Physics in Medicine & Biology
Jul 16, 2012
Real-time tumor motion estimation using respiratory surrogate via memory-based learning
Ruijiang Li, John H Lewis, Ross I Berbeco and Lei Xing 2012 Phys. Med. Biol. 57 4771
Respiratory tumor motion is a major challenge in radiation therapy for thoracic and abdominal cancers. Effective motion management requires an accurate knowledge of the real-time tumor motion. External respiration monitoring devices provide a noninvasive, non-ionizing, low-cost, and practical approach to obtain the respiratory signal. We propose to use a powerful memory-based learning method to find the complex internal/external relations. The method first stores the training data in memory and then finds relevant data to answer a particular query. Nearby data points are assigned high relevance (or weights) and conversely distant data are assigned low relevance. Due to the local nature of weighting functions, the method is inherently robust to outliers in the training data. Moreover, both training and adapting to new data is performed instantaneously, making it suitable for dynamically following variable internal/external relations. These desirable properties make it an ideal candidate for accurate and robust tumor gating/tracking using respiratory surrogates.
About the author
Ruijiang Li (Stanford University, CA, USA), John H Lewis, Ross I Berbeco (Brigham and Women's Hospital, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA) and Lei Xing (Stanford University, CA, USA)