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 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.

Flying triangulation--an optical 3D sensor for the motion-robust acquisition of complex objects.

Three-dimensional (3D) shape acquisition is difficult if an all-around measurement of an object is desired or if a relative motion between object and sensor is unavoidable. An optical sensor principle is presented-we call it "flying triangulation"-that enables a motion-robust acquisition of 3D surface topography. It combines a simple handheld sensor with sophisticated registration algorithms. An easy acquisition of complex objects is possible-just by freely hand-guiding the sensor around the object. Real-time feedback of the sequential measurement results enables a comfortable handling for the user. No tracking is necessary. In contrast to most other eligible sensors, the presented sensor generates 3D data from each single camera image.

Marker-less reconstruction of dense 4-D surface motion fields using active laser triangulation for respiratory motion management.

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.