thickened myocardial tissue. Existing surgical methods include traditional open-chest surgery, such as the Morrow procedure, which treats HCM by directly removing the myocardium from the ventricular septum.

[0039] Cardiac imaging examinations: Cardiac imaging examinations, such as echocardiography, CT, and MRI, are the most commonly used tools for assessing hypertrophic cardiomyopathy. Echocardiography is particularly often used to assess the degree of left ventricular outflow tract obstruction, myocardial thickness, and interventricular septum morphology. Through ultrasound examination, physicians can visually understand the cardiac anatomy, the location of the obstruction, and the extent of myocardial thickening, helping to determine the area of ​​myocardium to be removed. Its limitations are: although cardiac imaging examinations can provide relatively intuitive information about cardiac structure, the perspective presented depends on the operator's experience, and in some cases, such as in occult obstruction or irregular morphology, its accuracy and precision are limited. Furthermore, imaging examinations cannot comprehensively assess the hemodynamic status of the heart, especially lacking sufficient predictability regarding potential postoperative blood flow changes and obstruction.

[0040] Isoproterenol Provocation Test: In traditional surgical procedures, the isoproterenol provocation test is often used to assess the effectiveness of the current myocardial resection surgery (whether residual obstruction exists). This test involves injecting isoproterenol to stimulate the patient's cardiac stress response, simulating the postoperative cardiac workload. Through this test, physicians can detect whether residual obstruction or hemodynamic instability will occur postoperatively. Its limitations are as follows: While the isoproterenol provocation test can help assess cardiac function and obstruction status postoperatively, it has several problems: First, the test can only be performed after surgery and cannot provide sufficient information for preoperative planning. Second, the provocation test is highly dependent on the patient's physiological state, and not all patients are suitable for this test. Furthermore, the test only reflects the short-term postoperative effect and cannot accurately predict long-term prognosis.

[0041] Cardiac hemodynamic assessment: In traditional surgery, catheterization (such as cardiac catheterization) is often used to directly measure cardiac hemodynamic parameters, such as pressure gradient and cardiac output. These examinations help assess the degree of outflow tract obstruction and provide some reference data for surgery. Its limitations are: while cardiac catheterization can directly measure hemodynamic parameters, this method is invasive and cannot accurately assess the overall cardiac blood flow status. Catheterization only provides data at a specific point in time, lacking dynamic monitoring of continuous postoperative blood flow changes, and cannot comprehensively predict possible postoperative blood flow changes and long-term prognosis.

[0042] Intraoperative visual observation and experience-based judgment: In traditional surgeries, such as ventricular septal myocardectomy (Morrow procedure), the anatomical view of the heart is limited after an aortic incision. Surgeons typically determine the extent of myocardial resection based on experience and anatomical information from imaging data. During the procedure, the surgeon adjusts the resection plan based on the heart's real-time performance and physiological responses. Its limitations lie in the fact that this anatomical and experience-based judgment lacks precise quantification and systematicity, and it is difficult to fully assess and optimize the effectiveness of the resection plan. Precise hemodynamic assessment cannot be performed intraoperatively, nor can the post-resection effect be simulated in real time, potentially leading to inadequate resection plans and consequently affecting postoperative outcomes.

[0043] However, the above methods have problems such as difficulty in localization, incomplete resection, and inaccurate postoperative assessment.

[0044] The simulation-based target excision region determination method provided in this application embodiment can be applied to the application environment shown in Figure 1. The terminal 102 communicates with the server 104 via a network. A data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104 or placed on a cloud or other network server. The server 104 is used to execute the simulation-based target excision region determination method. The terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can be smart speakers, smart TVs, smart air conditioners, smart vehicle devices, etc. Portable wearable devices can be smartwatches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0045] To address the aforementioned problems, in one embodiment of this application, as shown in Figure 2, a method for determining the target excision region through simulation is provided, comprising the following steps:

[0046] Step 201: Obtain the heart simulation model and the corresponding blood flow field information.

[0047] The cardiac simulation model includes a left cardiac cavity model and a ventricular septum model; the blood flow field information includes the blood flow field information of the left cardiac cavity. It should be noted that the cardiac simulation model is a 4D image of the heart of the object whose myocardial region needs to be removed, obtained through simulation modeling using existing simulation software. The left cardiac cavity model is a simulation model of the left cardiac cavity of the object whose myocardial region needs to be removed during systole, including the left ventricle, left atrium, part of the aorta, and its cavity. As shown in Figure 12, the ventricular septum model is a simulation model of the ventricular septum of the object whose myocardial region needs to be removed during systole; the ventricular septum is the septal muscle between the left and right ventricles in the heart. It should be noted that the heart's pumping process includes systole and diastole. In this embodiment, only the relevant data during systole are considered. The blood flow field information is the flow field information of each cavity unit in the cavity portion of the left cardiac cavity model. It should be noted that the lumen portion of the left heart cavity model comprises multiple cavity units. Blood flow field information corresponds to each cavity unit, including flow velocity, wall shear stress, and pressure. The flow velocity is the blood velocity at a specific cavity unit, the wall shear stress is the wall shear stress at the cavity unit near the muscle of the left heart, and the pressure is the pressure at a specific cavity unit. The blood flow field information is obtained through fluid dynamics simulation calculations based on the left heart cavity model using pre-defined boundary conditions.

[0048] Step 202: Based on the left heart cavity model and blood flow field information, determine the first target region in the interventricular septum model.

[0049] The primary target region is the muscular region in the left ventricular model corresponding to the obstructed area near the interventricular septum. The obstructed area is the region near the interventricular septum in the left heart where blood flow is impeded during the process of blood flow.

[0050] It should be noted that, since the interventricular septum is the muscular barrier between the left and right ventricles in the heart, there is a corresponding relationship between the muscular region near the interventricular septum in the left ventricular cavity model and the interventricular septum itself.

[0051] Based on the pre-set threshold range of blood flow field information, and based on the left ventricular cavity model and the blood flow field information, the obstruction region near the interventricular septum in the left ventricular cavity model is determined. Then, based on the correspondence between the left ventricular cavity model and the interventricular septum model and the obstruction region, the first target region in the interventricular septum model is determined.

[0052] It should be noted that the blood flow field information threshold range is a pre-set threshold range corresponding to each item in the blood flow field information. For example, in this embodiment, the blood flow field information includes flow velocity, wall shear stress, and pressure; therefore, the blood flow field information threshold range includes the flow velocity threshold range, the wall shear stress threshold range, and the pressure threshold range. In this embodiment, regions where the corresponding flow velocity, wall shear stress, and pressure are all outside their respective threshold ranges are selected as obstruction regions.

