Microbubble Acoustic Control Using ML Collapse Prediction
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Solution Overview
Problem
Current ultrasound systems for treating and imaging with microbubbles are limited in both imaging and targeting, and existing machine learning applications for focused ultrasound with microbubbles (MB-FUS) are focused on imaging and treatment outcome predictions rather than real-time control of bubble dynamics.
Innovation Solution
A system and method using machine learning models to predict and control microbubble dynamics in real-time by monitoring acoustic emissions, adjusting acoustic energy to prevent bubble collapse and minimize tissue damage, utilizing controllers that include open-loop, closed-loop, and reactive control algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to predict and control microbubble dynamics in real-time, then control precision and treatment safety are improved, but system complexity and computational requirements increase
Solution Approach 1:
The machine learning model is trained offline on historical microbubble acoustic emission data to learn patterns and predict collapse events. This preliminary training phase separates the complex computational work from real-time operation, allowing the model to make rapid predictions during actual MB-FUS procedures without burdening the real-time control system with extensive training computations
Solution Approach 2:
The machine learning model acts as an intermediary layer between acoustic emission monitoring and control decisions. It processes acoustic emission signals and translates them into predictive insights about microbubble collapse risk, which then guide the control system's adjustments to acoustic pressure parameters, bridging the gap between observation and action
2Productivity
If acoustic energy is increased to enhance therapeutic effect, then treatment efficiency is improved, but risk of tissue damage increases
Solution Approach 1:
The system continuously monitors acoustic emission signals from microbubbles during MB-FUS treatment and feeds this information back to the machine learning model. The model predicts the likelihood of microbubble collapse and adjusts acoustic pressure parameters accordingly, creating a closed-loop control system that adapts to real-time conditions and prevents excessive energy delivery that could cause tissue damage
Solution Approach 2:
The machine learning model predicts microbubble collapse events before they occur by analyzing patterns in acoustic emission data. This early warning allows the control system to preemptively reduce acoustic pressure or adjust other parameters to prevent collapse-related tissue damage, rather than reacting after damage has occurred
3Reliability
If machine learning algorithms are integrated into MB-FUS control systems, then treatment safety and precision are improved, but computational time and processing requirements increase
Solution Approach 1:
The machine learning model undergoes extensive training on historical acoustic emission data before deployment, learning to recognize patterns indicative of microbubble collapse. This offline training phase performs the computationally intensive work of pattern recognition, enabling the model to make rapid predictions during real-time MB-FUS operations without requiring extensive computational resources during critical treatment moments
Solution Approach 2:
The patent replaces traditional rule-based control algorithms with machine learning-based predictive control. Instead of using fixed thresholds or simple heuristics to determine when to adjust acoustic pressure, the system employs trained neural networks or other ML models that can process acoustic emission patterns and predict collapse risk more accurately and efficiently
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables precise control of microbubble dynamics for improved ultrasound imaging and therapeutic interventions, preventing tissue damage and enhancing treatment efficiency in applications such as tumor therapy, drug delivery, and blood-brain barrier permeability.
Implementation Method 1
acoustic transducer to emit acoustic energy to the one or more bubbles
Implementation Method 2
monitor acoustic emission levels of one or more bubbles
Implementation Method 3
predict at least one acoustic emission level that would indicate a collapse of the one or more bubbles
Data Source
AI summary
Systems and methods for predicting and controlling bubble dynamics. The controller may comprise one or more processors. The controller may also comprise a machine learning model trained based on bubble acoustic emission data. The controller may also comprise at least one memory in communication with the controller and the machine learning model and storing computer program code. The computer program code may cause the controller to monitor acoustic emission levels of one or more bubbles in a body of a subject. The computer program code may further cause the controller to predict at least one acoustic emission level indicative of a collapse of the one or more bubbles. The computer program code may also cause the controller to cause an acoustic transducer to emit acoustic energy based at least in part on the at least one predicted acoustic emission level indicative of the collapse of the one or more bubbles.