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

Abstract

The invention provides a thermal ablation area monitoring method and system based on radio frequency processing and fuzzy clustering. The 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, related to fuzzy clustering; s3, performing iterative processing on fuzzy clustering according to the processed radio frequency data and set fuzzy clustering parameters to obtain a clustering result; and S4, displaying a thermal ablation area according to the obtained clustering result. On one hand, the difference between thermal ablation and normal tissue is maximized and highlighted from the perspective of signal processing; on the other hand, an unsupervised fuzzy clustering algorithm is used for automatically identifying the thermal ablation area, the scheme can be well compatible with a current ultrasonic system, and compared with other disclosed thermal ablation monitoring methods, the method has the advantages of being reliable in result, easy to operate and easy to implement.

Description

technical field [0001] The invention relates to the technical field of ultrasonic monitoring imaging, in particular to a method and system for monitoring thermal ablation regions based on radio frequency processing and fuzzy clustering. Background technique [0002] Malignant tumors have become the number one killer threatening human life and health. Minimally invasive thermal ablation of tumors has become commonplace since the advent of modern imaging techniques. Percutaneous radiofrequency ablation, microwave ablation, cryoablation, irreversible electroporation, and high-intensity focused ultrasound are playing an increasingly important role in the treatment of solid tumors. [0003] At present, B-ultrasound is commonly used in clinical practice to monitor the effect of thermal ablation therapy on tumors, but only from the appearance of B-ultrasound images, the echo difference between thermal ablation and normal tissue may be low, and this monitoring method is not necessa...

AI Technical Summary

Problems solved by technology

[0004] However, the above studies only performed Hilbert transformation on the RF signal and directly used it as a CNN data set, and did not maximize the thermal ablation area from the perspective of signal processing.

In addition, before CNN training, it is necessary to manually mark the ablation area in the data set, which will inevitably introduce individual differences in marking. Even if the same operator marks the same data at different times, there will be differences, resulting in model The training result of the model depends heavily on the quality of the label, which affects the generalization ability of the model

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