Ultrasound-Based Registration for the Computer-Assisted Navigated Percutaneous Scaphoid Fixation

An ultrasound (US)-based computer-assisted approach has the potential to improve the accuracy and precision of screw placement for the percutaneous fixation of scaphoid fractures and also reduce the radiation dose for patient and clinical staff. Therefore, a surgical plan based on preoperative diagnostic computed tomography (CT) is registered with intraoperative US images, enabling a navigated percutaneous fracture fixation. However, approaches published so far rely on semimanual methods for intraoperative registration and are limited by long computation times. To address these challenges, we propose the employment of deep learning-based methods for US segmentation and registration in order to achieve a fast and fully automated yet robust registration process. For validation of the proposed US-based approach, we first provide a comparison of methods for segmentation and registration, assess their contribution to the overall error throughout our pipeline, and, finally, evaluate navigated screw placement in an in vitro study on 3-D printed carpal phantoms. Successful screw placement has been achieved for all ten screws, with deviations from the planned axis of 1.0 +/- 0.6 and 0.7 +/- 0.3 mm at the distal and proximal pole, respectively. The complete automation and total duration of about 12 s also allow seamless integration of our approach into the surgical workflow.

Automated summary

An ultrasound-based computer-assisted approach is proposed to improve the accuracy and precision of screw placement for percutaneous fixation of scaphoid fractures. This approach also reduces radiation dose and computation time by employing deep learning-based methods for US segmentation and registration. Validation experiments on 3-D printed carpal phantoms demonstrate successful screw placement with minimal deviations from the planned axis and a total duration of about 12 seconds, allowing seamless integration into the surgical workflow.