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Abstract #0394

Deep-learning-informed parameter estimation improves reliability of spinal cord diffusion MRI

Ting Gong1, Francesco Grussu2,3, Claudia AM Gandini Wheeler-Kingshott4,5,6, Daniel C Alexander1, and Hui Zhang1
1Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom, 2Radiomics Group, Vall d’Hebron Institute of Oncology, Vall d’Hebron Barcelona Hospital Campus, Barcelona, Spain, 3Queen Square MS Centre, Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom, 4NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom, 5Brain Connectivity Centre, IRCCS Mondino Foundation, Pavia, Italy, 6Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy


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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