The problem of interslice magnetic resonance (MR) image reconstruction arises in a broad range of medical applications. In such cases, there is a need to approximate information present in the original subject that is not reflected in contiguously acquired MR images because of hardware sampling limitations. In the context of vascular morphology reconstruction, this information is required in order for subsequent visualization and computational analysis of blood vessels to be most effective. Toward that end we have developed a method of vascular morphology reconstruction based on adaptive control grid interpolation (ACGI) to function as a precursor to visualization and computational analysis. ACGI has previously been implemented in addressing various problems including video coding and tracking. This paper focuses on the novel application of the technique to medical image processing. ACGI combines features of optical flow-based and block-based motion estimation algorithms to enhance insufficiently dense MR data sets accurately with a minimal degree of computational complexity. The resulting enhanced data sets describe vascular geometries. These reconstructions can then be used as visualization tools and in conjunction with computational fluid dynamics (CFD) simulations to offer the pressure and velocity information necessary to quantify power loss. The proposed ACGI methodology is envisioned ultimately to play a role in surgical planning aimed at producing optimal vascular configurations for successful surgical outcomes.