CN111429432B - Thermal ablation area monitoring method and system based on radio frequency processing and fuzzy clustering - Google Patents

Thermal ablation area monitoring method and system based on radio frequency processing and fuzzy clustering Download PDF

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CN111429432B
CN111429432B CN202010213186.4A CN202010213186A CN111429432B CN 111429432 B CN111429432 B CN 111429432B CN 202010213186 A CN202010213186 A CN 202010213186A CN 111429432 B CN111429432 B CN 111429432B
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CN111429432A (en
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肖梦楠
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Jurong Medical Technology Hangzhou Co ltd
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Abstract

The invention provides a thermal ablation area monitoring method and a thermal ablation area monitoring system based on radio frequency processing and fuzzy clustering. The related thermal ablation area 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; s4, displaying the thermal ablation area according to the obtained clustering result. The method has the advantages that the difference between the thermal ablation and normal tissues is maximized from the aspect of signal processing, the thermal ablation area is automatically identified by using an unsupervised fuzzy clustering algorithm, the scheme can be well compatible with the current ultrasonic system, and compared with other disclosed thermal ablation monitoring methods, the method has the advantages of reliable results, simplicity in operation and easiness in implementation.

Description

Thermal ablation area monitoring method and system based on radio frequency processing and fuzzy clustering
Technical Field
The invention relates to the technical field of ultrasonic monitoring imaging, in particular to a thermal ablation area monitoring method and system based on radio frequency processing and fuzzy clustering.
Background
Malignant tumors have become the first leading killer to human life and health. Minimally invasive thermal ablation of tumors has become common since the advent of modern imaging technology. Percutaneous radio frequency ablation, microwave ablation, cryoablation, irreversible electroporation and high intensity focused ultrasound play an increasingly important role in the treatment of solid tumors.
At present, the effect of thermal ablation treatment on tumors is commonly monitored clinically by using B-ultrasonic, but the echo difference between thermal ablation and normal tissues is probably lower only from the point of view of B-ultrasonic image representation, and the monitoring method is not necessarily the most effective. In view of this problem, research team (Detection and Monitoring of Thermal Lesions Induced by Microwave Ablation Using Ultrasound Imaging and Convolutional Neural Networks[J].IEEE Journal of Biomedical and Health Informatics,2019.) of the western union transportation university proposes to analyze the ultrasonic signals using convolutional neural network (Convolutional neural network, CNN) and to construct a data set of CNN based on the backscattered RF signals, and to obtain a segmentation model through training, considering that the ultrasonic Radio Frequency (RF) signals can reflect various characteristics of the ultrasonic scatterers, and finally to realize monitoring of the thermal ablation region.
However, the above study only Hilbert transformed the RF signal and directly served as a data set for CNN, and did not maximize the salient thermal ablation region from the point of view of signal processing. In addition, the ablation region in the data set needs to be manually marked before CNN training, which inevitably introduces individual variability of marking, and even the marking results of the same operator on the same data at different moments can have differences, so that the training results of the model are seriously dependent on marking quality, and the generalization capability of the model is affected.
Disclosure of Invention
The invention aims at overcoming the defects of the prior art and provides a thermal ablation area monitoring method and a thermal ablation area monitoring system based on radio frequency processing and fuzzy clustering. The method comprises the steps of collecting back scattering RF signals in a thermal ablation process, realizing automatic monitoring of a thermal ablation area based on a fuzzy clustering algorithm, carrying out demodulation detection and dynamic range adjustment on the RF signals to maximize the outstanding thermal ablation area, and then realizing identification of the thermal ablation area through the fuzzy clustering algorithm, wherein human intervention is not needed in the process, and a segmentation result has higher reliability.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the method for monitoring the thermal ablation area based on radio frequency treatment 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;
S4, displaying the thermal ablation area according to the obtained clustering result.
Further, the step S1 specifically includes:
S11, acquiring a back scattering radio frequency signal in the thermal ablation process;
S12, performing detection demodulation processing on the obtained radio frequency signals to obtain envelope signals;
s13, carrying out dynamic range adjustment on the obtained envelope signal so as to maximally distinguish thermal ablation areas;
S14, rearranging the envelope signals.
Further, the rearranging of the envelope signals in the step S14 is rearranged with a column priority; wherein, the envelope signal before rearrangement is a two-dimensional matrix, and the Row number and the column number are Row and Col respectively; the rearranged envelope signal is one-dimensional data RESHAPEENV, with a length Row.
Further, the parameters in the step S2 include the number of clustering categories and iteration termination conditions of fuzzy clustering; and the iteration termination condition of the fuzzy clustering in the step S2 is the variation of two adjacent iterations of the clustering loss function.
Further, the step S3 specifically includes:
s31, initializing a membership matrix U; the number of rows of the membership matrix U is cluster, and the number of columns Row is Col;
s32, calculating a current clustering center;
S33, updating a membership matrix;
S34, judging whether the variation of two adjacent iterations of the clustering loss function is smaller than a preset threshold value, and if yes, stopping iteration; if not, step S32 is re-executed based on the updated membership matrix.
Further, in the step S32, a current cluster center is calculated, which is expressed as:
wherein Center i represents a cluster Center.
Further, in the step S33, the membership matrix is updated, which is expressed as:
Wherein U (i, j) represents the updated membership matrix.
Further, in the step S34, it is determined whether the variation of two adjacent iterations of the cluster loss function is smaller than a preset threshold, which is expressed as:
where Loss represents the cluster Loss function.
Further, the step S4 specifically includes: respectively calculating the average signal strength of the two clusters according to the clustering result; wherein clusters with large average signal intensities correspond to thermal ablation regions.
Correspondingly, a thermal ablation region monitoring system based on radio frequency processing and fuzzy clustering is also provided, 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.
Compared with the prior art, the invention has the following advantages:
1. The thermal ablation region is distinguished based on the back-scattered RF signal, and the RF signal is subjected to a specific process of demodulation by detection and dynamic range adjustment, so that the thermal ablation region can be distinguished to the greatest extent from the input data.
