CN111429432A - Thermal ablation region monitoring method and system based on radio frequency processing and fuzzy clustering - Google Patents
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Abstract
The invention provides a thermal ablation region monitoring method and system based on radio frequency processing and fuzzy clustering. The related thermal ablation region monitoring method based on radio frequency processing and fuzzy clustering comprises the following steps: s1, acquiring radio frequency data in a thermal ablation process, and processing the acquired radio frequency data; s2, setting parameters related to fuzzy clustering; s3, performing iterative processing on fuzzy clustering according to the processed radio frequency data and the set fuzzy clustering parameters to obtain a clustering result; and S4, displaying the thermal ablation area according to the obtained clustering result. The method and the device have the advantages that on one hand, the difference between the thermal ablation and the normal tissue is highlighted to the maximum from the signal processing perspective, on the other hand, the unsupervised fuzzy clustering algorithm is used for automatically identifying the thermal ablation area, in addition, the scheme can be well compatible with the current ultrasonic system, and compared with other disclosed thermal ablation monitoring methods, the method and the device have the advantages of reliable results, simplicity in operation and easiness in implementation.
Description
Technical Field
The invention relates to the technical field of ultrasonic monitoring imaging, in particular to a thermal ablation region monitoring method and system based on radio frequency processing and fuzzy clustering.
Background
Malignant tumors have become the first leading killer threatening human life and health. Minimally invasive thermal ablation of tumors has become common since the advent of modern imaging techniques. Percutaneous radiofrequency ablation, microwave ablation, cryoablation, irreversible electroporation, and high-intensity focused ultrasound play increasingly important roles in treating solid tumors.
For this problem, the research team of the western traffic university (Detection and Monitoring of Thermal L aspects Induced by B microwave Ablation Using ultrasonic Imaging and capacitive Neural Networks [ J ]. IEEE Journal of biological and Health information, 2019.) proposes to analyze ultrasonic signals Using a Convolutional Neural Network (CNN), and considering that ultrasonic Radio Frequency (RF) signals can reflect various characteristics of ultrasonic scatterers, so a data set of CNN is constructed based on backscattered RF signals, and a model is obtained by training to finally monitor the Thermal Ablation region.
However, the above studies performed only Hilbert transform on RF signals and directly as a data set for CNN, and did not maximize the thermal ablation area from the viewpoint of signal processing. In addition, before the CNN training, the ablation regions in the data set need to be labeled manually, which inevitably introduces individual differences of the labels, and even the labeling results of the same operator on the same data at different times will also have differences, so that the training results of the model are heavily dependent on the quality of the labels, and the generalization capability of the model is affected.
Disclosure of Invention
The invention aims to provide a thermal ablation region monitoring method and system based on radio frequency processing and fuzzy clustering, aiming at the defects of the prior art. The method comprises the steps of collecting back scattering RF signals in the thermal ablation process, automatically monitoring a thermal ablation area based on a fuzzy clustering algorithm, conducting detection demodulation and dynamic range adjustment on the RF signals to maximize the thermal ablation area, and identifying the thermal ablation area through the fuzzy clustering algorithm.
In order to achieve the purpose, the invention adopts the following technical scheme:
a thermal ablation region monitoring method based on radio frequency processing and fuzzy clustering comprises the following steps:
s1, acquiring radio frequency data in a thermal ablation process, and processing the acquired radio frequency data;
s2, setting parameters related to fuzzy clustering;
s3, performing iterative processing on fuzzy clustering according to the processed radio frequency data and the set fuzzy clustering parameters to obtain a clustering result;
and S4, displaying the thermal ablation area according to the obtained clustering result.
Further, the step S1 specifically includes:
s11, obtaining a back scattering radio frequency signal in a thermal ablation process;
s12, carrying out detection demodulation processing on the obtained radio frequency signal to obtain an envelope signal;
s13, adjusting the dynamic range of the obtained envelope signals to maximally distinguish thermal ablation areas;
s14, rearranging the envelope signals.
Further, the rearranging of the envelope signals in the step S14 is performed with column-first rearrangement; the envelope signals before rearrangement are two-dimensional matrixes, and Row numbers and column numbers are Row and Col respectively; the rearranged envelope signal is one-dimensional data reshapeEnv with a length Row Col.
Further, the parameters in step S2 include the number of cluster categories and the iteration termination condition of fuzzy clustering; the iteration termination condition of the fuzzy clustering in the step S2 is the variation of the cluster loss function between two adjacent iterations.
