Purpose. Model-based image registration and reconstruction offer strong potential for improved safety and precision in image-guided interventions. Advantages include reduced radiation dose, improved soft-Tissue visibility (detection of complications), and accurate guidance with/without a dedicated navigation system. This work reports the development and performance of such methods on an O-Arm system for intraoperative imaging and translates them to first clinical studies. Methods. Two novel methodologies predicate the work: (1) Known-Component Registration (KC-Reg) for 3D localization of the patient and interventional devices from 2D radiographs; and (2) Penalized-Likelihood reconstruction (PLH) for improved 3D image quality and dose reduction. A thorough assessment of geometric stability, dosimetry, and image quality was performed to define algorithm parameters for imaging and guidance protocols. Laboratory studies included: evaluation of KC-Reg in localization of spine screws delivered in cadaver; and PLH performance in contrast, noise, and resolution in phantoms/cadaver compared to filtered backprojection (FBP). Results. KC-Reg was shown to successfully register screw implants within ∼1 mm based on as few as 3 radiographs. PLH was shown to improve soft-Tissue visibility (61% improvement in CNR) compared to FBP at matched resolution. Cadaver studies verified the selection of algorithm parameters and the methods were successfully translated to clinical studies under an IRB protocol. Conclusions. Model-based registration and reconstruction approaches were shown to reduce dose and provide improved visualization of anatomy and surgical instrumentation. Immediate future work will focus on further integration of KC-Reg and PLH for Known-Component Reconstruction (KC-Recon) to provide high-quality intraoperative imaging in the presence of dense instrumentation.