Muge Karaman^{1}, Lingdao Sha^{2}, Tingqi Shi^{1}, Weiguo Li^{3,4}, Dan Schonfeld^{2,5,6}, Tibor Valyi-Nagy^{7}, and Xiaohong Joe Zhou^{1,6,8}

Tissue heterogeneity is an important consideration for diagnosing many diseases. Recently, a novel non-Gaussian diffusion model – continuous-time random-walk model (CTRW) – provided promising evidence indicating a possible link between voxel-level spatiotemporal diffusion heterogeneity and microscopic intravoxel tissue heterogeneity. Establishing a correlation between imaging-based and histology-based measurements, however, has been challenging because of the lack of efficient and subjective evaluation of tissue heterogeneity histologically. In this study, we applied a machine-learning algorithm to quantitatively determine microscopic tissue heterogeneity, enabling a correlation between intravoxel diffusion heterogeneity based on CTRW parameters and structural heterogeneity from histopathology.

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