Further, the step S3 specifically includes:
s31, initializing a membership matrix U; wherein, the Row number of the membership degree matrix U is cluster, and the column number Row is Col;
s32, calculating a current clustering center;
s33, updating the membership matrix;
s34, judging whether the variation of the clustering loss function in two adjacent iterations is smaller than a preset threshold value, and if so, stopping the iteration; if not, step S32 is re-executed based on the updated membership matrix.
Further, in step S32, a current cluster center is calculated, which is represented as:
wherein, the CenteriRepresenting the cluster center.
Further, in step S33, the membership matrix is updated as:
wherein U (i, j) represents the updated membership matrix.
Further, in the step S34, it is determined whether a variation of the clustering loss function between two adjacent iterations is smaller than a preset threshold, which is expressed as:
where L oss represents the cluster loss function.
Further, the step S4 is specifically: respectively calculating the average signal intensity of the two clusters according to the clustering result; wherein the cluster with the larger average signal intensity corresponds to the thermal ablation region.
Correspondingly, a thermal ablation region monitoring system based on radio frequency processing and fuzzy clustering is further provided, and comprises:
the acquisition module is used for acquiring radio frequency data in the thermal ablation process and processing the acquired radio frequency data;
the setting module is used for setting parameters related to fuzzy clustering;
the processing module is used for carrying out iterative processing on fuzzy clustering according to the processed radio frequency data and the set fuzzy clustering parameters to obtain a clustering result;
and the display module is used for displaying the thermal ablation area according to the obtained clustering result.
Compared with the prior art, the invention has the following advantages:
1. the thermal ablation region can be maximally distinguished from the input data by discriminating the thermal ablation region based on the backscattered RF signal and subjecting the RF signal to the specific processing of demodulation and dynamic range adjustment.
2. Fuzzy clustering is an unsupervised segmentation method, a data set does not need to be manually marked, and the difference of individual marks is eliminated, so that the monitoring result of thermal ablation is more reliable.
Drawings
FIG. 1 is a flowchart of a thermal ablation region monitoring method based on RF processing and fuzzy clustering according to an embodiment;
fig. 2 is a structural diagram of a thermal ablation region monitoring system based on rf processing and fuzzy clustering according to the second embodiment.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
The invention aims to provide a thermal ablation region monitoring method and system based on radio frequency processing and fuzzy clustering, aiming at the defects of the prior art.
Example one
The embodiment provides a thermal ablation region monitoring method based on radio frequency processing and fuzzy clustering, as shown in fig. 1, including the steps of:
s1, acquiring radio frequency data in a thermal ablation process, and processing the acquired radio frequency data;
s2, setting parameters related to fuzzy clustering;
s3, performing iterative processing on fuzzy clustering according to the processed radio frequency data and the set fuzzy clustering parameters to obtain a clustering result;
and S4, displaying the thermal ablation area according to the obtained clustering result.
In step S1, rf data during thermal ablation is acquired and processed. The method specifically comprises the following steps:
s11, obtaining a back scattering Radio Frequency (RF) signal in a thermal ablation process;
s12, carrying out detection demodulation processing on the obtained radio frequency signal to obtain an envelope signal;
specifically, the RF signal is demodulated to obtain an envelope signal Env. Common demodulation methods include quadrature demodulation, Hilbert transform demodulation, subsampling, and the like, and the Hilbert transform is used in this embodiment.
S13, adjusting the dynamic range of the obtained envelope signals to maximally distinguish thermal ablation areas;
in particular, the dynamic range of Env is adjusted to maximize the discrimination of thermal ablation zones. Common dynamic range compression methods include logarithmic mapping, piecewise function mapping, linear shift method, S-curve mapping, and the like, and the present embodiment uses an S-curve for mapping.
Wherein, the expression of the S curve is as follows:
wherein, y0The initial value is K, the final value is K, and r is used for measuring the change speed of the curve.
S14, rearranging the envelope signals.
Specifically, the envelope signal Env is rearranged. Env before rearrangement is a two-dimensional matrix, the number of rows and columns is Row and Col respectively, rearrangement is performed with column priority, and Env after rearrangement is one-dimensional data reshapeEnv with the length of Row Col.
In step S2, parameters related to the fuzzy clustering are set. The method specifically comprises the following steps:
s21, setting cluster category number cluster;
in the present embodiment, since it is only necessary to distinguish whether or not the current region is a thermal ablation region, cluster is set to 2. It should be noted that the value of cluster may be set according to actual conditions, and is not limited to the value set forth in the present embodiment.
