The goal of this work is to enable visualization of fine level details for cervical spine imaging. To this end, we are tackling the problem from two different fronts. One, we are developing state-of-the-art image reconstruction algorithms that can potentially halve the acquisition times. Second, we are developing algorithms that can personalize complex anatomical models based on noisy and even corrupted images to create highly detailed digital twins of the patient from which diagnostic information can be drawn and potentially used for treatment planning. The developed algorithms are planned to be deployed in the research setting at the Balgrist university hospital.