Purpose With the help of an intra-operative mobile C-arm CT, medical interventions can be verified and corrected, avoiding the need for a post-operative CT and a second intervention. An exact adjustment of standard plane positions is necessary for the best possible assessment of the anatomical regions of interest but the mobility of the C-arm causes the need for a time-consuming manual adjustment. In this article, we present an automatic plane adjustment at the example of calcaneal fractures. Methods We developed two feature detection methods (2D and pseudo-3D) based on SURF key points and also transferred the SURF approach to 3D. Combined with an atlas-based registration, our algorithm adjusts the standard planes of the calcaneal C-arm images automatically. The robustness of the algorithms is evaluated using a clinical data set. Additionally, we tested the algorithm’s performance for two registration approaches, two resolutions of C-arm images and two methods for metal artifact reduction. Results For the feature extraction, the novel 3D-SURF approach performs best. As expected, a higher resolution ( 512^3 voxel) leads also to more robust feature points and is therefore slightly better than the 256^3 voxel images (standard setting of device). Our comparison of two different artifact reduction methods and the complete removal of metal in the images shows that our approach is highly robust against artifacts and the number and position of metal implants. Conclusions By introducing our fast algorithmic processing pipeline, we developed the first steps for a fully automatic assistance system for the assessment of C-arm CT images.

In orthopedic surgeries, it is important to avoid intra-articular implant placements, which increase revision rates and the risk of arthritis. In order to support the intraoperative assessment and correction of surgical implants, we present an automatic detection approach using cone-beam computed tomography (CBCT).

PURPOSE:The assessment of intra-operatively acquired volumetric data is a difficult and often time-consuming task, which demands a new set of skills from the surgeons. In the case of orthopedic surgeries such as the treatment of calcaneal fractures, the correctness of the reduction of the bone fragments can be verified with the help of C-arm CT volumetric images. For an accurate intra-operative assessment of the displaced fragments, an automatic segmentation of the articular surfaces and color-coded visualization was developed.METHODS:Our automatic approach consists of three major steps: first, using adjusted standard planes intersecting the articular region, the joint space is localized with an intensity profile-based method. In a second step, the localized joint space is segmented on the Laplacian of Gaussian filtered volumetric image by a modified binary flood fill algorithm. Finally, a 3D surface model of the segmented joint space is analyzed and visualized with focus on critical displacements of the surface.RESULTS:A specifically designed human cadaver study consisting of ten lower legs of ten different donors was conducted to acquire 48 realistic C-arm CT images of misaligned bone fragments (steps of varying sizes) in the posterior talar articular surface of the calcaneus. The proposed algorithmic pipeline was verified by the acquired image data and showed very good results with no false positives and an overall correct displacement assessment of 93.8%.CONCLUSIONS:The proposed algorithmic pipeline can be easily integrated into the clinical workflow and qualifies for intra-operative usage. It showed very good results on the reference data set of the cadaver study. With the help of such an assistance system, the time-consuming process of 2D view adjustment and visual assessment of the gray value images can be greatly simplified.

Intraarticular fractures of the calcaneus are routinely treated by open reduction and internal fixation followed by intraoperative imaging to validate the repositioning of bone fragments. C-Arm CT offers surgeons the possibility to directly verify the alignment of the fracture parts in 3D. Although the device provides more mobility, there is no sufficient information about the device-to-patient orientation for standard plane reconstruction. Hence, physicians have to manually align the image planes in a position that intersects with the articular surfaces. This can be a time-consuming step and imprecise adjustments lead to diagnostic errors. We address this issue by introducing novel semi-/automatic methods for adjustment of the standard planes on mobile C-Arm CT images. With the semi-automatic method, physicians can quickly adjust the planes by setting six points based on anatomical landmarks. The automatic method reconstructs the standard planes in two steps, first SURF keypoints (2D and newly introduced pseudo-3D) are generated for each image slice; secondly, these features are registered to an atlas point set and the parameters of the image planes are transformed accordingly. The accuracy of our method was evaluated on 51 mobile C-Arm CT images from clinical routine with manually adjusted standard planes by three physicians of different expertise. The average time of the experts (46s) deviated from the intermediate user (55s) by 9 seconds. By applying 2D SURF keypoints 88% of the articular surfaces were intersected correctly by the transformed standard planes with a calculation time of 10 seconds. The pseudo-3D features performed even better with 91% and 8 seconds.

