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

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 reconstructionmore » 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.« less
Authors:
;  [1] ;  [2] ; ;  [3] ;  [4]
  1. Institute for Numerical Simulation, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115 Bonn (Germany)
  2. Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen (Germany)
  3. Institute of Optics, Information, and Photonics, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen (Germany)
  4. Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany and Erlangen Graduate School in Advanced Optical Technologies (SAOT), 91058 Erlangen (Germany)
Publication Date:
OSTI Identifier:
22220429
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 40; Journal Issue: 9; Other Information: (c) 2013 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
62 RADIOLOGY AND NUCLEAR MEDICINE; ALGORITHMS; COMPUTERIZED TOMOGRAPHY; DATASETS; IMAGE PROCESSING; IMAGES; PATIENTS; PHANTOMS; RESPIRATION EXTERNAL BEAM RADIATION THERAPY