Marker-Less Reconstruction of Dense 4-D Surface Motion Fields Using Active Laser Triangulation for Respiratory Motion Management

Abstract

To manage respiratory motion in image-guided interventions a novel sparse-to-dense registration approach is presented. We apply an emerging laser-based active triangulation (AT) sensor that delivers sparse but highly accurate 3-D measurements in real-time. These sparse position measurements are registered with a dense reference surface extracted from planning data. Thereby a dense displacement field is reconstructed which describes the 4-D deformation of the complete patient body surface and recovers a multi-dimensional respiratory signal for application in respiratory motion management. The method is validated on real data from an AT prototype and synthetic data sampled from dense surface scans acquired with a structured light scanner. In a study on 16 subjects, the proposed algorithm achieved a mean reconstruction accuracy of ±0.22 mm w.r.t. ground truth data.

Real-Time Range Imaging in Health Care: A Survey

“…1c). More specifically, Placht et al presented a mono-modal ToF-based solution where the intra-fractional patient shape is aligned to an RI reference shape acquired prior to the [18] from sparse RI measurements (depicted in blue), using prior shape information from planning data (in gray). The magnitude of the local displacements is color-coded.…”

“…Schaerer et al studied the application of a non-rigid extension of the ICP algorithm with a commercially available stereo vision based RT solution [28]. Further promising approaches for dense surface deformation tracking include sparse-to-dense shape registration based on a grid-type triangulation sensor [18], and photometry-driven surface registration [29]. Let us fur-ther remark that the analysis of dense displacement fields also allows for an automatic distinction between abdominal and thoracic respiration [30].…”

Joint surface reconstruction and 4D deformation estimation from sparse data and prior knowledge for marker‐less Respiratory motion tracking

“…Compared to the scheme used in Ref. 27, the scheme used here treats the distance d G implicitly and the direction ∇d G explicitly, while 27 treated both d G and ∇d G explicitly. Thus, the improved projection approximation scheme reflects the underlying projective geometry much better and is substantially more efficient.…”

“…Different from our first approach in Ref. 27, we propose here a scheme which much better reflects the underlying projective geometry and is substantially more efficient.…”

Co-registration of intra-operative brain surface photographs and pre-operative MR images

“…A method for the matching of photos of human faces with 3D surface models extracted from MRI data using rigid deformations has been suggested in [8]. Recently, a registration method for sparse but highly accurate 3-D line measurements with a surface extracted from volumetric planning data based on the consistent registration idea and higher order regularization was introduced in [2,4].…”