The very low SNR in spinal cord diffusion MRI can make robust microstructure parameter estimation challenging. To address unreliable parameter estimations under such low SNR, we propose a deep-learning-informed maximum likelihood estimation (MLE) approach, where a deep-learning model is trained to initialise MLE efficiently and optimally. In testing NODDI-derived parameters, simulation and in vivo experiments suggest the DL-informed method can reduce outlier estimates from conventional grid-search MLE, and at the same time, avoid biases from pure DL estimation under the low SNR. The proposed method also speeds up the computation, making it a promising tool for future applications.
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