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- W4385543664 abstract "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." @default.
- W4385543664 created "2023-08-04" @default.
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- W4385543664 date "2023-08-03" @default.
- W4385543664 modified "2023-09-27" @default.
- W4385543664 title "Deep-learning-informed parameter estimation improves reliability of spinal cord diffusion MRI" @default.
- W4385543664 doi "https://doi.org/10.58530/2022/0394" @default.
- W4385543664 hasPublicationYear "2023" @default.
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