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A Data-Driven Approach for Estimating Temperature Variations Based on B-mode Ultrasound Images and Changes in
Luiz F R Oliveira1, Felipe M G França1,2, Wagner C A Pereira3
1Program Systems Engineering and Computer Science Program, COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
Ultrasonic Imaging
|December 2, 2023
Summary
Accurate temperature monitoring during ultrasound thermal treatments is crucial. This study proposes a machine learning approach using B-mode ultrasound images to estimate regional temperatures, achieving high accuracy for safer patient treatments.
Area of Science:
- Medical Imaging
- Ultrasound Technology
- Machine Learning in Medicine
Background:
- Precise temperature control is essential for ultrasound thermal therapies to prevent treatment limitations or injury aggravation.
- Real-time temperature determination in targeted body regions remains a significant challenge in current medical practice.
- Machine learning is emerging as a powerful tool for enhancing ultrasound imaging and diagnostics.
Purpose of the Study:
- To develop a data-driven, supervised learning method for estimating temperature in specific regions of B-mode ultrasound images.
- To introduce a novel data modeling approach incorporating conventional B-mode images and parametric images based on changes in backscattered energy (CBE).
- To evaluate the performance of the proposed method against existing models in simulated scenarios.
Main Methods:
- Utilized a supervised learning framework to address the temperature estimation problem.
- Developed a novel data model integrating B-mode ultrasound image data with a parametric image derived from changes in backscattered energy (CBE).
- Compared the proposed approach with established machine learning models.
Main Results:
- The proposed data-driven approach, utilizing a Gradient Boosting model, demonstrated the ability to estimate temperature with a mean absolute error of approximately 0.5°C in a simulated environment.
- This level of accuracy is considered acceptable for practical applications in both physiotherapeutic treatments and high-intensity focused ultrasound (HIFU).
Conclusions:
- The developed machine learning model offers a promising solution for accurate real-time temperature estimation during ultrasound thermal treatments.
- The novel data modeling approach enhances the precision of temperature monitoring, potentially improving the safety and efficacy of ultrasound-guided therapies.
- This method holds significant potential for clinical applications, including physiotherapy and HIFU, by providing reliable temperature feedback.
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