Biomechanically Constrained Ultrasound to Computed Tomography Registration of the Lumbar Spine

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Gill, Sean
Ultrasound , Spine , Multimodal Registration , Biomechanical Model
Spinal injections for back-pain management are frequently carried out in hospitals and radiological clinics. Currently, these procedures are performed under fluoroscopy or CT guidance in specialized interventional radiology facilities, and thus incur a major financial burden on the healthcare system. Additionally, the current practice exposes patients and surgeons to X-ray radiation. The use of US for image guided navigation of the spine would greatly reduce the exposure of both the patient and the physician to ionizing radiation and allow the procedure to be performed outside of a specialized facility. However, US as the sole guidance modality has its own challenges. In particular, due to the significant level of occlusion in spinal US images, it can be difficult to accurately identify the appropriate injection site. Here, a groupwise US to CT registration algorithm for guiding percutaneous spinal interventions is presented. In our registration methodology, each vertebra in CT is treated as a sub-volume and transformed individually. A biomechanical model is used to constrain the displacement of the vertebrae relative to one another. The sub-volumes are then reconstructed into a single volume. In each iteration of registration, an US image is simulated from the reconstructed CT volume and an intensity-based similarity metric with the real US image is calculated. Validation studies are performed on datasets from a lamb cadaver, five patient-based phantoms designed to preserve realistic curvatures of the spine and a sixth patient-based phantom where the curvature of the spine is changed between preoperative and intraoperative imaging. For datasets where the spine curve between two imaging modalities was artificially perturbed, the proposed methodology was able to register initial misalignments of up to 20 mm with a success rate of 95%. For the phantom with a physical change in the curvature of the spine introduced between the US and CT datasets, the registration success rate was 98.5%. Finally, the registration success rate for the lamb cadaver with soft tissue information was 87%. The results demonstrate that our algorithm robustly registers US and CT datasets of the spine, regardless of a change in the patients pose between preoperative and intraoperative image acquisitions.
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