Intra-fraction tumor tracking methods can improve radiation delivery during radiotherapy sessions. Image acquisition for tumor tracking and subsequent adjustment of the treatment beam with gating or beam tracking introduces time latency and necessitates predicting the future position of the tumor. This study evaluates the use of multi-dimensional linear adaptive filters and support vector regression to predict the motion of lung tumors tracked at 30 Hz. We expand on the prior work of other groups who have looked at adaptive filters by using a general framework of a multiple-input single-output (MISO) adaptive system that uses multiple correlated signals to predict the motion of a tumor. We compare the performance of these two novel methods to conventional methods like linear regression and single-input, single-output adaptive filters. At 400 ms latency the average root-mean-square-errors (RMSEs) for the 14 treatment sessions studied using no prediction, linear regression, single-output adaptive filter, MISO and support vector regression are 2.58, 1.60, 1.58, 1.71 and 1.26 mm, respectively. At 1 s, the RMSEs are 4.40, 2.61, 3.34, 2.66 and 1.93 mm, respectively. We find that support vector regression most accurately predicts the future tumor position of the methods studied and can provide a RMSE of less than 2 mm at 1 s latency. Also, a multi-dimensional adaptive filter framework provides improved performance over single-dimension adaptive filters. Work is underway to combine these two frameworks to improve performance.

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We present here a new InSAR persistent scatterer selection method using maximum likelihood estimation to identify persistent scattering pixels, which results in a denser network of reliable phase measurements than do existing methods. We analyze the phase of each pixel in a series of interferograms and estimate the relative strength of any slowly fluctuating component of the radar echo from a dominant scatterer to the background scattering within a pixel. We find a fairly dense network of scatterers with stable phase characteristics in areas where conventional InSAR fails due to decorrelation. We examined data over two vegetated regions in the San Francisco Bay Area. The average phases of these pixels clearly show the slip along the Hayward fault, and set upper bounds on any slip along the Bay Area segment of the San Andreas fault.