Image registration combines data from multiple imaging modalities or time points by aligning images. Existing 2D-3D registration methods often require manual intervention, introducing variability and increasing processing time, and rely on Computed Tomography (CT) imaging, which exposes patients to radiation. This study aims to develop an automated registration pipeline that ensures accurate registration between 3D Magnetic Resonance Imaging (MRI) and 2D fluoroscopic images by preprocessing techniques.

This study involved two registration experiments to evaluate the pipeline. The DiffDRR package was used with normalized cross-correlation (NCC) as similarity metric and gradient-based Adam optimizer for optimization. The first experiment validated the accuracy of registration between 3D MRI models (solid bone, empty shell, and filled shell) and 2D CT-derived digitally reconstructed radiographs (DRRs) with known rotations and translations as reference standard. The second experiment aimed to determine the most effective fluoroscopy preprocessing technique for aligning 3D MRI models with 2D fluoroscopic images. Preprocessing techniques for the fluoroscopic images included contrast enhancement via thresholding and normalization, and region of interest selection or segmentation.

Validation results showed smaller rotational and translational errors for both shell models compared to the solid bone model. In-plane errors for the shell models were 0.1-3.3mm and out-of-plane 15.8-55.0mm, while solid bone reported in-plane errors of 1.0-4.7mm and out-of-plane of 68.3-282.7mm. Registration between empty shell models and differently preprocessed fluoroscopic images showed that segmentation of the bones in fluoroscopic images yielded the highest NCC values. However, visual assessment revealed inconsistencies in plausible bone positioning.

This automated 2D-3D registration method shows potential for registration between 3D MRI and 2D fluoroscopic images of the knee joint. The current method requires further refinement, particularly in integrating soft tissue data, possibly via synthetic CTs, and adopting more anatomically sensitive metrics, such as mutual information or gradient correlation.