[0053] In other embodiments of this application, if any one of the flow field information items in the blood flow field information corresponding to a certain region in the left ventricular model is not within the corresponding threshold range, it is also determined to be an obstruction region.

[0054] Step 203: Perform stress calculations based on the interventricular septum model to determine the second target region in the interventricular septum model.

[0055] The second target region is the area in the interventricular septum model where the stress tensor is greater than the preset stress threshold, i.e., the part of the myocardium that needs to be removed.

[0056] It should be noted that the interventricular septum model consists of multiple volume units, and each volume unit contains multiple nodes.

[0057] Based on the interventricular septum model, the coordinates of each node of the target volume element at the initial stage of contraction and at the final stage of contraction are determined. Then, based on the coordinates of each node at the initial stage of contraction, a first distance matrix between each node and other nodes of the same element at the initial stage of contraction is calculated. Based on the coordinates of each node at the final stage of contraction, a second distance matrix between each node and other nodes of the same element at the final stage of contraction is calculated. Then, based on the first and second distance matrices, the displacement gradient tensor of the volume element is constructed, and the strain tensor matrix corresponding to the target volume element is calculated. Then, based on a preset tensor transformation relationship and the strain tensor matrix corresponding to the target volume element, the stress tensor matrix corresponding to the target volume element is determined. Finally, based on the stress tensor matrix corresponding to each volume element and a preset stress threshold, the region formed by volume elements whose stress tensor modulus is greater than the preset stress threshold is taken as the second target region.

[0058] In this model, the initial contraction time is the beginning of the contraction period, and the final contraction time is the end of the contraction period. The first distance matrix includes the distances between each node of the target element and other nodes of the same element at the initial contraction time, and the second distance matrix includes the distances between each node of the target element and other nodes of the same element at the final contraction time. The strain tensor matrix is ​​obtained by subtracting the corresponding vectors in the first distance matrix from the relative position vectors in the second distance matrix, resulting in a set of relative displacement vectors. Based on the geometric relationship between displacement and position changes, a displacement gradient tensor describing the overall deformation of the element is constructed by solving a system of linear equations. This displacement gradient tensor is decomposed, and its symmetric part is extracted to obtain the strain tensor matrix representing the strain tensor corresponding to the target element. The tensor transformation relationship is a pre-defined transformation relationship between the strain tensor and the stress tensor. The stress tensor matrix is ​​the stress tensor matrix representing the stress tensor corresponding to the target element. The stress threshold is a pre-defined threshold for the stress tensor.

[0059] Step 204: Determine the initial target area based on the first target area and the second target area.

[0060] The initial target area is the area that needs to be removed, as initially determined in the ventricular septum model.

[0061] The union of the first target region and the second target region is taken as the initial target region.

[0062] Step 205: Based on the ventricular septum model, determine the thickness range of the initial target region at the end of systole.

[0063] End-systole is the moment when systole is about to end, that is, the instant the aortic valve closes.

[0064] The thickness of the muscles at various points in the initial target region of the ventricular septum model at the end of systole is measured to determine the maximum and minimum thicknesses, which are used as the upper and lower limits of the thickness range, respectively, thereby determining the thickness range of the initial target region at the end of systole.

[0065] Step 206: Determine the target cut thickness based on the preset base thickness and thickness range.

[0066] The preset base thickness is the minimum thickness that needs to be retained in the pre-defined ventricular septum model, and the target resection thickness is the thickness of the muscle that needs to be removed in the ventricular septum model.

[0067] Step 207: Determine the first target resection area based on the cardiac simulation model, the initial target area, the target resection thickness, and the blood flow field information.

[0068] The primary target resection area is the region of muscle that needs to be removed in the ventricular septum model.

[0069] The initial target region is projected onto a cardiac simulation model under a long-axis section view of the heart to determine the two-dimensional resection region. Based on the obstruction region and blood flow field information, the blood flow field information corresponding to the obstruction region is determined. Then, based on the preset long-axis direction and the blood flow field information corresponding to the obstruction region, the thickness adjustment coefficients corresponding to multiple two-dimensional sub-regions of the two-dimensional resection region in the long-axis direction are determined. Finally, based on the multiple thickness adjustment coefficients, the target resection thickness, and the two-dimensional resection region, the first target resection region is determined.

[0070] The long-axis section view of the heart is a section view with the long axis as the reference, which is the axis pointing from the center of the aortic valve to the apex of the heart. The two-dimensional resection region is the projection area of ​​the initial target region onto the cardiac simulation model under the long-axis section view of the heart. The thickness adjustment coefficient is the thickness weighting coefficient corresponding to multiple two-dimensional sub-regions of the two-dimensional resection region along the long axis direction.

[0071] Based on the preset long axis direction, and according to the blood flow field information corresponding to the obstruction area, the thickness adjustment coefficients corresponding to multiple two-dimensional sub-regions of the two-dimensional resection area in the long axis direction are determined. Specifically, the multiple two-dimensional sub-regions of the two-dimensional resection area in the long axis direction are standardized according to the blood flow field information corresponding to the obstruction area, so as to obtain the thickness adjustment coefficients corresponding to multiple two-dimensional sub-regions.

[0072] In the above simulation-based method for determining the target resection area, after acquiring the cardiac simulation model and the corresponding blood flow field information, the first target area in the interventricular septum model is determined based on the left cardiac cavity model and the blood flow field information; stress calculation is performed based on the interventricular septum model to determine the second target area in the interventricular septum model; then, based on the first and second target areas, the initial target area is determined; then, based on the interventricular septum model, the thickness range of the initial target area at end-systole is determined; finally, based on the preset baseline thickness and thickness range, the target resection thickness is determined; and based on the cardiac simulation model, the initial target area, the target resection thickness, and the blood flow field information, the first target resection area is determined, thereby accurately determining the area of ​​myocardial tissue to be resected.

[0073] It should be noted that this application integrates data from multiple dimensions through virtual simulation, including information on the patient's individual anatomical structure, myocardial function, and hemodynamics, and performs multiple rounds of iterative optimization. Through precise planning of the resection area and simulation testing of postoperative blood flow, the resection plan is ensured to be optimal before surgery, thereby reducing postoperative complications and residual obstruction. Furthermore, by combining specific data of the patient with CFD simulation, personalized myocardial resection plans are provided. Unlike traditional methods that rely on physician experience and intraoperative judgment, this invention provides scientific, data-driven resection plans through computational models, thereby improving the accuracy and safety of preoperative planning.

