Fig 1.
Probe assembly, mold design, and positioning schematic.
(A) Temperature probe assembly details: a. Thermocouple wire—Responsible for transmitting temperature signals. b. Hollow needle tip—Serves as the protective and guiding part for the sensor wire. c. Assembled temperature probe—Demonstrates the assembly of the sensor wire and needle tip. (B) Mold design for temperature probes and ablation needle: The diagram shows the expected layout of the temperature probes and ablation needle within the mold, with the ablation needle marked in red and the temperature probes marked in blue. (C) Expected relative positions of the temperature measurement points: This details the precise layout of the temperature measurement points relative to the ablation needle. The ablation needle is indicated by blue arrows (tip to the left), the temperature measurement points are represented by red dots (indexed at the bottom), and black lines denote the expected spatial distances between the measurement points.
Fig 2.
Experimental setup photo.
Fig 3.
Ultrasound temperature prediction method.
This figure outlines the methodological framework for extracting keyframes from ultrasound videos with corresponding temperature data. It then details the precise segmentation of regions of interest (ROIs) around temperature measurement points. The figure further illustrates the process of extracting imaging biomarker features from these ROIs, including comprehensive first-order statistical features and higher-order texture features. These features play a crucial role in capturing the details of temperature changes within the ablation zone and are subsequently used to generate a temperature estimation prediction model.
Fig 4.
Ultrasound image showing the ablation needle and temperature measurement ROIs.
This pre-ablation image captures the initial setup, with the ablation needle positioned accurately and the ROIs for temperature measurement clearly marked. Each 64 × 64 pixel square ROI is centered on a temperature measurement point, indicating the areas where temperature changes will be monitored throughout the ablation process.
Fig 5.
Ranking of features by importance in the 15 W and 20 W ablation groups.
This figure displays the importance ranking of temperature prediction features identified by the random forest algorithm, based on their contribution to predicting temperature changes. It highlights the features with the greatest predictive value during the microwave ablation process.
Fig 6.
Comparison of predicted and actual temperatures at 15W ablation (data presented in S1 Table).
Fig 7.
Comparison of predicted and actual temperatures at 20W ablation (data presented in S2 Table).
Fig 8.
Ultrasound images and corresponding predicted heat maps at the end of ablation.
This figure includes ex vivo porcine liver grayscale ultrasound images, machine learning-predicted thermographs, threshold thermographs, and tissue cross-sections. All images are categorized by ablation power, with the 15 W and 20 W groups displayed side by side for comparison.
Fig 9.
Ultrasound hyperechoic area measurement.
Table 1.
Prediction results of the coagulation area (P = 15W, t = 180s).
Table 2.
Prediction results of the coagulation area (P = 20W, t = 180s).
Fig 10.
Scatter plots of representative features with temperature in the 15 W ablation group.
(a) Cluster Prominence; (b) Variance; (c) Mean Absolute Deviation.
Fig 11.
Scatter plots of representative features with temperature in the 20 W ablation group.
(a) Variance; (b) Mean Absolute Deviation; (c) Minimum Feature Value.