Real-Time Wireless Tumor Tracking in Navigated Liver Resections: An Ex Vivo Feasibility Study.

BACKGROUND: Surgical navigation systems generally require intraoperative steps, such as intraoperative imaging and registration, to link the system to the patient anatomy. Because this hampers surgical workflow, we developed a plug-and-play wireless navigation system that does not require any intraoperative steps. In this ex vivo study on human hepatectomy specimens, the feasibility was assessed of using this navigation system to accurately resect a planned volume with small margins to the lesion. METHODS: For ten hepatectomy specimens, a planning CT was acquired in which a virtual spherical lesion with 5 mm margin was delineated, inside the healthy parenchyma. Using two implanted trackers, the real-time position of this planned resection volume was visualized on a screen, relative to the used tracked pointer. Experienced liver surgeons were asked to accurately resect the nonpalpable planned volume, fully relying on the navigation screen. Resected and planned volumes were compared using CT. RESULTS: The surgeons resected the planned volume while cutting along its border with a mean accuracy of - 0.1 ± 2.4 mm and resected 98 ± 12% of the planned volume. Nine out of ten resections were radical and one case showed a cut of 0.8 mm into the lesion. The sessions took approximately 10 min each, and no considerable technical issues were encountered. CONCLUSIONS: This ex vivo liver study showed that it is feasible to accurately resect virtual hepatic lesions with small planned margins using our novel navigation system, which is promising for clinical applications where nonpalpable hepatic metastases have to be resected with small resection margins.

Computed tomography (CT)-compatible remote center of motion needle steering robot: Fusing CT images and electromagnetic sensor data.

Lung cancer is the most common cause of cancer-related death, and early detection can reduce the mortality rate. Patients with lung nodules greater than 10 mm usually undergo a computed tomography (CT)-guided biopsy. However, aligning the needle with the target is difficult and the needle tends to deflect from a straight path. In this work, we present a CT-compatible robotic system, which can both position the needle at the puncture point and also insert and rotate the needle. The robot has a remote-center-of-motion arm which is achieved through a parallel mechanism. A new needle steering scheme is also developed where CT images are fused with electromagnetic (EM) sensor data using an unscented Kalman filter. The data fusion allows us to steer the needle using the real-time EM tracker data. The robot design and the steering scheme are validated using three experimental cases. Experimental Case I and II evaluate the accuracy and CT-compatibility of the robot arm, respectively. In experimental Case III, the needle is steered towards 5 real targets embedded in an anthropomorphic gelatin phantom of the thorax. The mean targeting error for the 5 experiments is 1.78 ± 0.70 mm. The proposed robotic system is shown to be CT-compatible with low targeting error. Small nodule size and large needle diameter are two risk factors that can lead to complications in lung biopsy. Our results suggest that nodules larger than 5 mm in diameter can be targeted using our method which may result in lower complication rate.

A novel region-growing based semi-automatic segmentation protocol for three-dimensional condylar reconstruction using cone beam computed tomography (CBCT).

OBJECTIVE: To present and validate a semi-automatic segmentation protocol to enable an accurate 3D reconstruction of the mandibular condyles using cone beam computed tomography (CBCT). MATERIALS AND METHODS: Approval from the regional medical ethics review board was obtained for this study. Bilateral mandibular condyles in ten CBCT datasets of patients were segmented using the currently proposed semi-automatic segmentation protocol. This segmentation protocol combined 3D region-growing and local thresholding algorithms. The segmentation of a total of twenty condyles was performed by two observers. The Dice-coefficient and distance map calculations were used to evaluate the accuracy and reproducibility of the segmented and 3D rendered condyles. RESULTS: The mean inter-observer Dice-coefficient was 0.98 (range [0.95-0.99]). An average 90th percentile distance of 0.32 mm was found, indicating an excellent inter-observer similarity of the segmented and 3D rendered condyles. No systematic errors were observed in the currently proposed segmentation protocol. CONCLUSION: The novel semi-automated segmentation protocol is an accurate and reproducible tool to segment and render condyles in 3D. The implementation of this protocol in the clinical practice allows the CBCT to be used as an imaging modality for the quantitative analysis of condylar morphology.