[0074] In other embodiments of this application, as shown in Figure 3, obtaining the cardiac simulation model and the corresponding blood flow field information includes:

[0075] Step 301: Obtain 4D images of the target heart and the first simulation boundary conditions.

[0076] The target heart is the heart of the object whose myocardial region needs to be removed. The 4D image is a three-dimensional image of the target heart over time. The 4D image is obtained through cardiac imaging examinations, such as echocardiography, CT, and MRI. The first simulation boundary conditions are pre-defined boundary conditions required for fluid dynamics simulation calculations, including cardiac output flow, atrial systolic and diastolic pressures, ventricular systolic and diastolic pressures, and heart rate.

[0077] Step 302: Based on the 4D images, establish a simulation model of the target heart and determine the heart simulation model.

[0078] The cardiac simulation model includes a left ventricular cavity model and a ventricular septum model. It is a model obtained by simulating and modeling a 4D image of the heart of an object from which the myocardial region needs to be removed, using existing simulation software. The cardiac simulation model includes a left ventricular cavity model and a ventricular septum model.

[0079] Step 303: Based on the first simulation boundary conditions and the left heart cavity model, perform fluid dynamics simulation calculations to determine the blood flow field information corresponding to the left heart cavity model.

[0080] Fluid dynamics simulation is a technique that uses mathematical methods to simulate fluids. The blood flow field information provided here refers to the blood flow field of the left heart. It should be noted that the inner cavity of the left heart model comprises multiple cavity units, and the blood flow field information corresponds to each cavity unit. This information includes the flow velocity, wall shear stress, and pressure for each cavity unit. The flow velocity is the blood velocity at a specific cavity unit, the wall shear stress is the wall shear stress at the cavity unit near the muscle of the left heart, and the pressure is the pressure at a specific cavity unit.

[0081] Based on the left ventricular model, the first simulation boundary condition is used as a constraint to perform fluid dynamics simulation calculations, thereby obtaining the blood flow field information corresponding to the left ventricular model.

[0082] In other embodiments of this application, determining the first target region in the interventricular septum model based on the left ventricular cavity model and the corresponding blood flow field information includes:

[0083] Step 1: Based on the preset blood flow field information threshold range, the left heart cavity model, and the blood flow field information, determine the obstruction area in the left heart cavity model.

[0084] The blood flow field information threshold range is a pre-defined threshold range corresponding to each item in the blood flow field information. For example, in this embodiment, the blood flow field information includes flow velocity, wall shear stress, and pressure; therefore, the blood flow field information threshold range includes the flow velocity threshold range, the wall shear stress threshold range, and the pressure threshold range. The obstruction region is the area near the interventricular septum in the left heart where blood flow is obstructed during the process.

[0085] In this embodiment, regions in the left ventricular model where the flow velocity, wall shear stress, and pressure are all outside the corresponding threshold ranges are selected as obstruction regions. In other embodiments of this application, if any flow field information of a region in the left ventricular model is outside the corresponding threshold range, it is also determined to be an obstruction region.

[0086] It should be noted that the obstruction area in the left ventricular model was determined manually or based on existing algorithms.

[0087] Step 2: Based on the correspondence between the left ventricular cavity model and the ventricular septum model in the cardiac simulation model, as well as the obstruction area, determine the first target area in the ventricular septum model.

[0088] The correspondence between the left ventricular cavity model and the interventricular septum model is a pre-defined correspondence between the muscular region near the interventricular septum in the left ventricular cavity model and the interventricular septum model. It should be noted that because the interventricular septum is the muscular septum separating the left and right ventricles in the heart, there is a correspondence between the muscular region near the interventricular septum in the left ventricular cavity model and the interventricular septum itself. The first target region is the muscular region in the interventricular septum model corresponding to the obstruction region near the interventricular septum in the left ventricular cavity model.

[0089] Based on the correspondence between the left ventricular cavity model and the interventricular septum model in the cardiac simulation model, the region in the interventricular septum corresponding to the obstruction area is determined as the first target region in the interventricular septum model.

[0090] In other embodiments of this application, the ventricular septum model includes multiple volume elements, and each volume element includes multiple nodes; stress calculation based on the ventricular septum model to determine the second target region in the ventricular septum model includes:

[0091] Step 1: Based on the ventricular septum model, determine the coordinates of each node of the target body unit at the initial stage of systole and the coordinates of each node of the target body unit at the end of systole.

[0092] It should be noted that the ventricular septal model comprises multiple volumetric units, and each volumetric unit consists of multiple nodes. The volumetric unit is the smallest constituent unit of the ventricular septal model. The initial systolic time is the beginning of the systolic phase, and the final systolic time is the end of the systolic phase.

[0093] Based on the ventricular septum model, the coordinates of each node of the target body unit at the initial stage of systole and the coordinates of each node of the target body unit at the end of systole are obtained.

[0094] The target body unit is any one body unit in the interventricular septum model. Although in this embodiment only the target body unit is subjected to the relevant steps, in the actual scenario, the relevant steps will be performed on each body unit.

[0095] Step 2: Based on the coordinates of each node of the target element at the initial moment of contraction, determine the first distance matrix between each node and other nodes of the same element at the initial moment of contraction.

[0096] The first distance matrix includes the distances between each node of the target volume element and other nodes of the same element at the initial moment of contraction.

[0097] Based on the coordinates of each node of the target volume element at the initial moment of contraction, the distance between each node and other nodes of the same element at the initial moment of contraction is calculated to obtain the first distance matrix.

[0098] In this embodiment, the first distance matrix is ​​a 3×3 matrix.

[0099] Step 3: Based on the coordinates of each node of the target element at the end of the contraction period, determine the second distance matrix between each node and other nodes of the same element at the end of the contraction period.

[0100] The second distance matrix includes the distances between each node of the target volume element and other nodes of the same element at the end of the contraction phase.

[0101] Based on the coordinates of each node of the target volume element at the end of the contraction period, the distance between each node and other nodes of the same element at the end of the contraction period is calculated to obtain the second distance matrix.

[0102] In this embodiment, the second distance matrix is ​​a 3×3 matrix.

[0103] Step 4: Determine the strain tensor matrix corresponding to the target volume element based on the first distance matrix and the second distance matrix.

