A continuous surface reconstruction method on point cloud captured from a 3D surface photogrammetry system

Abstract

Purpose: To accurately and efficiently reconstruct a continuous surface from noisy point clouds captured by a surface photogrammetry system (VisionRT). Methods: The authors have developed a level-set based surface reconstruction method on point clouds captured by a surface photogrammetry system (VisionRT). The proposed method reconstructs an implicit and continuous representation of the underlying patient surface by optimizing a regularized fitting energy, offering extra robustness to noise and missing measurements. By contrast to explicit/discrete meshing-type schemes, their continuous representation is particularly advantageous for subsequent surface registration and motion tracking by eliminating the need for maintaining explicit point correspondences as in discrete models. The authors solve the proposed method with an efficient narrowband evolving scheme. The authors evaluated the proposed method on both phantom and human subject data with two sets of complementary experiments. In the first set of experiment, the authors generated a series of surfaces each with different black patches placed on one chest phantom. The resulting VisionRT measurements from the patched area had different degree of noise and missing levels, since VisionRT has difficulties in detecting dark surfaces. The authors applied the proposed method to point clouds acquired under these different configurations, and quantitatively evaluated reconstructed surfaces by comparing against a high-quality reference surface with respect to root mean squared error (RMSE). In the second set of experiment, the authors applied their method to 100 clinical point clouds acquired from one human subject. In the absence of ground-truth, the authors qualitatively validated reconstructed surfaces by comparing the local geometry, specifically mean curvature distributions, against that of the surface extracted from a high-quality CT obtained from the same patient. Results: On phantom point clouds, their method achieved submillimeter reconstruction RMSE under different configurations, demonstrating quantitatively the faith of the proposed method in preserving local structural properties of the underlying surface in the presence of noise and missing measurements, and its robustness toward variations of such characteristics. On point clouds from the human subject, the proposed method successfully reconstructed all patient surfaces, filling regions where raw point coordinate readings were missing. Within two comparable regions of interest in the chest area, similar mean curvature distributions were acquired from both their reconstructed surface and CT surface, with mean and standard deviation of (µ recon = −2.7 × 10 −3 mm −1 ,σ recon = 7.0 × 10 −3 mm −1 ) and (µ CT = −2.5 × 10 −3 mm −1 ,σ CT = 5.3 × 10 −3 mm −1 ), respectively. The agreement of local geometry properties between the reconstructed surfaces and the CT surface demonstrated the ability of the proposed method in faithfully representing the underlying patient surface. Conclusions: The authors have integrat...

Prediction of high-dimensional states subject to respiratory motion: a manifold learning approach

“…Fig. 2 presents the example level set surface reconstructed from one acquired point cloud using the variational method [11]. We used the first 100 surfaces as the training set for manifold learning and training the VAR prediction algorithm.…”

Development and prospective in‐patient proof‐of‐concept validation of a surface photogrammetry + CT‐based volumetric motion model for lung radiotherapy

“…This missing patch was then “filled in” using the Poisson surface reconstruction process described above, and the reconstructed surface was used for model construction. Previous phantom studies performed by our group have found this technique to have submillimeter accuracy (RMSE) and maximum point errors lower than 2 mm …”

“…Previous phantom studies performed by our group have found this technique to have submillimeter accuracy (RMSE) and maximum point errors lower than 2 mm. 33 Figure 2(a) shows the patient setup for the 4DCT + VRT data acquisition. Prior to patient placement on the couch, the VRT system underwent daily calibration.…”

Multi-Modality Imaging: A Software Fusion and Image-Guided Therapy Perspective

“…Again, it is interesting to see the link between multimodal image fusion-the application this method was intended for in Levin et al [29]-and image-guided therapy, where ICP is still a mainstay when it comes to merging physical coordinate systems such as a stereotactic fixation device or a LINAC to the coordinate system of a volume image [32][33][34][35][36][37][38][39][40][41][42][43]. In the latter case, surface points can either be manually digitized by using a 3D input device [40] or by optical or other non-invasive technologies such as A-mode US [34,35,37,41,42].…”