Purpose: The current axial coverage of intraoperative 3D cone-beam CT imaging is limited by detector length. For neurosurgical interventions, for example, this prevents the alignment of the entire spine to be assessed from a single 3D scan. Here, we investigate the feasibility of realising whole spine 3D cone-beam CT imaging for the first time within the interventional suite.
Methods: A Siemens ARTIS pheno robotic angiography imaging system in conjunction with a Siemens Test Automation Control System (TACS) was used to image a 3D printed spine phantom (Sawbones) containing cervical, thoracic, and lumbar vertebra with a multi-turn reverse helical scan. The TACS was used to control both the C-arm rotation and table movement. The C-arm was rotated continuously through a total of 1350° (400° clockwise, 400° anticlockwise, 400° clockwise and 150° anticlockwise) around the table at 20°/s, acquiring projections at a fixed frame rate of 10 f/s, while the table was translated backward from the C-arm at a rate of 10 mm/s. The 3D images were reconstructed using penalized-likelihood reconstructions. To compare the axial coverage with current 3D imaging capacity, a conventional circular scan (4 second, 248 projections) was acquired of the thoracic spine. Image quality was examined visually and via the Structural Similarity Index Metric (SSIM).
Results: The multi-turn reverse helical scan enabled a total axial coverage of 76 cm, capturing all 24 vertebrae. Comparatively, the conventional scan enabled 17 cm of axial coverage, encapsulating 6 thoracic vertebrae. The multi-turn reverse helical scan took 80 seconds to complete, acquiring 790 projections. Structural similarity of the multi-turn to the conventional scan from T-4 to T-9 was 97.83%.
Conclusion: This is the first time a continuous multi-turn reverse helical scan has been implemented on a clinical robotic angiography system, allowing the entire spine to be imaged in a single scan.
Funding Support, Disclosures, and Conflict of Interest: This research was supported in part by G201166 IPA2 with Siemens Healthineers; ACRF grant G175269; Ricky O'Brien Fellowship CI NSW Fellowship G195559, NHMRC Project Grant G193048; Tess Reynolds USyd Postdoctoral Fellowship Scheme G200793; NIH grant R01EB027127