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To improve the positioning accuracy of tunnels for anterior cruciate ligament (ACL) reconstruction, we proposed an intensity‐based 2D‐3D registration method for an ACL reconstruction navigation system. Methods for digitally reconstructed radiograph (DRR) generation, similarity measurement, and optimization are crucial to 2D‐3D registration. We evaluated the accuracy, success rate, and processing time of different methods: (a) ray‐casting and splating were compared for DRR generation; (b) normalized mutual information (NMI), Mattes mutual information (MMI), and Spearman's rank correlation coefficient (SRC) were assessed for similarity between registrations; and (c) gradient descent (GD) and downhill simplex (DS) were compared for optimization. The combination of splating, SRC, and GD provided the best composite performance and was applied in an augmented reality (AR) ACL reconstruction navigation system. The accuracy of the navigation system could fulfill the clinical needs of ACL reconstruction, with an end pose error of 2.50 mm and an angle error of 2.74°.

To eliminate unnecessary background information, such as soft tissues in original CT images and the adverse impact of the similarity of adjacent spines on lumbar image segmentation and surgical path planning, a two-stage approach for localising lumbar segments is proposed. First, based on the multi-scale feature fusion technology, a nonlinear regression method is used to achieve accurate localisation of the overall spatial region of the lumbar spine, effectively eliminating useless background information, such as soft tissues. In the second stage, we directly realised the precise positioning of each segment in the lumbar spine space region based on the non-linear regression method, thus effectively eliminating the interference caused by the adjacent spine. The 3D Intersection over Union (3D_IOU) is used as the main evaluation indicator for the positioning accuracy. On an open dataset, 3D_IOU values of 0.8339 � 0.0990 and 0.8559 � 0.0332 in the first and second stages, respectively is achieved. In addition, the average time required for the proposed method in the two stages is 0.3274 and 0.2105 s respectively. Therefore, the proposed method performs very well in terms of both precision and speed and can effectively improve the accuracy of lumbar image segmentation and the effect of surgical path planning. K E Y W O R D SCT image, lumbar spatial orientation, multi-scale information fusion | INTRODUCTIONLumbar spine disease is a very common type of spinal disease that is often associated with pain. In particular, lumbar spinal stenosis (LSS) is a very common disease of the lumbar spine that can lead to back and lower limb pain, mobility problems and other disabilities [1]. In the United States, LSS is the most common reason for spinal surgery in people over the age of Yonghong Zhang, Ning Hu and Zhuofu Li contributed equally to this work.