BackgroundDue to the heterogeneity of patient’s individual respiratory motion pattern in lung stereotactic body radiotherapy (SBRT), treatment planning dose assessment using a traditional four-dimensional computed tomography (4DCT_traditional) images based on a uniform breathing curve may not represent the true treatment dose delivered to the patient. The purpose of this study was to evaluate the accumulated dose discrepancy between based on the 4DCT_traditional and true 4DCT (4DCT_true) that incorporated with the patient’s real entire breathing motion. The study also explored a novel 4D robust planning strategy to compensate for such heterogeneity respiratory motion uncertainties.MethodsSimulated and measured patient specific breathing curves were used to generate 4D targets motion CT images. Volumetric-modulated arc therapy (VMAT) was planned using two arcs. Accumulated dose was obtained by recalculating the plan dose on each individual phase image and then deformed the dose from each phase image to the reference image. The “4 D dose” (D4D) and “true dose” (Dtrue) were the accumulated dose based on the 4DCT_traditional and 4DCT_true respectively. The average worse case dose discrepancy () between D4D and Dtrue in all treatment fraction was calculated to evaluate dosimetric /planning parameters and correlate them with the heterogeneity of respiratory-induced motion patterns. A novel 4D robust optimization strategy for VMAT (4D Ro-VMAT) based on the probability density function(pdf) of breathing curve was proposed to improve the target coverage in the presence of heterogeneity respiratory motion. The data were assessed with a paired t-tests.ResultsWith increasing breathing amplitude from 5 to 20 mm, target , increased from 1.59,1.39 to 10.15%,8.66% respectively. When the standard deviation of breathing amplitude increased from 15 to 35% of the mean amplitude, , increased from 4.06,3.48 to 10.25%,6.63% respectively. The 4D Ro-VMAT plan significantly improve the target dose compared to VMAT plan.ConclusionWhen the breathing curve amplitude is more than 10 mm and standard deviation of amplitude is higher than 25% of mean amplitude, special care is needed to choose an appropriated dose accumulation approach to evaluate lung SBRT plan target coverage robustness. The proposed 4D Ro_VMAT strategy based on the pdf of patient specific breathing curve could effectively compensate such uncertainties.

IT problem management calls for quick identification of resolvers to reported problems. The efficiency of this process highly depends on ticket routing-transferring problem ticket among various expert groups in search of the right resolver to the ticket. To achieve efficient ticket routing, wise decision needs to be made at each step of ticket transfer to determine which expert group is likely to be, or to lead to the resolver.In this paper, we address the possibility of improving ticket routing efficiency by mining ticket resolution sequences alone, without accessing ticket content. To demonstrate this possibility, a Markov model is developed to statistically capture the right decisions that have been made toward problem resolution, where the order of the Markov model is carefully chosen according to the conditional entropy obtained from ticket data. We also design a search algorithm, called Variable-order Multiple active State search (VMS), that generates ticket transfer recommendations based on our model. The proposed framework is evaluated on a large set of realworld problem tickets. The results demonstrate that VMS significantly improves human decisions: Problem resolvers can often be identified with fewer ticket transfers.

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