Keywords: Signal Modeling, Diffusion Tensor Imaging Deep Neural Network based Network Estimation Operator (NEO) was developed to differentiate and quantify parallel fibers of different axial diffusivities. Data from both Monte Carlo simulations and an In-Silico imaging phantom were analyzed by both DBSI and NEO. Results reveal that NEO distinguished parallel fibers of different axial diffusivity while DBSI only identified a single fiber. NEO can detect and quantify injured vs. non-injured axons in a coherent fiber bundle. Thus, NEO can quantify the extent of axonal injury and loss more accurately than DBSI and other advanced diffusion MRI models.
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