Joint surface reconstruction and 4D deformation estimation from sparse data and prior knowledge for marker‐less Respiratory motion tracking. Issue 9 (5th August 2013)

• Record Type: Journal Article
• Authors: Berkels, Benjamin; Bauer, Sebastian; Ettl, Svenja; Arold, Oliver; Hornegger, Joachim; Rumpf, Martin
• Is Part Of: Medical physics. Volume 40:Issue 9(2013)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2013-08-05
• Subjects: Computed tomography -- Therapeutic applications, including brachytherapy -- Pneumodyamics, respiration -- Dose‐volume analysis -- Reconstruction -- Registration; computerised tomography -- dosimetry -- estimation theory -- feature extraction -- image reconstruction -- image registration -- image sampling -- medical image processing -- motion estimation -- patient monitoring -- phantoms -- pneumodynamics -- radiation therapy -- spatiotemporal phenomena; radiation therapy -- respiratory motion -- motion modeling -- surface registration -- nonrigid registration -- inverse‐consistent registration -- range imaging; Computerised tomographs -- Radiation therapy -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Analysis of motion; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4816675
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: : The intraprocedural tracking of respiratory motion has the potential to substantially improve image‐guided diagnosis and interventions. The authors have developed a sparse‐to‐dense registration approach that is capable of recovering the patient's external 3D body surface and estimating a 4D (3D + time) surface motion field from sparse sampling data and patient‐specific prior shape knowledge. Methods: : The system utilizes an emerging marker‐less and laser‐based active triangulation (AT) sensor that delivers sparse but highly accurate 3D 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 recovered, which describes the spatio‐temporal 4D deformation of the complete patient body surface, depending on the type and state of respiration. It yields both a reconstruction of the instantaneous patient shape and a high‐dimensional respiratory surrogate for respiratory motion tracking. The method is validated on a 4D CT respiration phantom and evaluated on both real data from an AT prototype and synthetic data sampled from dense surface scans acquired with a structured‐light scanner. Results: : In the experiments, the authors estimated surface motion fields with the proposed algorithm on 256 datasets from 16 subjects and in different respiration states, achieving a mean surface reconstruction accuracy of ±0.23 mm with respect to ground truth data—down from a mean initial surface mismatch of 5.66 mm. The 95th percentile of the local residual mesh‐to‐mesh distance after registration did not exceed 1.17 mm for any subject. On average, the total runtime of our proof of concept CPU implementation is 2.3 s per frame, outperforming related work substantially. Conclusions: : In external beam radiation therapy, the approach holds potential for patient monitoring during treatment using the reconstructed surface, and for motion‐compensated dose delivery using the estimated 4D surface motion field in combination with external‐internal correlation models.