MOdel-free Diffusion-wEighted MRI (MODEM) with Machine Learning for Accurate Tissue Characterization

Motivation: Mathematical, biophysical, and/or statistical models are typically used to analyze diffusion-weighted imaging signals, yielding quantitative biomarkers. Those model-based approaches, however, often suffer from limited model capability, fitting instability, and degeneracy. Goal(s): To use a MOdel-free Diffusion-wEighted MRI technique (MODEM) to differentiate underlying tissues based on diffusion signal intensities. Approach: We developed a machine-learning-based approach which we call MOdel-free Diffusion-wEighted MRI technique(MODEM) and assess its performance by using synthetic DWI data from Monte Carlo simulations and cervical staging dataset. Results: MODEM exhibited superior diagnostic performance to the model-based approach in both Monte Carlo simulations and cervical cancer staging data. Impact: A model-free machine-learning-based approach provides superior performance to the conventional diffusion-model-based approach for differentiating the underlying tissue properties.