Keywords: AI Diffusion Models, Pelvis
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.
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