CN-122005096-A - Method and system for fusing nano indentation sensing and real-time visual feedback

CN122005096ACN 122005096 ACN122005096 ACN 122005096ACN-122005096-A

Abstract

The invention belongs to the technical field of medical diagnosis, and particularly relates to a method and a system for fusing nano indentation sensing and real-time visual feedback. According to the invention, the mechanical characteristic parameters of lung tissues are obtained in real time through a nanoindentation sensing technology, the mechanical heterogeneity between tumor tissues and normal tissues is effectively captured, the mechanical characteristic parameters and an operation navigation image are spatially registered, the generated fusion visual image overcomes the limitation that the traditional preoperative image cannot reflect the tissue change in operation in real time, the stable mechanical characteristic parameters are effectively screened out by adopting a sliding window mechanism and combining secondary indentation verification, the lesion area can be accurately identified and mapped to the navigation image through multi-dimensional mechanical characteristic comparison and geometric characteristic analysis, the problems of mismatching of images and anatomy and difficult identification of small lesions are solved, and the fusion visual image is beneficial to an operator to accurately plan the excision scope and reduce the risk of excessive excision or lesion residues.

Inventors

WANG ZHIJIE
YANG GUANG
WANG XIAOBIN
LIU JIN

Assignees

中国人民解放军空军军医大学

Dates

Publication Date: 20260512
Application Date: 20260415

Claims (10)

