Fiducial marker recovery and detection from severely truncated data in navigation assisted spine surgery
This addresses a practical issue in minimally invasive spine surgery by improving marker detection accuracy, though it is incremental as it builds on existing detection algorithms with neural network enhancements.
The paper tackles the problem of fiducial marker detection in navigation-assisted spine surgery when markers are distorted due to limited field-of-view in CBCT systems, proposing two methods that achieve a marker registration error smaller than 0.2 mm on simulated and real data.
Fiducial markers are commonly used in navigation assisted minimally invasive spine surgery (MISS) and they help transfer image coordinates into real world coordinates. In practice, these markers might be located outside the field-of-view (FOV), due to the limited detector sizes of C-arm cone-beam computed tomography (CBCT) systems used in intraoperative surgeries. As a consequence, reconstructed markers in CBCT volumes suffer from artifacts and have distorted shapes, which sets an obstacle for navigation. In this work, we propose two fiducial marker detection methods: direct detection from distorted markers (direct method) and detection after marker recovery (recovery method). For direct detection from distorted markers in reconstructed volumes, an efficient automatic marker detection method using two neural networks and a conventional circle detection algorithm is proposed. For marker recovery, a task-specific learning strategy is proposed to recover markers from severely truncated data. Afterwards, a conventional marker detection algorithm is applied for position detection. The two methods are evaluated on simulated data and real data, both achieving a marker registration error smaller than 0.2 mm. Our experiments demonstrate that the direct method is capable of detecting distorted markers accurately and the recovery method with task-specific learning has high robustness and generalizability on various data sets.