Purpose. A system for long-length intraoperative imaging is reported based on longitudinal motion of an O-arm gantry featuring a multi-slot collimator. We assess the utility of long-length tomosynthesis and the geometric accuracy of 3D image registration for surgical guidance and evaluation of long spinal constructs. Methods. A multi-slot collimator with tilted apertures was integrated into an O-arm system for long-length imaging. The multi-slot projective geometry leads to slight view disparity in both long-length projection images (referred to as ‘line scans’) and tomosynthesis ‘slot reconstructions’ produced using a weighted-backprojection method. The radiation dose for long-length imaging was measured, and the utility of long-length, intraoperative tomosynthesis was evaluated in phantom and cadaver studies. Leveraging the depth resolution provided by parallax views, an algorithm for 3D-2D registration of the patient and surgical devices was adapted for registration with line scans and slot reconstructions. Registration performance using single-plane or dual-plane long-length images was evaluated and compared to registration accuracy achieved using standard dual-plane radiographs. Results. Longitudinal coverage of ∼50–64 cm was achieved with a single long-length slot scan, providing a field-of-view (FOV) up to (40 × 64) cm2, depending on patient positioning. The dose-area product (reference point air kerma × x-ray field area) for a slot scan ranged from ∼702–1757 mGy·cm2, equivalent to ∼2.5 s of fluoroscopy and comparable to other long-length imaging systems. Long-length scanning produced high-resolution tomosynthesis reconstructions, covering ∼12–16 vertebral levels. 3D image registration using dual-plane slot reconstructions achieved median target registration error (TRE) of 1.2 mm and 0.6° in cadaver studies, outperforming registration to dual-plane line scans (TRE = 2.8 mm and 2.2°) and radiographs (TRE = 2.5 mm and 1.1°). 3D registration using single-plane slot reconstructions leveraged the ∼7–14° angular separation between slots to achieve median TRE ∼2 mm and <2° from a single scan. Conclusion. The multi-slot configuration provided intraoperative visualization of long spine segments, facilitating target localization, assessment of global spinal alignment, and evaluation of long surgical constructs. 3D-2D registration to long-length tomosynthesis reconstructions yielded a promising means of guidance and verification with accuracy exceeding that of 3D-2D registration to conventional radiographs.

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Purpose. Accurate localization and labeling of vertebrae in computed tomography (CT) is an important step toward more quantitative, automated diagnostic analysis and surgical planning. In this paper, we present a framework (called Ortho2D) for vertebral labeling in CT in a manner that is accurate and memory-efficient. Methods. Ortho2D uses two independent faster R-convolutional neural network networks to detect and classify vertebrae in orthogonal (sagittal and coronal) CT slices. The 2D detections are clustered in 3D to localize vertebrae centroids in the volumetric CT and classify the region (cervical, thoracic, lumbar, or sacral) and vertebral level. A post-process sorting method incorporates the confidence in network output to refine classifications and reduce outliers. Ortho2D was evaluated on a publicly available dataset containing 302 normal and pathological spine CT images with and without surgical instrumentation. Labeling accuracy and memory requirements were assessed in comparison to other recently reported methods. The memory efficiency of Ortho2D permitted extension to high-resolution CT to investigate the potential for further boosts to labeling performance. Results. Ortho2D achieved overall vertebrae detection accuracy of 97.1%, region identification accuracy of 94.3%, and individual vertebral level identification accuracy of 91.0%. The framework achieved 95.8% and 83.6% level identification accuracy in images without and with surgical instrumentation, respectively. Ortho2D met or exceeded the performance of previously reported 2D and 3D labeling methods and reduced memory consumption by a factor of ∼50 (at 1 mm voxel size) compared to a 3D U-Net, allowing extension to higher resolution datasets than normally afforded. The accuracy of level identification increased from 80.1% (for standard/low resolution CT) to 95.1% (for high-resolution CT). Conclusions. The Ortho2D method achieved vertebrae labeling performance that is comparable to other recently reported methods with significant reduction in memory consumption, permitting further performance boosts via application to high-resolution CT.

Background Spinal deformation during surgical intervention (caused by patient positioning and/or the correction of malalignment) confounds conventional navigation due to the assumptions of rigid transformation. Moreover, the ability to accurately quantify spinal alignment in the operating room would provide an assessment of the surgical product via metrics that correlate with clinical outcomes. Purpose A method for deformable 3D–2D registration of preoperative CT to intraoperative long‐length tomosynthesis images is reported for an accurate 3D evaluation of device placement in the presence of spinal deformation and automated evaluation of global spinal alignment (GSA). Methods Long‐length tomosynthesis (“Long Film,” LF) images were acquired using an O‐arm imaging system (Medtronic, Minneapolis USA). A deformable 3D–2D patient registration was developed using multi‐scale masking (proceeding from the full‐length image to local subvolumes about each vertebra) to transform vertebral labels and planning information from preoperative CT to the LF images. Automatic measurement of GSA (main thoracic kyphosis [MThK] and lumbar lordosis [LL]) was obtained using a spline fit to registered labels. The “Known‐Component Registration” method for device registration was adapted to the multi‐scale process for 3D device localization from orthogonal LF images. The multi‐scale framework was evaluated using a deformable spine phantom in which pedicle screws were inserted, and deformations were induced over a range in LL ∼25°–80°. Further validation was carried out in a cadaver study with implanted pedicle screws and a similar range of spinal deformation. The accuracy of patient and device registration was evaluated in terms of 3D translational error and target registration error, respectively, and the accuracies of automatic GSA measurements were compared to manual annotation. Results Phantom studies demonstrated accurate registration via the multi‐scale framework for all vertebral levels in both the neutral and deformed spine: median (interquartile range, IQR) patient registration error was 1.1 mm (0.7–1.9 mm IQR). Automatic measures of MThK and LL agreed with manual delineation within −1.1° ± 2.2° and 0.7° ± 2.0° (mean and standard deviation), respectively. Device registration error was 0.7 mm (0.4–1.0 mm IQR) at the screw tip and 0.9° (1.0°–1.5°) about the screw trajectory. Deformable 3D–2D registration significantly outperformed conventional rigid registration (p < 0.05), which exhibited device registration errors of 2.1 mm (0.8–4.1 mm) and 4.1° (1.2°–9.5°). Cadaver studies verified performance under realistic conditions, demonstrating patient registration error of 1.6 mm (0.9–2.1 mm); MThK within −4.2° ± 6.8° and LL within 1.7° ± 3.5°; and device registration error of 0.8 mm (0.5–1.9 mm) and 0.7° (0.4°–1.2°) for the multi‐scale deformable method, compared to 2.5 mm (1.0–7.9 mm) and 2.3° (1.6°–8.1°) for rigid registration (p < 0.05). Conclusion The deformable 3D–2D registration framework leverages long‐length intraoperat...

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