Keywords: Diffusion Reconstruction, Diffusion Denoising, Deep Neural Network, CSF suppression, Multi parameter, ADC
Motivation: T2wsup-dMRI combining Triangle-pattern sampling in (TE, b) space with the nonlinear least square (LSQ) fitting provides lower errors (RE and CV) for the quantitative maps but is time-consuming.
Goal(s): To shorten the analysis time of LSQ fitting by applying a Deep Neural Network (DNN) while minimizing errors.
Approach: The errors between DNN and 2dLSQ fitting for several sampling patterns were compared using digital phantom and MRI data by simulation.
Results: The DNN outperformed the 2dLSQ fitting in terms of time (~1/500) while maintaining comparable errors. The caveat for the DNN is that the learning is required depending on the data pattern and SNR.
Impact: The analysis time for the 2d LSQ fitting with a Triangle-pattern in (TE, b) space was significantly shortened by the DNN while maintaining comparable REs and CVs. Although further optimization is required, clinical application is promising.
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