Основные понятия
UORO, SnAp-1, and DNI efficiently forecast respiratory movements using minimal data, enhancing radiotherapy safety.
Аннотация
This study explores the effectiveness of online learning algorithms for predicting respiratory motion in radiotherapy. Comparisons are made between different methods such as RTRL, UORO, SnAp-1, DNI, LMS, linear regression, and fixed-weight RNNs. The research focuses on improving safety and accuracy in externally guided radiotherapy through advanced forecasting techniques.
The study involves training recurrent neural networks to predict the 3D positions of external markers on the chest during breathing cycles. Various algorithms like UORO, SnAp-1, and DNI are compared with traditional linear methods like LMS and linear regression.
Results show that RNNs trained with UORO, SnAp-1, and DNI achieve accurate predictions with minimal data usage. These algorithms offer a resource-efficient solution for forecasting respiratory motion during radiotherapy treatments.
Key metrics include normalized root mean square errors (nRMSE) at different sampling frequencies and horizons. The study also evaluates hyperparameters such as learning rates and hidden layer sizes to optimize prediction performance.
Overall, the research highlights the potential of online learning algorithms in enhancing safety and precision in radiotherapy by accurately forecasting respiratory movements.
Статистика
SnAp-1 had nRMSE values of 0.335 at 3.33Hz and 0.157 at 10Hz.
UORO achieved an nRMSE of 0.0897 at 30Hz.
Linear regression showed an nRMSE of 0.0979 for h = 100ms at 10Hz.
DNI's inference time was lowest among RNN methods examined: 0.14ms at 3.33Hz, 1.0ms at 10Hz, and 6.8ms at 30Hz.
Цитаты
"UORO, SnAp-1, and DNI can accurately forecast respiratory movements using little data." - Research Findings