Real-Time RNN Tumor Motion Prediction for Respiratory Tracking
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Solution Overview
Problem
Conventional techniques for counteracting respiratory motion during tumor imaging and treatment, such as breath-hold and respiration gating, cause patient discomfort and increased radiation exposure due to variations in patient respiration patterns.
Innovation Solution
A recurrent neural network (RNN) is trained in real-time using a sliding window of respiratory motion data to predict future tumor movement, updating its weights based on current patient respiration, facilitating accurate targeting of the tumor region.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If breath-hold or respiration gating methods are used to counteract respiratory motion, then tumor targeting accuracy is improved, but patient discomfort increases and treatment time increases leading to increased radiation exposure
Solution Approach 1:
The system performs preliminary action by predicting future tumor position based on current and past respiratory motion data. The RNN model forecasts where the tumor will be at a future time point, allowing the treatment system to proactively adjust beam targeting before the tumor actually moves, rather than reactively responding to detected motion. This predictive approach eliminates the need for patients to hold breath or pause treatment, thereby maintaining targeting accuracy while avoiding patient discomfort and reduced radiation exposure.
2Measurement precision
If conventional motion tracking methods are used, then tumor position monitoring is achieved, but treatment efficiency decreases due to increased treatment time
Solution Approach 1:
The system implements feedback by continuously monitoring current respiratory motion and using this information to update the RNN model in real-time. The model processes incoming motion data and generates predictions about future tumor position, creating a closed-loop system where measurement information directly informs corrective action. This continuous feedback mechanism enables accurate tumor position monitoring without requiring treatment interruptions, thereby maintaining measurement precision while preserving treatment efficiency.
3Measurement precision
If real-time prediction updating is performed, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The system applies dynamics by implementing adaptive updating of the RNN model where the frequency and depth of updates are adjusted based on actual changes in patient respiratory patterns. Rather than performing fixed-interval updates regardless of need, the system dynamically determines when updates are necessary based on detected variations in breathing patterns. This dynamic approach improves prediction accuracy when respiratory patterns change while avoiding unnecessary computational complexity during stable breathing phases.
Data Source
AI summary
In an embodiment, there is provided a method of predicting respiratory motion in real-time. The method includes training, by a training module, a recurrent neural network circuitry in real time. The training is based, at least in part, on a training data set. The training data set includes a first number of motion samples. The motion samples are related to respiratory motion of a patient target region. The method further includes predicting, by the trained recurrent neural network circuitry, a future motion of the patient target region at a future point in time based, at least in part, on a prediction data set. The future point in time is a look ahead time period in the future relative to a prediction data window. The prediction data set includes a second number of motion samples.