This work introduces a novel, joint reconstruction of vascular structure and microvascular function maps directly from highly undersampled data in k - t space using vascular heterogeneity priors for high-definition, dynamic contrast-enhanced (DCE) MRI. In DCE MRI, arteries and veins are characterized by rapid, high uptake and wash-out of contrast agents (CA). On the other hand, depending on CA uptake and wash-out signal patterns, capillary tissues can be categorized into highly perfused, moderately perfused, and necrotic regions. Given the above considerations, macrovascular maps are generated as a prior to differentiate penalties on arteries relative to capillary tissues during image reconstruction. Furthermore, as a microvascular prior, contrast dynamics in capillary regions are represented in a low dimensional space using a finite number of basic vectors that reflect actual tissue-specific signal patterns. Both vascular structure and microvascular function maps are jointly estimated by solving a constrained optimization problem in which the above vascular heterogeneity priors are represented by spatially weighted nonnegative matrix factorization. Retrospective and prospective experiments are performed to validate the effectiveness of the proposed method in generating well-defined vascular structure and microvascular function maps for patients with brain tumor at high reduction factors.