[0104] The strain tensor matrix is ​​the stress tensor matrix that characterizes the stress tensor corresponding to the target body element.

[0105] Subtract the relative position vectors between the corresponding node pairs in the first distance matrix from the relative position vectors between the node pairs (node ​​pairs are combinations formed by connecting nodes with other nodes in the same unit) in the second distance matrix to obtain the relative displacement vectors between the node pairs. Based on the relationship between the relative displacement vectors and the relative position vectors of the first distance matrix between the corresponding node pairs, construct the displacement gradient tensor of the volume element through a system of linear equations. Decompose the displacement gradient tensor and extract its symmetric components to obtain the strain tensor matrix corresponding to the target volume element.

[0106] For example, the first distance matrix contains relative position vectors of multiple node pairs, with the vector components numbered U1, U2, U3...U1 from top to bottom. n (The value n is determined according to the volume element type), the second distance matrix contains the relative position vectors of corresponding node pairs, and the vector components are u1, u2, u3...u from top to bottom. n (Corresponding to the n value of the first distance matrix), the relative displacement vector components are calculated as Δu1=u1-U1, Δu2=u2-U2, Δu3=u3-U3……Δu n =u n -U n By solving the linear equation system Δu i =▽u•U i (where i = 1, 2, 3...n, and ▽u is the displacement gradient tensor) to obtain the displacement gradient tensor, and finally obtain the displacement gradient tensor using the formula ε = (▽u + ▽u) T The strain tensor matrix ε is obtained by calculating ε / 2.

[0107] In this embodiment, the strain tensor matrix is ​​a 3×3 matrix.

[0108] Step 5: Determine the stress tensor matrix corresponding to the target element based on the preset tensor transformation relationship and the strain tensor matrix corresponding to the target element.

[0109] The tensor transformation relationship is a pre-defined transformation relationship between the strain tensor and the stress tensor. The stress tensor matrix is ​​the stress tensor matrix that represents the stress tensor corresponding to the target element.

[0110] The stress tensor matrix corresponding to the target element is obtained by performing variable transformation based on the tensor transformation relationship and the strain tensor matrix corresponding to the target element.

[0111] In practice, the tensor transformation relation is:

[0112] .

[0113] in, It is the strain tensor The first invariant, Let be the strain invariant along the fiber direction. These are material parameters that can be optimized by fitting the pressure-volume relationship during a patient's specific systolic phase. It should be noted that it is assumed that the myocardium has different stiffness characteristics in the fiber direction and transverse direction to simulate its anisotropy. Given the strain energy density, the stress tensor can be obtained by differentiating the strain energy density.

[0114] It should be noted that in this embodiment, the stress tensor matrix is ​​obtained by substituting the strain tensor matrix into the tensor transformation relation to perform variable transformation.

[0115] Step 6: Based on the stress tensor matrix corresponding to each volume element and the preset stress threshold, the region formed by volume elements whose stress tensor is greater than the preset stress threshold is taken as the second target region.

[0116] The stress threshold is a pre-set threshold for the stress tensor. The second target region is the area in the interventricular septum model where the stress tensor is greater than the pre-set stress threshold.

[0117] Based on the values ​​of the stress tensor matrix corresponding to each volume element, the corresponding equivalent stress value is calculated. According to the preset stress threshold, the region formed by volume elements in the chamber septum model whose stress tensor is greater than the preset stress threshold is determined as the second target region.

[0118] It should be noted that the equivalent stress value corresponding to each body element is obtained by calculating the von Mises equivalent stress.

[0119] In other embodiments of this application, determining the initial target region based on the first target region and the second target region includes:

[0120] The union of the first target region and the second target region is taken as the initial target region.

[0121] The initial target area is the area that needs to be removed, as initially determined in the ventricular septum model.

[0122] The union of the first target region and the second target region is taken as the initial target region.

[0123] In other embodiments of this application, determining the target resection thickness based on a preset base thickness and thickness range includes:

[0124] Step 1: Subtract the base thickness from the upper and lower limits of the thickness range to determine the target thickness range.

[0125] The preset base thickness is the minimum thickness that needs to be retained in the pre-defined ventricular septum model, typically ranging from 10mm to 15mm. For example, the upper limit of the thickness range is typically between 40mm and 20mm, and the lower limit is typically between 20mm and 15mm. The target thickness range is the range of muscle thickness that needs to be removed from the ventricular septum model.

[0126] Subtract the base thickness from the upper and lower limits of the thickness range, and use the range from the upper limit of the thickness range minus the base thickness to the lower limit of the thickness range minus the base thickness as the target thickness range.

[0127] Step 2: Use the median value of the target thickness range as the target cut-off thickness.

[0128] The target resection thickness is the thickness of the muscle to be removed in the ventricular septum model.

[0129] Calculate the median value of the target thickness range and use it as the target cut-off thickness.

[0130] It should be noted that in this embodiment, the target thickness range is determined by subtracting the base thickness from the upper and lower limits of the thickness range, in order to prevent the risk of the target cut-off thickness being too small. Then, the median value of the target thickness range is used as the target cut-off thickness.

[0131] In other embodiments of this application, determining the first target resection area based on the cardiac simulation model, the initial target region, the target resection thickness, and blood flow field information includes:

[0132] Step 1: Project the initial target area onto the cardiac simulation model under the long axis section view of the heart to determine the two-dimensional resection area.

[0133] The long-axis section view of the heart is a section view with the long axis as the reference, which is the axis pointing from the center of the aortic valve to the apex of the heart. The two-dimensional resection region is the projection area of ​​the initial target region onto the cardiac simulation model under the long-axis section view of the heart.

[0134] The projection area of ​​the initial target region onto the cardiac simulation model in the long axis section view of the heart is used as the two-dimensional resection area.

[0135] Step 2: Determine the blood flow field information corresponding to the obstruction area based on the obstruction area and blood flow field information.

[0136] It should be noted that the blood flow field information is the blood flow field information corresponding to the left ventricular cavity model, while the obstruction area is the area near the interventricular septum in which blood flow is obstructed during the blood flow process in the left ventricular cavity model. Therefore, it is possible to filter out the blood flow field information corresponding to the obstruction area from the blood flow field information corresponding to the left ventricular cavity model.

[0137] The blood flow field information corresponding to the left ventricular model is filtered according to the obstruction area to determine the blood flow field information corresponding to the obstruction area.