1. A method for fusing nano indentation sensing and real-time visual feedback is characterized by comprising the following steps: Obtaining a nano indentation sensing signal representing mechanical properties of the tissue by carrying out indentation test on the surface of the lung tissue; Preprocessing the nano indentation sensing signals, and resolving mechanical characteristic parameters of the preprocessed nano indentation sensing signals to generate mechanical characteristic parameters for real-time visual feedback; spatially registering the mechanical characteristic parameters and the surgical navigation image to generate a fusion visual image overlapped with the mechanical characteristic parameters; Based on a sliding window mechanism, carrying out statistics on mechanical characteristic parameters to obtain a plurality of stable mechanical characteristic parameters for representing local mechanical heterogeneity of lung tissues; And identifying a lesion area according to the stable mechanical characteristic parameters, mapping the lesion area to a surgical navigation image coordinate system, generating a lesion positioning mark, and synchronously triggering an early warning signal.
2. The method for fusing nano-indentation sensing and real-time visual feedback as set forth in claim 1, wherein the step of obtaining the nano-indentation sensing signal characterizing mechanical properties of the tissue by indentation testing the surface of the lung tissue comprises the steps of: the surface of lung tissue is contacted by the indentation sensing probe, controllable nanoscale displacement load is applied, and tissue deformation response signals are synchronously acquired; calculating and constructing a load displacement curve based on the corresponding relation between the tissue deformation response signals and the displacement load; And carrying out sectional fitting on the load displacement curve, extracting elastic modulus, creep rate and stress relaxation time constant as basic mechanical characteristic parameters, and summarizing the basic mechanical characteristic parameters into an original data set of the nano indentation sensing signal.
3. The method for fusing nano-indentation sensing with real-time visual feedback as set forth in claim 1, wherein the step of preprocessing the nano-indentation sensing signal comprises the steps of: filtering the acquired original nano indentation sensing signals to inhibit interference signals caused by respiratory motion, instrument vibration and electronic noise; Comparing the signal-to-noise ratio of the nano indentation sensing signal after filtering with a preset signal-to-noise ratio threshold; if the signal-to-noise ratio of the nano indentation sensing signal is lower than a preset threshold value, the fact that the indentation sensing probe does not form stable contact with the surface of lung tissue or has slippage is indicated, and indentation test retry operation is carried out until the signal-to-noise ratio reaches the standard; If the signal-to-noise ratio of the nano indentation sensing signal is not lower than a preset threshold, the fact that the indentation sensing signal meets the real-time requirement is indicated, and real-time calculation is carried out on the nano indentation sensing signal after filtering.
4. The method for fusing nano-indentation sensing and real-time visual feedback as set forth in claim 1, wherein the step of performing mechanical feature parameter calculation on the preprocessed nano-indentation sensing signal to generate mechanical feature parameters for real-time visual feedback comprises the steps of: Respectively calculating the slope and curvature change rate of each section according to the load displacement curve after the sectional fitting, and then calculating the elastic modulus based on the slope and curvature change rate; Collecting indentation depth in the tissue deformation process in real time, and fitting by combining the depth change rate with a time sequence to obtain a creep rate; Taking the time point after the stress of the lung tissue is unloaded as the initial time, counting the time when the stress decays to the initial value, and recording as a stress relaxation time constant.
5. The method for fusing nanoindentation sensing and real-time visual feedback as claimed in claim 1, wherein the step of spatially registering the mechanical feature parameter with the surgical navigation image to generate the fused visual image superimposed with the mechanical feature parameter comprises: acquiring the space coordinates of the indentation sensing probe in real time, and mapping the probe coordinates into a navigation image coordinate system based on a preset coordinate system conversion rule; Taking an indentation test point as a center, establishing a circular visual marking area, calculating an arithmetic average value of elastic modulus, creep rate and stress relaxation time constant of the visual marking area, and recording the arithmetic average value as a first mechanical characteristic value, a second mechanical characteristic value and a third mechanical characteristic value respectively; Selecting an anatomical landmark point in the navigation image as a registration reference, and mapping the first mechanical characteristic value, the second mechanical characteristic value and the third mechanical characteristic value to corresponding anatomical regions; And rendering the first mechanical characteristic value, the second mechanical characteristic value and the third mechanical characteristic value in real time through the color gradient and the numerical label to form a fused visual image.
6. The method for fusing nanoindentation sensing and real-time visual feedback as claimed in claim 1, wherein the step of counting the mechanical characteristic parameters based on the sliding window mechanism to obtain a plurality of stable mechanical characteristic parameters for characterizing local mechanical heterogeneity of lung tissue comprises the steps of: Setting the size of a sliding window as continuous N indentation test points, wherein the movement compensation of the sliding window is M test points, wherein M is less than N; in each sliding window, carrying out arithmetic average treatment on the elastic modulus, the creep rate and the stress to obtain an elastic modulus average value, a creep rate average value and a stress average value; Respectively calculating standard deviation of the elastic modulus mean value, the creep rate mean value and the stress mean value, and determining a local mechanical heterogeneity index according to the ratio of the standard deviation to the mean value; Comparing the local mechanical heterogeneity index with a preset heterogeneity threshold; If the local mechanical heterogeneity index exceeds the heterogeneity threshold, indicating that the mechanical characteristic abnormality exists in the corresponding sliding window area, and marking the elastic modulus mean value, the creep rate mean value and the stress mean value in the sliding window area as characteristic parameters to be verified; If the local mechanical heterogeneity index does not exceed the heterogeneity threshold, the mean value of the mechanical characteristic parameters corresponding to the sliding window area is directly used as the stable mechanical characteristic parameter.
7. The method for fusing nanoindentation sensing and real-time visual feedback as set forth in claim 6, wherein the secondary indentation verification is performed after the characteristic parameters to be verified are output, and the specific steps are as follows: repeatedly carrying out indentation tests for a plurality of times on the region to be verified, and calculating the elasticity modulus variation, the creep rate variation and the stress relaxation time constant variation of the region to be verified; A comprehensive variation matrix is constructed based on the variation degree of the elastic modulus and the creep rate of the next test point of the region to be verified and the variation degree of the stress relaxation time constant; the mechanical behavior consistency index is generated through principal component analysis, and the specific steps are as follows: Decomposing the characteristic values of the comprehensive variation matrix, selecting a plurality of main components, and calculating the accumulated variance contribution rate of the corresponding characteristic values; calculating and outputting the weight of the feature vector of each main component, and obtaining a mechanical behavior consistency index through weighted summation; If the mechanical behavior consistency index is lower than a preset consistency threshold, indicating that stable mechanical characteristics are abnormal in the corresponding region to be verified, and determining the mean value of the mechanical characteristic parameters in the region to be verified as the stable mechanical characteristic parameters; If the mechanical behavior consistency index is not lower than a preset consistency threshold, determining that the test error is caused, and discarding the corresponding characteristic parameters to be verified.
8. The method for fusing nanoindentation sensing and real-time visual feedback as set forth in claim 1, wherein the step of identifying the lesion region based on the stable mechanical characteristic parameter and mapping the lesion region to the surgical navigation image coordinate system comprises: The stable mechanical characteristic parameters are characterized on the surface of lung tissues according to the mode of spatial interpolation and gridding mapping and adopting the combination of rigid registration and non-rigid deformation correction based on characteristic points; Comparing the stable mechanical characteristic parameter with a preset lesion mechanical characteristic threshold value, directly identifying a region with the stable mechanical characteristic parameter exceeding the lesion mechanical characteristic threshold value as a lesion region, and judging a region with the stable mechanical characteristic parameter lower than the lesion mechanical characteristic threshold value as a region to be verified; under the region to be verified, calculating the spatial gradient amplitude and direction of each mechanical characteristic parameter according to the arrangement of the stable mechanical characteristic parameters, and marking the region with the spatial gradient amplitude larger than a preset gradient threshold as a mechanical boundary abrupt change region; Taking the connected domain outline of the mechanical boundary mutation region as a candidate lesion region mask, extracting geometric characteristic parameters of each connected domain, and screening a lesion region and a risk region from the candidate lesion region mask according to the geometric characteristic parameters; wherein, the step of screening the lesion area and the risk area from the candidate lesion area mask according to the geometric characteristics comprises the following steps: Comparing the geometric characteristic parameter with a preset geometric threshold range of the lesion area; If the geometric characteristic parameters all fall into a preset threshold range, directly judging the corresponding connected domain as a lesion area; If any index of the geometric characteristic parameters does not fall within a preset threshold range, marking the corresponding connected domain as a risk region; if the geometric characteristic parameters do not fall into the preset threshold range, judging that the corresponding connected domain is a normal tissue region; And mapping the lesion area and the risk area to a surgical navigation image coordinate system to generate a fused visual feedback image.
9. A system for fusing nano-indentation sensing and real-time visual feedback is characterized in that the method for fusing nano-indentation sensing and real-time visual feedback according to any one of claims 1 to 8 comprises the following steps: the test module is used for obtaining nano indentation sensing signals representing mechanical properties of the tissues by carrying out indentation test on the surface of the lung tissues; The signal processing module is used for preprocessing the nano indentation sensing signals, and resolving mechanical characteristic parameters of the preprocessed nano indentation sensing signals to generate mechanical characteristic parameters for real-time visual feedback; the image registration module is used for carrying out spatial registration on the mechanical characteristic parameters and the operation navigation image to generate a fusion visual image overlapped with the mechanical characteristic parameters; The feature extraction module is used for counting the mechanical feature parameters based on a sliding window mechanism to obtain a plurality of stable mechanical feature parameters for representing local mechanical heterogeneity of lung tissues; The lesion identification module is used for identifying a lesion area according to the stable mechanical characteristic parameters, mapping the lesion area to a surgical navigation image coordinate system, generating a lesion positioning mark and synchronously triggering an early warning signal.
10. An electronic device, characterized in that the electronic device comprises: At least one processor; and a memory communicatively coupled to the at least one processor; Wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of fused nano-indentation sensing and real-time visual feedback of any of claims 1 to 8.

