Intraoperative imaging systems are seeing an increased role in support of surgical guidance and quality assurance in the operating room for interventional approaches. However, image quality sufficient to detect complications and provide quantitative assessment of the surgical product are often confounded by image noise and artifacts. In this work, we translated a 3D image reconstruction method (referred to as “Known-Component Reconstruction,” KC-Recon) for the first time to clinical studies with the aim of resolving both limitations. KC-Recon builds upon an optimization-based reconstruction method to reduce noise and incorporates a model of surgical instruments in the image to reduce artifacts. The first clinical pilot study involved 17 spine surgery patients imaged using the O-arm before and after spinal instrumentation. Imaging performance was evaluated in terms of low-contrast soft-tissue visibility, the ability to assess screw placement within bone margins, and the potential to image at lower radiation doses. Depending on the imaging task, dose reduction up to an order of magnitude appeared feasible while maintaining soft-tissue visibility. KC-Recon also yielded ~30% reduction in blooming artifact about the screw shafts and ~60% higher tissue homogeneity at the screw tips, providing clearer depiction of pedicle and vertebral body for assessment of potential breaches. Overall, the method offers a promising means to reduce patient dose in image-guided procedures, extend the use of cone-beam CT to soft-tissue surgeries, provide a valuable check against complications in the operating room (cf., post-operative CT), and serve as a basis for quantitative evaluation of quality of the surgical construct.