[0138] Step 3: Based on the preset long axis direction, determine the thickness adjustment coefficients of multiple two-dimensional sub-regions of the two-dimensional resection area in the long axis direction according to the blood flow field information corresponding to the obstruction area.

[0139] The preset major axis direction is the direction of the major axis in a pre-defined long-axis cross-sectional view of the heart. In this embodiment, the major axis direction is the axis pointing from the center of the aortic valve to the apex of the heart. Multiple two-dimensional sub-regions are multiple sub-regions obtained by equally dividing the two-dimensional resection area along the major axis direction, and each two-dimensional sub-region has the same length along the major axis direction. The thickness adjustment coefficient is the thickness weighting coefficient corresponding to the multiple two-dimensional sub-regions of the two-dimensional resection area along the major axis direction.

[0140] The two-dimensional resection area is divided equally along its long axis to obtain multiple two-dimensional sub-regions. Based on the blood flow field information corresponding to the obstruction area and each two-dimensional sub-region, the blood flow field information corresponding to each two-dimensional sub-region is determined. Then, the blood flow field information corresponding to each two-dimensional sub-region is standardized to obtain the thickness adjustment coefficient corresponding to each two-dimensional sub-region.

[0141] It should be noted that, regarding the standardization based on the blood flow field information corresponding to each two-dimensional sub-region, in this embodiment, the standardization is based on a certain flow field information in the blood flow field information, such as flow velocity or pressure. In other embodiments of this application, multiple or all flow field information in the blood flow field information will be standardized, and then the results of each standardization will be multiplied by the corresponding preset weight and added together to obtain the thickness adjustment coefficient corresponding to each two-dimensional sub-region.

[0142] For example, flow velocity is used as an example. The flow velocity corresponding to each two-dimensional sub-region is traversed to obtain the maximum flow velocity among the flow velocities corresponding to each two-dimensional sub-region. Then, the flow velocity corresponding to each two-dimensional sub-region is divided by the maximum flow velocity to obtain the thickness adjustment coefficient corresponding to each two-dimensional sub-region.

[0143] Step 4: Determine the first target excision area based on multiple thickness adjustment coefficients, target excision thickness, and two-dimensional excision area.

[0144] The primary target resection area is the region of muscle that needs to be removed in the ventricular septum model.

[0145] The thickness of each two-dimensional sub-region of the two-dimensional excision area is obtained by multiplying the thickness adjustment coefficient of each two-dimensional sub-region by the target excision thickness. Based on the two-dimensional excision area, the first target excision area is determined.

[0146] In other embodiments of this application, determining the first target resection area based on multiple thickness adjustment coefficients, the target resection thickness, and the two-dimensional resection area includes:

[0147] Step 1: Multiply the multiple thickness adjustment coefficients by the target cut thickness to determine the thickness of multiple two-dimensional sub-regions of the two-dimensional cut area along the long axis.

[0148] The thickness adjustment coefficient of each two-dimensional sub-region of the two-dimensional excision region is multiplied by the target excision thickness to obtain the thickness of multiple two-dimensional sub-regions of the two-dimensional excision region along the long axis.

[0149] Step 2: Determine the first target excision area based on the thickness of multiple two-dimensional sub-regions in the long axis direction of the two-dimensional excision area and the two-dimensional excision area.

[0150] The primary target resection area is the region of muscle that needs to be removed in the ventricular septum model.

[0151] The first target excision region is ultimately determined based on the thickness of multiple two-dimensional sub-regions along the long axis of the two-dimensional excision region and the two-dimensional excision region itself. The first target excision region is a three-dimensional region jointly formed by the thickness of multiple two-dimensional sub-regions and the two-dimensional excision region.

[0152] In other embodiments of this application, the method further includes:

[0153] Step 1: Update the cardiac simulation model based on the first target resection area.

[0154] The first target excision region is removed from the cardiac simulation model, thereby updating the cardiac simulation model.

[0155] Step 2: Obtain the second simulation boundary conditions.

[0156] The second simulation boundary condition is a pre-set boundary condition required for performing fluid dynamics simulation calculations, including cardiac output flow, atrial systolic and diastolic pressure, ventricular systolic and diastolic pressure, and heart rate.

[0157] It should be noted that the second simulation boundary condition is set based on the heart parameters of the object during strenuous exercise.

[0158] Step 3: Based on the second simulation boundary conditions and the updated left heart cavity model, perform fluid dynamics simulation calculations to determine the blood flow field information corresponding to the updated left heart cavity model.

[0159] Fluid dynamics simulation is a technique for simulating fluids using mathematical methods. The blood flow field information is from the updated left heart cavity model. It should be noted that the cavity portion of the left heart cavity model comprises multiple hollow units, and the blood flow field information corresponds to each hollow unit. This information includes the flow velocity, wall shear stress, and pressure for each hollow unit. The flow velocity is the blood velocity at a specific hollow unit, the wall shear stress is the wall shear stress at the hollow unit near the muscle of the left heart, and the pressure is the pressure at a specific hollow unit.

[0160] Based on the left ventricular model, the second simulation boundary condition is used as a constraint to perform fluid dynamics simulation calculations, thereby obtaining the blood flow field information corresponding to the updated left ventricular model.

[0161] Step 4: If the blood flow field information corresponding to the updated left ventricular model meets the preset conditions, then the first target resection area is taken as the final resection area.

[0162] The preset conditions are the range of pre-defined blood flow field information, representing the range of blood flow field information in the left ventricular model under healthy conditions. The final resection area is the muscle region that ultimately needs to be removed.

[0163] If the blood flow field information corresponding to the updated left ventricular model is within the preset conditions, then the first target resection area will be taken as the final resection area.

[0164] Step 5: If the blood flow field information corresponding to the updated left heart cavity model does not meet the preset conditions, then determine the second target resection area based on the updated heart simulation model and the blood flow field information corresponding to the updated left heart cavity model. Then, update the heart simulation model based on the second target resection area until the blood flow field information corresponding to the updated left heart cavity model meets the preset conditions. Finally, take the first target resection area and at least one second target resection area as the final resection area.

[0165] If the blood flow field information corresponding to the updated left ventricular model does not meet the preset conditions, it indicates that the updated left ventricular model still has problems and requires the removal of some myocardial tissue. Therefore, the second target resection area will be determined based on the updated cardiac simulation model and the blood flow field information corresponding to the updated left ventricular model. The cardiac simulation model will be updated again based on the second target resection area until the blood flow field information corresponding to the left ventricular model meets the preset conditions. The first target resection area and at least one second target resection area will be summarized to determine the final resection area.

