No abstract

Abstract. The purpose of this study is to design and fabricate an anthropopathic abdominal phantom for accuracy evaluation of deformable image registration (DIR) algorithms in adaptive radiation therapy. The constructed deformable organs, including the liver, kidney, spleen and stomach, are made of mixture of polyvinyl chloride (PVC) and softener dioctyl terephthalate, while the rigid structures, i.e. vertebrae, are made of white cement. Relation between the PVC-softener blending ratio and organ CT number is studied, and three-dimensional printing technic is employed to create highly anthropopathic organs in terms of organ shape and density. Detailed steps for phantom construction, landmark point placement and choice of phantom ingredients and construction recipe are introduced. Preliminary results of the mechanical properties of the fabricated organs are also presented. The experimental results indicate that the constructed phantom has satisfactory elastic characteristics and close CT number with corporal organs, and can potentially be applied to simulate real abdominal organ deformation in geometric accuracy validation of DIR algorithms.

This paper presents an accurate and robust dense 3D reconstruction system for detail preserving surface modeling of large-scale scenes from multi-view images, which we named DP-MVS. Our system performs high-quality large-scale dense reconstruction, which preserves geometric details for thin structures, especially for linear objects. Our framework begins with a sparse reconstruction carried out by an incremental Structure-from-Motion. Based on the reconstructed sparse map, a novel detail preserving PatchMatch approach is applied for depth estimation of each image view. The estimated depth maps of multiple views are then fused to a dense point cloud in a memory-efficient way, followed by a detail-aware surface meshing method to extract the final surface mesh of the captured scene. Experiments on ETH3D benchmark show that the proposed method outperforms other state-of-the-art methods on F1-score, with the running time more than 4 times faster. More experiments on large-scale photo collections demonstrate the effectiveness of the proposed framework for large-scale scene reconstruction in terms of accuracy, completeness, memory saving, and time efficiency.

No abstract