DECT-based proton treatment planning in a commercial treatment planning system was successfully demonstrated for the first time. DECT is an attractive imaging modality for proton therapy treatment planning owing to its ability to characterize density and chemical composition of patient tissues. SECT and DECT scans of a phantom of known composition have been used to demonstrate the dosimetric advantages obtainable in proton therapy treatment planning with DECT over the current approach based on SECT.

Three-dimensional (3D) object detection is an important research in 3D computer vision with significant applications in many fields, such as automatic driving, robotics, and human–computer interaction. However, the low precision is an urgent problem in the field of 3D object detection. To solve it, we present a framework for 3D object detection in point cloud. To be specific, a designed Backbone Network is used to make fusion of low-level features and high-level features, which makes full use of various information advantages. Moreover, the two-dimensional (2D) Generalized Intersection over Union is extended to 3D use as part of the loss function in our framework. Empirical experiments of Car, Cyclist, and Pedestrian detection have been conducted respectively on the KITTI benchmark. Experimental results with average precision (AP) have shown the effectiveness of the proposed network.

ObjectivesThe purpose of this study is to independently compare the performance of the inverse planning algorithm utilized in Gamma Knife (GK) Lightning Treatment Planning System (TPS) to manual forward planning, between experienced and inexperienced users, for different types of targets.Materials and MethodsForty patients treated with GK stereotactic radiosurgery (SRS) for pituitary adenoma (PA), vestibular schwannoma (VS), post-operative brain metastases (pBM), and intact brain metastases (iBM) were randomly selected, ten for each site. Three inversely optimized plans were generated for each case by two experienced planners (OptExp1 and OptExp2) and a novice planner (OptNov) using GK Lightning TPS. For each treatment site, the Gradient Index (GI), the Paddick Conformity Index (PCI), the prescription percentage, the scaled beam-on time (sBOT), the number of shots used, and dosimetric metrics to OARs were compared first between the inversely optimized plans and the manually generated clinical plans, and then among the inversely optimized plans. Statistical analyses were performed using the Student’s t-test and the ANOVA followed by the post-hoc Tukey tests.ResultsThe GI for the inversely optimized plans significantly outperformed the clinical plans for all sites. PCIs were similar between the inversely optimized and clinical plans for PA and VS, but were significantly improved in the inversely optimized plans for iBM and pBM. There were no significant differences in the sBOT between the inversely optimized and clinical plans, except for the PA cases. No significant differences were observed in dosimetric metrics, except for lower brain V12Gy and PTV D98% in the inversely optimized plans for iBM. There were no noticeable differences in plan qualities among the inversely optimized plans created by the novice and experienced planners.ConclusionInverse planning in GK Lightning TPS produces GK SRS plans at least equivalent in plan quality and similar in sBOT compared to manual forward planning in this independent validation study. The automatic workflow of inversed planning ensures a consistent plan quality regardless of a planner’s experience.

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