Multipararametric magnetic resonance imaging (MRI) uses advanced parameters such as diffusion weighted imaging (DWI) and dynamic contrast enhanced (DCE) imaging to probe underlying tissue biology and pathology for many different tissue types. Furthermore, different MRI parameters are entangled in a highly correlated complex network, which makes it desirable to understand the organizational structure and characteristics of this complex network to create a unique imaging biomarker of patient pathology. We developed the technique of contribution scattergram to uncover the underlying multidimensional complex network for each patient, bringing a new perspective to personalized radiological diagnosis and treatment planning.

Patent Disclosure

  • M. A. Jacobs, V S. Parekh. “MIRAGE: Radiomics-Geodesic Features Extraction Decision Support System.” (D14297, 2016)


  • V. Parekh and M. Jacobs, "Multidimensional Imaging Radiomics-Geodesics: A Novel Manifold Learning Based Automatic
    Feature Extraction Method for Diagnostic Prediction in Multiparametric Imaging," Medical Physics, vol. 43, pp. 3373-3374, 2016.