Fiducial marker recovery and detection from severely truncated data in navigation assisted spine surgery

TL;DR

This paper introduces two novel methods for detecting fiducial markers in CBCT volumes during spine surgery, addressing issues caused by truncated data and artifacts to improve navigation accuracy.

Contribution

It proposes a direct detection approach using neural networks and a recovery method with task-specific learning for robust marker detection in severely truncated CBCT data.

Findings

Both methods achieve marker registration errors below 0.2 mm.

The direct method accurately detects distorted markers.

The recovery method shows high robustness and generalizability.

Abstract

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.