Das Standardvorgehen bei der Behandlung von Calcaneusfrakturen ist eine Osteosynthese. Mit Hilfe der intraoperativen Bildgebung wie dem mobilen C-Bogen CT kann der Chirurg das Repositionsergebnis noch im Operationssaal verifizieren und wenn nötig korrigieren. Die Mobilität des C-Bogen CT hat jedoch zur Folge, dass Informationen über die Orientierung des Patienten zum Gerät verloren gehen. Dadurch kann keine Standard-Ausrichtung der dreidimensionalen Daten an die Anatomie erfolgen. Eine manuelle Einstellung des Volumendatensatzes durch den Chirurgen ist damit unabdingbar. Dies ist ein zeitaufwendiger Schritt und kann bei einer unpräzisen Einstellung zu Fehlern bei der Beurteilung der Daten führen. In diesem Paper stellen wir zwei automatische Methoden zur Einstellung der Standard-Ebenen auf mobilen C-Bogen CT Daten vor. Die automatischen Methoden rekonstruieren die Standard-Ebenen in zwei Schritten: als Erstes werden SURF-Keypoints (2D und neu eingeführte Pseudo-3D-Punkte) für das Bildvolumen berechnet, in einem zweiten Schritt wird eine Atlas-Punktwolke auf diese Merkmale registriert und die Parameter der Standard-Ebenen transformiert. Die Genauigkeit unserer Methoden wurde an 51 klinischen mobilen C-Bogen CT Bildern mit manuell eingestellten Standard-Ebenen evaluiert. Die Referenzdaten wurden von drei Chirurgen mit unterschiedlichem Erfahrungsstand erstellt. Die durchschnittlich benötigte Zeit der Experten (46 s) unterscheidet sich von der des fortgeschrittenen Benutzers (55 s) um neun Sekunden. Die Berechnungszeit des 2D-Surf Ansatzes beträgt 10 Sekunden und liefert bei 88

Orthopedic fractures are often fixed using metal implants. The correct positioning of cylindrical implants such as surgical screws, rods and guide wires is highly important. Intraoperative 3D imaging is often used to ensure proper implant placement. However, 3D image interaction is time-consuming and requires experience. We developed an automatic method that simplifies and accelerates location assessment of cylindrical implants in 3D images.

Frakturen am Fersenbein werden mit Hilfe offener Reduktion und interner Fixation korrigiert. Eine anatomisch korrekte Rekonstruktion beteiligter Gelenke ist notwendig, um Knorpelschäden und verfrühte Arthrose vorzubeugen. Um intraartikuläre Schraubenplatzierungen zu vermeiden wird der mobile 3D C-Bogen eingesetzt. Die detaillierte Analyse der Schraubenlage anhand des erzeugten 3D Bildes ist jedoch auf eine zeitaufwändige Mensch-Computer-Interaktion angewiesen. Etablierte Interaktionsprozeduren basieren auf wiederholtem Positionieren und Rotieren von Schnittebenen, wodurch die intraoperative Kontrolle der Schraubenplatzierung die Dauer der Operation wesentlich verlängert. Um die Interaktion mit 3D C-Bogen Daten zu erleichtern schlagen wir eine automatische Schraubendetektion vor, mit der eine direkte Anwahl relevanter Schnittebenen möglich wird. Unser Ansatz setzt sich aus zwei Schritten zusammen. Im ersten Schritt werden zylindrische Charakteristiken anhand lokaler Gradientstrukturen mit Hilfe von RANSAC ermittelt. Diese Charakteristiken werden dann durch die Anwendung des DBScan Clustering Algorithmus im zweiten Schritt gruppiert. Jedes detektierte Cluster repräsentiert abschließend eine Schraube. Unsere Evaluation mit 309 Schrauben in 50 Bildern zeigt robuste Ergebnisse. Der Algorithmus detektierte 97.4

Calcaneal fractures are commonly treated by open reduction and internal fixation. An anatomical reconstruction of involved joints is mandatory to prevent cartilage damage and premature arthritis. In order to avoid intraarticular screw placements, the use of mobile C-arm CT devices is required. However, for analyzing the screw placement in detail, a time-consuming human-computer interaction is necessary to navigate through 3D images and therefore to view a single screw in detail. Established interaction procedures of repeatedly positioning and rotating sectional planes are inconvenient and impede the intraoperative assessment of the screw positioning. To simplify the interaction with 3D images, we propose an automatic screw segmentation that allows for an immediate selection of relevant sectional planes. Our algorithm consists of three major steps. At first, cylindrical characteristics are determined from local gradient structures with the help of RANSAC. In a second step, a DBScan clustering algorithm is applied to group similar cylinder characteristics. Each detected cluster represents a screw, whose determined location is then refined by a cylinder-to-image registration in a third step. Our evaluation with 309 screws in 50 images shows robust and precise results. The algorithm detected 98% (303) of the screws correctly. Thirteen clusters led to falsely identified screws. The mean distance error for the screw tip was 0.8 ± 0.8 mm and for the screw head 1.2 ± 1 mm. The mean orientation error was 1.4 ± 1.2 degrees.