Purpose: Improving soft-tissue contrast resolution beyond the capability of current cone-beam CT (CBCT) systems is essential to a growing range of image guidance and diagnostic imaging scenarios. We present a framework for CBCT model-based image reconstruction (MBIR) combining artifact corrections with multi-resolution reconstruction and multiregion motion compensation and apply the method for the first time in a clinical study of CBCT for high-quality imaging of head injury. Methods: A CBCT prototype was developed for mobile point-of-care imaging in the neuro-critical care unit (NCCU). Projection data were processed via poly-energetic gain correction and an artifacts correction pipeline treating scatter, beam hardening, and motion compensation. The scatter correction was modified to use a penalized weighted least-squares (PWLS) image in the Monte-Carlo (MC) object model for better uniformity in truncated data. The PWLS method included: (1) multi-resolution reconstruction to mitigate lateral truncation from the head-holder; (2) multi-motion compensation allowing separate motion of the head and head-holder; and (3) modified statistical weights to account for electronics noise and fluence modulation by the bowtie filter. Imaging performance was evaluated in simulation and in the first clinical study (N = 54 patients) conducted with the system. Results: Using a PWLS object model in the final iteration of the MC scatter estimate improved image uniformity by 40.4% for truncated datasets. The multi-resolution, multi-motion PWLS method greatly reduced streak artifacts and nonuniformity both in simulation (RMSE reduced by 65.5%) and in the clinical study (visual image quality assessed by a neuroradiologist). Up to 15% reduction in variance was achieved using statistical weights modified according to a model for electronic noise from the detector. Each component was important for improved contrast resolution in the patient data. Conclusion: An integrated pipeline for artifacts correction and PWLS reconstruction mitigated artifacts and noise to a level supporting visualization of low-contrast brain lesions and warranting future studies of diagnostic performance in the NCCU.