Analysis of couch position tolerance limits to detect mistakes in patient setup

This work investigates the use of the tolerance limits on the treatment couch position to detect mistakes in patient positioning and warn users of possible treatment errors. Computer controlled radiotherapy systems use the position of the treatment couch as a surrogate for patient position, and a tolerance limit is applied against a planned position. When the couch is out of tolerance, a warning is sent to a user to indicate a possible mistake in setup. A tight tolerance may catch all positioning mistakes while at the same time sending too many warnings; a loose tolerance will not catch all mistakes. We developed a statistical model of the absolute position for the three translational axes of the couch. The couch position for any fraction is considered a random variable x i . The ideal planned couch position x p is unknown before a patient starts treatment and must be estimated from the daily positions of x i . As such, x p is also a random variable. The tolerance, tol , is applied to the difference between the daily and planned position, d i = x i − x p . The d i is a linear combination of random variables and therefore the density of d i is the convolution of distributions of x i and x p . Tolerance limits are based on the standard deviation of d i such that couch positions that are more than two standard deviations away are considered out of tolerance. Using this framework, we investigated two methods of setting x p and tolerance limits. The first, called first day acquire (FDA), is to take couch position on the first day as the planned position. The second is to use the cumulative average (CumA) over previous fractions as the planned position. The standard deviation of d i shrinks as more samples are used to determine x p and, as a result, the tolerance limit shrinks as a function of fraction number when a CumA technique is used. The metrics of sensitivity and specificity were used to characterize the performance of the two methods to correctly identify a couch position as in‐ or out‐of‐tolerance. These two methods were tested using simulated and real patient data. Five clinical sites with different indexed immobilization were tested. These were whole brain, head and neck, breast, thorax, and prostate. Analysis of the head and neck data shows that it is reasonable to model the daily couch position as a random variable in this treatment site. Using an average couch position for x p increased the sensitivity of the couch interlock and reduced the chances of acquiring a couch position that was a statistical outlier. Analysis of variation in couch position for different sites allowed the tolerance limit to be set specifically for a site and immobilization device. The CumA technique was able to increase the sensitivity of detecting out‐of‐tolerance positions while shrinking tolerance limits for a treatment course. Making better use of the software interlock on the couch positions could have a positive impact on patient safety and reduce mistakes in treatment delivery. PACS number: 87.55.Ne, 87.55.Qr, 87.55.tg, 87.55.tm