Description

Method and system for fusing nano indentation sensing and real-time visual feedback Technical Field The invention belongs to the technical field of medical diagnosis, and particularly relates to a method and a system for fusing nano indentation sensing and real-time visual feedback. Background With the continuous progress of medical technology, the detection rate of lung nodules and early lung cancer is remarkably improved, the thoracoscopic surgery is used as a main means of minimally invasive treatment, the accuracy and the safety of the thoracoscopic surgery depend on the accurate positioning of a lesion area, and the traditional surgical navigation mainly depends on preoperative images (such as CT and MRI) but has the following limitations; Firstly, the preoperative image cannot reflect the change of mechanical properties of tissue in operation in real time, and the tumor tissue and normal lung tissue have obvious differences in mechanical properties such as hardness, elasticity and the like, and the mechanical heterogeneity is an important diagnostic marker; Secondly, the lung tissue in the operation is influenced by factors such as respiratory motion, traction and the like, and is easy to deform, so that deviation between the preoperative image and the actual anatomy structure in the operation is caused, namely the problem of mismatching between the image and the anatomy is solved; thirdly, for glass grinding nodules with smaller diameters or deeper positions, the boundaries of the glass grinding nodules are difficult to accurately identify by visual observation and tactile feedback, so that the excessive cutting range or residual lesions can be caused, and the prognosis of a patient is influenced; Therefore, how to acquire the mechanical properties of the lung tissue in real time during operation and fuse the mechanical properties with the navigation image to realize the accurate identification and positioning of the lesion area becomes a key challenge for improving the accuracy of thoracoscopic operation. Disclosure of Invention The invention aims to provide a method and a system for fusing nanoindentation sensing and real-time visual feedback, which can acquire mechanical characteristic parameters of lung tissues in real time in an operation, and perform spatial registration and fusion visualization on the mechanical characteristic parameters and an operation navigation image, so as to realize accurate identification of lesion areas of the lung tissues. The technical scheme adopted by the invention is as follows: A method of fusing nanoindentation sensing with real-time visual feedback, comprising: Obtaining a nano indentation sensing signal representing mechanical properties of the tissue by carrying out indentation test on the surface of the lung tissue; Preprocessing the nano indentation sensing signals, and resolving mechanical characteristic parameters of the preprocessed nano indentation sensing signals to generate mechanical characteristic parameters for real-time visual feedback; spatially registering the mechanical characteristic parameters and the surgical navigation image to generate a fusion visual image overlapped with the mechanical characteristic parameters; Based on a sliding window mechanism, carrying out statistics on mechanical characteristic parameters to obtain a plurality of stable mechanical characteristic parameters for representing local mechanical heterogeneity of lung tissues; And identifying a lesion area according to the stable mechanical characteristic parameters, mapping the lesion area to a surgical navigation image coordinate system, generating a lesion positioning mark, and synchronously triggering an early warning signal. In a preferred embodiment, the step of obtaining the nanoindentation sensing signal characterizing mechanical properties of the tissue by indentation testing of the surface of the lung tissue comprises: the surface of lung tissue is contacted by the indentation sensing probe, controllable nanoscale displacement load is applied, and tissue deformation response signals are synchronously acquired; calculating and constructing a load displacement curve based on the corresponding relation between the tissue deformation response signals and the displacement load; And carrying out sectional fitting on the load displacement curve, extracting elastic modulus, creep rate and stress relaxation time constant as basic mechanical characteristic parameters, and summarizing the basic mechanical characteristic parameters into an original data set of the nano indentation sensing signal. In a preferred embodiment, the step of preprocessing the nanoindentation sensing signal includes: filtering the acquired original nano indentation sensing signals to inhibit interference signals caused by respiratory motion, instrument vibration and electronic noise; Comparing the signal-to-noise ratio of the nano indentation sensing signal after filtering with a preset signal-to-n