[0166] It should be noted that the method for determining the second target resection area based on the updated heart simulation model and the blood flow field information corresponding to the updated left heart cavity model is the same as the method for determining the first target resection area, and will not be repeated here.

[0167] This application introduces a virtual provocation test, using computational fluid dynamics (CFD) to simulate virtual postoperative cardiac hemodynamic changes. This virtual provocation test can assess in advance whether residual obstruction will exist after resection, avoiding the limitations of traditional provocation tests, which are only performed after actual resection and the intolerance of some patients to provocation test agents. Virtual provocation allows for the evaluation of the impact of different resection approaches on cardiac hemodynamics, enabling preoperative adjustments to the resection strategy to ensure more ideal postoperative outcomes. Furthermore, this application not only optimizes preoperative myocardial resection planning but also uses simulation technology to assess long-term changes in cardiac blood flow after resection. By simulating postoperative hemodynamic status, long-term prognosis can be predicted, and potential complications such as heart failure or recurrence of obstruction can be anticipated in advance, thus allowing for the development of more appropriate postoperative rehabilitation plans for patients.

[0168] In a specific embodiment of this application, the simulation-based method for determining the target excision region includes:

[0169] Step 1: Based on 4D imaging CT / MRI, obtain a three-dimensional model of the heart during the systolic phase before surgery for patients with myocardial hypertrophy as a reference model. The reference model includes two parts: the intracardiac blood lumen model and the interventricular septum myocardial model. The intracardiac blood lumen model covers the left ventricle, left atrium, part of the aorta, etc.

[0170] Step 2: Based on the three-dimensional cardiac cavity model obtained in Step 1, perform CFD simulation calculations to obtain the blood flow field information of the left heart in patients with myocardial hypertrophy before surgery.

[0171] Step 3: Based on the blood flow field information of the left heart of patients with myocardial hypertrophy obtained in Step 2, the region with the most severe obstruction is determined by using composite parameters such as flow velocity, wall shear stress, and pressure difference in the blood flow field information of the left heart. The region of the interventricular septum model corresponding to this region is the first region.

[0172] Step 4: Based on the interventricular septum myocardial model obtained in Step 1, spatial displacement information of the interventricular septum in a cardiac cycle is obtained through registration technology for patients with myocardial hypertrophy before surgery.

[0173] The spatial displacement information consists of the first distance matrix and the second distance matrix mentioned above.

[0174] Step 5: Calculate the strain tensor based on the spatial displacement information of the interventricular septum under one cardiac cycle obtained in Step 4, and calculate the stress tensor based on the constitutive relation. Exclude the abnormal region of excessive stress gradient in the interventricular septum, and define the remaining region as the second region.

[0175] Step 6: Use the union of the second region determined in Step 5 and the first region determined in Step 3 as the initial target region.

[0176] Step 7: Interventricular septal myocardial model, measuring its maximum thickness a mm and minimum thickness b mm based on the initial target region determined in Step 6.

[0177] Step 8: Based on the maximum and minimum thickness obtained in Step 7, define the initial target area at the location of the heart to determine the base thickness e mm (at least 10-15 mm), determine the target thickness range as (be, ae), and determine the target resection thickness c mm according to the thickness range.

[0178] Step 10: Determine the initial resection plan.

[0179] Step 10.1: Based on the initial target area determined in Step 6, draw a curved surface on the long axis section view of the heart (baseline model) along the starting point (the obstruction area near the aortic valve) and the ending point (the obstruction area away from the aortic valve) as a two-dimensional resection area, as shown in the green area of ​​Figure 4.

[0180] Step 10.2: Simultaneously, based on the blood flow field information of the left heart obtained in Step 2, the blood flow field information along the axis of the obstruction region is standardized (Pn=Pi / Pmax) to obtain the thickness adjustment coefficient of the obstruction surface along the axis corresponding to the long axis section. The thickness of the two-dimensional initial resection region in Step 10.1 is adjusted according to the thickness adjustment coefficient, as shown in the pink area of ​​Figure 5.

[0181] Step 10.3: Generate the first target excision area by stretching and rotating using modeling software, as shown in the green area of ​​Figure 6.

[0182] Step 11: Based on CFD calculations, the initial virtual postoperative cardiac blood flow information of the initial resection plan generated in Step 10 is used to evaluate the blood flow improvement effect of the initial resection plan. As shown in Figure 7, some blood flow parameters in the initial resection area have high values. Using the thickness threshold baseline formed by the baseline thickness e and the maximum resection thickness ae, as shown by the dashed line in Figure 4, Step 10.2 is repeated for the initial resection plan 1 to obtain a new round of thickness adjustment coefficients. The ventricular septum within the baseline range (the same start and end point) is divided into 12 equal parts, and the thickness of the first target resection area and the corresponding section is adjusted. The final resection thickness must not exceed the thickness threshold baseline.

[0183] Step 12: Repeat step 11 to obtain the final iterative optimization scheme, whose left ventricular blood flow state is shown in Figure 8.

[0184] Step 13: Adjust the boundary conditions of the iterative optimization scheme obtained in Step 12 and conduct a virtual excitation test to determine the scheme. If there are still residual obstructions, Steps 11-12 need to be repeated for further optimization.

[0185] In a specific embodiment of this application, as shown in Figure 11, the obstruction area is determined by preoperative assessment of cardiac hemodynamic parameters, the area of ​​abnormal myocardial function is determined by preoperative assessment of interventricular septum myocardial function, the area of ​​myocardial tissue to be resected in the interventricular septum is determined based on the obstruction area and the area of ​​abnormal myocardial function, and the ratio of hemodynamic parameters along the axis of the obstruction area is used as a thickness coefficient. An initial resection plan is generated based on the thickness coefficient and the resectable myocardial tissue area of ​​the interventricular septum, and the resection plan is iteratively optimized based on CFD. Finally, a virtual provocation test is performed on the iteratively optimized resection plan.

[0186] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0187] Based on the same inventive concept, this application also provides a simulation-based target excision region determination device for implementing the simulation-based target excision region determination method described above. The solution provided by this device is similar to the implementation described in the above method; therefore, the specific limitations of one or more simulation-based target excision region determination device embodiments provided below can be found in the limitations of the simulation-based target excision region determination method described above, and will not be repeated here.