And S22, setting an iteration termination condition of fuzzy clustering, wherein whether iteration is terminated depends on the variation of two adjacent iterations of a clustering loss function L oss, and a minimum threshold of the variation of L oss is set to be eps, wherein eps is 1 e-5.
In step S3, performing iterative processing on the fuzzy clusters according to the processed radio frequency data and the set fuzzy clustering parameters to obtain a clustering result. The method specifically comprises the following steps:
s31, initializing a membership matrix U; wherein, the Row number of the membership degree matrix U is cluster, and the column number Row is Col;
specifically, the membership matrix U is initialized. The number of rows of U is cluster and the number of columns Row Col. U is randomly initialized to a number between 0-1 and normalized by column.
S32, calculating a current clustering center;
specifically, the current clustering Center is calculated. The calculation formula is as follows:
wherein, the CenteriRepresenting a cluster center; i represents the number of cluster categories; j denotes the length of the one-dimensional data reshapeEnv.
S33, updating the membership matrix;
specifically, the membership matrix U is updated. The update formula is as follows:
wherein U (i, j) represents the updated membership matrix.
S34, judging whether the variation of the clustering loss function in two adjacent iterations is smaller than a preset threshold value, and if so, stopping the iteration; if not, step S32 is re-executed based on the updated membership matrix.
Judging iteration termination conditions, if the variation of two adjacent iterations of the clustering loss function L oss is smaller than eps (such as 1e-5), considering that the iteration is terminated, otherwise, recalculating the clustering center based on the updated U value and repeating the processes of the steps S32-S34, wherein the calculation formula of L oss is as follows:
where L oss represents the cluster loss function.
In step S4, a thermal ablation area is displayed based on the obtained clustering result. The method specifically comprises the following steps:
and S41, respectively calculating the average signal intensity of the two classes according to the clustering result.
And S42, obtaining the class-corresponding thermal ablation area with high average signal intensity by combining the characteristics of the thermal ablation area.
The embodiment maximizes the difference between the thermal ablation and normal tissues from the aspect of signal processing, and performs automatic identification of the thermal ablation area by using an unsupervised fuzzy clustering algorithm, and the scheme is well compatible with the current ultrasonic system, and has the advantages of reliable result, simple operation and easy realization compared with other disclosed thermal ablation monitoring methods.
Compared with the prior art, the embodiment has the following technical effects:
1. the thermal ablation region can be maximally distinguished from the input data by discriminating the thermal ablation region based on the backscattered RF signal and subjecting the RF signal to the specific processing of demodulation and dynamic range adjustment.
2. Fuzzy clustering is an unsupervised segmentation method, a data set does not need to be manually marked, and the difference of individual marks is eliminated, so that the monitoring result of thermal ablation is more reliable.
Example two
The present embodiment provides a thermal ablation region monitoring system based on rf processing and fuzzy clustering, as shown in fig. 2, including:
the acquisition module 11 is configured to acquire radio frequency data in a thermal ablation process and process the acquired radio frequency data;
a setting module 12, configured to set parameters related to fuzzy clustering;
the processing module 13 is configured to perform iterative processing on the fuzzy clustering according to the processed radio frequency data and the set fuzzy clustering parameters to obtain a clustering result;
and the display module 14 is configured to display the thermal ablation area according to the obtained clustering result.
It should be noted that the thermal ablation region monitoring system based on radio frequency processing and fuzzy clustering provided in this embodiment is similar to the embodiment, and will not be described herein again.
Compared with the prior art, the invention has the following advantages:
1. the thermal ablation region can be maximally distinguished from the input data by discriminating the thermal ablation region based on the backscattered RF signal and subjecting the RF signal to the specific processing of demodulation and dynamic range adjustment.
2. Fuzzy clustering is an unsupervised segmentation method, a data set does not need to be manually marked, and the difference of individual marks is eliminated, so that the monitoring result of thermal ablation is more reliable.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (10)
1. The thermal ablation region monitoring method based on radio frequency processing and fuzzy clustering is characterized by comprising the following steps of:
s1, acquiring radio frequency data in a thermal ablation process, and processing the acquired radio frequency data;
s2, setting parameters related to fuzzy clustering;
s3, performing iterative processing on fuzzy clustering according to the processed radio frequency data and the set fuzzy clustering parameters to obtain a clustering result;
and S4, displaying the thermal ablation area according to the obtained clustering result.
2. The method for monitoring the thermal ablation area based on radio frequency processing and fuzzy clustering as claimed in claim 1, wherein the step S1 specifically comprises:
s11, obtaining a back scattering radio frequency signal in a thermal ablation process;
s12, carrying out detection demodulation processing on the obtained radio frequency signal to obtain an envelope signal;
s13, adjusting the dynamic range of the obtained envelope signals to maximally distinguish thermal ablation areas;
s14, rearranging the envelope signals.
