Real-time 3D-2D pose regression using intraoperative long-length tomosynthesis images for MR navigation in spine surgery

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Purpose. Magnetic resonance (MR) imaging offers high-quality soft tissue visualization for diagnosing spinal conditions; however, its use in surgical navigation is challenged by anatomical deformations resulting from differences in patient positioning. This study introduces a multi-stage, multi-resolution approach for automated initialization of registration between preoperative 3D and intraoperative Long-Film (LF) images, thus eliminating the need for manual segmentation and labeling of individual vertebrae. Methods. A convolutional neural network was designed and implemented to regress the 3D poses of vertebrae using features extracted from soft-tissue-suppressed 2D images. The approach operates within a multi-resolution pyramid to initialize registrations from global to local scale. To improve robustness against large misalignments and outliers, a batched multi-start strategy was used. The network was trained using simulated LF images from 365 CT and evaluated on real CT and LF images from a cadaver study of 14 cases. Results. The proposed multi-stage initialization and batched inference approach achieved a median target registration error (TRE) of 6.6 mm (interquartile range: 3.9–10.2 mm) in cadaver studies, demonstrating accuracy comparable to the initial TRE range reported in previous studies that utilized model-based optimization techniques. Conclusions. This study reports a multi-stage initialization method for 3D-2D pose regression. Initial results demonstrate the feasibility of using pose regression networks for initial alignment of preoperative 3D and intraoperative LF images. Ongoing work aims to extend this approach to a fully automated, end-to-end multi-modal registration pipeline.

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