Oliver Arold

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.

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 patients 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 - A motion-robust optical 3D sensor for the real-time shape acquisition of complex objects

The three-dimensional shape acquisition of objects has become more and more important in the last years. Up to now, there are several well-established methods which already yield impressive results. However, even under quite common conditions like object movement or a complex shaping, most methods become unsatisfying. Thus, the 3D shape acquisition is still a difficult and non-trivial task. We present our measurement principle “Flying Triangulation” which enables a motion-robust 3D acquisition of complex-shaped object surfaces by a freely movable handheld sensor. Since “Flying Triangulation” is scalable, a whole sensor-zoo for different object sizes is presented. Concluding, an overview of current and future fields of investigation is given.

Optimized data processing for an optical 3D sensor based on flying triangulation

We present data processing methods for an optical 3D sensor based on the measurement principle “Flying Triangulation”. The principle enables a motion-robust acquisition of the 3D shape of even complex objects: A hand-held sensor is freely guided around the object while real-time feedback of the measurement progress is delivered during the captioning. Although of high precision, the resulting 3D data usually may exhibit some weaknesses: e.g. outliers might be present and the data size might be too large. We describe the measurement principle and the data processing and conclude with measurement results.

Flying Triangulation - Acquiring the 360 Topography of the Human Body on the Fly

We introduce a novel optical measurement principle: “Flying Triangulation”. It fills an important gap in 3D metrology because it enables an acquisition of the topography of moving objects. The immunity against relative motion between object and sensor also allows for medical applications. An easy acquisition of complex objects is possible – just by freely hand guiding the sensor around the object. No tracking is necessary. We will present a “Flying Triangulation” sensor for the intraoral measurement of teeth and a sensor realization for the full 360° 3D acquisition of a person’s head. Parts of the body can be captured with high precision by comfortably guiding the sensor, with real-time control of the result.

Optimierte Segmentierung der Papille in HRT-Retinaaufnahmen

Der Papillenrand ist ein entscheidendes Merkmal zur Erkennung von krankhaften Veranderungen am Augenhintergrund. Zur Auswertung ist eine Segmentierung notig, die meist manuell durch den Augenarzt vorgenommen werden muss. Eine robuste, automatische Segmentierung der Papille kann den Arzt unterstutzen, die Reliabilitat der Segmentierung erhohen und eine Basis fur eine automatische Diagnose schaffen. Die vorgestellte Methode optimiert ein Segmentierungsverfahren mittels Ausreiserdetektion und Spline-Interpolation auf radial abgetasteten binarisierten Reflektionsbildern des Heidelberg Retina Tomographen (HRT). Der Vergleich mit bestehenden Verfahren zeigt, dass der Segmentierungsfehler um 9% reduziert werden konnte und das Verfahren stabiler gegen Artefakte ist.