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The X-ray machine serves as a crucial auxiliary tool in surgical settings, collaborating with surgical navigation systems to facilitate intraoperative 2-D fluoroscopic image navigation and provide a reliable foundation for 2-D–3-D registration navigation technology. The attainment of precise navigation for both 2-D fluoroscopic images and 3-D preoperative CT/MRI images hinges upon the efficient and accurate calibration of X-ray machine. Conventional calibration techniques for X-ray machines, whether conducted online or offline, are characterized by excessively intricate calibration processes, imposing significant time demands during surgery and resulting in diminished surgical efficiency. We propose a novel permanent calibration (PerCal) strategy employing a self-developed X-ray source tracker (TRKR-S), image plate tracker (TRKR-IP), and portable calibrator (CAL-P), aiming to streamline surgical procedures and enhance overall surgical efficiency. This strategy reframes the X-ray machine calibration challenge into the determination of the spatial position of the X-ray source in relation to TRKR-S and the 2-D fluoroscopic image relative to TRKR-IP. In the intraoperative navigation scenario, solely collecting position information of TRKR-S and TRKR-IP under the optical camera during perspective scanning enables real-time and precise intraoperative 2-D fluoroscopic image navigation. Meanwhile, the initialization mechanism for the projection (IM-Proj) coordinate frame has been proposed to ensure the effective solution of the PerCal problem. Furthermore, the membership criterion for 2-D/3-D (MC-2D/3D) marker points has been formulated with the aim of expeditiously and precisely determining the mapping correlation between spatial marker points within the 3-D calibration scene and their corresponding marker points on the 2-D fluoroscopic image. The efficacy of the proposed PerCal strategy combining IM-Proj and MC-2D/3D is validated through ablation experiments using a G-Type Arm, confirming its calibration accuracy and timeliness. The AMSE errors for frontal and lateral perspectives were denoted as $0.332~ \pm ~0.026$ and $0.53~ \pm ~0.023$ mm, respectively. Notably, it necessitates only one calibration session to attain permanent reuse of calibration data in spinal surgery navigation. The navigation registration time is confirmed not to exceed 10 s, affirming the accuracy and efficiency of PerCal.

Multi-focus image fusion (MFIF) creates an image from different source images with various sensors or optical settings as the devices can’t focus all objects at different distances. Most of the MFIF methods have several limitations in encoder enough features from the images and the result are not robust. To overcome the primary issue, we present a robust fusion algorithm based on the Frequency mask and the Hyperdimensional computing. We propose the Frequency Mask Filter (FMF) to get the narrow-band signals by encoding the frequency domain vector through the mask filter in the frequency domain. The Hyperdimensional encoder uses monogenic mapping, in which the multi-modulation features (MMF) such as the frequency, phase and amplitude are dynamically selected to obtain robust focus maps. Generated by multiscale monogenic representations of each image, the narrow-band image are mapped to hypervector encoding. Hyperdimensional encoder shows the energetic and structural information and leads to robust fusion results. Our proposed method is far superior to the existing MFIF method in terms of both objective evaluation metrics and visual effects on three publicly available datasets.Additionally, our proposed method requires only 0.88 seconds and has a parameter count of 0.13 million for multi-focus image fusion.