[0188] In one embodiment of this application, as shown in FIG9, a simulation-based target excision region determination device is provided, comprising:

[0189] The acquisition module 100 is used to acquire a cardiac simulation model and corresponding blood flow field information; the cardiac simulation model includes a left cardiac cavity model and a ventricular septum model; the blood flow field information includes blood flow field information of the left cardiac cavity.

[0190] The first determining module 200 is used to determine the first target region in the interventricular septum model based on the left heart cavity model and the blood flow field information;

[0191] The second determining module 300 is used to perform stress calculations based on the interventricular septum model to determine the second target region in the interventricular septum model.

[0192] The third determining module 400 is used to determine an initial target region based on the first target region and the second target region;

[0193] The fourth determining module 500 is used to determine the thickness range of the initial target region at the end of systole based on the interventricular septum model.

[0194] The fifth determining module 600 is used to determine the target cut-off thickness based on the preset base thickness and the thickness range;

[0195] The sixth determining module 700 is used to determine the first target resection area based on the heart simulation model, the initial target area, the target resection thickness, and the blood flow field information.

[0196] In one embodiment of this application, the acquisition module 100 is further configured to acquire a 4D image of the target heart and a first simulation boundary condition; establish a simulation model of the target heart based on the 4D image, and determine the heart simulation model; the heart simulation model includes a left ventricular cavity model and a ventricular septum model; and perform fluid dynamics simulation calculations based on the first simulation boundary condition and the left ventricular cavity model to determine the blood flow field information corresponding to the left ventricular cavity model.

[0197] In one embodiment of this application, the first determining module 200 is further configured to determine the obstruction region in the left heart cavity model based on a preset blood flow field information threshold range, the left heart cavity model, and the blood flow field information; and to determine the first target region in the ventricular septum model based on the correspondence between the left heart cavity model and the ventricular septum model in the heart simulation model and the obstruction region.

[0198] In one embodiment of this application, the second determining module 300 is further configured to: determine the coordinates of each node of the target body unit at the initial stage of systole and the coordinates of each node of the target body unit at the end of systole, based on the interventricular septum model; determine a first distance matrix between each node and other nodes of the same unit at the initial stage of systole, based on the coordinates of each node of the target body unit at the end of systole; determine a second distance matrix between each node and other nodes of the same unit, based on the coordinates of each node of the target body unit at the end of systole; determine the strain tensor matrix corresponding to the target body unit based on the first distance matrix and the second distance matrix; determine the stress tensor matrix corresponding to the target body unit based on the strain tensor matrix corresponding to the target body unit according to a preset tensor transformation relationship; and, based on the stress tensor matrix corresponding to each body unit and a preset stress threshold, designate the region formed by body units whose stress tensor is greater than the preset stress threshold as the second target region.

[0199] In one embodiment of this application, the third determining module 400 is further configured to take the union of the first target region and the second target region as the initial target region.

[0200] In one embodiment of this application, the fifth determining module 600 is further configured to subtract the base thickness from the upper and lower limits of the thickness range to determine the target thickness range; and to use the median value of the target thickness range as the target cut-off thickness.

[0201] In one embodiment of this application, the sixth determining module 700 is further configured to project the initial target region onto a cardiac simulation model under a long-axis section view of the heart to determine a two-dimensional resection region; determine the blood flow field information corresponding to the obstruction region based on the obstruction region and the blood flow field information; determine the thickness adjustment coefficients corresponding to multiple two-dimensional sub-regions of the two-dimensional resection region in the long-axis direction based on a preset long-axis direction and the blood flow field information corresponding to the obstruction region; and determine a first target resection region based on the multiple thickness adjustment coefficients, the target resection thickness, and the two-dimensional resection region.

[0202] In one embodiment of this application, the sixth determining module 700 is further configured to multiply the plurality of thickness adjustment coefficients by the target cutting thickness to determine the thickness of the plurality of two-dimensional sub-regions of the two-dimensional cutting region in the long axis direction; and to determine the first target cutting region based on the thickness of the plurality of two-dimensional sub-regions of the two-dimensional cutting region in the long axis direction and the two-dimensional cutting region.

[0203] In one embodiment of this application, the sixth determining module 700 is further configured to update the heart simulation model based on the first target resection area; obtain second simulation boundary conditions; perform fluid dynamics simulation calculations based on the second simulation boundary conditions and the updated left heart cavity model to determine the blood flow field information corresponding to the updated left heart cavity model; if the blood flow field information corresponding to the updated left heart cavity model meets preset conditions, then the first target resection area is taken as the final resection area; if the blood flow field information corresponding to the updated left heart cavity model does not meet preset conditions, then a second target resection area is determined based on the updated heart simulation model and the blood flow field information corresponding to the updated left heart cavity model, and the heart simulation model is updated based on the second target resection area until the blood flow field information corresponding to the updated left heart cavity model meets preset conditions, and the first target resection area and at least one second target resection area are taken as the final resection area.

[0204] The modules in the aforementioned simulation-based target excision region determination device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0205] In one embodiment of this application, a computer device is provided, which may be a server, and its internal structure diagram is shown in Figure 10. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores all relevant data for executing a simulation-based target excision region determination method. The network interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a simulation-based target excision region determination method.

[0206] Those skilled in the art will understand that the structure shown in Figure 10 is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or may combine certain components, or may have different component arrangements.

[0207] In one embodiment of this application, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the simulation-based target excision region determination method in the above embodiments.

[0208] In one embodiment of this application, a computer-readable storage medium is provided, on which a computer program is stored, the computer program being executed by a processor to implement the steps of the simulation-based target excision region determination method in the above-described method embodiments.

[0209] In one embodiment of this application, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the simulation-based target excision region determination method in the above-described method embodiments.

