Knowledge based automatic beam angle determination for lung IMRT planning
© Springer International Publishing Switzerland 2015. We present an efficient knowledge based automatic beam configuration determination method by utilizing patient-specific anatomy and tumor geometry information and a beam bouquet atlas. The proposed technique is based on learning the relationship between patient anatomy and beam configurations from clinical plans designed by experienced planners. The training dataset contains 60 lung IMRT plans with plan prescription dose between 45 and 70 Gy. The method involves three major steps. First, a beam bouquet atlas was established by classifying the clinical plans into 6 beam configuration groups using the k-medoids cluster analysis method. Second, a beam efficiency map was constructed to characterize the geometry of the tumor relative to the lungs, the body and the other OARs at each candidate beam direction. Finally, the beam efficiency maps of the clinical cases and the cluster assignments of their beam bouquets were paired to train a Bayesian classification model. This classification model was used to select a suitable beam bouquet from the atlas for a new case based on its beam efficiency map. This technique was validated by leave-one-out cross validation with 16 cases randomly selected from the original dataset. The dosimetric parameters (mean±S.D. in percentage of prescription dose) in the auto-beam plans and in the clinical plans, respectively, and the p-values by a paired t-test (in parenthesis) are: lung mean dose (Dmean): 16.3±9.3, 18.6±7.4 (0.48), esophagus Dmean: 28.4±18, 30.7±19.3 (0.02), Heart Dmean: 21.5±17.5,21.1±17.2 (0.76), Spinal Cord D2%: 48±23, 51.2±21.8 (0.01), PTV dose homogeneity (D2%-D99%): 22±27.4, 20.4±12.8 (0.10). The other dosimetric parameters are not statistically different. In conclusion, plans generated by the automatic beam angle determination method can achieve dosimetric quality equivalent to that of clinical plans. This method can help improve the quality and efficiency of lung IMRT planning.
Yuan, L; Ge, Y; Sheng, Y; Yin, F; Wu, QJ
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International Standard Book Number 13 (ISBN-13)
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