2. The fuzzy clustering is an unsupervised segmentation method, a manual marking dataset is not needed, individual marking differences are eliminated, and the monitoring result of thermal ablation is more reliable.
Drawings
FIG. 1 is a flowchart of a thermal ablation zone monitoring method based on RF processing and fuzzy clustering according to an embodiment I;
Fig. 2 is a diagram of a thermal ablation zone monitoring system based on radio frequency processing and fuzzy clustering according to the second embodiment.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
The invention aims at overcoming the defects of the prior art and provides a thermal ablation area monitoring method and a thermal ablation area monitoring system based on radio frequency processing and fuzzy clustering.
Example 1
The embodiment provides a thermal ablation area monitoring method based on radio frequency processing and fuzzy clustering, as shown in fig. 1, comprising 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;
S4, displaying the thermal ablation area according to the obtained clustering result.
In step S1, radio frequency data in a thermal ablation process is acquired, and the acquired radio frequency data is processed. The method specifically comprises the following steps:
S11, acquiring a back scattering Radio Frequency (RF) signal in the thermal ablation process;
S12, performing detection demodulation processing on the obtained radio frequency signals to obtain envelope signals;
specifically, the RF signal is detected and demodulated to obtain an envelope signal Env. Common demodulation methods include quadrature demodulation, hilbert transform demodulation, subsampling, and the like, and Hilbert transform is used in this embodiment.
S13, carrying out dynamic range adjustment on the obtained envelope signal so as to maximally distinguish thermal ablation areas;
specifically, the Env is dynamically adjusted to maximize discrimination between thermal ablation regions. Common dynamic range compression methods include logarithmic mapping, piecewise function mapping, linear shifting, S-curve mapping, and the like, and the embodiment uses S-curves for mapping.
Wherein, the expression of the S curve is:
Wherein y 0 is an initial value, K is a final value, and r measures the change speed of the curve.
S14, rearranging the envelope signals.
Specifically, the envelope signal Env is rearranged. The Env before rearrangement is a two-dimensional matrix, the number of rows and columns are Row and Col respectively, rearrangement is performed with priority of columns, the Env after rearrangement is one-dimensional data RESHAPEENV, and the length is Row.
In step S2, parameters related to fuzzy clustering are set. The method specifically comprises the following steps:
s21, setting a cluster category number cluster;
In this embodiment, since it is only necessary to distinguish whether the current region is a thermal ablation region, cluster=2 is set. It should be noted that the numerical value of the cluster may be set according to the actual situation, and is not limited to the numerical value set forth in the present embodiment.
S22, setting iteration termination conditions of fuzzy clustering. Whether an iteration is terminated depends on the amount of change of two adjacent iterations of the cluster Loss function Loss, setting the minimum threshold for the amount of change of Loss to eps, where eps = 1e-5. It should be noted that the values of eps are not limited to the values set forth in this embodiment.
In step S3, according to the processed radio frequency data and the set fuzzy clustering parameters, iterative processing is performed on fuzzy clustering, so as to obtain a clustering result. The method specifically comprises the following steps:
s31, initializing a membership matrix U; the number of rows of the membership matrix U is cluster, and the number of columns Row is Col;
Specifically, the membership matrix U is initialized. The number of rows of U is cluster and the number of columns Row. U is randomly initialized to a number between 0 and 1 and normalized by column.
S32, calculating a current clustering center;
Specifically, a current cluster Center is calculated. The calculation formula is as follows:
wherein Center i represents a cluster Center; i represents the number of clustering categories; j represents the length of one-dimensional data RESHAPEENV.
S33, updating a 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 two adjacent iterations of the clustering loss function is smaller than a preset threshold value, and if yes, stopping iteration; if not, step S32 is re-executed based on the updated membership matrix.
Specifically, the iteration termination condition is judged. If the variation of two adjacent iterations of the cluster Loss function Loss is smaller than eps (e.g. 1 e-5), the iteration is considered to be terminated, otherwise, the cluster center is recalculated based on the updated U value, and the process of steps S32-S34 is repeated. The formula of Loss is as follows:
where Loss represents the cluster Loss function.
In step S4, a thermal ablation region is displayed according to the obtained clustering result. The method specifically comprises the following steps:
s41, respectively calculating the average signal intensity of the two classes according to the clustering result.
S42, combining the characteristics of the thermal ablation areas to obtain the class corresponding thermal ablation areas with large average signal intensity.
The embodiment maximizes the difference between the outstanding thermal ablation and the normal tissue from the signal processing angle, and uses an unsupervised fuzzy clustering algorithm to automatically identify the thermal ablation area.
Compared with the prior art, the embodiment achieves the following technical effects:
1. The thermal ablation region is distinguished based on the back-scattered RF signal, and the RF signal is subjected to a specific process of demodulation by detection and dynamic range adjustment, so that the thermal ablation region can be distinguished to the greatest extent from the input data.
2. The fuzzy clustering is an unsupervised segmentation method, a manual marking dataset is not needed, individual marking differences are eliminated, and the monitoring result of thermal ablation is more reliable.
Example two
The present embodiment provides a thermal ablation zone monitoring system based on radio frequency processing and fuzzy clustering, as shown in fig. 2, including:
The acquisition module 11 is used for acquiring radio frequency data in the thermal ablation process and processing the acquired radio frequency data;
a setting module 12, configured to set parameters related to fuzzy clustering;
the processing module 13 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 14 is used for displaying the thermal ablation area according to the obtained clustering result.
It should be noted that, the thermal ablation area monitoring system based on radio frequency processing and fuzzy clustering provided in this embodiment is similar to the embodiment, and will not be described in detail herein.
Compared with the prior art, the invention has the following advantages:
1. The thermal ablation region is distinguished based on the back-scattered RF signal, and the RF signal is subjected to a specific process of demodulation by detection and dynamic range adjustment, so that the thermal ablation region can be distinguished to the greatest extent from the input data.
2. The fuzzy clustering is an unsupervised segmentation method, a manual marking dataset is not needed, individual marking differences are eliminated, and the monitoring result of thermal ablation is more reliable.
The specific embodiments described herein are offered by way of example only to illustrate the spirit of the invention. Those skilled in the art may make various modifications or additions to the described embodiments or substitutions thereof without departing from the spirit of the invention or exceeding the scope of the invention as defined in the accompanying claims.