[0210] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0211] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0212] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0213] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A simulation-based method for determining the target excision region, characterized in that, The method includes: acquiring a cardiac simulation model and corresponding blood flow field information; the cardiac simulation model includes a left cardiac cavity model and a ventricular septum model; the blood flow field information includes blood flow field information of the left cardiac cavity; determining a first target region in the ventricular septum model based on the left cardiac cavity model and the blood flow field information; including determining an obstruction region in the left cardiac cavity model based on a preset blood flow field information threshold range, the left cardiac cavity model, and the blood flow field information; determining a first target region in the ventricular septum model based on the correspondence between the left cardiac cavity model and the ventricular septum model in the cardiac simulation model and the obstruction region; performing stress calculation based on the ventricular septum model to determine a second target region in the ventricular septum model; taking the union of the first target region and the second target region as an initial target region; determining the thickness range of the initial target region at end-systole based on the ventricular septum model; determining a target resection thickness based on a preset baseline thickness and the thickness range; and determining a first target resection region based on the cardiac simulation model, the initial target region, the target resection thickness, and the blood flow field information. The ventricular septum model comprises multiple volume elements, and each volume element comprises multiple nodes. The step of performing stress calculations based on the ventricular septum model to determine the second target region within the model includes: determining the coordinates of multiple nodes of the target volume element at the initial stage of contraction and at the end of contraction based on the ventricular septum model; determining a first distance matrix between each node and other nodes within the same volume element at the initial stage of contraction based on the coordinates of the multiple nodes of the target volume element at the end of contraction; determining a second distance matrix between each node and other nodes within the same volume element at the end of contraction based on the coordinates of the multiple nodes of the target volume element at the end of contraction; constructing the displacement gradient tensor of the volume element based on the first and second distance matrices, and determining the strain tensor matrix corresponding to the target volume element; determining the stress tensor matrix corresponding to the target volume element based on a preset tensor transformation relationship and the strain tensor matrix corresponding to the target volume element; and defining the region formed by volume elements whose equivalent stress in the stress tensor is greater than the preset stress threshold as the second target region based on the stress tensor matrices of each volume element and a preset stress threshold.

2. The simulation-based target excision region determination method according to claim 1, characterized in that, The acquisition of the heart simulation model and corresponding blood flow field information includes: acquiring a 4D image of the target heart and a first simulation boundary condition; establishing a simulation model of the target heart based on the 4D image to determine the heart simulation model; the heart simulation model includes a left ventricular cavity model and a ventricular septum model; and performing fluid dynamics simulation calculations based on the first simulation boundary condition and the left ventricular cavity model to determine the blood flow field information corresponding to the left ventricular cavity model.

3. The simulation-based target excision region determination method according to claim 1, characterized in that, The step of determining the target resection thickness based on the preset base thickness and the thickness range includes: subtracting the base thickness from the upper and lower limits of the thickness range to determine the target thickness range; and using the median value of the target thickness range as the target resection thickness.

4. The simulation-based target excision region determination method according to claim 1, characterized in that, The step of determining the first target resection area based on the cardiac simulation model, the initial target area, the target resection thickness, and the blood flow field information includes: projecting the initial target area onto the cardiac simulation model under the long axis section view of the heart to determine the two-dimensional resection area; determining the blood flow field information corresponding to the obstruction area based on the obstruction area and the blood flow field information; determining the thickness adjustment coefficients corresponding to multiple two-dimensional sub-regions of the two-dimensional resection area in the long axis direction based on a preset long axis direction and the blood flow field information corresponding to the obstruction area; and determining the first target resection area based on the multiple thickness adjustment coefficients, the target resection thickness, and the two-dimensional resection area.

5. The simulation-based target excision region determination method according to claim 4, characterized in that, The step of determining the first target excision region based on the plurality of thickness adjustment coefficients, the target excision thickness, and the two-dimensional excision region includes: multiplying the plurality of thickness adjustment coefficients by the target excision thickness to determine the thickness of the plurality of two-dimensional sub-regions of the two-dimensional excision region in the long axis direction; and determining the first target excision region based on the thickness of the plurality of two-dimensional sub-regions of the two-dimensional excision region in the long axis direction and the two-dimensional excision region.

6. The simulation-based target excision region determination method according to claim 2, characterized in that, The method further includes: updating the heart simulation model according to the first target resection area; obtaining second simulation boundary conditions; performing fluid dynamics simulation calculations according to the second simulation boundary conditions and the updated left heart cavity model to determine the blood flow field information corresponding to the updated left heart cavity model; if the blood flow field information corresponding to the updated left heart cavity model meets preset conditions, then the first target resection area is taken as the final resection area; if the blood flow field information corresponding to the updated left heart cavity model does not meet preset conditions, then determining a second target resection area according to the updated heart simulation model and the blood flow field information corresponding to the updated left heart cavity model, and updating the heart simulation model according to the second target resection area until the blood flow field information corresponding to the updated left heart cavity model meets preset conditions, and taking the first target resection area and at least one second target resection area as the final resection area.

7. A simulation-based target excision region determination device, characterized in that, The device includes: an acquisition module for acquiring a cardiac simulation model and corresponding blood flow field information; the cardiac simulation model includes a left cardiac cavity model and a ventricular septum model; the blood flow field information includes blood flow field information of the left heart; a first determination module for determining an obstruction region in the left cardiac cavity model based on a preset blood flow field information threshold range, the left cardiac cavity model, and the blood flow field information; and determining a first target region in the ventricular septum model based on the correspondence between the left cardiac cavity model and the ventricular septum model in the cardiac simulation model and the obstruction region; and a second determination module. A block is used to perform stress calculations based on the ventricular septum model to determine a second target region within the ventricular septum model. The ventricular septum model includes multiple volume elements, and each volume element includes multiple nodes. The step of performing stress calculations based on the ventricular septum model to determine the second target region within the ventricular septum model includes: determining the coordinates of multiple nodes of the target volume element at the initial stage of systole and the coordinates of multiple nodes of the target volume element at the end of systole, based on the coordinates of multiple nodes of the target volume element at the initial stage of systole; and determining the coordinates of each node relative to other nodes within the same volume element at the initial stage of systole, based on the coordinates of the multiple nodes of the target volume element at the initial stage of systole. The system comprises: a first distance matrix; a second distance matrix between each node and other nodes of the same unit at the end of systole, based on the coordinates of multiple nodes of the target unit at the end of systole; a displacement gradient tensor of the unit, constructed based on the first and second distance matrices, and a strain tensor matrix corresponding to the target unit; a stress tensor matrix, determined based on a preset tensor transformation relationship and the strain tensor matrix corresponding to the target unit; a region composed of units whose equivalent stress of the stress tensor is greater than the preset stress threshold, defined as the second target region, based on the stress tensor matrix of each unit and a preset stress threshold; a third determining module, used to take the union of the first and second target regions as the initial target region; a fourth determining module, used to determine the thickness range of the initial target region at the end of systole based on the interventricular septum model; a fifth determining module, used to determine the target resection thickness based on the preset base thickness and the thickness range; and a sixth determining module, used to determine the first target resection region based on the cardiac simulation model, the initial target region, the target resection thickness, and the blood flow field information.

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