Comparative study of ultrasound shape completion and CBCT-based AR workflows for spinal needle interventions

Area of Science:

• Medical Imaging
• Augmented Reality
• Spinal Interventions

Background:

• Augmented reality (AR) is increasingly used to guide medical procedures.
• Accurate imaging is crucial for planning and executing lumbar needle interventions.
• Comparing different imaging modalities within AR frameworks is essential for optimizing surgical workflows.

Purpose of the Study:

• To compare two AR-guided imaging workflows (ultrasound shape completion vs. CBCT) for lumbar needle interventions.
• To assess the impact of imaging modality on user performance, usability, and trust.
• To evaluate AR-assisted spinal procedures using different imaging techniques.

Main Methods:

• Integrated ultrasound and CBCT imaging systems into an AR framework for in situ visualization and guidance.
• Developed an ultrasound workflow with AR-guided robotic scanning and shape completion.
• Developed a CBCT workflow with AR-assisted scan planning and acquisition.
• Conducted a between-subject user study evaluating planning, image acquisition, and needle insertion phases.

Main Results:

• CBCT-based AR workflow demonstrated significantly shorter planning times.
• Needle insertion phase showed faster times, lower placement error, and higher user trust with CBCT-based AR.
• Ultrasound-based AR was adequate for facet joint insertion but less accurate for lumbar puncture due to shape completion dependency.
• Usability metrics (SUS, SEQ, NASA-TLX) were comparable between modalities.

Conclusions:

• Both AR-guided imaging pipelines are viable for spinal intervention support.
• CBCT-based AR offers superior efficiency, precision, usability, and user confidence.
• Ultrasound-based AR provides radiation-free imaging but has limitations in deeper spinal regions.
• A hybrid AR approach combining CBCT for context and ultrasound for updates is proposed.