3. The method for monitoring the thermal ablation region based on rf processing and fuzzy clustering according to claim 2, wherein the rearranging of the envelope signals in step S14 is rearranged with column priority; the envelope signals before rearrangement are two-dimensional matrixes, and Row numbers and column numbers are Row and Col respectively; the rearranged envelope signal is one-dimensional data reshapeEnv with a length Row Col.
4. The method for monitoring the thermal ablation area based on radio frequency processing and fuzzy clustering as claimed in claim 1, wherein the parameters in the step S2 include the clustering category number, the iteration termination condition of fuzzy clustering; the iteration termination condition of the fuzzy clustering in the step S2 is the variation of the cluster loss function between two adjacent iterations.
5. The method for monitoring the thermal ablation area based on radio frequency processing and fuzzy clustering as claimed in claim 1, wherein the step S3 specifically comprises:
s31, initializing a membership matrix U; wherein, the Row number of the membership degree matrix U is cluster, and the column number Row is Col;
s32, calculating a current clustering center;
s33, updating the membership matrix;
s34, judging whether the variation of the clustering loss function in two adjacent iterations is smaller than a preset threshold value, and if so, stopping the iteration; if not, step S32 is re-executed based on the updated membership matrix.
6. The method for monitoring the thermal ablation area based on radio frequency processing and fuzzy clustering as claimed in claim 5, wherein the current clustering center is calculated in step S32 and is represented as:
wherein, the CenteriRepresenting the cluster center.
7. The method for monitoring the thermal ablation area based on radio frequency processing and fuzzy clustering as claimed in claim 5, wherein the step S33 is implemented by updating the membership matrix as:
wherein U (i, j) represents the updated membership matrix.
8. The method for monitoring the thermal ablation area based on rf processing and fuzzy clustering as claimed in claim 5, wherein the step S34 is implemented to determine whether the variation of the cluster loss function between two adjacent iterations is smaller than a preset threshold, which is expressed as:
where L oss represents the cluster loss function.
9. The method for monitoring the thermal ablation area based on radio frequency processing and fuzzy clustering as claimed in claim 1, wherein the step S4 is specifically as follows: respectively calculating the average signal intensity of the two clusters according to the clustering result; wherein the cluster with the larger average signal intensity corresponds to the thermal ablation region.
10. Thermal ablation area monitoring system based on radio frequency processing and fuzzy clustering, characterized by comprising:
the acquisition module is used for acquiring radio frequency data in the thermal ablation process and processing the acquired radio frequency data;
the setting module is used for setting parameters related to fuzzy clustering;
the processing module is used for carrying out iterative processing on fuzzy clustering according to the processed radio frequency data and the set fuzzy clustering parameters to obtain a clustering result;
and the display module is used for displaying the thermal ablation area according to the obtained clustering result.
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| CN112007289A (en) * | 2020-09-09 | 2020-12-01 | 上海沈德医疗器械科技有限公司 | Automatic planning method and device for tissue ablation |
| CN112790858A (en) * | 2020-12-31 | 2021-05-14 | 杭州堃博生物科技有限公司 | Ablation parameter configuration method, device, system and computer readable storage medium |
| CN113456213A (en) * | 2021-08-13 | 2021-10-01 | 卡本(深圳)医疗器械有限公司 | Artificial intelligence-based radio frequency ablation parameter optimization and information synthesis method and system |
| CN114305667A (en) * | 2021-12-22 | 2022-04-12 | 聚融医疗科技(杭州)有限公司 | Ultrasonic thermal ablation monitoring method and system based on non-relevant characteristics |
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| CN112790858B (en) * | 2020-12-31 | 2021-11-09 | 杭州堃博生物科技有限公司 | Ablation parameter configuration method, device, system and computer readable storage medium |
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| CN114305669B (en) * | 2021-12-22 | 2023-10-31 | 聚融医疗科技(杭州)有限公司 | An ultrasonic thermal ablation monitoring method and system based on acoustic attenuation characteristics |
| CN114305668B (en) * | 2021-12-22 | 2023-10-31 | 聚融医疗科技(杭州)有限公司 | Ultrasonic thermal ablation multi-parameter monitoring method and system based on demodulation domain parameter imaging |
| CN114305667B (en) * | 2021-12-22 | 2023-10-31 | 聚融医疗科技(杭州)有限公司 | An ultrasonic thermal ablation monitoring method and system based on non-correlated features |
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