Claims (3)

1. The method for monitoring the thermal ablation area based on radio frequency processing and fuzzy clustering is characterized by comprising 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;
S4, displaying a thermal ablation area according to the obtained clustering result;
The step S1 specifically comprises the following steps:
S11, acquiring a back scattering radio frequency signal in the thermal ablation process;
s12, performing detection demodulation processing on the obtained radio frequency signals to obtain envelope signals;
s13, adjusting the dynamic range of the obtained envelope signal to maximize and distinguish thermal ablation areas;
S14, rearranging the envelope signals;
in step S14, rearranging the envelope signals is rearranged with column preference; wherein, the envelope signal before rearrangement is a two-dimensional matrix, and the Row number and the column number are Row and Col respectively; the rearranged envelope signal is one-dimensional data RESHAPEENV, and the length is Row;
the step S3 specifically comprises the following steps:
s31, initializing a membership matrix U; the number of rows of the membership matrix U is cluster, and the number of columns Row is Col;
s32, calculating a current clustering center;
S33, updating a membership matrix;
S34, judging whether the variation of two adjacent iterations of the clustering loss function is smaller than a preset threshold value, and if yes, stopping the iteration; if not, re-executing the step S32 based on the updated membership matrix;
in step S32, a current cluster center is calculated, expressed as:
wherein Centeri denotes a cluster center;
In step S33, the membership matrix is updated, expressed as:
wherein U (i, j) represents the updated membership matrix;
in step S34, it is determined whether the variation of two adjacent iterations of the clustering loss function is smaller than a preset threshold, which is expressed as:
where Loss represents the cluster Loss function.
2. The method for monitoring a thermal ablation region based on radio frequency processing and fuzzy clustering according to claim 1, wherein the parameters in the step S2 include the number of clustering categories, and iteration termination conditions of fuzzy clustering; in step S2, the iteration termination condition of the fuzzy clustering is the variation of two adjacent iterations of the clustering loss function.
3. The method for monitoring a thermal ablation region based on radio frequency processing and fuzzy clustering according to claim 1, wherein step S4 is specifically: respectively calculating the average signal strength of the two clusters according to the clustering result; wherein clusters with large average signal intensities correspond to thermal ablation regions.
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