SU-E-T-613: Dosimetric Consequences of Systematic MLC Leaf Positioning Errors




The purpose of this study is to determine the dosimetric consequences of systematic MLC leaf positioning errors for clinical IMRT patient plans so as to establish detection tolerances for quality assurance programs.

Materials and Methods:

Dosimetric consequences were simulated by extracting mlc delivery instructions from the TPS, altering the file by the specified error, reloading the delivery instructions into the TPS, recomputing dose, and extracting dose-volume metrics for one head-andneck and one prostate patient. Machine error was simulated by offsetting MLC leaves in Pinnacle in a systematic way. Three different algorithms were followed for these systematic offsets, and are as follows: a systematic sequential one-leaf offset (one leaf offset in one segment per beam), a systematic uniform one-leaf offset (same one leaf offset per segment per beam) and a systematic offset of a given number of leaves picked uniformly at random from a given number of segments (5 out of 10 total). Dose to the PTV and normal tissue was simulated.


A systematic 5 mm offset of 1 leaf for all delivery segments of all beams resulted in a maximum PTV D98 deviation of 1%. Results showed very low dose error in all reasonably possible machine configurations, rare or otherwise, which could be simulated. Very low error in dose to PTV and OARs was shown in all possible cases of one leaf per beam per segment being offset (<1%), or that of only one leaf per beam being offset (<.2%). The errors resulting from a high number of adjacent leaves (maximum of 5 out of 60 total leaf-pairs) being simultaneously offset in many (5) of the control points (total 10–18 in all beams) per beam, in both the PTV and the OARs analyzed, were similarly low (<2–3%).


The above results show that patient shifts and anatomical changes are the main source of errors in dose delivered, not machine delivery. These two sources of error are “visually complementary” and uncorrelated (albeit not additive in the final error) and one can easily incorporate error resulting from machine delivery in an error model based purely on tumor motion.